Numeric.SpecFunctions:logBeta from math-functions-0.1.5.2, A

Percentage Accurate: 99.9% → 99.9%

Time: 5.4s

Alternatives: 16

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ (- (+ (+ x y) z) (* z (log t))) (* (- a 0.5) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return (((x + y) + z) - (z * log(t))) + ((a - 0.5) * 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) - (z * log(t))) + ((a - 0.5d0) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return (((x + y) + z) - (z * Math.log(t))) + ((a - 0.5) * b);
}

Python

def code(x, y, z, t, a, b):
	return (((x + y) + z) - (z * math.log(t))) + ((a - 0.5) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(Float64(x + y) + z) - Float64(z * log(t))) + Float64(Float64(a - 0.5) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = (((x + y) + z) - (z * log(t))) + ((a - 0.5) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(N[(x + y), $MachinePrecision] + z), $MachinePrecision] - N[(z * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ (- (+ (+ x y) z) (* z (log t))) (* (- a 0.5) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return (((x + y) + z) - (z * log(t))) + ((a - 0.5) * 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) - (z * log(t))) + ((a - 0.5d0) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return (((x + y) + z) - (z * Math.log(t))) + ((a - 0.5) * b);
}

Python

def code(x, y, z, t, a, b):
	return (((x + y) + z) - (z * math.log(t))) + ((a - 0.5) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(Float64(x + y) + z) - Float64(z * log(t))) + Float64(Float64(a - 0.5) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = (((x + y) + z) - (z * log(t))) + ((a - 0.5) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(N[(x + y), $MachinePrecision] + z), $MachinePrecision] - N[(z * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\left(-\log t\right) - -1, z, \mathsf{fma}\left(b, a - 0.5, y + x\right)\right) \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (fma (- (- (log t)) -1.0) z (fma b (- a 0.5) (+ y x))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return fma((-log(t) - -1.0), z, fma(b, (a - 0.5), (y + x)));
}

Julia

function code(x, y, z, t, a, b)
	return fma(Float64(Float64(-log(t)) - -1.0), z, fma(b, Float64(a - 0.5), Float64(y + x)))
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[((-N[Log[t], $MachinePrecision]) - -1.0), $MachinePrecision] * z + N[(b * N[(a - 0.5), $MachinePrecision] + N[(y + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift--.f64 N/A

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

   3. lift-+.f64 N/A

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

   4. associate--l+ N/A

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

   5. +-commutative N/A

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

   6. associate-+l+ N/A

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

   7. lift-*.f64 N/A

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

   8. *-commutative N/A

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

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

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

   10. distribute-rgt1-in N/A

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

   11. +-commutative N/A

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

   12. lower-fma.f64 N/A

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

   13. add-flip N/A

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

   14. metadata-eval N/A

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

   15. lower--.f64 N/A

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

   16. lower-neg.f64 N/A

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

   17. lift-*.f64 N/A

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

   18. *-commutative N/A

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

3. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\left(-\log t\right) - -1, z, \mathsf{fma}\left(b, a - 0.5, y + x\right)\right)} \]
4. Add Preprocessing

Alternative 2: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \left(y + \mathsf{fma}\left(b, a - 0.5, z \cdot \left(1 - \log t\right)\right)\right) \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ x (+ y (fma b (- a 0.5) (* z (- 1.0 (log t)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return x + (y + fma(b, (a - 0.5), (z * (1.0 - log(t)))));
}

Julia

function code(x, y, z, t, a, b)
	return Float64(x + Float64(y + fma(b, Float64(a - 0.5), Float64(z * Float64(1.0 - log(t))))))
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(x + N[(y + N[(b * N[(a - 0.5), $MachinePrecision] + N[(z * N[(1.0 - N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

2. Taylor expanded in z around 0

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

   1. lower-+.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-fma.f64 N/A

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

   4. lower--.f64 N/A

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

   5. lower-*.f64 N/A

      \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - \frac{1}{2}, z \cdot \left(1 - \log t\right)\right)\right) \]

   6. lower--.f64 N/A

      \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - \frac{1}{2}, z \cdot \left(1 - \log t\right)\right)\right) \]

   7. lower-log.f64 99.9

      \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - 0.5, z \cdot \left(1 - \log t\right)\right)\right) \]

4. Applied rewrites 99.9%

   \[\leadsto \color{blue}{x + \left(y + \mathsf{fma}\left(b, a - 0.5, z \cdot \left(1 - \log t\right)\right)\right)} \]
5. Add Preprocessing

