Statistics.Distribution.Beta:$centropy from math-functions-0.1.5.2

Percentage Accurate: 95.2% → 97.5%

Time: 3.8s

Alternatives: 18

Speedup: 0.5×

• 63.4% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* (- (+ y t) 2.0) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = ((x - ((y - 1.0d0) * z)) - ((t - 1.0d0) * a)) + (((y + t) - 2.0d0) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

Python

def code(x, y, z, t, a, b):
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x - Float64(Float64(y - 1.0) * z)) - Float64(Float64(t - 1.0) * a)) + Float64(Float64(Float64(y + t) - 2.0) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x - N[(N[(y - 1.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - N[(N[(t - 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Initial Program: 95.2% accurate, 1.0× speedup

Math

\[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* (- (+ y t) 2.0) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = ((x - ((y - 1.0d0) * z)) - ((t - 1.0d0) * a)) + (((y + t) - 2.0d0) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

Python

def code(x, y, z, t, a, b):
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x - Float64(Float64(y - 1.0) * z)) - Float64(Float64(t - 1.0) * a)) + Float64(Float64(Float64(y + t) - 2.0) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x - N[(N[(y - 1.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - N[(N[(t - 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Alternative 1: 97.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} t_1 := \left(t - 1\right) \cdot a\\ t_2 := \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{if}\;\left(\left(x - \left(y - 1\right) \cdot z\right) - t\_1\right) + t\_2 \leq \infty:\\ \;\;\;\;\left(\left(\left(x - z \cdot y\right) - \left(-z\right)\right) - t\_1\right) + t\_2\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* (- t 1.0) a)) (t_2 (* (- (+ y t) 2.0) b)))
  (if (<= (+ (- (- x (* (- y 1.0) z)) t_1) t_2) INFINITY)
    (+ (- (- (- x (* z y)) (- z)) t_1) t_2)
    (* y (- b z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (t - 1.0) * a;
	double t_2 = ((y + t) - 2.0) * b;
	double tmp;
	if ((((x - ((y - 1.0) * z)) - t_1) + t_2) <= ((double) INFINITY)) {
		tmp = (((x - (z * y)) - -z) - t_1) + t_2;
	} else {
		tmp = y * (b - z);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (t - 1.0) * a;
	double t_2 = ((y + t) - 2.0) * b;
	double tmp;
	if ((((x - ((y - 1.0) * z)) - t_1) + t_2) <= Double.POSITIVE_INFINITY) {
		tmp = (((x - (z * y)) - -z) - t_1) + t_2;
	} else {
		tmp = y * (b - z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (t - 1.0) * a
	t_2 = ((y + t) - 2.0) * b
	tmp = 0
	if (((x - ((y - 1.0) * z)) - t_1) + t_2) <= math.inf:
		tmp = (((x - (z * y)) - -z) - t_1) + t_2
	else:
		tmp = y * (b - z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(t - 1.0) * a)
	t_2 = Float64(Float64(Float64(y + t) - 2.0) * b)
	tmp = 0.0
	if (Float64(Float64(Float64(x - Float64(Float64(y - 1.0) * z)) - t_1) + t_2) <= Inf)
		tmp = Float64(Float64(Float64(Float64(x - Float64(z * y)) - Float64(-z)) - t_1) + t_2);
	else
		tmp = Float64(y * Float64(b - z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (t - 1.0) * a;
	t_2 = ((y + t) - 2.0) * b;
	tmp = 0.0;
	if ((((x - ((y - 1.0) * z)) - t_1) + t_2) <= Inf)
		tmp = (((x - (z * y)) - -z) - t_1) + t_2;
	else
		tmp = y * (b - z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(t - 1.0), $MachinePrecision] * a), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]}, If[LessEqual[N[(N[(N[(x - N[(N[(y - 1.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision] + t$95$2), $MachinePrecision], Infinity], N[(N[(N[(N[(x - N[(z * y), $MachinePrecision]), $MachinePrecision] - (-z)), $MachinePrecision] - t$95$1), $MachinePrecision] + t$95$2), $MachinePrecision], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (-.f64 x (*.f64 (-.f64 y #s(literal 1 binary64)) z)) (*.f64 (-.f64 t #s(literal 1 binary64)) a)) (*.f64 (-.f64 (+.f64 y t) #s(literal 2 binary64)) b)) < +inf.0

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\color{blue}{\left(x - \left(y - 1\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. sub-flip N/A

         \[\leadsto \left(\color{blue}{\left(x + \left(\mathsf{neg}\left(\left(y - 1\right) \cdot z\right)\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      3. add-flip N/A

         \[\leadsto \left(\color{blue}{\left(x - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\left(y - 1\right) \cdot z\right)\right)\right)\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      4. remove-double-neg N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y - 1\right) \cdot z}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      5. lift-*.f64 N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y - 1\right) \cdot z}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      6. *-commutative N/A

         \[\leadsto \left(\left(x - \color{blue}{z \cdot \left(y - 1\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      7. lift--.f64 N/A

         \[\leadsto \left(\left(x - z \cdot \color{blue}{\left(y - 1\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      8. sub-flip N/A

         \[\leadsto \left(\left(x - z \cdot \color{blue}{\left(y + \left(\mathsf{neg}\left(1\right)\right)\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      9. distribute-rgt-in N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y \cdot z + \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      10. associate--r+ N/A

          \[\leadsto \left(\color{blue}{\left(\left(x - y \cdot z\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      11. lower--.f64 N/A

          \[\leadsto \left(\color{blue}{\left(\left(x - y \cdot z\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      12. lower--.f64 N/A

          \[\leadsto \left(\left(\color{blue}{\left(x - y \cdot z\right)} - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      13. *-commutative N/A

          \[\leadsto \left(\left(\left(x - \color{blue}{z \cdot y}\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      14. lower-*.f64 N/A

          \[\leadsto \left(\left(\left(x - \color{blue}{z \cdot y}\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      15. metadata-eval N/A

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{-1} \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      16. mul-1-neg N/A

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{\left(\mathsf{neg}\left(z\right)\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      17. lower-neg.f64 95.2%

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{\left(-z\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   3. Applied rewrites 95.2%

      \[\leadsto \left(\color{blue}{\left(\left(x - z \cdot y\right) - \left(-z\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   if +inf.0 < (+.f64 (-.f64 (-.f64 x (*.f64 (-.f64 y #s(literal 1 binary64)) z)) (*.f64 (-.f64 t #s(literal 1 binary64)) a)) (*.f64 (-.f64 (+.f64 y t) #s(literal 2 binary64)) b))

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\left(b - z\right)} \]

      2. lower--.f64 32.6%

         \[\leadsto y \cdot \left(b - \color{blue}{z}\right) \]

   4. Applied rewrites 32.6%

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 97.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} t_1 := \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{if}\;t\_1 \leq \infty:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1
        (+
         (- (- x (* (- y 1.0) z)) (* (- t 1.0) a))
         (* (- (+ y t) 2.0) b))))
  (if (<= t_1 INFINITY) t_1 (* y (- b z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
	double tmp;
	if (t_1 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = y * (b - z);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
	double tmp;
	if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else {
		tmp = y * (b - z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b)
	tmp = 0
	if t_1 <= math.inf:
		tmp = t_1
	else:
		tmp = y * (b - z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(x - Float64(Float64(y - 1.0) * z)) - Float64(Float64(t - 1.0) * a)) + Float64(Float64(Float64(y + t) - 2.0) * b))
	tmp = 0.0
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = Float64(y * Float64(b - z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
	tmp = 0.0;
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = y * (b - z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(x - N[(N[(y - 1.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - N[(N[(t - 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, Infinity], t$95$1, N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (-.f64 x (*.f64 (-.f64 y #s(literal 1 binary64)) z)) (*.f64 (-.f64 t #s(literal 1 binary64)) a)) (*.f64 (-.f64 (+.f64 y t) #s(literal 2 binary64)) b)) < +inf.0

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   if +inf.0 < (+.f64 (-.f64 (-.f64 x (*.f64 (-.f64 y #s(literal 1 binary64)) z)) (*.f64 (-.f64 t #s(literal 1 binary64)) a)) (*.f64 (-.f64 (+.f64 y t) #s(literal 2 binary64)) b))

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\left(b - z\right)} \]

      2. lower--.f64 32.6%

         \[\leadsto y \cdot \left(b - \color{blue}{z}\right) \]

   4. Applied rewrites 32.6%

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 88.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} t_1 := \left(x - a \cdot \left(t - 1\right)\right) + \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{if}\;b \leq -5.8 \cdot 10^{+147}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;b \leq 1.1 \cdot 10^{+62}:\\ \;\;\;\;\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + b \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (+ (- x (* a (- t 1.0))) (* (- (+ y t) 2.0) b))))
  (if (<= b -5.8e+147)
    t_1
    (if (<= b 1.1e+62)
      (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* b y))
      t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x - (a * (t - 1.0))) + (((y + t) - 2.0) * b);
	double tmp;
	if (b <= -5.8e+147) {
		tmp = t_1;
	} else if (b <= 1.1e+62) {
		tmp = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (b * y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (x - (a * (t - 1.0d0))) + (((y + t) - 2.0d0) * b)
    if (b <= (-5.8d+147)) then
        tmp = t_1
    else if (b <= 1.1d+62) then
        tmp = ((x - ((y - 1.0d0) * z)) - ((t - 1.0d0) * a)) + (b * y)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x - (a * (t - 1.0))) + (((y + t) - 2.0) * b);
	double tmp;
	if (b <= -5.8e+147) {
		tmp = t_1;
	} else if (b <= 1.1e+62) {
		tmp = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (b * y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x - (a * (t - 1.0))) + (((y + t) - 2.0) * b)
	tmp = 0
	if b <= -5.8e+147:
		tmp = t_1
	elif b <= 1.1e+62:
		tmp = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (b * y)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x - Float64(a * Float64(t - 1.0))) + Float64(Float64(Float64(y + t) - 2.0) * b))
	tmp = 0.0
	if (b <= -5.8e+147)
		tmp = t_1;
	elseif (b <= 1.1e+62)
		tmp = Float64(Float64(Float64(x - Float64(Float64(y - 1.0) * z)) - Float64(Float64(t - 1.0) * a)) + Float64(b * y));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (x - (a * (t - 1.0))) + (((y + t) - 2.0) * b);
	tmp = 0.0;
	if (b <= -5.8e+147)
		tmp = t_1;
	elseif (b <= 1.1e+62)
		tmp = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (b * y);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x - N[(a * N[(t - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -5.8e+147], t$95$1, If[LessEqual[b, 1.1e+62], N[(N[(N[(x - N[(N[(y - 1.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - N[(N[(t - 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + N[(b * y), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if b < -5.7999999999999997e147 or 1.1000000000000001e62 < b

