Graphics.Rasterific.CubicBezier:cachedBezierAt from Rasterific-0.6.1

Percentage Accurate: 92.8% → 96.6%

Time: 5.6s

Alternatives: 12

Speedup: 1.0×

• 67.4% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x + (y * z)) + (t * a)) + ((a * z) * 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 * z)) + (t * a)) + ((a * z) * b)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 92.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x + (y * z)) + (t * a)) + ((a * z) * 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 * z)) + (t * a)) + ((a * z) * b)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 96.6% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Split input into 2 regimes

2. if (+.f64 (+.f64 (+.f64 x (*.f64 y z)) (*.f64 t a)) (*.f64 (*.f64 a z) b)) < +inf.0

   1. Initial program 92.8%

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

   if +inf.0 < (+.f64 (+.f64 (+.f64 x (*.f64 y z)) (*.f64 t a)) (*.f64 (*.f64 a z) b))

   1. Initial program 92.8%

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

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 52.1%

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

   4. Applied rewrites 52.1%

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

Alternative 2: 94.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \mathbf{if}\;z \leq -24000000000000000375037519247012851550741234072148780799542595377534166112665414846552412052370866763671531590001685424143056259581476864:\\ \;\;\;\;z \cdot \left(y + a \cdot b\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(b \cdot z + t\right) \cdot a + z \cdot y\right) + x\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (if (<=
     z
     -24000000000000000375037519247012851550741234072148780799542595377534166112665414846552412052370866763671531590001685424143056259581476864)
  (* z (+ y (* a b)))
  (+ (+ (* (+ (* b z) t) a) (* z y)) x)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -2.4e+136) {
		tmp = z * (y + (a * b));
	} else {
		tmp = ((((b * z) + t) * a) + (z * y)) + x;
	}
	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 <= (-2.4d+136)) then
        tmp = z * (y + (a * b))
    else
        tmp = ((((b * z) + t) * a) + (z * y)) + x
    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 <= -2.4e+136) {
		tmp = z * (y + (a * b));
	} else {
		tmp = ((((b * z) + t) * a) + (z * y)) + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -2.4e+136:
		tmp = z * (y + (a * b))
	else:
		tmp = ((((b * z) + t) * a) + (z * y)) + x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -2.4e+136)
		tmp = Float64(z * Float64(y + Float64(a * b)));
	else
		tmp = Float64(Float64(Float64(Float64(Float64(b * z) + t) * a) + Float64(z * y)) + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -2.4e+136)
		tmp = z * (y + (a * b));
	else
		tmp = ((((b * z) + t) * a) + (z * y)) + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -24000000000000000375037519247012851550741234072148780799542595377534166112665414846552412052370866763671531590001685424143056259581476864], N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(N[(b * z), $MachinePrecision] + t), $MachinePrecision] * a), $MachinePrecision] + N[(z * y), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -2.4e136

   1. Initial program 92.8%

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

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 52.1%

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

   4. Applied rewrites 52.1%

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

   if -2.4e136 < z

   1. Initial program 92.8%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. lift-+.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. associate-+l+ N/A