Alternative 3: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x - \mathsf{fma}\left(0.5 - a, b, \left(\log t \cdot z - z\right) - y\right) \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (- x (fma (- 0.5 a) b (- (- (* (log t) z) z) y))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return x - fma((0.5 - a), b, (((log(t) * z) - z) - y));
}

Julia

function code(x, y, z, t, a, b)
	return Float64(x - fma(Float64(0.5 - a), b, Float64(Float64(Float64(log(t) * z) - z) - y)))
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(x - N[(N[(0.5 - a), $MachinePrecision] * b + N[(N[(N[(N[Log[t], $MachinePrecision] * z), $MachinePrecision] - z), $MachinePrecision] - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift--.f64 N/A

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

   3. associate-+l- N/A

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

   4. lift-+.f64 N/A

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

   5. lift-+.f64 N/A

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

   6. associate-+l+ N/A

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

   7. associate--l+ N/A

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

   8. add-flip N/A

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

   9. sub-negate-rev N/A

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

   10. lower--.f64 N/A

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

   11. sub-negate-rev N/A

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

   12. associate--l+ N/A

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

   13. associate--r- N/A

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

   14. add-flip-rev N/A

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

   15. distribute-neg-out N/A

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

3. Applied rewrites 99.9%

   \[\leadsto \color{blue}{x - \mathsf{fma}\left(0.5 - a, b, \left(\log t \cdot z - z\right) - y\right)} \]
4. Add Preprocessing

Alternative 4: 90.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x + y \leq -1 \cdot 10^{-64}:\\ \;\;\;\;\left(x + \left(z + b \cdot \left(a - 0.5\right)\right)\right) - z \cdot \log t\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(1 - \log t, z, \mathsf{fma}\left(a - 0.5, b, y\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= (+ x y) -1e-64)
   (- (+ x (+ z (* b (- a 0.5)))) (* z (log t)))
   (fma (- 1.0 (log t)) z (fma (- a 0.5) b y))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((x + y) <= -1e-64) {
		tmp = (x + (z + (b * (a - 0.5)))) - (z * log(t));
	} else {
		tmp = fma((1.0 - log(t)), z, fma((a - 0.5), b, y));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (Float64(x + y) <= -1e-64)
		tmp = Float64(Float64(x + Float64(z + Float64(b * Float64(a - 0.5)))) - Float64(z * log(t)));
	else
		tmp = fma(Float64(1.0 - log(t)), z, fma(Float64(a - 0.5), b, y));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[N[(x + y), $MachinePrecision], -1e-64], N[(N[(x + N[(z + N[(b * N[(a - 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(z * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 - N[Log[t], $MachinePrecision]), $MachinePrecision] * z + N[(N[(a - 0.5), $MachinePrecision] * b + y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 x y) < -9.99999999999999965e-65

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 79.2

         \[\leadsto \left(x + \left(z + b \cdot \left(a - 0.5\right)\right)\right) - z \cdot \log t \]

   4. Applied rewrites 79.2%

      \[\leadsto \color{blue}{\left(x + \left(z + b \cdot \left(a - 0.5\right)\right)\right) - z \cdot \log t} \]

   if -9.99999999999999965e-65 < (+.f64 x y)

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 77.8

         \[\leadsto \left(y + \left(z + b \cdot \left(a - 0.5\right)\right)\right) - z \cdot \log t \]

   4. Applied rewrites 77.8%

      \[\leadsto \color{blue}{\left(y + \left(z + b \cdot \left(a - 0.5\right)\right)\right) - z \cdot \log t} \]
   5. Step-by-step derivation

      1. lift--.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. +-commutative N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-+.f64 N/A

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

      8. associate-+l+ N/A

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

      9. +-commutative N/A

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

      10. associate-+l+ N/A

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

   6. Applied rewrites 77.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(1 - \log t, z, \mathsf{fma}\left(a - 0.5, b, y\right)\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 86.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x + y \leq -0.5:\\ \;\;\;\;\mathsf{fma}\left(a - 0.5, b, y + x\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(1 - \log t, z, \mathsf{fma}\left(a - 0.5, b, y\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= (+ x y) -0.5)
   (fma (- a 0.5) b (+ y x))
   (fma (- 1.0 (log t)) z (fma (- a 0.5) b y))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((x + y) <= -0.5) {
		tmp = fma((a - 0.5), b, (y + x));
	} else {
		tmp = fma((1.0 - log(t)), z, fma((a - 0.5), b, y));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (Float64(x + y) <= -0.5)
		tmp = fma(Float64(a - 0.5), b, Float64(y + x));
	else
		tmp = fma(Float64(1.0 - log(t)), z, fma(Float64(a - 0.5), b, y));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[N[(x + y), $MachinePrecision], -0.5], N[(N[(a - 0.5), $MachinePrecision] * b + N[(y + x), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 - N[Log[t], $MachinePrecision]), $MachinePrecision] * z + N[(N[(a - 0.5), $MachinePrecision] * b + y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 x y) < -0.5