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{\left(x - a \cdot \left(t - 1\right)\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x - \color{blue}{a \cdot \left(t - 1\right)}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. lower-*.f64 N/A

         \[\leadsto \left(x - a \cdot \color{blue}{\left(t - 1\right)}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      3. lower--.f64 73.3%

         \[\leadsto \left(x - a \cdot \left(t - \color{blue}{1}\right)\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Applied rewrites 73.3%

      \[\leadsto \color{blue}{\left(x - a \cdot \left(t - 1\right)\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   if -5.7999999999999997e147 < b < 1.1000000000000001e62

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in y around inf

      \[\leadsto \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \color{blue}{b \cdot y} \]
   3. Step-by-step derivation

      1. lower-*.f64 78.7%

         \[\leadsto \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + b \cdot \color{blue}{y} \]

   4. Applied rewrites 78.7%

      \[\leadsto \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \color{blue}{b \cdot y} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 83.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \mathbf{if}\;z \leq -2.03 \cdot 10^{+186}:\\ \;\;\;\;\left(x + z\right) - y \cdot z\\ \mathbf{elif}\;z \leq 3.9 \cdot 10^{+118}:\\ \;\;\;\;\left(x - a \cdot \left(t - 1\right)\right) + \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (if (<= z -2.03e+186)
  (- (+ x z) (* y z))
  (if (<= z 3.9e+118)
    (+ (- x (* a (- t 1.0))) (* (- (+ y t) 2.0) b))
    (- (+ x (+ z (* b (- (+ t y) 2.0)))) (* y z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -2.03e+186) {
		tmp = (x + z) - (y * z);
	} else if (z <= 3.9e+118) {
		tmp = (x - (a * (t - 1.0))) + (((y + t) - 2.0) * b);
	} else {
		tmp = (x + (z + (b * ((t + y) - 2.0)))) - (y * z);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (z <= (-2.03d+186)) then
        tmp = (x + z) - (y * z)
    else if (z <= 3.9d+118) then
        tmp = (x - (a * (t - 1.0d0))) + (((y + t) - 2.0d0) * b)
    else
        tmp = (x + (z + (b * ((t + y) - 2.0d0)))) - (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -2.03e+186) {
		tmp = (x + z) - (y * z);
	} else if (z <= 3.9e+118) {
		tmp = (x - (a * (t - 1.0))) + (((y + t) - 2.0) * b);
	} else {
		tmp = (x + (z + (b * ((t + y) - 2.0)))) - (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -2.03e+186:
		tmp = (x + z) - (y * z)
	elif z <= 3.9e+118:
		tmp = (x - (a * (t - 1.0))) + (((y + t) - 2.0) * b)
	else:
		tmp = (x + (z + (b * ((t + y) - 2.0)))) - (y * z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -2.03e+186)
		tmp = Float64(Float64(x + z) - Float64(y * z));
	elseif (z <= 3.9e+118)
		tmp = Float64(Float64(x - Float64(a * Float64(t - 1.0))) + Float64(Float64(Float64(y + t) - 2.0) * b));
	else
		tmp = Float64(Float64(x + Float64(z + Float64(b * Float64(Float64(t + y) - 2.0)))) - Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -2.03e+186)
		tmp = (x + z) - (y * z);
	elseif (z <= 3.9e+118)
		tmp = (x - (a * (t - 1.0))) + (((y + t) - 2.0) * b);
	else
		tmp = (x + (z + (b * ((t + y) - 2.0)))) - (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -2.03e+186], N[(N[(x + z), $MachinePrecision] - N[(y * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.9e+118], N[(N[(x - N[(a * N[(t - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(z + N[(b * N[(N[(t + y), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y * z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.0300000000000001e186

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\color{blue}{\left(x - \left(y - 1\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. sub-flip N/A

         \[\leadsto \left(\color{blue}{\left(x + \left(\mathsf{neg}\left(\left(y - 1\right) \cdot z\right)\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      3. add-flip N/A

         \[\leadsto \left(\color{blue}{\left(x - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\left(y - 1\right) \cdot z\right)\right)\right)\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      4. remove-double-neg N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y - 1\right) \cdot z}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      5. lift-*.f64 N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y - 1\right) \cdot z}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      6. *-commutative N/A

         \[\leadsto \left(\left(x - \color{blue}{z \cdot \left(y - 1\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      7. lift--.f64 N/A

         \[\leadsto \left(\left(x - z \cdot \color{blue}{\left(y - 1\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      8. sub-flip N/A

         \[\leadsto \left(\left(x - z \cdot \color{blue}{\left(y + \left(\mathsf{neg}\left(1\right)\right)\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      9. distribute-rgt-in N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y \cdot z + \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      10. associate--r+ N/A

          \[\leadsto \left(\color{blue}{\left(\left(x - y \cdot z\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      11. lower--.f64 N/A

          \[\leadsto \left(\color{blue}{\left(\left(x - y \cdot z\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      12. lower--.f64 N/A

          \[\leadsto \left(\left(\color{blue}{\left(x - y \cdot z\right)} - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      13. *-commutative N/A

          \[\leadsto \left(\left(\left(x - \color{blue}{z \cdot y}\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      14. lower-*.f64 N/A

          \[\leadsto \left(\left(\left(x - \color{blue}{z \cdot y}\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      15. metadata-eval N/A

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{-1} \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      16. mul-1-neg N/A

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{\left(\mathsf{neg}\left(z\right)\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      17. lower-neg.f64 95.2%

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{\left(-z\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   3. Applied rewrites 95.2%

      \[\leadsto \left(\color{blue}{\left(\left(x - z \cdot y\right) - \left(-z\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z} \]
   5. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - \color{blue}{y \cdot z} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - \color{blue}{y} \cdot z \]

      3. lower-+.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      4. lower-*.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      5. lower--.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      6. lower-+.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      7. lower-*.f64 73.4%

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot \color{blue}{z} \]

   6. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z} \]

   7. Taylor expanded in b around 0

      \[\leadsto \left(x + z\right) - \color{blue}{y} \cdot z \]
   8. Step-by-step derivation

      1. lower-+.f64 41.5%

         \[\leadsto \left(x + z\right) - y \cdot z \]

   9. Applied rewrites 41.5%

      \[\leadsto \left(x + z\right) - \color{blue}{y} \cdot z \]

   if -2.0300000000000001e186 < z < 3.9e118

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{\left(x - a \cdot \left(t - 1\right)\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x - \color{blue}{a \cdot \left(t - 1\right)}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. lower-*.f64 N/A

         \[\leadsto \left(x - a \cdot \color{blue}{\left(t - 1\right)}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      3. lower--.f64 73.3%

         \[\leadsto \left(x - a \cdot \left(t - \color{blue}{1}\right)\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Applied rewrites 73.3%

      \[\leadsto \color{blue}{\left(x - a \cdot \left(t - 1\right)\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   if 3.9e118 < z

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\color{blue}{\left(x - \left(y - 1\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. sub-flip N/A

         \[\leadsto \left(\color{blue}{\left(x + \left(\mathsf{neg}\left(\left(y - 1\right) \cdot z\right)\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      3. add-flip N/A

         \[\leadsto \left(\color{blue}{\left(x - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\left(y - 1\right) \cdot z\right)\right)\right)\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      4. remove-double-neg N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y - 1\right) \cdot z}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      5. lift-*.f64 N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y - 1\right) \cdot z}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      6. *-commutative N/A

         \[\leadsto \left(\left(x - \color{blue}{z \cdot \left(y - 1\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      7. lift--.f64 N/A

         \[\leadsto \left(\left(x - z \cdot \color{blue}{\left(y - 1\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      8. sub-flip N/A

         \[\leadsto \left(\left(x - z \cdot \color{blue}{\left(y + \left(\mathsf{neg}\left(1\right)\right)\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      9. distribute-rgt-in N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y \cdot z + \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      10. associate--r+ N/A

          \[\leadsto \left(\color{blue}{\left(\left(x - y \cdot z\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      11. lower--.f64 N/A

          \[\leadsto \left(\color{blue}{\left(\left(x - y \cdot z\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      12. lower--.f64 N/A

          \[\leadsto \left(\left(\color{blue}{\left(x - y \cdot z\right)} - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      13. *-commutative N/A

          \[\leadsto \left(\left(\left(x - \color{blue}{z \cdot y}\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      14. lower-*.f64 N/A

          \[\leadsto \left(\left(\left(x - \color{blue}{z \cdot y}\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      15. metadata-eval N/A

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{-1} \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      16. mul-1-neg N/A

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{\left(\mathsf{neg}\left(z\right)\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      17. lower-neg.f64 95.2%

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{\left(-z\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   3. Applied rewrites 95.2%

      \[\leadsto \left(\color{blue}{\left(\left(x - z \cdot y\right) - \left(-z\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z} \]
   5. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - \color{blue}{y \cdot z} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - \color{blue}{y} \cdot z \]

      3. lower-+.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      4. lower-*.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      5. lower--.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      6. lower-+.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      7. lower-*.f64 73.4%

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot \color{blue}{z} \]