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

      7. associate--l+ N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 N/A

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

   3. Applied rewrites 94.2%

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

Alternative 3: 86.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} t_1 := \left(t \cdot a + z \cdot y\right) + x\\ \mathbf{if}\;t \leq \frac{-4942654315294039}{13729595320261219429963801598162786434538870600286610818788926918371086366795312104245119281322909109954592622782961716074243975999433287625148056582230114304}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 204999999999999997902848:\\ \;\;\;\;x + \left(a \cdot \left(b \cdot z\right) + y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (+ (+ (* t a) (* z y)) x)))
  (if (<=
       t
       -4942654315294039/13729595320261219429963801598162786434538870600286610818788926918371086366795312104245119281322909109954592622782961716074243975999433287625148056582230114304)
    t_1
    (if (<= t 204999999999999997902848)
      (+ x (+ (* a (* b z)) (* y z)))
      t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((t * a) + (z * y)) + x;
	double tmp;
	if (t <= -3.6e-142) {
		tmp = t_1;
	} else if (t <= 2.05e+23) {
		tmp = x + ((a * (b * z)) + (y * 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 * a) + (z * y)) + x
    if (t <= (-3.6d-142)) then
        tmp = t_1
    else if (t <= 2.05d+23) then
        tmp = x + ((a * (b * z)) + (y * 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 * a) + (z * y)) + x;
	double tmp;
	if (t <= -3.6e-142) {
		tmp = t_1;
	} else if (t <= 2.05e+23) {
		tmp = x + ((a * (b * z)) + (y * z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((t * a) + (z * y)) + x
	tmp = 0
	if t <= -3.6e-142:
		tmp = t_1
	elif t <= 2.05e+23:
		tmp = x + ((a * (b * z)) + (y * z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(t * a) + Float64(z * y)) + x)
	tmp = 0.0
	if (t <= -3.6e-142)
		tmp = t_1;
	elseif (t <= 2.05e+23)
		tmp = Float64(x + Float64(Float64(a * Float64(b * z)) + Float64(y * z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((t * a) + (z * y)) + x;
	tmp = 0.0;
	if (t <= -3.6e-142)
		tmp = t_1;
	elseif (t <= 2.05e+23)
		tmp = x + ((a * (b * z)) + (y * z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(t * a), $MachinePrecision] + N[(z * y), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]}, If[LessEqual[t, -4942654315294039/13729595320261219429963801598162786434538870600286610818788926918371086366795312104245119281322909109954592622782961716074243975999433287625148056582230114304], t$95$1, If[LessEqual[t, 204999999999999997902848], N[(x + N[(N[(a * N[(b * z), $MachinePrecision]), $MachinePrecision] + N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if t < -3.6e-142 or 2.05e23 < t

   1. Initial program 92.8%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. lift-+.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. associate-+l+ N/A

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

      7. associate--l+ N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 N/A

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

   3. Applied rewrites 94.2%

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

   4. Taylor expanded in z around 0

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

   6. Applied rewrites 76.3%

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

   if -3.6e-142 < t < 2.05e23

   1. Initial program 92.8%

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

   2. Taylor expanded in t around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-*.f64 70.9%

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

   4. Applied rewrites 70.9%

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

Alternative 4: 83.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} t_1 := \left(t \cdot a + z \cdot y\right) + x\\ \mathbf{if}\;y \leq \frac{-8652089692998945}{23384026197294446691258957323460528314494920687616}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 13000000000000000250220688488498292662086011053465034426066648794239336448:\\ \;\;\;\;a \cdot \left(t + b \cdot z\right) + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (+ (+ (* t a) (* z y)) x)))
  (if (<=
       y
       -8652089692998945/23384026197294446691258957323460528314494920687616)
    t_1
    (if (<=
         y
         13000000000000000250220688488498292662086011053465034426066648794239336448)
      (+ (* a (+ t (* b z))) x)
      t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((t * a) + (z * y)) + x;
	double tmp;
	if (y <= -3.7e-34) {
		tmp = t_1;
	} else if (y <= 1.3e+73) {
		tmp = (a * (t + (b * 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 * a) + (z * y)) + x
    if (y <= (-3.7d-34)) then
        tmp = t_1
    else if (y <= 1.3d+73) then
        tmp = (a * (t + (b * 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 * a) + (z * y)) + x;
	double tmp;
	if (y <= -3.7e-34) {
		tmp = t_1;
	} else if (y <= 1.3e+73) {
		tmp = (a * (t + (b * z))) + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((t * a) + (z * y)) + x
	tmp = 0
	if y <= -3.7e-34:
		tmp = t_1
	elif y <= 1.3e+73:
		tmp = (a * (t + (b * z))) + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(t * a) + Float64(z * y)) + x)
	tmp = 0.0
	if (y <= -3.7e-34)
		tmp = t_1;
	elseif (y <= 1.3e+73)
		tmp = Float64(Float64(a * Float64(t + Float64(b * z))) + x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((t * a) + (z * y)) + x;
	tmp = 0.0;
	if (y <= -3.7e-34)
		tmp = t_1;
	elseif (y <= 1.3e+73)
		tmp = (a * (t + (b * z))) + x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(t * a), $MachinePrecision] + N[(z * y), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]}, If[LessEqual[y, -8652089692998945/23384026197294446691258957323460528314494920687616], t$95$1, If[LessEqual[y, 13000000000000000250220688488498292662086011053465034426066648794239336448], N[(N[(a * N[(t + N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -3.6999999999999999e-34 or 1.3e73 < y

   1. Initial program 92.8%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. lift-+.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. associate-+l+ N/A

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

      7. associate--l+ N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 N/A

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

   3. Applied rewrites 94.2%

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

   4. Taylor expanded in z around 0

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

   6. Applied rewrites 76.3%

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

   if -3.6999999999999999e-34 < y < 1.3e73

   1. Initial program 92.8%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. lift-+.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. associate-+l+ N/A