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. +-commutative N/A

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

      5. associate-+l+ N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. +-commutative N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, \color{blue}{b}, x + y\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y + x\right) \]

      11. lower-+.f64 79.4

          \[\leadsto \mathsf{fma}\left(a - 0.5, b, y + x\right) \]

   6. Applied rewrites 79.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y + x\right)} \]

   if -0.5 < (+.f64 x y)

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 77.8

         \[\leadsto \left(y + \left(z + b \cdot \left(a - 0.5\right)\right)\right) - z \cdot \log t \]

   4. Applied rewrites 77.8%

      \[\leadsto \color{blue}{\left(y + \left(z + b \cdot \left(a - 0.5\right)\right)\right) - z \cdot \log t} \]
   5. Step-by-step derivation

      1. lift--.f64 N/A

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

      2. lift-*.f64 N/A

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

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

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

      4. +-commutative N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-+.f64 N/A

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

      8. associate-+l+ N/A

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

      9. +-commutative N/A

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

      10. associate-+l+ N/A

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

   6. Applied rewrites 77.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(1 - \log t, z, \mathsf{fma}\left(a - 0.5, b, y\right)\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 86.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a - 0.5\right) \cdot b\\ t_2 := \mathsf{fma}\left(a - 0.5, b, y + x\right)\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+100}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{+30}:\\ \;\;\;\;\mathsf{fma}\left(1 - \log t, z, y\right) + x\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* (- a 0.5) b)) (t_2 (fma (- a 0.5) b (+ y x))))
   (if (<= t_1 -5e+100)
     t_2
     (if (<= t_1 2e+30) (+ (fma (- 1.0 (log t)) z y) x) t_2))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (a - 0.5) * b;
	double t_2 = fma((a - 0.5), b, (y + x));
	double tmp;
	if (t_1 <= -5e+100) {
		tmp = t_2;
	} else if (t_1 <= 2e+30) {
		tmp = fma((1.0 - log(t)), z, y) + x;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(a - 0.5) * b)
	t_2 = fma(Float64(a - 0.5), b, Float64(y + x))
	tmp = 0.0
	if (t_1 <= -5e+100)
		tmp = t_2;
	elseif (t_1 <= 2e+30)
		tmp = Float64(fma(Float64(1.0 - log(t)), z, y) + x);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a - 0.5), $MachinePrecision] * b + N[(y + x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -5e+100], t$95$2, If[LessEqual[t$95$1, 2e+30], N[(N[(N[(1.0 - N[Log[t], $MachinePrecision]), $MachinePrecision] * z + y), $MachinePrecision] + x), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 a #s(literal 1/2 binary64)) b) < -4.9999999999999999e100 or 2e30 < (*.f64 (-.f64 a #s(literal 1/2 binary64)) b)

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. +-commutative N/A

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

      5. associate-+l+ N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. +-commutative N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, \color{blue}{b}, x + y\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y + x\right) \]

      11. lower-+.f64 79.4

          \[\leadsto \mathsf{fma}\left(a - 0.5, b, y + x\right) \]

   6. Applied rewrites 79.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y + x\right)} \]

   if -4.9999999999999999e100 < (*.f64 (-.f64 a #s(literal 1/2 binary64)) b) < 2e30

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-fma.f64 N/A

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

      4. lower--.f64 N/A

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

      5. lower-*.f64 N/A

         \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - \frac{1}{2}, z \cdot \left(1 - \log t\right)\right)\right) \]

      6. lower--.f64 N/A

         \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - \frac{1}{2}, z \cdot \left(1 - \log t\right)\right)\right) \]

      7. lower-log.f64 99.9

         \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - 0.5, z \cdot \left(1 - \log t\right)\right)\right) \]

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{x + \left(y + \mathsf{fma}\left(b, a - 0.5, z \cdot \left(1 - \log t\right)\right)\right)} \]

   5. Taylor expanded in b around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-log.f64 62.9

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

   7. Applied rewrites 62.9%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 62.9

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-fma.f64 62.9

         \[\leadsto \mathsf{fma}\left(1 - \log t, z, y\right) + x \]