   6. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 83.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} t_1 := a \cdot \left(1 - t\right) + \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{if}\;a \leq -1.15 \cdot 10^{+98}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 7.5 \cdot 10^{+71}:\\ \;\;\;\;\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (+ (* a (- 1.0 t)) (* (- (+ y t) 2.0) b))))
  (if (<= a -1.15e+98)
    t_1
    (if (<= a 7.5e+71)
      (- (+ x (* b (- (+ t y) 2.0))) (* z (- y 1.0)))
      t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (a * (1.0 - t)) + (((y + t) - 2.0) * b);
	double tmp;
	if (a <= -1.15e+98) {
		tmp = t_1;
	} else if (a <= 7.5e+71) {
		tmp = (x + (b * ((t + y) - 2.0))) - (z * (y - 1.0));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (a * (1.0d0 - t)) + (((y + t) - 2.0d0) * b)
    if (a <= (-1.15d+98)) then
        tmp = t_1
    else if (a <= 7.5d+71) then
        tmp = (x + (b * ((t + y) - 2.0d0))) - (z * (y - 1.0d0))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (a * (1.0 - t)) + (((y + t) - 2.0) * b);
	double tmp;
	if (a <= -1.15e+98) {
		tmp = t_1;
	} else if (a <= 7.5e+71) {
		tmp = (x + (b * ((t + y) - 2.0))) - (z * (y - 1.0));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (a * (1.0 - t)) + (((y + t) - 2.0) * b)
	tmp = 0
	if a <= -1.15e+98:
		tmp = t_1
	elif a <= 7.5e+71:
		tmp = (x + (b * ((t + y) - 2.0))) - (z * (y - 1.0))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(a * Float64(1.0 - t)) + Float64(Float64(Float64(y + t) - 2.0) * b))
	tmp = 0.0
	if (a <= -1.15e+98)
		tmp = t_1;
	elseif (a <= 7.5e+71)
		tmp = Float64(Float64(x + Float64(b * Float64(Float64(t + y) - 2.0))) - Float64(z * Float64(y - 1.0)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (a * (1.0 - t)) + (((y + t) - 2.0) * b);
	tmp = 0.0;
	if (a <= -1.15e+98)
		tmp = t_1;
	elseif (a <= 7.5e+71)
		tmp = (x + (b * ((t + y) - 2.0))) - (z * (y - 1.0));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.15e+98], t$95$1, If[LessEqual[a, 7.5e+71], N[(N[(x + N[(b * N[(N[(t + y), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(z * N[(y - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -1.1500000000000001e98 or 7.5000000000000001e71 < a

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto a \cdot \color{blue}{\left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. lower--.f64 59.7%

         \[\leadsto a \cdot \left(1 - \color{blue}{t}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Applied rewrites 59.7%

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   if -1.1500000000000001e98 < a < 7.5000000000000001e71

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z \cdot \left(y - 1\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      5. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      6. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \color{blue}{\left(y - 1\right)} \]

      7. lower--.f64 73.4%

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - \color{blue}{1}\right) \]

   4. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 67.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \mathbf{if}\;x \leq -1.85 \cdot 10^{+61}:\\ \;\;\;\;\left(x + b \cdot \left(y - 2\right)\right) - z \cdot \left(y - 1\right)\\ \mathbf{elif}\;x \leq 1.85 \cdot 10^{+34}:\\ \;\;\;\;a \cdot \left(1 - t\right) + \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(\left(x - y \cdot z\right) - a \cdot t\right) + b \cdot y\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (if (<= x -1.85e+61)
  (- (+ x (* b (- y 2.0))) (* z (- y 1.0)))
  (if (<= x 1.85e+34)
    (+ (* a (- 1.0 t)) (* (- (+ y t) 2.0) b))
    (+ (- (- x (* y z)) (* a t)) (* b y)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -1.85e+61) {
		tmp = (x + (b * (y - 2.0))) - (z * (y - 1.0));
	} else if (x <= 1.85e+34) {
		tmp = (a * (1.0 - t)) + (((y + t) - 2.0) * b);
	} else {
		tmp = ((x - (y * z)) - (a * t)) + (b * y);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (x <= (-1.85d+61)) then
        tmp = (x + (b * (y - 2.0d0))) - (z * (y - 1.0d0))
    else if (x <= 1.85d+34) then
        tmp = (a * (1.0d0 - t)) + (((y + t) - 2.0d0) * b)
    else
        tmp = ((x - (y * z)) - (a * t)) + (b * y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -1.85e+61) {
		tmp = (x + (b * (y - 2.0))) - (z * (y - 1.0));
	} else if (x <= 1.85e+34) {
		tmp = (a * (1.0 - t)) + (((y + t) - 2.0) * b);
	} else {
		tmp = ((x - (y * z)) - (a * t)) + (b * y);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if x <= -1.85e+61:
		tmp = (x + (b * (y - 2.0))) - (z * (y - 1.0))
	elif x <= 1.85e+34:
		tmp = (a * (1.0 - t)) + (((y + t) - 2.0) * b)
	else:
		tmp = ((x - (y * z)) - (a * t)) + (b * y)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (x <= -1.85e+61)
		tmp = Float64(Float64(x + Float64(b * Float64(y - 2.0))) - Float64(z * Float64(y - 1.0)));
	elseif (x <= 1.85e+34)
		tmp = Float64(Float64(a * Float64(1.0 - t)) + Float64(Float64(Float64(y + t) - 2.0) * b));
	else
		tmp = Float64(Float64(Float64(x - Float64(y * z)) - Float64(a * t)) + Float64(b * y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (x <= -1.85e+61)
		tmp = (x + (b * (y - 2.0))) - (z * (y - 1.0));
	elseif (x <= 1.85e+34)
		tmp = (a * (1.0 - t)) + (((y + t) - 2.0) * b);
	else
		tmp = ((x - (y * z)) - (a * t)) + (b * y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[x, -1.85e+61], N[(N[(x + N[(b * N[(y - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(z * N[(y - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.85e+34], N[(N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision], N[(N[(N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision] + N[(b * y), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.85e61

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z \cdot \left(y - 1\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      5. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      6. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \color{blue}{\left(y - 1\right)} \]

      7. lower--.f64 73.4%

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - \color{blue}{1}\right) \]

   4. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]

   5. Taylor expanded in t around 0

      \[\leadsto \left(x + b \cdot \left(y - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]
   6. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(y - 2\right)\right) - z \cdot \left(y - 1\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(y - 2\right)\right) - z \cdot \left(y - 1\right) \]

      3. lower--.f64 60.4%

         \[\leadsto \left(x + b \cdot \left(y - 2\right)\right) - z \cdot \left(y - 1\right) \]

   7. Applied rewrites 60.4%

      \[\leadsto \left(x + b \cdot \left(y - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

   if -1.85e61 < x < 1.85e34

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto a \cdot \color{blue}{\left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. lower--.f64 59.7%

         \[\leadsto a \cdot \left(1 - \color{blue}{t}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Applied rewrites 59.7%

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   if 1.85e34 < x

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in y around inf

      \[\leadsto \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \color{blue}{b \cdot y} \]
   3. Step-by-step derivation

      1. lower-*.f64 78.7%

         \[\leadsto \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + b \cdot \color{blue}{y} \]

   4. Applied rewrites 78.7%

      \[\leadsto \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \color{blue}{b \cdot y} \]

   5. Taylor expanded in t around inf

      \[\leadsto \left(\left(x - \left(y - 1\right) \cdot z\right) - \color{blue}{a \cdot t}\right) + b \cdot y \]
   6. Step-by-step derivation

      1. lower-*.f64 69.5%

         \[\leadsto \left(\left(x - \left(y - 1\right) \cdot z\right) - a \cdot \color{blue}{t}\right) + b \cdot y \]

   7. Applied rewrites 69.5%

      \[\leadsto \left(\left(x - \left(y - 1\right) \cdot z\right) - \color{blue}{a \cdot t}\right) + b \cdot y \]

   8. Taylor expanded in y around inf

      \[\leadsto \left(\left(x - \color{blue}{y \cdot z}\right) - a \cdot t\right) + b \cdot y \]
   9. Step-by-step derivation

      1. lower-*.f64 59.5%

         \[\leadsto \left(\left(x - y \cdot \color{blue}{z}\right) - a \cdot t\right) + b \cdot y \]

   10. Applied rewrites 59.5%

       \[\leadsto \left(\left(x - \color{blue}{y \cdot z}\right) - a \cdot t\right) + b \cdot y \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 7: 67.0% accurate, 1.1× speedup

Math

\[\begin{array}{l} t_1 := \left(x + b \cdot \left(y - 2\right)\right) - z \cdot \left(y - 1\right)\\ \mathbf{if}\;x \leq -1.85 \cdot 10^{+61}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 7.2 \cdot 10^{+84}:\\ \;\;\;\;a \cdot \left(1 - t\right) + \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (- (+ x (* b (- y 2.0))) (* z (- y 1.0)))))
  (if (<= x -1.85e+61)
    t_1
    (if (<= x 7.2e+84)
      (+ (* a (- 1.0 t)) (* (- (+ y t) 2.0) b))
      t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + (b * (y - 2.0))) - (z * (y - 1.0));
	double tmp;
	if (x <= -1.85e+61) {
		tmp = t_1;
	} else if (x <= 7.2e+84) {
		tmp = (a * (1.0 - t)) + (((y + t) - 2.0) * b);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (x + (b * (y - 2.0d0))) - (z * (y - 1.0d0))
    if (x <= (-1.85d+61)) then
        tmp = t_1
    else if (x <= 7.2d+84) then
        tmp = (a * (1.0d0 - t)) + (((y + t) - 2.0d0) * b)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + (b * (y - 2.0))) - (z * (y - 1.0));
	double tmp;
	if (x <= -1.85e+61) {
		tmp = t_1;
	} else if (x <= 7.2e+84) {
		tmp = (a * (1.0 - t)) + (((y + t) - 2.0) * b);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + (b * (y - 2.0))) - (z * (y - 1.0))
	tmp = 0
	if x <= -1.85e+61:
		tmp = t_1
	elif x <= 7.2e+84:
		tmp = (a * (1.0 - t)) + (((y + t) - 2.0) * b)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + Float64(b * Float64(y - 2.0))) - Float64(z * Float64(y - 1.0)))
	tmp = 0.0
	if (x <= -1.85e+61)
		tmp = t_1;
	elseif (x <= 7.2e+84)
		tmp = Float64(Float64(a * Float64(1.0 - t)) + Float64(Float64(Float64(y + t) - 2.0) * b));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (x + (b * (y - 2.0))) - (z * (y - 1.0));
	tmp = 0.0;
	if (x <= -1.85e+61)
		tmp = t_1;
	elseif (x <= 7.2e+84)
		tmp = (a * (1.0 - t)) + (((y + t) - 2.0) * b);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x + N[(b * N[(y - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(z * N[(y - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.85e+61], t$95$1, If[LessEqual[x, 7.2e+84], N[(N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.85e61 or 7.1999999999999999e84 < x