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

      7. associate--l+ N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 N/A

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

   3. Applied rewrites 94.2%

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

   4. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 73.7%

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

   6. Applied rewrites 73.7%

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

Alternative 5: 82.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} t_1 := z \cdot \left(y + a \cdot b\right)\\ \mathbf{if}\;z \leq -2199999999999999885260233413848058330640843229616793674173628649221800130588415029452562203177085987418885945263028424059069640212480:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 35999999999999999245740906339966790167030859524511658159104163103288924474876839835625062400:\\ \;\;\;\;a \cdot \left(t + b \cdot z\right) + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* z (+ y (* a b)))))
  (if (<=
       z
       -2199999999999999885260233413848058330640843229616793674173628649221800130588415029452562203177085987418885945263028424059069640212480)
    t_1
    (if (<=
         z
         35999999999999999245740906339966790167030859524511658159104163103288924474876839835625062400)
      (+ (* a (+ t (* b z))) x)
      t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (y + (a * b));
	double tmp;
	if (z <= -2.2e+132) {
		tmp = t_1;
	} else if (z <= 3.6e+91) {
		tmp = (a * (t + (b * 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 = z * (y + (a * b))
    if (z <= (-2.2d+132)) then
        tmp = t_1
    else if (z <= 3.6d+91) then
        tmp = (a * (t + (b * 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 = z * (y + (a * b));
	double tmp;
	if (z <= -2.2e+132) {
		tmp = t_1;
	} else if (z <= 3.6e+91) {
		tmp = (a * (t + (b * z))) + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (y + (a * b))
	tmp = 0
	if z <= -2.2e+132:
		tmp = t_1
	elif z <= 3.6e+91:
		tmp = (a * (t + (b * z))) + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(y + Float64(a * b)))
	tmp = 0.0
	if (z <= -2.2e+132)
		tmp = t_1;
	elseif (z <= 3.6e+91)
		tmp = Float64(Float64(a * Float64(t + Float64(b * z))) + x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z * (y + (a * b));
	tmp = 0.0;
	if (z <= -2.2e+132)
		tmp = t_1;
	elseif (z <= 3.6e+91)
		tmp = (a * (t + (b * z))) + x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2199999999999999885260233413848058330640843229616793674173628649221800130588415029452562203177085987418885945263028424059069640212480], t$95$1, If[LessEqual[z, 35999999999999999245740906339966790167030859524511658159104163103288924474876839835625062400], N[(N[(a * N[(t + N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -2.1999999999999999e132 or 3.5999999999999999e91 < z

   1. Initial program 92.8%

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

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 52.1%

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

   4. Applied rewrites 52.1%

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

   if -2.1999999999999999e132 < z < 3.5999999999999999e91

   1. Initial program 92.8%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. lift-+.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. associate-+l+ N/A