   9. Applied rewrites 62.9%

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

Alternative 7: 84.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.5 \cdot 10^{+214}:\\ \;\;\;\;x + z \cdot \left(1 - \log t\right)\\ \mathbf{elif}\;z \leq 5.2 \cdot 10^{+225}:\\ \;\;\;\;\mathsf{fma}\left(a - 0.5, b, y + x\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y + z\right) - z \cdot \log t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -3.5e+214)
   (+ x (* z (- 1.0 (log t))))
   (if (<= z 5.2e+225) (fma (- a 0.5) b (+ y x)) (- (+ y z) (* z (log t))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -3.5e+214) {
		tmp = x + (z * (1.0 - log(t)));
	} else if (z <= 5.2e+225) {
		tmp = fma((a - 0.5), b, (y + x));
	} else {
		tmp = (y + z) - (z * log(t));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -3.5e+214)
		tmp = Float64(x + Float64(z * Float64(1.0 - log(t))));
	elseif (z <= 5.2e+225)
		tmp = fma(Float64(a - 0.5), b, Float64(y + x));
	else
		tmp = Float64(Float64(y + z) - Float64(z * log(t)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -3.5e+214], N[(x + N[(z * N[(1.0 - N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 5.2e+225], N[(N[(a - 0.5), $MachinePrecision] * b + N[(y + x), $MachinePrecision]), $MachinePrecision], N[(N[(y + z), $MachinePrecision] - N[(z * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -3.5e214

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-fma.f64 N/A

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

      4. lower--.f64 N/A

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

      5. lower-*.f64 N/A

         \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - \frac{1}{2}, z \cdot \left(1 - \log t\right)\right)\right) \]

      6. lower--.f64 N/A

         \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - \frac{1}{2}, z \cdot \left(1 - \log t\right)\right)\right) \]

      7. lower-log.f64 99.9

         \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - 0.5, z \cdot \left(1 - \log t\right)\right)\right) \]

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{x + \left(y + \mathsf{fma}\left(b, a - 0.5, z \cdot \left(1 - \log t\right)\right)\right)} \]

   5. Taylor expanded in b around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-log.f64 62.9

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

   7. Applied rewrites 62.9%

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

   8. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-log.f64 42.9

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

   10. Applied rewrites 42.9%

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

   if -3.5e214 < z < 5.20000000000000009e225

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. +-commutative N/A

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

      5. associate-+l+ N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. +-commutative N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, \color{blue}{b}, x + y\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y + x\right) \]

      11. lower-+.f64 79.4

          \[\leadsto \mathsf{fma}\left(a - 0.5, b, y + x\right) \]

   6. Applied rewrites 79.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y + x\right)} \]

   if 5.20000000000000009e225 < z

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 77.8

         \[\leadsto \left(y + \left(z + b \cdot \left(a - 0.5\right)\right)\right) - z \cdot \log t \]

   4. Applied rewrites 77.8%

      \[\leadsto \color{blue}{\left(y + \left(z + b \cdot \left(a - 0.5\right)\right)\right) - z \cdot \log t} \]

   5. Taylor expanded in b around 0

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

      1. lower-+.f64 41.4

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

   7. Applied rewrites 41.4%

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

Alternative 8: 82.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + z \cdot \left(1 - \log t\right)\\ \mathbf{if}\;z \leq -3.5 \cdot 10^{+214}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 9.2 \cdot 10^{+225}:\\ \;\;\;\;\mathsf{fma}\left(a - 0.5, b, y + x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* z (- 1.0 (log t))))))
   (if (<= z -3.5e+214)
     t_1
     (if (<= z 9.2e+225) (fma (- a 0.5) b (+ y x)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (z * (1.0 - log(t)));
	double tmp;
	if (z <= -3.5e+214) {
		tmp = t_1;
	} else if (z <= 9.2e+225) {
		tmp = fma((a - 0.5), b, (y + x));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(z * Float64(1.0 - log(t))))
	tmp = 0.0
	if (z <= -3.5e+214)
		tmp = t_1;
	elseif (z <= 9.2e+225)
		tmp = fma(Float64(a - 0.5), b, Float64(y + x));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(z * N[(1.0 - N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -3.5e+214], t$95$1, If[LessEqual[z, 9.2e+225], N[(N[(a - 0.5), $MachinePrecision] * b + N[(y + x), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -3.5e214 or 9.1999999999999998e225 < z