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z \cdot \left(y - 1\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      5. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      6. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \color{blue}{\left(y - 1\right)} \]

      7. lower--.f64 73.4%

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - \color{blue}{1}\right) \]

   4. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]

   5. Taylor expanded in t around 0

      \[\leadsto \left(x + b \cdot \left(y - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]
   6. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(y - 2\right)\right) - z \cdot \left(y - 1\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(y - 2\right)\right) - z \cdot \left(y - 1\right) \]

      3. lower--.f64 60.4%

         \[\leadsto \left(x + b \cdot \left(y - 2\right)\right) - z \cdot \left(y - 1\right) \]

   7. Applied rewrites 60.4%

      \[\leadsto \left(x + b \cdot \left(y - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

   if -1.85e61 < x < 7.1999999999999999e84

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto a \cdot \color{blue}{\left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. lower--.f64 59.7%

         \[\leadsto a \cdot \left(1 - \color{blue}{t}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Applied rewrites 59.7%

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 65.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \mathbf{if}\;b \leq -7.8 \cdot 10^{+129}:\\ \;\;\;\;a + \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{elif}\;b \leq 1.6 \cdot 10^{-45}:\\ \;\;\;\;\left(x - a \cdot \left(t - 1\right)\right) + b \cdot y\\ \mathbf{else}:\\ \;\;\;\;x + b \cdot \left(\left(t + y\right) - 2\right)\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (if (<= b -7.8e+129)
  (+ a (* (- (+ y t) 2.0) b))
  (if (<= b 1.6e-45)
    (+ (- x (* a (- t 1.0))) (* b y))
    (+ x (* b (- (+ t y) 2.0))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (b <= -7.8e+129) {
		tmp = a + (((y + t) - 2.0) * b);
	} else if (b <= 1.6e-45) {
		tmp = (x - (a * (t - 1.0))) + (b * y);
	} else {
		tmp = x + (b * ((t + y) - 2.0));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (b <= (-7.8d+129)) then
        tmp = a + (((y + t) - 2.0d0) * b)
    else if (b <= 1.6d-45) then
        tmp = (x - (a * (t - 1.0d0))) + (b * y)
    else
        tmp = x + (b * ((t + y) - 2.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (b <= -7.8e+129) {
		tmp = a + (((y + t) - 2.0) * b);
	} else if (b <= 1.6e-45) {
		tmp = (x - (a * (t - 1.0))) + (b * y);
	} else {
		tmp = x + (b * ((t + y) - 2.0));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if b <= -7.8e+129:
		tmp = a + (((y + t) - 2.0) * b)
	elif b <= 1.6e-45:
		tmp = (x - (a * (t - 1.0))) + (b * y)
	else:
		tmp = x + (b * ((t + y) - 2.0))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (b <= -7.8e+129)
		tmp = Float64(a + Float64(Float64(Float64(y + t) - 2.0) * b));
	elseif (b <= 1.6e-45)
		tmp = Float64(Float64(x - Float64(a * Float64(t - 1.0))) + Float64(b * y));
	else
		tmp = Float64(x + Float64(b * Float64(Float64(t + y) - 2.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (b <= -7.8e+129)
		tmp = a + (((y + t) - 2.0) * b);
	elseif (b <= 1.6e-45)
		tmp = (x - (a * (t - 1.0))) + (b * y);
	else
		tmp = x + (b * ((t + y) - 2.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[b, -7.8e+129], N[(a + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.6e-45], N[(N[(x - N[(a * N[(t - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(b * y), $MachinePrecision]), $MachinePrecision], N[(x + N[(b * N[(N[(t + y), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b < -7.7999999999999994e129

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto a \cdot \color{blue}{\left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. lower--.f64 59.7%

         \[\leadsto a \cdot \left(1 - \color{blue}{t}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Applied rewrites 59.7%

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   5. Taylor expanded in t around 0

      \[\leadsto a + \left(\left(y + t\right) - 2\right) \cdot b \]
   6. Step-by-step derivation

   7. Applied rewrites 46.3%

      \[\leadsto a + \left(\left(y + t\right) - 2\right) \cdot b \]

   if -7.7999999999999994e129 < b < 1.6e-45

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\color{blue}{\left(x - \left(y - 1\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. sub-flip N/A

         \[\leadsto \left(\color{blue}{\left(x + \left(\mathsf{neg}\left(\left(y - 1\right) \cdot z\right)\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      3. add-flip N/A

         \[\leadsto \left(\color{blue}{\left(x - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\left(y - 1\right) \cdot z\right)\right)\right)\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      4. remove-double-neg N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y - 1\right) \cdot z}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      5. lift-*.f64 N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y - 1\right) \cdot z}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      6. *-commutative N/A

         \[\leadsto \left(\left(x - \color{blue}{z \cdot \left(y - 1\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      7. lift--.f64 N/A

         \[\leadsto \left(\left(x - z \cdot \color{blue}{\left(y - 1\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      8. sub-flip N/A

         \[\leadsto \left(\left(x - z \cdot \color{blue}{\left(y + \left(\mathsf{neg}\left(1\right)\right)\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      9. distribute-rgt-in N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y \cdot z + \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      10. associate--r+ N/A

          \[\leadsto \left(\color{blue}{\left(\left(x - y \cdot z\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      11. lower--.f64 N/A

          \[\leadsto \left(\color{blue}{\left(\left(x - y \cdot z\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      12. lower--.f64 N/A

          \[\leadsto \left(\left(\color{blue}{\left(x - y \cdot z\right)} - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      13. *-commutative N/A

          \[\leadsto \left(\left(\left(x - \color{blue}{z \cdot y}\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      14. lower-*.f64 N/A

          \[\leadsto \left(\left(\left(x - \color{blue}{z \cdot y}\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      15. metadata-eval N/A

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{-1} \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      16. mul-1-neg N/A

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{\left(\mathsf{neg}\left(z\right)\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      17. lower-neg.f64 95.2%

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{\left(-z\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   3. Applied rewrites 95.2%

      \[\leadsto \left(\color{blue}{\left(\left(x - z \cdot y\right) - \left(-z\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Taylor expanded in z around 0

      \[\leadsto \color{blue}{\left(x - a \cdot \left(t - 1\right)\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   5. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x - \color{blue}{a \cdot \left(t - 1\right)}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. lower-*.f64 N/A

         \[\leadsto \left(x - a \cdot \color{blue}{\left(t - 1\right)}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      3. lower--.f64 73.3%

         \[\leadsto \left(x - a \cdot \left(t - \color{blue}{1}\right)\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   6. Applied rewrites 73.3%

      \[\leadsto \color{blue}{\left(x - a \cdot \left(t - 1\right)\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   7. Taylor expanded in y around inf

      \[\leadsto \left(x - a \cdot \left(t - 1\right)\right) + \color{blue}{b \cdot y} \]
   8. Step-by-step derivation

      1. lower-*.f64 56.8%

         \[\leadsto \left(x - a \cdot \left(t - 1\right)\right) + b \cdot \color{blue}{y} \]

   9. Applied rewrites 56.8%

      \[\leadsto \left(x - a \cdot \left(t - 1\right)\right) + \color{blue}{b \cdot y} \]

   if 1.6e-45 < b

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z \cdot \left(y - 1\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      5. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      6. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \color{blue}{\left(y - 1\right)} \]

      7. lower--.f64 73.4%

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - \color{blue}{1}\right) \]

   4. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]

   5. Taylor expanded in y around 0

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]
   6. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot \color{blue}{z} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      5. lower-*.f64 46.7%

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   7. Applied rewrites 46.7%

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]

   8. Taylor expanded in z around 0

      \[\leadsto x + \color{blue}{b \cdot \left(\left(t + y\right) - 2\right)} \]
   9. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto x + b \cdot \color{blue}{\left(\left(t + y\right) - 2\right)} \]

      2. lower-*.f64 N/A

         \[\leadsto x + b \cdot \left(\left(t + y\right) - \color{blue}{2}\right) \]

      3. lower--.f64 N/A

         \[\leadsto x + b \cdot \left(\left(t + y\right) - 2\right) \]

      4. lower-+.f64 50.8%

         \[\leadsto x + b \cdot \left(\left(t + y\right) - 2\right) \]