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

      7. associate--l+ N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 N/A

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

   3. Applied rewrites 94.2%

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

   4. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 73.7%

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

   6. Applied rewrites 73.7%

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

Alternative 6: 73.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} t_1 := z \cdot \left(y + a \cdot b\right)\\ \mathbf{if}\;z \leq \frac{-2374940160662717}{91343852333181432387730302044767688728495783936}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq \frac{3470978933371479}{266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072}:\\ \;\;\;\;a \cdot t + x\\ \mathbf{elif}\;z \leq 299999999999999999088617516181804470671953197798974385752637440:\\ \;\;\;\;x + \left(a \cdot z\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* z (+ y (* a b)))))
  (if (<=
       z
       -2374940160662717/91343852333181432387730302044767688728495783936)
    t_1
    (if (<=
         z
         3470978933371479/266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072)
      (+ (* a t) x)
      (if (<=
           z
           299999999999999999088617516181804470671953197798974385752637440)
        (+ x (* (* a z) b))
        t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (y + (a * b));
	double tmp;
	if (z <= -2.6e-32) {
		tmp = t_1;
	} else if (z <= 1.3e-80) {
		tmp = (a * t) + x;
	} else if (z <= 3e+62) {
		tmp = x + ((a * z) * 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 = z * (y + (a * b))
    if (z <= (-2.6d-32)) then
        tmp = t_1
    else if (z <= 1.3d-80) then
        tmp = (a * t) + x
    else if (z <= 3d+62) then
        tmp = x + ((a * z) * 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 = z * (y + (a * b));
	double tmp;
	if (z <= -2.6e-32) {
		tmp = t_1;
	} else if (z <= 1.3e-80) {
		tmp = (a * t) + x;
	} else if (z <= 3e+62) {
		tmp = x + ((a * z) * b);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (y + (a * b))
	tmp = 0
	if z <= -2.6e-32:
		tmp = t_1
	elif z <= 1.3e-80:
		tmp = (a * t) + x
	elif z <= 3e+62:
		tmp = x + ((a * z) * b)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(y + Float64(a * b)))
	tmp = 0.0
	if (z <= -2.6e-32)
		tmp = t_1;
	elseif (z <= 1.3e-80)
		tmp = Float64(Float64(a * t) + x);
	elseif (z <= 3e+62)
		tmp = Float64(x + Float64(Float64(a * z) * b));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z * (y + (a * b));
	tmp = 0.0;
	if (z <= -2.6e-32)
		tmp = t_1;
	elseif (z <= 1.3e-80)
		tmp = (a * t) + x;
	elseif (z <= 3e+62)
		tmp = x + ((a * z) * b);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2374940160662717/91343852333181432387730302044767688728495783936], t$95$1, If[LessEqual[z, 3470978933371479/266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072], N[(N[(a * t), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[z, 299999999999999999088617516181804470671953197798974385752637440], N[(x + N[(N[(a * z), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.5999999999999997e-32 or 3e62 < z

   1. Initial program 92.8%

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

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 52.1%

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

   4. Applied rewrites 52.1%

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

   if -2.5999999999999997e-32 < z < 1.3e-80

   1. Initial program 92.8%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. lift-+.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. associate-+l+ N/A

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

      7. associate--l+ N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 N/A

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

   3. Applied rewrites 94.2%

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

   4. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 73.7%

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

   6. Applied rewrites 73.7%

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

   7. Taylor expanded in z around 0

      \[\leadsto a \cdot t + x \]
   8. Step-by-step derivation

   9. Applied rewrites 50.8%

      \[\leadsto a \cdot t + x \]

   if 1.3e-80 < z < 3e62

   1. Initial program 92.8%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-*.f64 71.4%

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

   4. Applied rewrites 71.4%

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

   5. Taylor expanded in x around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 48.4%

         \[\leadsto a \cdot t + a \cdot \left(b \cdot z\right) \]

   7. Applied rewrites 48.4%

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

   8. Taylor expanded in t around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 50.1%

         \[\leadsto x + a \cdot \left(b \cdot z\right) \]

   10. Applied rewrites 50.1%

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

       1. lift-*.f64 N/A

          \[\leadsto x + a \cdot \left(b \cdot z\right) \]

       2. lift-*.f64 N/A

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

       3. *-commutative N/A

          \[\leadsto x + a \cdot \left(z \cdot b\right) \]

       4. associate-*l* N/A

          \[\leadsto x + \left(a \cdot z\right) \cdot b \]

       5. lift-*.f64 N/A

          \[\leadsto x + \left(a \cdot z\right) \cdot b \]

       6. lift-*.f64 51.3%

          \[\leadsto x + \left(a \cdot z\right) \cdot b \]

   12. Applied rewrites 51.3%

       \[\leadsto x + \left(a \cdot z\right) \cdot b \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 7: 62.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \mathbf{if}\;z \leq \frac{-2374940160662717}{91343852333181432387730302044767688728495783936}:\\ \;\;\;\;a \cdot \left(t + b \cdot z\right)\\ \mathbf{elif}\;z \leq \frac{3470978933371479}{266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072}:\\ \;\;\;\;a \cdot t + x\\ \mathbf{else}:\\ \;\;\;\;x + \left(a \cdot z\right) \cdot b\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (if (<=
     z
     -2374940160662717/91343852333181432387730302044767688728495783936)
  (* a (+ t (* b z)))
  (if (<=
       z
       3470978933371479/266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072)
    (+ (* a t) x)
    (+ x (* (* a z) b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -2.6e-32) {
		tmp = a * (t + (b * z));
	} else if (z <= 1.3e-80) {
		tmp = (a * t) + x;
	} else {
		tmp = x + ((a * z) * 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 (z <= (-2.6d-32)) then
        tmp = a * (t + (b * z))
    else if (z <= 1.3d-80) then
        tmp = (a * t) + x
    else
        tmp = x + ((a * z) * 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 (z <= -2.6e-32) {
		tmp = a * (t + (b * z));
	} else if (z <= 1.3e-80) {
		tmp = (a * t) + x;
	} else {
		tmp = x + ((a * z) * b);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -2.6e-32:
		tmp = a * (t + (b * z))
	elif z <= 1.3e-80:
		tmp = (a * t) + x
	else:
		tmp = x + ((a * z) * b)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -2.6e-32)
		tmp = Float64(a * Float64(t + Float64(b * z)));
	elseif (z <= 1.3e-80)
		tmp = Float64(Float64(a * t) + x);
	else
		tmp = Float64(x + Float64(Float64(a * z) * b));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -2.6e-32)
		tmp = a * (t + (b * z));
	elseif (z <= 1.3e-80)
		tmp = (a * t) + x;
	else
		tmp = x + ((a * z) * b);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -2374940160662717/91343852333181432387730302044767688728495783936], N[(a * N[(t + N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3470978933371479/266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072], N[(N[(a * t), $MachinePrecision] + x), $MachinePrecision], N[(x + N[(N[(a * z), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.5999999999999997e-32