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-fma.f64 N/A

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

      4. lower--.f64 N/A

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

      5. lower-*.f64 N/A

         \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - \frac{1}{2}, z \cdot \left(1 - \log t\right)\right)\right) \]

      6. lower--.f64 N/A

         \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - \frac{1}{2}, z \cdot \left(1 - \log t\right)\right)\right) \]

      7. lower-log.f64 99.9

         \[\leadsto x + \left(y + \mathsf{fma}\left(b, a - 0.5, z \cdot \left(1 - \log t\right)\right)\right) \]

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{x + \left(y + \mathsf{fma}\left(b, a - 0.5, z \cdot \left(1 - \log t\right)\right)\right)} \]

   5. Taylor expanded in b around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-log.f64 62.9

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

   7. Applied rewrites 62.9%

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

   8. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-log.f64 42.9

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

   10. Applied rewrites 42.9%

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

   if -3.5e214 < z < 9.1999999999999998e225

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. +-commutative N/A

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

      5. associate-+l+ N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. +-commutative N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, \color{blue}{b}, x + y\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y + x\right) \]

      11. lower-+.f64 79.4

          \[\leadsto \mathsf{fma}\left(a - 0.5, b, y + x\right) \]

   6. Applied rewrites 79.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y + x\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 79.4% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - \log t\right)\\ \mathbf{if}\;z \leq -7.4 \cdot 10^{+264}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 9.2 \cdot 10^{+225}:\\ \;\;\;\;\mathsf{fma}\left(a - 0.5, b, y + x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 (log t)))))
   (if (<= z -7.4e+264)
     t_1
     (if (<= z 9.2e+225) (fma (- a 0.5) b (+ y x)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (1.0 - log(t));
	double tmp;
	if (z <= -7.4e+264) {
		tmp = t_1;
	} else if (z <= 9.2e+225) {
		tmp = fma((a - 0.5), b, (y + x));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(1.0 - log(t)))
	tmp = 0.0
	if (z <= -7.4e+264)
		tmp = t_1;
	elseif (z <= 9.2e+225)
		tmp = fma(Float64(a - 0.5), b, Float64(y + x));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z * N[(1.0 - N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -7.4e+264], t$95$1, If[LessEqual[z, 9.2e+225], N[(N[(a - 0.5), $MachinePrecision] * b + N[(y + x), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -7.3999999999999998e264 or 9.1999999999999998e225 < z

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z \cdot \left(1 - \log t\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto z \cdot \color{blue}{\left(1 - \log t\right)} \]

      2. lower--.f64 N/A

         \[\leadsto z \cdot \left(1 - \color{blue}{\log t}\right) \]

      3. lower-log.f64 21.7

         \[\leadsto z \cdot \left(1 - \log t\right) \]

   4. Applied rewrites 21.7%

      \[\leadsto \color{blue}{z \cdot \left(1 - \log t\right)} \]

   if -7.3999999999999998e264 < z < 9.1999999999999998e225

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. +-commutative N/A

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

      5. associate-+l+ N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. +-commutative N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, \color{blue}{b}, x + y\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y + x\right) \]

      11. lower-+.f64 79.4

          \[\leadsto \mathsf{fma}\left(a - 0.5, b, y + x\right) \]

   6. Applied rewrites 79.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y + x\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 78.2% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(a - 0.5, b, y + x\right) \end{array} \]

FPCore

(FPCore (x y z t a b) :precision binary64 (fma (- a 0.5) b (+ y x)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return fma((a - 0.5), b, (y + x));
}

Julia

function code(x, y, z, t, a, b)
	return fma(Float64(a - 0.5), b, Float64(y + x))
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(a - 0.5), $MachinePrecision] * b + N[(y + x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

2. Taylor expanded in z around 0

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

   1. lower-+.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower--.f64 79.4

      \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

4. Applied rewrites 79.4%

   \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]
5. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. +-commutative N/A

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

   3. lift-+.f64 N/A

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

   4. +-commutative N/A

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

   5. associate-+l+ N/A

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

   6. lift-*.f64 N/A

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

   7. *-commutative N/A

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

   8. +-commutative N/A

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

   9. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, \color{blue}{b}, x + y\right) \]

   10. +-commutative N/A

       \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y + x\right) \]

   11. lower-+.f64 79.4

       \[\leadsto \mathsf{fma}\left(a - 0.5, b, y + x\right) \]