   10. Applied rewrites 50.8%

       \[\leadsto x + \color{blue}{b \cdot \left(\left(t + y\right) - 2\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 65.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} t_1 := \left(x + z\right) - y \cdot z\\ t_2 := a \cdot \left(1 - t\right) + b \cdot t\\ \mathbf{if}\;b \leq -9 \cdot 10^{-27}:\\ \;\;\;\;a + \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{elif}\;b \leq -2.6 \cdot 10^{-136}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;b \leq 2.5 \cdot 10^{-304}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;b \leq 3 \cdot 10^{-143}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;b \leq 9:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;x + b \cdot \left(\left(t + y\right) - 2\right)\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (- (+ x z) (* y z))) (t_2 (+ (* a (- 1.0 t)) (* b t))))
  (if (<= b -9e-27)
    (+ a (* (- (+ y t) 2.0) b))
    (if (<= b -2.6e-136)
      t_1
      (if (<= b 2.5e-304)
        t_2
        (if (<= b 3e-143)
          t_1
          (if (<= b 9.0) t_2 (+ x (* b (- (+ t y) 2.0))))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + z) - (y * z);
	double t_2 = (a * (1.0 - t)) + (b * t);
	double tmp;
	if (b <= -9e-27) {
		tmp = a + (((y + t) - 2.0) * b);
	} else if (b <= -2.6e-136) {
		tmp = t_1;
	} else if (b <= 2.5e-304) {
		tmp = t_2;
	} else if (b <= 3e-143) {
		tmp = t_1;
	} else if (b <= 9.0) {
		tmp = t_2;
	} else {
		tmp = x + (b * ((t + y) - 2.0));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (x + z) - (y * z)
    t_2 = (a * (1.0d0 - t)) + (b * t)
    if (b <= (-9d-27)) then
        tmp = a + (((y + t) - 2.0d0) * b)
    else if (b <= (-2.6d-136)) then
        tmp = t_1
    else if (b <= 2.5d-304) then
        tmp = t_2
    else if (b <= 3d-143) then
        tmp = t_1
    else if (b <= 9.0d0) then
        tmp = t_2
    else
        tmp = x + (b * ((t + y) - 2.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + z) - (y * z);
	double t_2 = (a * (1.0 - t)) + (b * t);
	double tmp;
	if (b <= -9e-27) {
		tmp = a + (((y + t) - 2.0) * b);
	} else if (b <= -2.6e-136) {
		tmp = t_1;
	} else if (b <= 2.5e-304) {
		tmp = t_2;
	} else if (b <= 3e-143) {
		tmp = t_1;
	} else if (b <= 9.0) {
		tmp = t_2;
	} else {
		tmp = x + (b * ((t + y) - 2.0));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + z) - (y * z)
	t_2 = (a * (1.0 - t)) + (b * t)
	tmp = 0
	if b <= -9e-27:
		tmp = a + (((y + t) - 2.0) * b)
	elif b <= -2.6e-136:
		tmp = t_1
	elif b <= 2.5e-304:
		tmp = t_2
	elif b <= 3e-143:
		tmp = t_1
	elif b <= 9.0:
		tmp = t_2
	else:
		tmp = x + (b * ((t + y) - 2.0))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + z) - Float64(y * z))
	t_2 = Float64(Float64(a * Float64(1.0 - t)) + Float64(b * t))
	tmp = 0.0
	if (b <= -9e-27)
		tmp = Float64(a + Float64(Float64(Float64(y + t) - 2.0) * b));
	elseif (b <= -2.6e-136)
		tmp = t_1;
	elseif (b <= 2.5e-304)
		tmp = t_2;
	elseif (b <= 3e-143)
		tmp = t_1;
	elseif (b <= 9.0)
		tmp = t_2;
	else
		tmp = Float64(x + Float64(b * Float64(Float64(t + y) - 2.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (x + z) - (y * z);
	t_2 = (a * (1.0 - t)) + (b * t);
	tmp = 0.0;
	if (b <= -9e-27)
		tmp = a + (((y + t) - 2.0) * b);
	elseif (b <= -2.6e-136)
		tmp = t_1;
	elseif (b <= 2.5e-304)
		tmp = t_2;
	elseif (b <= 3e-143)
		tmp = t_1;
	elseif (b <= 9.0)
		tmp = t_2;
	else
		tmp = x + (b * ((t + y) - 2.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x + z), $MachinePrecision] - N[(y * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision] + N[(b * t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -9e-27], N[(a + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -2.6e-136], t$95$1, If[LessEqual[b, 2.5e-304], t$95$2, If[LessEqual[b, 3e-143], t$95$1, If[LessEqual[b, 9.0], t$95$2, N[(x + N[(b * N[(N[(t + y), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if b < -9.0000000000000003e-27

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto a \cdot \color{blue}{\left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. lower--.f64 59.7%

         \[\leadsto a \cdot \left(1 - \color{blue}{t}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Applied rewrites 59.7%

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   5. Taylor expanded in t around 0

      \[\leadsto a + \left(\left(y + t\right) - 2\right) \cdot b \]
   6. Step-by-step derivation

   7. Applied rewrites 46.3%

      \[\leadsto a + \left(\left(y + t\right) - 2\right) \cdot b \]

   if -9.0000000000000003e-27 < b < -2.6e-136 or 2.4999999999999998e-304 < b < 2.9999999999999999e-143

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\color{blue}{\left(x - \left(y - 1\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. sub-flip N/A

         \[\leadsto \left(\color{blue}{\left(x + \left(\mathsf{neg}\left(\left(y - 1\right) \cdot z\right)\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      3. add-flip N/A

         \[\leadsto \left(\color{blue}{\left(x - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\left(y - 1\right) \cdot z\right)\right)\right)\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      4. remove-double-neg N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y - 1\right) \cdot z}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      5. lift-*.f64 N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y - 1\right) \cdot z}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      6. *-commutative N/A

         \[\leadsto \left(\left(x - \color{blue}{z \cdot \left(y - 1\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      7. lift--.f64 N/A

         \[\leadsto \left(\left(x - z \cdot \color{blue}{\left(y - 1\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      8. sub-flip N/A

         \[\leadsto \left(\left(x - z \cdot \color{blue}{\left(y + \left(\mathsf{neg}\left(1\right)\right)\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      9. distribute-rgt-in N/A

         \[\leadsto \left(\left(x - \color{blue}{\left(y \cdot z + \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      10. associate--r+ N/A

          \[\leadsto \left(\color{blue}{\left(\left(x - y \cdot z\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      11. lower--.f64 N/A

          \[\leadsto \left(\color{blue}{\left(\left(x - y \cdot z\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      12. lower--.f64 N/A

          \[\leadsto \left(\left(\color{blue}{\left(x - y \cdot z\right)} - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      13. *-commutative N/A

          \[\leadsto \left(\left(\left(x - \color{blue}{z \cdot y}\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      14. lower-*.f64 N/A

          \[\leadsto \left(\left(\left(x - \color{blue}{z \cdot y}\right) - \left(\mathsf{neg}\left(1\right)\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      15. metadata-eval N/A

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{-1} \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      16. mul-1-neg N/A

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{\left(\mathsf{neg}\left(z\right)\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      17. lower-neg.f64 95.2%

          \[\leadsto \left(\left(\left(x - z \cdot y\right) - \color{blue}{\left(-z\right)}\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   3. Applied rewrites 95.2%

      \[\leadsto \left(\color{blue}{\left(\left(x - z \cdot y\right) - \left(-z\right)\right)} - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z} \]
   5. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - \color{blue}{y \cdot z} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - \color{blue}{y} \cdot z \]

      3. lower-+.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      4. lower-*.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      5. lower--.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      6. lower-+.f64 N/A

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z \]

      7. lower-*.f64 73.4%

         \[\leadsto \left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot \color{blue}{z} \]

   6. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + \left(z + b \cdot \left(\left(t + y\right) - 2\right)\right)\right) - y \cdot z} \]

   7. Taylor expanded in b around 0

      \[\leadsto \left(x + z\right) - \color{blue}{y} \cdot z \]
   8. Step-by-step derivation

      1. lower-+.f64 41.5%

         \[\leadsto \left(x + z\right) - y \cdot z \]

   9. Applied rewrites 41.5%

      \[\leadsto \left(x + z\right) - \color{blue}{y} \cdot z \]

   if -2.6e-136 < b < 2.4999999999999998e-304 or 2.9999999999999999e-143 < b < 9

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto a \cdot \color{blue}{\left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. lower--.f64 59.7%

         \[\leadsto a \cdot \left(1 - \color{blue}{t}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Applied rewrites 59.7%

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   5. Taylor expanded in t around inf

      \[\leadsto a \cdot \left(1 - t\right) + \color{blue}{b \cdot t} \]
   6. Step-by-step derivation

      1. lower-*.f64 39.8%

         \[\leadsto a \cdot \left(1 - t\right) + b \cdot \color{blue}{t} \]

   7. Applied rewrites 39.8%

      \[\leadsto a \cdot \left(1 - t\right) + \color{blue}{b \cdot t} \]

   if 9 < b

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z \cdot \left(y - 1\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      5. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      6. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \color{blue}{\left(y - 1\right)} \]

      7. lower--.f64 73.4%

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - \color{blue}{1}\right) \]

   4. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]

   5. Taylor expanded in y around 0

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]
   6. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot \color{blue}{z} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      5. lower-*.f64 46.7%

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   7. Applied rewrites 46.7%

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]

   8. Taylor expanded in z around 0

      \[\leadsto x + \color{blue}{b \cdot \left(\left(t + y\right) - 2\right)} \]
   9. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto x + b \cdot \color{blue}{\left(\left(t + y\right) - 2\right)} \]

      2. lower-*.f64 N/A

         \[\leadsto x + b \cdot \left(\left(t + y\right) - \color{blue}{2}\right) \]

      3. lower--.f64 N/A

         \[\leadsto x + b \cdot \left(\left(t + y\right) - 2\right) \]

      4. lower-+.f64 50.8%

         \[\leadsto x + b \cdot \left(\left(t + y\right) - 2\right) \]