   1. Initial program 92.8%

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

   2. Taylor expanded in a around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 50.7%

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

   4. Applied rewrites 50.7%

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

   if -2.5999999999999997e-32 < z < 1.3e-80

   1. Initial program 92.8%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. lift-+.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. associate-+l+ N/A

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

      7. associate--l+ N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 N/A

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

   3. Applied rewrites 94.2%

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

   4. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 73.7%

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

   6. Applied rewrites 73.7%

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

   7. Taylor expanded in z around 0

      \[\leadsto a \cdot t + x \]
   8. Step-by-step derivation

   9. Applied rewrites 50.8%

      \[\leadsto a \cdot t + x \]

   if 1.3e-80 < z

   1. Initial program 92.8%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-*.f64 71.4%

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

   4. Applied rewrites 71.4%

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

   5. Taylor expanded in x around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 48.4%

         \[\leadsto a \cdot t + a \cdot \left(b \cdot z\right) \]

   7. Applied rewrites 48.4%

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

   8. Taylor expanded in t around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 50.1%

         \[\leadsto x + a \cdot \left(b \cdot z\right) \]

   10. Applied rewrites 50.1%

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

       1. lift-*.f64 N/A

          \[\leadsto x + a \cdot \left(b \cdot z\right) \]

       2. lift-*.f64 N/A

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

       3. *-commutative N/A

          \[\leadsto x + a \cdot \left(z \cdot b\right) \]

       4. associate-*l* N/A

          \[\leadsto x + \left(a \cdot z\right) \cdot b \]

       5. lift-*.f64 N/A

          \[\leadsto x + \left(a \cdot z\right) \cdot b \]

       6. lift-*.f64 51.3%

          \[\leadsto x + \left(a \cdot z\right) \cdot b \]

   12. Applied rewrites 51.3%

       \[\leadsto x + \left(a \cdot z\right) \cdot b \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 8: 61.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} t_1 := x + \left(a \cdot z\right) \cdot b\\ \mathbf{if}\;z \leq \frac{-2374940160662717}{91343852333181432387730302044767688728495783936}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq \frac{3470978933371479}{266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072}:\\ \;\;\;\;a \cdot t + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (+ x (* (* a z) b))))
  (if (<=
       z
       -2374940160662717/91343852333181432387730302044767688728495783936)
    t_1
    (if (<=
         z
         3470978933371479/266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072)
      (+ (* a t) x)
      t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + ((a * z) * b);
	double tmp;
	if (z <= -2.6e-32) {
		tmp = t_1;
	} else if (z <= 1.3e-80) {
		tmp = (a * t) + 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 = x + ((a * z) * b)
    if (z <= (-2.6d-32)) then
        tmp = t_1
    else if (z <= 1.3d-80) then
        tmp = (a * t) + 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 = x + ((a * z) * b);
	double tmp;
	if (z <= -2.6e-32) {
		tmp = t_1;
	} else if (z <= 1.3e-80) {
		tmp = (a * t) + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + ((a * z) * b)
	tmp = 0
	if z <= -2.6e-32:
		tmp = t_1
	elif z <= 1.3e-80:
		tmp = (a * t) + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(Float64(a * z) * b))
	tmp = 0.0
	if (z <= -2.6e-32)
		tmp = t_1;
	elseif (z <= 1.3e-80)
		tmp = Float64(Float64(a * t) + x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + ((a * z) * b);
	tmp = 0.0;
	if (z <= -2.6e-32)
		tmp = t_1;
	elseif (z <= 1.3e-80)
		tmp = (a * t) + x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(N[(a * z), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2374940160662717/91343852333181432387730302044767688728495783936], t$95$1, If[LessEqual[z, 3470978933371479/266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072], N[(N[(a * t), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -2.5999999999999997e-32 or 1.3e-80 < z