6. Applied rewrites 79.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y + x\right)} \]
7. Add Preprocessing

Alternative 11: 70.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\left(x + y\right) + z\right) - z \cdot \log t \leq -5 \cdot 10^{-70}:\\ \;\;\;\;x + b \cdot \left(a - 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(a - 0.5, b, y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= (- (+ (+ x y) z) (* z (log t))) -5e-70)
   (+ x (* b (- a 0.5)))
   (fma (- a 0.5) b y)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((((x + y) + z) - (z * log(t))) <= -5e-70) {
		tmp = x + (b * (a - 0.5));
	} else {
		tmp = fma((a - 0.5), b, y);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (Float64(Float64(Float64(x + y) + z) - Float64(z * log(t))) <= -5e-70)
		tmp = Float64(x + Float64(b * Float64(a - 0.5)));
	else
		tmp = fma(Float64(a - 0.5), b, y);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[N[(N[(N[(x + y), $MachinePrecision] + z), $MachinePrecision] - N[(z * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -5e-70], N[(x + N[(b * N[(a - 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a - 0.5), $MachinePrecision] * b + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (+.f64 (+.f64 x y) z) (*.f64 z (log.f64 t))) < -4.9999999999999998e-70

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]

   5. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 59.2

         \[\leadsto x + b \cdot \left(a - 0.5\right) \]

   7. Applied rewrites 59.2%

      \[\leadsto x + b \cdot \color{blue}{\left(a - 0.5\right)} \]

   if -4.9999999999999998e-70 < (-.f64 (+.f64 (+.f64 x y) z) (*.f64 z (log.f64 t)))

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. +-commutative N/A

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

      5. associate-+l+ N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. +-commutative N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, \color{blue}{b}, x + y\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y + x\right) \]

      11. lower-+.f64 79.4

          \[\leadsto \mathsf{fma}\left(a - 0.5, b, y + x\right) \]

   6. Applied rewrites 79.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y + x\right)} \]

   7. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y\right) \]
   8. Step-by-step derivation

   9. Applied rewrites 57.8%

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, y\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 69.9% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(a - 0.5, b, y\right)\\ \mathbf{if}\;a \leq -1.62 \cdot 10^{+63}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 8.5 \cdot 10^{+17}:\\ \;\;\;\;\mathsf{fma}\left(-0.5, b, y + x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (fma (- a 0.5) b y)))
   (if (<= a -1.62e+63) t_1 (if (<= a 8.5e+17) (fma -0.5 b (+ y x)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = fma((a - 0.5), b, y);
	double tmp;
	if (a <= -1.62e+63) {
		tmp = t_1;
	} else if (a <= 8.5e+17) {
		tmp = fma(-0.5, b, (y + x));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = fma(Float64(a - 0.5), b, y)
	tmp = 0.0
	if (a <= -1.62e+63)
		tmp = t_1;
	elseif (a <= 8.5e+17)
		tmp = fma(-0.5, b, Float64(y + x));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(a - 0.5), $MachinePrecision] * b + y), $MachinePrecision]}, If[LessEqual[a, -1.62e+63], t$95$1, If[LessEqual[a, 8.5e+17], N[(-0.5 * b + N[(y + x), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -1.62e63 or 8.5e17 < a

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. +-commutative N/A

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

      5. associate-+l+ N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. +-commutative N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, \color{blue}{b}, x + y\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y + x\right) \]

      11. lower-+.f64 79.4

          \[\leadsto \mathsf{fma}\left(a - 0.5, b, y + x\right) \]

   6. Applied rewrites 79.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y + x\right)} \]

   7. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y\right) \]
   8. Step-by-step derivation

   9. Applied rewrites 57.8%

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, y\right) \]

   if -1.62e63 < a < 8.5e17

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. +-commutative N/A

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

      5. associate-+l+ N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. +-commutative N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, \color{blue}{b}, x + y\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y + x\right) \]

      11. lower-+.f64 79.4

          \[\leadsto \mathsf{fma}\left(a - 0.5, b, y + x\right) \]

   6. Applied rewrites 79.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y + x\right)} \]

   7. Taylor expanded in a around 0

      \[\leadsto \mathsf{fma}\left(\frac{-1}{2}, b, y + x\right) \]
   8. Step-by-step derivation