   10. Applied rewrites 50.8%

       \[\leadsto x + \color{blue}{b \cdot \left(\left(t + y\right) - 2\right)} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 10: 59.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} t_1 := y \cdot \left(b - z\right)\\ \mathbf{if}\;y \leq -2.9 \cdot 10^{+41}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 5.5 \cdot 10^{-25}:\\ \;\;\;\;x - \left(\left(2 - t\right) \cdot b - z\right)\\ \mathbf{elif}\;y \leq 7.6 \cdot 10^{+51}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* y (- b z))))
  (if (<= y -2.9e+41)
    t_1
    (if (<= y 5.5e-25)
      (- x (- (* (- 2.0 t) b) z))
      (if (<= y 7.6e+51) (* t (- b a)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y * (b - z);
	double tmp;
	if (y <= -2.9e+41) {
		tmp = t_1;
	} else if (y <= 5.5e-25) {
		tmp = x - (((2.0 - t) * b) - z);
	} else if (y <= 7.6e+51) {
		tmp = t * (b - a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (b - z)
    if (y <= (-2.9d+41)) then
        tmp = t_1
    else if (y <= 5.5d-25) then
        tmp = x - (((2.0d0 - t) * b) - z)
    else if (y <= 7.6d+51) then
        tmp = t * (b - a)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y * (b - z);
	double tmp;
	if (y <= -2.9e+41) {
		tmp = t_1;
	} else if (y <= 5.5e-25) {
		tmp = x - (((2.0 - t) * b) - z);
	} else if (y <= 7.6e+51) {
		tmp = t * (b - a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y * (b - z)
	tmp = 0
	if y <= -2.9e+41:
		tmp = t_1
	elif y <= 5.5e-25:
		tmp = x - (((2.0 - t) * b) - z)
	elif y <= 7.6e+51:
		tmp = t * (b - a)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y * Float64(b - z))
	tmp = 0.0
	if (y <= -2.9e+41)
		tmp = t_1;
	elseif (y <= 5.5e-25)
		tmp = Float64(x - Float64(Float64(Float64(2.0 - t) * b) - z));
	elseif (y <= 7.6e+51)
		tmp = Float64(t * Float64(b - a));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y * (b - z);
	tmp = 0.0;
	if (y <= -2.9e+41)
		tmp = t_1;
	elseif (y <= 5.5e-25)
		tmp = x - (((2.0 - t) * b) - z);
	elseif (y <= 7.6e+51)
		tmp = t * (b - a);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.9e+41], t$95$1, If[LessEqual[y, 5.5e-25], N[(x - N[(N[(N[(2.0 - t), $MachinePrecision] * b), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7.6e+51], N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y < -2.8999999999999999e41 or 7.5999999999999994e51 < y

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\left(b - z\right)} \]

      2. lower--.f64 32.6%

         \[\leadsto y \cdot \left(b - \color{blue}{z}\right) \]

   4. Applied rewrites 32.6%

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]

   if -2.8999999999999999e41 < y < 5.5e-25

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z \cdot \left(y - 1\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      5. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      6. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \color{blue}{\left(y - 1\right)} \]

      7. lower--.f64 73.4%

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - \color{blue}{1}\right) \]

   4. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]

   5. Taylor expanded in y around 0

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]
   6. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot \color{blue}{z} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      5. lower-*.f64 46.7%

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   7. Applied rewrites 46.7%

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]
   8. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot \color{blue}{z} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      3. mul-1-neg N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \left(\mathsf{neg}\left(z\right)\right) \]

      4. add-flip-rev N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) + z \]

      5. lift-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) + z \]

      6. lift-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) + z \]

      7. fp-cancel-sign-sub-inv N/A

         \[\leadsto \left(x - \left(\mathsf{neg}\left(b\right)\right) \cdot \left(t - 2\right)\right) + z \]

      8. associate-+l- N/A

         \[\leadsto x - \left(\left(\mathsf{neg}\left(b\right)\right) \cdot \left(t - 2\right) - \color{blue}{z}\right) \]

      9. lower--.f64 N/A

         \[\leadsto x - \left(\left(\mathsf{neg}\left(b\right)\right) \cdot \left(t - 2\right) - \color{blue}{z}\right) \]

      10. lower--.f64 N/A

          \[\leadsto x - \left(\left(\mathsf{neg}\left(b\right)\right) \cdot \left(t - 2\right) - z\right) \]

      11. *-commutative N/A

          \[\leadsto x - \left(\left(t - 2\right) \cdot \left(\mathsf{neg}\left(b\right)\right) - z\right) \]

      12. distribute-rgt-neg-in N/A

          \[\leadsto x - \left(\left(\mathsf{neg}\left(\left(t - 2\right) \cdot b\right)\right) - z\right) \]

      13. distribute-lft-neg-in N/A

          \[\leadsto x - \left(\left(\mathsf{neg}\left(\left(t - 2\right)\right)\right) \cdot b - z\right) \]

      14. lift--.f64 N/A

          \[\leadsto x - \left(\left(\mathsf{neg}\left(\left(t - 2\right)\right)\right) \cdot b - z\right) \]

      15. sub-negate-rev N/A

          \[\leadsto x - \left(\left(2 - t\right) \cdot b - z\right) \]

      16. lower-*.f64 N/A

          \[\leadsto x - \left(\left(2 - t\right) \cdot b - z\right) \]

      17. lower--.f64 46.7%

          \[\leadsto x - \left(\left(2 - t\right) \cdot b - z\right) \]

   9. Applied rewrites 46.7%

      \[\leadsto x - \left(\left(2 - t\right) \cdot b - \color{blue}{z}\right) \]

   if 5.5e-25 < y < 7.5999999999999994e51

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\left(b - a\right)} \]

      2. lower--.f64 33.1%

         \[\leadsto t \cdot \left(b - \color{blue}{a}\right) \]

   4. Applied rewrites 33.1%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 11: 57.2% accurate, 1.5× speedup

Math

\[\begin{array}{l} t_1 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -35000000000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 3 \cdot 10^{+25}:\\ \;\;\;\;a + \left(y - 2\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* t (- b a))))
  (if (<= t -35000000000.0)
    t_1
    (if (<= t 3e+25) (+ a (* (- y 2.0) b)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -35000000000.0) {
		tmp = t_1;
	} else if (t <= 3e+25) {
		tmp = a + ((y - 2.0) * b);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t * (b - a)
    if (t <= (-35000000000.0d0)) then
        tmp = t_1
    else if (t <= 3d+25) then
        tmp = a + ((y - 2.0d0) * b)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -35000000000.0) {
		tmp = t_1;
	} else if (t <= 3e+25) {
		tmp = a + ((y - 2.0) * b);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t * (b - a)
	tmp = 0
	if t <= -35000000000.0:
		tmp = t_1
	elif t <= 3e+25:
		tmp = a + ((y - 2.0) * b)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -35000000000.0)
		tmp = t_1;
	elseif (t <= 3e+25)
		tmp = Float64(a + Float64(Float64(y - 2.0) * b));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t * (b - a);
	tmp = 0.0;
	if (t <= -35000000000.0)
		tmp = t_1;
	elseif (t <= 3e+25)
		tmp = a + ((y - 2.0) * b);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -35000000000.0], t$95$1, If[LessEqual[t, 3e+25], N[(a + N[(N[(y - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if t < -3.5e10 or 3.0000000000000001e25 < t

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\left(b - a\right)} \]

      2. lower--.f64 33.1%

         \[\leadsto t \cdot \left(b - \color{blue}{a}\right) \]

   4. Applied rewrites 33.1%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   if -3.5e10 < t < 3.0000000000000001e25

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto a \cdot \color{blue}{\left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. lower--.f64 59.7%

         \[\leadsto a \cdot \left(1 - \color{blue}{t}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Applied rewrites 59.7%

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   5. Taylor expanded in t around 0

      \[\leadsto a + \left(\left(y + t\right) - 2\right) \cdot b \]
   6. Step-by-step derivation

   7. Applied rewrites 46.3%

      \[\leadsto a + \left(\left(y + t\right) - 2\right) \cdot b \]

   8. Taylor expanded in t around 0

      \[\leadsto a + \color{blue}{\left(y - 2\right)} \cdot b \]
   9. Step-by-step derivation

      1. lower--.f64 33.2%

         \[\leadsto a + \left(y - \color{blue}{2}\right) \cdot b \]

   10. Applied rewrites 33.2%

       \[\leadsto a + \color{blue}{\left(y - 2\right)} \cdot b \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 54.7% accurate, 1.4× speedup

Math

\[\begin{array}{l} t_1 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -35000000000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 2.9 \cdot 10^{-218}:\\ \;\;\;\;x + b \cdot \left(y - 2\right)\\ \mathbf{elif}\;t \leq 3 \cdot 10^{+25}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* t (- b a))))
  (if (<= t -35000000000.0)
    t_1
    (if (<= t 2.9e-218)
      (+ x (* b (- y 2.0)))
      (if (<= t 3e+25) (* y (- b z)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -35000000000.0) {
		tmp = t_1;
	} else if (t <= 2.9e-218) {
		tmp = x + (b * (y - 2.0));
	} else if (t <= 3e+25) {
		tmp = y * (b - z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t * (b - a)
    if (t <= (-35000000000.0d0)) then
        tmp = t_1
    else if (t <= 2.9d-218) then
        tmp = x + (b * (y - 2.0d0))
    else if (t <= 3d+25) then
        tmp = y * (b - z)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -35000000000.0) {
		tmp = t_1;
	} else if (t <= 2.9e-218) {
		tmp = x + (b * (y - 2.0));
	} else if (t <= 3e+25) {
		tmp = y * (b - z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t * (b - a)
	tmp = 0
	if t <= -35000000000.0:
		tmp = t_1
	elif t <= 2.9e-218:
		tmp = x + (b * (y - 2.0))
	elif t <= 3e+25:
		tmp = y * (b - z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -35000000000.0)
		tmp = t_1;
	elseif (t <= 2.9e-218)
		tmp = Float64(x + Float64(b * Float64(y - 2.0)));
	elseif (t <= 3e+25)
		tmp = Float64(y * Float64(b - z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t * (b - a);
	tmp = 0.0;
	if (t <= -35000000000.0)
		tmp = t_1;
	elseif (t <= 2.9e-218)
		tmp = x + (b * (y - 2.0));
	elseif (t <= 3e+25)
		tmp = y * (b - z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -35000000000.0], t$95$1, If[LessEqual[t, 2.9e-218], N[(x + N[(b * N[(y - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 3e+25], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if t < -3.5e10 or 3.0000000000000001e25 < t