   1. Initial program 92.8%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-*.f64 71.4%

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

   4. Applied rewrites 71.4%

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

   5. Taylor expanded in x around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 48.4%

         \[\leadsto a \cdot t + a \cdot \left(b \cdot z\right) \]

   7. Applied rewrites 48.4%

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

   8. Taylor expanded in t around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 50.1%

         \[\leadsto x + a \cdot \left(b \cdot z\right) \]

   10. Applied rewrites 50.1%

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

       1. lift-*.f64 N/A

          \[\leadsto x + a \cdot \left(b \cdot z\right) \]

       2. lift-*.f64 N/A

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

       3. *-commutative N/A

          \[\leadsto x + a \cdot \left(z \cdot b\right) \]

       4. associate-*l* N/A

          \[\leadsto x + \left(a \cdot z\right) \cdot b \]

       5. lift-*.f64 N/A

          \[\leadsto x + \left(a \cdot z\right) \cdot b \]

       6. lift-*.f64 51.3%

          \[\leadsto x + \left(a \cdot z\right) \cdot b \]

   12. Applied rewrites 51.3%

       \[\leadsto x + \left(a \cdot z\right) \cdot b \]

   if -2.5999999999999997e-32 < z < 1.3e-80

   1. Initial program 92.8%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. lift-+.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. associate-+l+ N/A

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

      7. associate--l+ N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 N/A

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

   3. Applied rewrites 94.2%

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

   4. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 73.7%

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

   6. Applied rewrites 73.7%

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

   7. Taylor expanded in z around 0

      \[\leadsto a \cdot t + x \]
   8. Step-by-step derivation

   9. Applied rewrites 50.8%

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

Alternative 9: 61.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} t_1 := x + a \cdot \left(b \cdot z\right)\\ \mathbf{if}\;z \leq \frac{-2374940160662717}{91343852333181432387730302044767688728495783936}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq \frac{3470978933371479}{266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072}:\\ \;\;\;\;a \cdot t + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (+ x (* a (* b z)))))
  (if (<=
       z
       -2374940160662717/91343852333181432387730302044767688728495783936)
    t_1
    (if (<=
         z
         3470978933371479/266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072)
      (+ (* a t) x)
      t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (a * (b * z));
	double tmp;
	if (z <= -2.6e-32) {
		tmp = t_1;
	} else if (z <= 1.3e-80) {
		tmp = (a * t) + 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 = x + (a * (b * z))
    if (z <= (-2.6d-32)) then
        tmp = t_1
    else if (z <= 1.3d-80) then
        tmp = (a * t) + 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 = x + (a * (b * z));
	double tmp;
	if (z <= -2.6e-32) {
		tmp = t_1;
	} else if (z <= 1.3e-80) {
		tmp = (a * t) + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (a * (b * z))
	tmp = 0
	if z <= -2.6e-32:
		tmp = t_1
	elif z <= 1.3e-80:
		tmp = (a * t) + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(a * Float64(b * z)))
	tmp = 0.0
	if (z <= -2.6e-32)
		tmp = t_1;
	elseif (z <= 1.3e-80)
		tmp = Float64(Float64(a * t) + x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (a * (b * z));
	tmp = 0.0;
	if (z <= -2.6e-32)
		tmp = t_1;
	elseif (z <= 1.3e-80)
		tmp = (a * t) + x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(a * N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2374940160662717/91343852333181432387730302044767688728495783936], t$95$1, If[LessEqual[z, 3470978933371479/266998379490113760299377713271194014325338065294581596243380200977777465722580068752870260867072], N[(N[(a * t), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -2.5999999999999997e-32 or 1.3e-80 < z

   1. Initial program 92.8%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-*.f64 71.4%

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

   4. Applied rewrites 71.4%

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

   5. Taylor expanded in x around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 48.4%

         \[\leadsto a \cdot t + a \cdot \left(b \cdot z\right) \]

   7. Applied rewrites 48.4%

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

   8. Taylor expanded in t around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 50.1%

         \[\leadsto x + a \cdot \left(b \cdot z\right) \]

