Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, B

Percentage Accurate: 85.5% → 99.8%

Time: 7.9s

Alternatives: 12

Speedup: 1.9×

Specification

Math

\[\begin{array}{l} \\ \left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (- (+ (* x (log y)) (* z (log (- 1.0 y)))) t))

C

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

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

Java

public static double code(double x, double y, double z, double t) {
	return ((x * Math.log(y)) + (z * Math.log((1.0 - y)))) - t;
}

Python

def code(x, y, z, t):
	return ((x * math.log(y)) + (z * math.log((1.0 - y)))) - t

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 85.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (- (+ (* x (log y)) (* z (log (- 1.0 y)))) t))

C

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

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

Java

public static double code(double x, double y, double z, double t) {
	return ((x * Math.log(y)) + (z * Math.log((1.0 - y)))) - t;
}

Python

def code(x, y, z, t):
	return ((x * math.log(y)) + (z * math.log((1.0 - y)))) - t

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\left(\left(-y\right) \cdot y\right) \cdot y\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, y\right)\right)\right)\right) - t \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (-
  (+ (* x (log y)) (* z (- (log1p (* (* (- y) y) y)) (log1p (fma y y y)))))
  t))

C

double code(double x, double y, double z, double t) {
	return ((x * log(y)) + (z * (log1p(((-y * y) * y)) - log1p(fma(y, y, y))))) - t;
}

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(x * log(y)) + Float64(z * Float64(log1p(Float64(Float64(Float64(-y) * y) * y)) - log1p(fma(y, y, y))))) - t)
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision] + N[(z * N[(N[Log[1 + N[(N[((-y) * y), $MachinePrecision] * y), $MachinePrecision]], $MachinePrecision] - N[Log[1 + N[(y * y + y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision]

Derivation

1. Initial program 88.1%

   \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
2. Add Preprocessing
3. Step-by-step derivation

   1. flip3-- N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \log \color{blue}{\left(\frac{{1}^{3} - {y}^{3}}{1 \cdot 1 + \left(y \cdot y + 1 \cdot y\right)}\right)}\right) - t \]

   2. log-div N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \color{blue}{\left(\log \left({1}^{3} - {y}^{3}\right) - \log \left(1 \cdot 1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)}\right) - t \]

   3. lower--.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \color{blue}{\left(\log \left({1}^{3} - {y}^{3}\right) - \log \left(1 \cdot 1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)}\right) - t \]

   4. lower-log.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\color{blue}{\log \left({1}^{3} - {y}^{3}\right)} - \log \left(1 \cdot 1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   5. metadata-eval N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \left(\color{blue}{1} - {y}^{3}\right) - \log \left(1 \cdot 1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   6. lower--.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \color{blue}{\left(1 - {y}^{3}\right)} - \log \left(1 \cdot 1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   7. lower-pow.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \left(1 - \color{blue}{{y}^{3}}\right) - \log \left(1 \cdot 1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   8. metadata-eval N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \left(1 - {y}^{3}\right) - \log \left(\color{blue}{1} + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   9. lower-log1p.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \left(1 - {y}^{3}\right) - \color{blue}{\mathsf{log1p}\left(y \cdot y + 1 \cdot y\right)}\right)\right) - t \]

   10. lower-fma.f64 N/A

       \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \left(1 - {y}^{3}\right) - \mathsf{log1p}\left(\color{blue}{\mathsf{fma}\left(y, y, 1 \cdot y\right)}\right)\right)\right) - t \]

   11. lower-*.f64 99.8

       \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \left(1 - {y}^{3}\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, \color{blue}{1 \cdot y}\right)\right)\right)\right) - t \]

4. Applied rewrites 99.8%

   \[\leadsto \left(x \cdot \log y + z \cdot \color{blue}{\left(\log \left(1 - {y}^{3}\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, 1 \cdot y\right)\right)\right)}\right) - t \]
5. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \color{blue}{\left(\log \left(1 - {y}^{3}\right) - \log \left(1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)}\right) - t \]

   2. *-lft-identity N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \left(1 - {\color{blue}{\left(1 \cdot y\right)}}^{3}\right) - \log \left(1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   3. unpow-prod-down N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \left(1 - \color{blue}{{1}^{3} \cdot {y}^{3}}\right) - \log \left(1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   4. metadata-eval N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \left(1 - \color{blue}{1} \cdot {y}^{3}\right) - \log \left(1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   5. metadata-eval N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \left(1 - \color{blue}{\left(\mathsf{neg}\left(-1\right)\right)} \cdot {y}^{3}\right) - \log \left(1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