   9. Applied rewrites 54.7%

      \[\leadsto \mathsf{fma}\left(-0.5, b, y + x\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 13: 65.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a - 0.5\right) \cdot b\\ t_2 := \mathsf{fma}\left(a - 0.5, b, y\right)\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+78}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{+21}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* (- a 0.5) b)) (t_2 (fma (- a 0.5) b y)))
   (if (<= t_1 -5e+78) t_2 (if (<= t_1 2e+21) (+ x y) t_2))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (a - 0.5) * b;
	double t_2 = fma((a - 0.5), b, y);
	double tmp;
	if (t_1 <= -5e+78) {
		tmp = t_2;
	} else if (t_1 <= 2e+21) {
		tmp = x + y;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(a - 0.5) * b)
	t_2 = fma(Float64(a - 0.5), b, y)
	tmp = 0.0
	if (t_1 <= -5e+78)
		tmp = t_2;
	elseif (t_1 <= 2e+21)
		tmp = Float64(x + y);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a - 0.5), $MachinePrecision] * b + y), $MachinePrecision]}, If[LessEqual[t$95$1, -5e+78], t$95$2, If[LessEqual[t$95$1, 2e+21], N[(x + y), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 a #s(literal 1/2 binary64)) b) < -4.99999999999999984e78 or 2e21 < (*.f64 (-.f64 a #s(literal 1/2 binary64)) b)

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. +-commutative N/A

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

      5. associate-+l+ N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. +-commutative N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, \color{blue}{b}, x + y\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y + x\right) \]

      11. lower-+.f64 79.4

          \[\leadsto \mathsf{fma}\left(a - 0.5, b, y + x\right) \]

   6. Applied rewrites 79.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y + x\right)} \]

   7. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y\right) \]
   8. Step-by-step derivation

   9. Applied rewrites 57.8%

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, y\right) \]

   if -4.99999999999999984e78 < (*.f64 (-.f64 a #s(literal 1/2 binary64)) b) < 2e21

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]

   5. Taylor expanded in b around 0

      \[\leadsto x + \color{blue}{y} \]
   6. Step-by-step derivation

      1. lower-+.f64 42.9

         \[\leadsto x + y \]

   7. Applied rewrites 42.9%

      \[\leadsto x + \color{blue}{y} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 59.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a - 0.5\right) \cdot b\\ t_2 := b \cdot \left(a - 0.5\right)\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+78}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+101}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* (- a 0.5) b)) (t_2 (* b (- a 0.5))))
   (if (<= t_1 -5e+78) t_2 (if (<= t_1 5e+101) (+ x y) t_2))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (a - 0.5) * b;
	double t_2 = b * (a - 0.5);
	double tmp;
	if (t_1 <= -5e+78) {
		tmp = t_2;
	} else if (t_1 <= 5e+101) {
		tmp = x + y;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (a - 0.5d0) * b
    t_2 = b * (a - 0.5d0)
    if (t_1 <= (-5d+78)) then
        tmp = t_2
    else if (t_1 <= 5d+101) then
        tmp = x + y
    else
        tmp = t_2
    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 - 0.5) * b;
	double t_2 = b * (a - 0.5);
	double tmp;
	if (t_1 <= -5e+78) {
		tmp = t_2;
	} else if (t_1 <= 5e+101) {
		tmp = x + y;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (a - 0.5) * b
	t_2 = b * (a - 0.5)
	tmp = 0
	if t_1 <= -5e+78:
		tmp = t_2
	elif t_1 <= 5e+101:
		tmp = x + y
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(a - 0.5) * b)
	t_2 = Float64(b * Float64(a - 0.5))
	tmp = 0.0
	if (t_1 <= -5e+78)
		tmp = t_2;
	elseif (t_1 <= 5e+101)
		tmp = Float64(x + y);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (a - 0.5) * b;
	t_2 = b * (a - 0.5);
	tmp = 0.0;
	if (t_1 <= -5e+78)
		tmp = t_2;
	elseif (t_1 <= 5e+101)
		tmp = x + y;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]}, Block[{t$95$2 = N[(b * N[(a - 0.5), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -5e+78], t$95$2, If[LessEqual[t$95$1, 5e+101], N[(x + y), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 a #s(literal 1/2 binary64)) b) < -4.99999999999999984e78 or 4.99999999999999989e101 < (*.f64 (-.f64 a #s(literal 1/2 binary64)) b)

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. +-commutative N/A

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

      5. associate-+l+ N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. +-commutative N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, \color{blue}{b}, x + y\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y + x\right) \]

      11. lower-+.f64 79.4

          \[\leadsto \mathsf{fma}\left(a - 0.5, b, y + x\right) \]

   6. Applied rewrites 79.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y + x\right)} \]

   7. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(a - \frac{1}{2}\right)} \]
   8. Step-by-step derivation

      1. lower-*.f64 N/A

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

      2. lower--.f64 38.0

         \[\leadsto b \cdot \left(a - \color{blue}{0.5}\right) \]