   10. Applied rewrites 50.1%

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

   if -2.5999999999999997e-32 < z < 1.3e-80

   1. Initial program 92.8%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. lift-+.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. associate-+l+ N/A

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

      7. associate--l+ N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 N/A

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

   3. Applied rewrites 94.2%

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

   4. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 73.7%

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

   6. Applied rewrites 73.7%

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

   7. Taylor expanded in z around 0

      \[\leadsto a \cdot t + x \]
   8. Step-by-step derivation

   9. Applied rewrites 50.8%

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

Alternative 10: 56.4% accurate, 1.3× speedup

Math

\[\begin{array}{l} \mathbf{if}\;z \leq \frac{-2374940160662717}{91343852333181432387730302044767688728495783936}:\\ \;\;\;\;a \cdot \left(b \cdot z\right)\\ \mathbf{elif}\;z \leq 519999999999999967972319583654650286587951527928992692472643584:\\ \;\;\;\;a \cdot t + x\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(a \cdot b\right)\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (if (<=
     z
     -2374940160662717/91343852333181432387730302044767688728495783936)
  (* a (* b z))
  (if (<=
       z
       519999999999999967972319583654650286587951527928992692472643584)
    (+ (* a t) x)
    (* z (* a b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -2.6e-32) {
		tmp = a * (b * z);
	} else if (z <= 5.2e+62) {
		tmp = (a * t) + x;
	} else {
		tmp = z * (a * 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 (z <= (-2.6d-32)) then
        tmp = a * (b * z)
    else if (z <= 5.2d+62) then
        tmp = (a * t) + x
    else
        tmp = z * (a * 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 (z <= -2.6e-32) {
		tmp = a * (b * z);
	} else if (z <= 5.2e+62) {
		tmp = (a * t) + x;
	} else {
		tmp = z * (a * b);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -2.6e-32:
		tmp = a * (b * z)
	elif z <= 5.2e+62:
		tmp = (a * t) + x
	else:
		tmp = z * (a * b)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -2.6e-32)
		tmp = Float64(a * Float64(b * z));
	elseif (z <= 5.2e+62)
		tmp = Float64(Float64(a * t) + x);
	else
		tmp = Float64(z * Float64(a * b));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -2.6e-32)
		tmp = a * (b * z);
	elseif (z <= 5.2e+62)
		tmp = (a * t) + x;
	else
		tmp = z * (a * b);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -2374940160662717/91343852333181432387730302044767688728495783936], N[(a * N[(b * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 519999999999999967972319583654650286587951527928992692472643584], N[(N[(a * t), $MachinePrecision] + x), $MachinePrecision], N[(z * N[(a * b), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.5999999999999997e-32

   1. Initial program 92.8%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-*.f64 71.4%

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

   4. Applied rewrites 71.4%

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

   5. Taylor expanded in x around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 48.4%

         \[\leadsto a \cdot t + a \cdot \left(b \cdot z\right) \]

   7. Applied rewrites 48.4%

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

   8. Taylor expanded in t around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 50.1%

         \[\leadsto x + a \cdot \left(b \cdot z\right) \]

   10. Applied rewrites 50.1%

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

   11. Taylor expanded in x around 0

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

       1. lower-*.f64 N/A

          \[\leadsto a \cdot \left(b \cdot z\right) \]

       2. lower-*.f64 27.4%

          \[\leadsto a \cdot \left(b \cdot z\right) \]

   13. Applied rewrites 27.4%

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

   if -2.5999999999999997e-32 < z < 5.1999999999999997e62

   1. Initial program 92.8%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. lift-+.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. associate-+l+ N/A

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

      7. associate--l+ N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 N/A

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

   3. Applied rewrites 94.2%

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

   4. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 73.7%

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

   6. Applied rewrites 73.7%

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

   7. Taylor expanded in z around 0

      \[\leadsto a \cdot t + x \]
   8. Step-by-step derivation

   9. Applied rewrites 50.8%

      \[\leadsto a \cdot t + x \]

   if 5.1999999999999997e62 < z

   1. Initial program 92.8%

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

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 52.1%

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

   4. Applied rewrites 52.1%

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

   5. Taylor expanded in y around 0

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

      1. lower-*.f64 27.6%

         \[\leadsto z \cdot \left(a \cdot b\right) \]