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\left(b - a\right)} \]

      2. lower--.f64 33.1%

         \[\leadsto t \cdot \left(b - \color{blue}{a}\right) \]

   4. Applied rewrites 33.1%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   if -3.5e10 < t < 2.9000000000000002e-218

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z \cdot \left(y - 1\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      5. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      6. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \color{blue}{\left(y - 1\right)} \]

      7. lower--.f64 73.4%

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - \color{blue}{1}\right) \]

   4. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]

   5. Taylor expanded in y around 0

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]
   6. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot \color{blue}{z} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      5. lower-*.f64 46.7%

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   7. Applied rewrites 46.7%

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]

   8. Taylor expanded in z around 0

      \[\leadsto x + \color{blue}{b \cdot \left(\left(t + y\right) - 2\right)} \]
   9. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto x + b \cdot \color{blue}{\left(\left(t + y\right) - 2\right)} \]

      2. lower-*.f64 N/A

         \[\leadsto x + b \cdot \left(\left(t + y\right) - \color{blue}{2}\right) \]

      3. lower--.f64 N/A

         \[\leadsto x + b \cdot \left(\left(t + y\right) - 2\right) \]

      4. lower-+.f64 50.8%

         \[\leadsto x + b \cdot \left(\left(t + y\right) - 2\right) \]

   10. Applied rewrites 50.8%

       \[\leadsto x + \color{blue}{b \cdot \left(\left(t + y\right) - 2\right)} \]

   11. Taylor expanded in t around 0

       \[\leadsto x + b \cdot \color{blue}{\left(y - 2\right)} \]
   12. Step-by-step derivation

       1. lower-+.f64 N/A

          \[\leadsto x + b \cdot \left(y - \color{blue}{2}\right) \]

       2. lower-*.f64 N/A

          \[\leadsto x + b \cdot \left(y - 2\right) \]

       3. lower--.f64 37.5%

          \[\leadsto x + b \cdot \left(y - 2\right) \]

   13. Applied rewrites 37.5%

       \[\leadsto x + b \cdot \color{blue}{\left(y - 2\right)} \]

   if 2.9000000000000002e-218 < t < 3.0000000000000001e25

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\left(b - z\right)} \]

      2. lower--.f64 32.6%

         \[\leadsto y \cdot \left(b - \color{blue}{z}\right) \]

   4. Applied rewrites 32.6%

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 13: 51.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} t_1 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -35000000000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 8.5 \cdot 10^{-216}:\\ \;\;\;\;a + b \cdot y\\ \mathbf{elif}\;t \leq 3 \cdot 10^{+25}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* t (- b a))))
  (if (<= t -35000000000.0)
    t_1
    (if (<= t 8.5e-216)
      (+ a (* b y))
      (if (<= t 3e+25) (* y (- b z)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -35000000000.0) {
		tmp = t_1;
	} else if (t <= 8.5e-216) {
		tmp = a + (b * y);
	} else if (t <= 3e+25) {
		tmp = y * (b - z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t * (b - a)
    if (t <= (-35000000000.0d0)) then
        tmp = t_1
    else if (t <= 8.5d-216) then
        tmp = a + (b * y)
    else if (t <= 3d+25) then
        tmp = y * (b - z)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -35000000000.0) {
		tmp = t_1;
	} else if (t <= 8.5e-216) {
		tmp = a + (b * y);
	} else if (t <= 3e+25) {
		tmp = y * (b - z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t * (b - a)
	tmp = 0
	if t <= -35000000000.0:
		tmp = t_1
	elif t <= 8.5e-216:
		tmp = a + (b * y)
	elif t <= 3e+25:
		tmp = y * (b - z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -35000000000.0)
		tmp = t_1;
	elseif (t <= 8.5e-216)
		tmp = Float64(a + Float64(b * y));
	elseif (t <= 3e+25)
		tmp = Float64(y * Float64(b - z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t * (b - a);
	tmp = 0.0;
	if (t <= -35000000000.0)
		tmp = t_1;
	elseif (t <= 8.5e-216)
		tmp = a + (b * y);
	elseif (t <= 3e+25)
		tmp = y * (b - z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -35000000000.0], t$95$1, If[LessEqual[t, 8.5e-216], N[(a + N[(b * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 3e+25], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if t < -3.5e10 or 3.0000000000000001e25 < t

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\left(b - a\right)} \]

      2. lower--.f64 33.1%

         \[\leadsto t \cdot \left(b - \color{blue}{a}\right) \]

   4. Applied rewrites 33.1%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   if -3.5e10 < t < 8.5e-216

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto a \cdot \color{blue}{\left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. lower--.f64 59.7%

         \[\leadsto a \cdot \left(1 - \color{blue}{t}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   4. Applied rewrites 59.7%

      \[\leadsto \color{blue}{a \cdot \left(1 - t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   5. Taylor expanded in t around 0

      \[\leadsto a + \left(\left(y + t\right) - 2\right) \cdot b \]
   6. Step-by-step derivation

   7. Applied rewrites 46.3%

      \[\leadsto a + \left(\left(y + t\right) - 2\right) \cdot b \]

   8. Taylor expanded in y around inf

      \[\leadsto a + \color{blue}{b \cdot y} \]
   9. Step-by-step derivation

      1. lower-*.f64 27.2%

         \[\leadsto a + b \cdot \color{blue}{y} \]

   10. Applied rewrites 27.2%

       \[\leadsto a + \color{blue}{b \cdot y} \]

   if 8.5e-216 < t < 3.0000000000000001e25

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\left(b - z\right)} \]

      2. lower--.f64 32.6%

         \[\leadsto y \cdot \left(b - \color{blue}{z}\right) \]

   4. Applied rewrites 32.6%

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 14: 48.1% accurate, 1.8× speedup

Math

\[\begin{array}{l} t_1 := y \cdot \left(b - z\right)\\ \mathbf{if}\;y \leq -1.72 \cdot 10^{-77}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 7.6 \cdot 10^{+51}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* y (- b z))))
  (if (<= y -1.72e-77) t_1 (if (<= y 7.6e+51) (* t (- b a)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y * (b - z);
	double tmp;
	if (y <= -1.72e-77) {
		tmp = t_1;
	} else if (y <= 7.6e+51) {
		tmp = t * (b - a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (b - z)
    if (y <= (-1.72d-77)) then
        tmp = t_1
    else if (y <= 7.6d+51) then
        tmp = t * (b - a)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y * (b - z);
	double tmp;
	if (y <= -1.72e-77) {
		tmp = t_1;
	} else if (y <= 7.6e+51) {
		tmp = t * (b - a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y * (b - z)
	tmp = 0
	if y <= -1.72e-77:
		tmp = t_1
	elif y <= 7.6e+51:
		tmp = t * (b - a)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y * Float64(b - z))
	tmp = 0.0
	if (y <= -1.72e-77)
		tmp = t_1;
	elseif (y <= 7.6e+51)
		tmp = Float64(t * Float64(b - a));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y * (b - z);
	tmp = 0.0;
	if (y <= -1.72e-77)
		tmp = t_1;
	elseif (y <= 7.6e+51)
		tmp = t * (b - a);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.72e-77], t$95$1, If[LessEqual[y, 7.6e+51], N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.72e-77 or 7.5999999999999994e51 < y

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\left(b - z\right)} \]

      2. lower--.f64 32.6%

         \[\leadsto y \cdot \left(b - \color{blue}{z}\right) \]

   4. Applied rewrites 32.6%

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]

   if -1.72e-77 < y < 7.5999999999999994e51

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\left(b - a\right)} \]

      2. lower--.f64 33.1%

         \[\leadsto t \cdot \left(b - \color{blue}{a}\right) \]

   4. Applied rewrites 33.1%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 15: 48.1% accurate, 1.8× speedup

Math

\[\begin{array}{l} t_1 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -6.2 \cdot 10^{+23}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 9.5 \cdot 10^{+24}:\\ \;\;\;\;z + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* t (- b a))))
  (if (<= t -6.2e+23) t_1 (if (<= t 9.5e+24) (+ z x) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -6.2e+23) {
		tmp = t_1;
	} else if (t <= 9.5e+24) {
		tmp = z + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t * (b - a)
    if (t <= (-6.2d+23)) then
        tmp = t_1
    else if (t <= 9.5d+24) then
        tmp = z + x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -6.2e+23) {
		tmp = t_1;
	} else if (t <= 9.5e+24) {
		tmp = z + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t * (b - a)
	tmp = 0
	if t <= -6.2e+23:
		tmp = t_1
	elif t <= 9.5e+24:
		tmp = z + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -6.2e+23)
		tmp = t_1;
	elseif (t <= 9.5e+24)
		tmp = Float64(z + x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t * (b - a);
	tmp = 0.0;
	if (t <= -6.2e+23)
		tmp = t_1;
	elseif (t <= 9.5e+24)
		tmp = z + x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -6.2e+23], t$95$1, If[LessEqual[t, 9.5e+24], N[(z + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if t < -6.1999999999999994e23 or 9.5000000000000001e24 < t

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\left(b - a\right)} \]

      2. lower--.f64 33.1%

         \[\leadsto t \cdot \left(b - \color{blue}{a}\right) \]

   4. Applied rewrites 33.1%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   if -6.1999999999999994e23 < t < 9.5000000000000001e24

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z \cdot \left(y - 1\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      5. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      6. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \color{blue}{\left(y - 1\right)} \]

      7. lower--.f64 73.4%

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - \color{blue}{1}\right) \]

   4. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]

   5. Taylor expanded in y around 0

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]
   6. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot \color{blue}{z} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      5. lower-*.f64 46.7%