   9. Applied rewrites 38.0%

      \[\leadsto \color{blue}{b \cdot \left(a - 0.5\right)} \]

   if -4.99999999999999984e78 < (*.f64 (-.f64 a #s(literal 1/2 binary64)) b) < 4.99999999999999989e101

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]

   5. Taylor expanded in b around 0

      \[\leadsto x + \color{blue}{y} \]
   6. Step-by-step derivation

      1. lower-+.f64 42.9

         \[\leadsto x + y \]

   7. Applied rewrites 42.9%

      \[\leadsto x + \color{blue}{y} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 15: 58.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a - 0.5\right) \cdot b\\ \mathbf{if}\;t\_1 \leq -4 \cdot 10^{+194}:\\ \;\;\;\;a \cdot b\\ \mathbf{elif}\;t\_1 \leq 10^{+229}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;a \cdot b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* (- a 0.5) b)))
   (if (<= t_1 -4e+194) (* a b) (if (<= t_1 1e+229) (+ x y) (* a b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (a - 0.5) * b;
	double tmp;
	if (t_1 <= -4e+194) {
		tmp = a * b;
	} else if (t_1 <= 1e+229) {
		tmp = x + y;
	} else {
		tmp = 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) :: t_1
    real(8) :: tmp
    t_1 = (a - 0.5d0) * b
    if (t_1 <= (-4d+194)) then
        tmp = a * b
    else if (t_1 <= 1d+229) then
        tmp = x + y
    else
        tmp = 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 t_1 = (a - 0.5) * b;
	double tmp;
	if (t_1 <= -4e+194) {
		tmp = a * b;
	} else if (t_1 <= 1e+229) {
		tmp = x + y;
	} else {
		tmp = a * b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (a - 0.5) * b
	tmp = 0
	if t_1 <= -4e+194:
		tmp = a * b
	elif t_1 <= 1e+229:
		tmp = x + y
	else:
		tmp = a * b
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(a - 0.5) * b)
	tmp = 0.0
	if (t_1 <= -4e+194)
		tmp = Float64(a * b);
	elseif (t_1 <= 1e+229)
		tmp = Float64(x + y);
	else
		tmp = Float64(a * b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (a - 0.5) * b;
	tmp = 0.0;
	if (t_1 <= -4e+194)
		tmp = a * b;
	elseif (t_1 <= 1e+229)
		tmp = x + y;
	else
		tmp = a * b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]}, If[LessEqual[t$95$1, -4e+194], N[(a * b), $MachinePrecision], If[LessEqual[t$95$1, 1e+229], N[(x + y), $MachinePrecision], N[(a * b), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 a #s(literal 1/2 binary64)) b) < -3.99999999999999978e194 or 9.9999999999999999e228 < (*.f64 (-.f64 a #s(literal 1/2 binary64)) b)

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in a around inf

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

      1. lower-*.f64 26.4

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

   4. Applied rewrites 26.4%

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

   if -3.99999999999999978e194 < (*.f64 (-.f64 a #s(literal 1/2 binary64)) b) < 9.9999999999999999e228

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 79.4

         \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

   4. Applied rewrites 79.4%

      \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]

   5. Taylor expanded in b around 0

      \[\leadsto x + \color{blue}{y} \]
   6. Step-by-step derivation

      1. lower-+.f64 42.9

         \[\leadsto x + y \]

   7. Applied rewrites 42.9%

      \[\leadsto x + \color{blue}{y} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 16: 42.9% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ x + y \end{array} \]

FPCore

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

C

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

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
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

2. Taylor expanded in z around 0

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

   1. lower-+.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower--.f64 79.4

      \[\leadsto x + \left(y + b \cdot \left(a - \color{blue}{0.5}\right)\right) \]

4. Applied rewrites 79.4%

   \[\leadsto \color{blue}{x + \left(y + b \cdot \left(a - 0.5\right)\right)} \]

5. Taylor expanded in b around 0

   \[\leadsto x + \color{blue}{y} \]
6. Step-by-step derivation

   1. lower-+.f64 42.9

      \[\leadsto x + y \]

7. Applied rewrites 42.9%

   \[\leadsto x + \color{blue}{y} \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025143 
(FPCore (x y z t a b)
  :name "Numeric.SpecFunctions:logBeta from math-functions-0.1.5.2, A"
  :precision binary64
  (+ (- (+ (+ x y) z) (* z (log t))) (* (- a 0.5) b)))