   7. Applied rewrites 27.6%

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

Alternative 11: 55.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} t_1 := a \cdot \left(b \cdot z\right)\\ \mathbf{if}\;z \leq \frac{-2374940160662717}{91343852333181432387730302044767688728495783936}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 6400000000000000093636449476319187398208275122333792379383109037886696731207348581826560:\\ \;\;\;\;a \cdot t + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* a (* b z))))
  (if (<=
       z
       -2374940160662717/91343852333181432387730302044767688728495783936)
    t_1
    (if (<=
         z
         6400000000000000093636449476319187398208275122333792379383109037886696731207348581826560)
      (+ (* a t) x)
      t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (b * z);
	double tmp;
	if (z <= -2.6e-32) {
		tmp = t_1;
	} else if (z <= 6.4e+87) {
		tmp = (a * t) + 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 = a * (b * z)
    if (z <= (-2.6d-32)) then
        tmp = t_1
    else if (z <= 6.4d+87) then
        tmp = (a * t) + 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 = a * (b * z);
	double tmp;
	if (z <= -2.6e-32) {
		tmp = t_1;
	} else if (z <= 6.4e+87) {
		tmp = (a * t) + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (b * z)
	tmp = 0
	if z <= -2.6e-32:
		tmp = t_1
	elif z <= 6.4e+87:
		tmp = (a * t) + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(b * z))
	tmp = 0.0
	if (z <= -2.6e-32)
		tmp = t_1;
	elseif (z <= 6.4e+87)
		tmp = Float64(Float64(a * t) + x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (b * z);
	tmp = 0.0;
	if (z <= -2.6e-32)
		tmp = t_1;
	elseif (z <= 6.4e+87)
		tmp = (a * t) + x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a * N[(b * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2374940160662717/91343852333181432387730302044767688728495783936], t$95$1, If[LessEqual[z, 6400000000000000093636449476319187398208275122333792379383109037886696731207348581826560], N[(N[(a * t), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -2.5999999999999997e-32 or 6.4000000000000001e87 < z

   1. Initial program 92.8%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-*.f64 71.4%

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

   4. Applied rewrites 71.4%

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

   5. Taylor expanded in x around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 48.4%

         \[\leadsto a \cdot t + a \cdot \left(b \cdot z\right) \]

   7. Applied rewrites 48.4%

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

   8. Taylor expanded in t around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 50.1%

         \[\leadsto x + a \cdot \left(b \cdot z\right) \]

   10. Applied rewrites 50.1%

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

   11. Taylor expanded in x around 0

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

       1. lower-*.f64 N/A

          \[\leadsto a \cdot \left(b \cdot z\right) \]

       2. lower-*.f64 27.4%

          \[\leadsto a \cdot \left(b \cdot z\right) \]

   13. Applied rewrites 27.4%

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

   if -2.5999999999999997e-32 < z < 6.4000000000000001e87

   1. Initial program 92.8%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. lift-+.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. associate-+l+ N/A

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

      7. associate--l+ N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 N/A

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

   3. Applied rewrites 94.2%

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

   4. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 73.7%

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

   6. Applied rewrites 73.7%

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

   7. Taylor expanded in z around 0

      \[\leadsto a \cdot t + x \]
   8. Step-by-step derivation

   9. Applied rewrites 50.8%

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

Alternative 12: 50.8% accurate, 3.3× speedup

Math

\[a \cdot t + x \]

FPCore

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

C

double code(double x, double y, double z, double t, double a, double b) {
	return (a * t) + 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 = (a * t) + x
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 92.8%

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

   1. lift-+.f64 N/A

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

   2. lift-*.f64 N/A

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

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

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

   4. lift-+.f64 N/A

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

   5. lift-+.f64 N/A

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

   6. associate-+l+ N/A

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

   7. associate--l+ N/A

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

   8. +-commutative N/A

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

   9. lower-+.f64 N/A

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

3. Applied rewrites 94.2%

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

4. Taylor expanded in y around 0

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

   1. lower-*.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 73.7%

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

6. Applied rewrites 73.7%

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

7. Taylor expanded in z around 0

   \[\leadsto a \cdot t + x \]
8. Step-by-step derivation

9. Applied rewrites 50.8%

   \[\leadsto a \cdot t + x \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2025271 -o generate:evaluate
(FPCore (x y z t a b)
  :name "Graphics.Rasterific.CubicBezier:cachedBezierAt from Rasterific-0.6.1"
  :precision binary64
  (+ (+ (+ x (* y z)) (* t a)) (* (* a z) b)))