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   7. Applied rewrites 46.7%

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]

   8. Taylor expanded in b around 0

      \[\leadsto x - -1 \cdot \color{blue}{z} \]
   9. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto x - -1 \cdot z \]

      2. lower-*.f64 25.4%

         \[\leadsto x - -1 \cdot z \]

   10. Applied rewrites 25.4%

       \[\leadsto x - -1 \cdot \color{blue}{z} \]
   11. Step-by-step derivation

       1. lift--.f64 N/A

          \[\leadsto x - -1 \cdot z \]

       2. sub-flip N/A

          \[\leadsto x + \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

       3. +-commutative N/A

          \[\leadsto \left(\mathsf{neg}\left(-1 \cdot z\right)\right) + x \]

       4. lower-+.f64 N/A

          \[\leadsto \left(\mathsf{neg}\left(-1 \cdot z\right)\right) + x \]

       5. lift-*.f64 N/A

          \[\leadsto \left(\mathsf{neg}\left(-1 \cdot z\right)\right) + x \]

       6. distribute-lft-neg-out N/A

          \[\leadsto \left(\mathsf{neg}\left(-1\right)\right) \cdot z + x \]

       7. metadata-eval N/A

          \[\leadsto 1 \cdot z + x \]

       8. *-lft-identity 25.4%

          \[\leadsto z + x \]

   12. Applied rewrites 25.4%

       \[\leadsto z + x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 16: 33.0% accurate, 2.1× speedup

Math

\[\begin{array}{l} \mathbf{if}\;t \leq -8.5 \cdot 10^{+24}:\\ \;\;\;\;t \cdot b\\ \mathbf{elif}\;t \leq 3 \cdot 10^{+25}:\\ \;\;\;\;z + x\\ \mathbf{else}:\\ \;\;\;\;t \cdot b\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (if (<= t -8.5e+24) (* t b) (if (<= t 3e+25) (+ z x) (* t b))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -8.5e+24) {
		tmp = t * b;
	} else if (t <= 3e+25) {
		tmp = z + x;
	} else {
		tmp = t * b;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (t <= (-8.5d+24)) then
        tmp = t * b
    else if (t <= 3d+25) then
        tmp = z + x
    else
        tmp = t * b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -8.5e+24) {
		tmp = t * b;
	} else if (t <= 3e+25) {
		tmp = z + x;
	} else {
		tmp = t * b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -8.5e+24:
		tmp = t * b
	elif t <= 3e+25:
		tmp = z + x
	else:
		tmp = t * b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -8.5e+24)
		tmp = Float64(t * b);
	elseif (t <= 3e+25)
		tmp = Float64(z + x);
	else
		tmp = Float64(t * b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -8.5e+24)
		tmp = t * b;
	elseif (t <= 3e+25)
		tmp = z + x;
	else
		tmp = t * b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -8.5e+24], N[(t * b), $MachinePrecision], If[LessEqual[t, 3e+25], N[(z + x), $MachinePrecision], N[(t * b), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if t < -8.4999999999999996e24 or 3.0000000000000001e25 < t

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\left(b - a\right)} \]

      2. lower--.f64 33.1%

         \[\leadsto t \cdot \left(b - \color{blue}{a}\right) \]

   4. Applied rewrites 33.1%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   5. Taylor expanded in a around 0

      \[\leadsto t \cdot b \]
   6. Step-by-step derivation

   7. Applied rewrites 17.3%

      \[\leadsto t \cdot b \]

   if -8.4999999999999996e24 < t < 3.0000000000000001e25

   1. Initial program 95.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z \cdot \left(y - 1\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      5. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

      6. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \color{blue}{\left(y - 1\right)} \]

      7. lower--.f64 73.4%

         \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - \color{blue}{1}\right) \]

   4. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]

   5. Taylor expanded in y around 0

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]
   6. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot \color{blue}{z} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      4. lower--.f64 N/A

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

      5. lower-*.f64 46.7%

         \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   7. Applied rewrites 46.7%

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]

   8. Taylor expanded in b around 0

      \[\leadsto x - -1 \cdot \color{blue}{z} \]
   9. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto x - -1 \cdot z \]

      2. lower-*.f64 25.4%

         \[\leadsto x - -1 \cdot z \]

   10. Applied rewrites 25.4%

       \[\leadsto x - -1 \cdot \color{blue}{z} \]
   11. Step-by-step derivation

       1. lift--.f64 N/A

          \[\leadsto x - -1 \cdot z \]

       2. sub-flip N/A

          \[\leadsto x + \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

       3. +-commutative N/A

          \[\leadsto \left(\mathsf{neg}\left(-1 \cdot z\right)\right) + x \]

       4. lower-+.f64 N/A

          \[\leadsto \left(\mathsf{neg}\left(-1 \cdot z\right)\right) + x \]

       5. lift-*.f64 N/A

          \[\leadsto \left(\mathsf{neg}\left(-1 \cdot z\right)\right) + x \]

       6. distribute-lft-neg-out N/A

          \[\leadsto \left(\mathsf{neg}\left(-1\right)\right) \cdot z + x \]

       7. metadata-eval N/A

          \[\leadsto 1 \cdot z + x \]

       8. *-lft-identity 25.4%

          \[\leadsto z + x \]

   12. Applied rewrites 25.4%

       \[\leadsto z + x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 17: 25.4% accurate, 9.3× speedup

Math

\[z + x \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (+ z x))

C

double code(double x, double y, double z, double t, double a, double b) {
	return z + x;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = z + x
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return z + x;
}

Python

def code(x, y, z, t, a, b):
	return z + x

Julia

function code(x, y, z, t, a, b)
	return Float64(z + x)
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = z + x;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(z + x), $MachinePrecision]

Derivation

1. Initial program 95.2%

   \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

2. Taylor expanded in a around 0

   \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
3. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z \cdot \left(y - 1\right)} \]

   2. lower-+.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

   3. lower-*.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

   4. lower--.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

   5. lower-+.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

   6. lower-*.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \color{blue}{\left(y - 1\right)} \]

   7. lower--.f64 73.4%

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - \color{blue}{1}\right) \]

4. Applied rewrites 73.4%

   \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]

5. Taylor expanded in y around 0

   \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]
6. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot \color{blue}{z} \]

   2. lower-+.f64 N/A

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   3. lower-*.f64 N/A

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   4. lower--.f64 N/A

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   5. lower-*.f64 46.7%

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

7. Applied rewrites 46.7%

   \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]

8. Taylor expanded in b around 0

   \[\leadsto x - -1 \cdot \color{blue}{z} \]
9. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto x - -1 \cdot z \]

   2. lower-*.f64 25.4%

      \[\leadsto x - -1 \cdot z \]

10. Applied rewrites 25.4%

    \[\leadsto x - -1 \cdot \color{blue}{z} \]
11. Step-by-step derivation

    1. lift--.f64 N/A

       \[\leadsto x - -1 \cdot z \]

    2. sub-flip N/A

       \[\leadsto x + \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

    3. +-commutative N/A

       \[\leadsto \left(\mathsf{neg}\left(-1 \cdot z\right)\right) + x \]

    4. lower-+.f64 N/A

       \[\leadsto \left(\mathsf{neg}\left(-1 \cdot z\right)\right) + x \]

    5. lift-*.f64 N/A

       \[\leadsto \left(\mathsf{neg}\left(-1 \cdot z\right)\right) + x \]

    6. distribute-lft-neg-out N/A

       \[\leadsto \left(\mathsf{neg}\left(-1\right)\right) \cdot z + x \]

    7. metadata-eval N/A

       \[\leadsto 1 \cdot z + x \]

    8. *-lft-identity 25.4%

       \[\leadsto z + x \]

12. Applied rewrites 25.4%

    \[\leadsto z + x \]
13. Add Preprocessing

Alternative 18: 12.1% accurate, 37.0× speedup

Math

\[z \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  z)

C

double code(double x, double y, double z, double t, double a, double b) {
	return z;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = z
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return z;
}

Python

def code(x, y, z, t, a, b):
	return z

Julia

function code(x, y, z, t, a, b)
	return z
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = z;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := z

Derivation

1. Initial program 95.2%

   \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

2. Taylor expanded in a around 0

   \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]
3. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z \cdot \left(y - 1\right)} \]

   2. lower-+.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - \color{blue}{z} \cdot \left(y - 1\right) \]

   3. lower-*.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

   4. lower--.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

   5. lower-+.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right) \]

   6. lower-*.f64 N/A

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \color{blue}{\left(y - 1\right)} \]

   7. lower--.f64 73.4%

      \[\leadsto \left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - \color{blue}{1}\right) \]

4. Applied rewrites 73.4%

   \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]

5. Taylor expanded in y around 0

   \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]
6. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot \color{blue}{z} \]

   2. lower-+.f64 N/A

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   3. lower-*.f64 N/A

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   4. lower--.f64 N/A

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

   5. lower-*.f64 46.7%

      \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - -1 \cdot z \]

7. Applied rewrites 46.7%

   \[\leadsto \left(x + b \cdot \left(t - 2\right)\right) - \color{blue}{-1 \cdot z} \]

8. Taylor expanded in b around 0

   \[\leadsto x - -1 \cdot \color{blue}{z} \]
9. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto x - -1 \cdot z \]

   2. lower-*.f64 25.4%

      \[\leadsto x - -1 \cdot z \]

10. Applied rewrites 25.4%

    \[\leadsto x - -1 \cdot \color{blue}{z} \]

11. Taylor expanded in x around 0

    \[\leadsto z \]
12. Step-by-step derivation

13. Applied rewrites 12.1%

    \[\leadsto z \]
14. Add Preprocessing

Reproduce

herbie shell --seed 2025258 
(FPCore (x y z t a b)
  :name "Statistics.Distribution.Beta:$centropy from math-functions-0.1.5.2"
  :precision binary64
  (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* (- (+ y t) 2.0) b)))