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

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\log \color{blue}{\left(1 + -1 \cdot {y}^{3}\right)} - \log \left(1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   7. lower-log1p.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\color{blue}{\mathsf{log1p}\left(-1 \cdot {y}^{3}\right)} - \log \left(1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   8. mul-1-neg N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\color{blue}{\mathsf{neg}\left({y}^{3}\right)}\right) - \log \left(1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   9. cube-neg-rev N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{3}}\right) - \log \left(1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   10. lower-pow.f64 N/A

       \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{3}}\right) - \log \left(1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   11. lower-neg.f64 N/A

       \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left({\color{blue}{\left(-y\right)}}^{3}\right) - \log \left(1 + \left(y \cdot y + 1 \cdot y\right)\right)\right)\right) - t \]

   12. lower-log1p.f64 N/A

       \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left({\left(-y\right)}^{3}\right) - \color{blue}{\mathsf{log1p}\left(y \cdot y + 1 \cdot y\right)}\right)\right) - t \]

   13. *-lft-identity N/A

       \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left({\left(-y\right)}^{3}\right) - \mathsf{log1p}\left(y \cdot y + \color{blue}{y}\right)\right)\right) - t \]

   14. lower-fma.f64 99.8

       \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left({\left(-y\right)}^{3}\right) - \mathsf{log1p}\left(\color{blue}{\mathsf{fma}\left(y, y, y\right)}\right)\right)\right) - t \]

6. Applied rewrites 99.8%

   \[\leadsto \left(x \cdot \log y + z \cdot \color{blue}{\left(\mathsf{log1p}\left({\left(-y\right)}^{3}\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, y\right)\right)\right)}\right) - t \]
7. Step-by-step derivation

   1. unpow3 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\color{blue}{\left(\left(\mathsf{neg}\left(y\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)}\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, y\right)\right)\right)\right) - t \]

   2. sqr-neg-rev N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\color{blue}{\left(y \cdot y\right)} \cdot \left(\mathsf{neg}\left(y\right)\right)\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, y\right)\right)\right)\right) - t \]

   3. pow2 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\color{blue}{{y}^{2}} \cdot \left(\mathsf{neg}\left(y\right)\right)\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, y\right)\right)\right)\right) - t \]

   4. lower-*.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\color{blue}{{y}^{2} \cdot \left(\mathsf{neg}\left(y\right)\right)}\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, y\right)\right)\right)\right) - t \]

   5. pow2 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\color{blue}{\left(y \cdot y\right)} \cdot \left(\mathsf{neg}\left(y\right)\right)\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, y\right)\right)\right)\right) - t \]

   6. lower-*.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\color{blue}{\left(y \cdot y\right)} \cdot \left(\mathsf{neg}\left(y\right)\right)\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, y\right)\right)\right)\right) - t \]

   7. lower-neg.f64 99.8

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\left(y \cdot y\right) \cdot \color{blue}{\left(-y\right)}\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, y\right)\right)\right)\right) - t \]

8. Applied rewrites 99.8%

   \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\color{blue}{\left(y \cdot y\right) \cdot \left(-y\right)}\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, y\right)\right)\right)\right) - t \]

9. Final simplification 99.8%

   \[\leadsto \left(x \cdot \log y + z \cdot \left(\mathsf{log1p}\left(\left(\left(-y\right) \cdot y\right) \cdot y\right) - \mathsf{log1p}\left(\mathsf{fma}\left(y, y, y\right)\right)\right)\right) - t \]
10. Add Preprocessing

Alternative 2: 99.7% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\log y, x, \left(\left(\left(\left(-0.25 \cdot y - 0.3333333333333333\right) \cdot y - 0.5\right) \cdot y - 1\right) \cdot y\right) \cdot z - t\right) \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (fma
  (log y)
  x
  (-
   (* (* (- (* (- (* (- (* -0.25 y) 0.3333333333333333) y) 0.5) y) 1.0) y) z)
   t)))

C

double code(double x, double y, double z, double t) {
	return fma(log(y), x, (((((((((-0.25 * y) - 0.3333333333333333) * y) - 0.5) * y) - 1.0) * y) * z) - t));
}

Julia

function code(x, y, z, t)
	return fma(log(y), x, Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(-0.25 * y) - 0.3333333333333333) * y) - 0.5) * y) - 1.0) * y) * z) - t))
end

Wolfram

code[x_, y_, z_, t_] := N[(N[Log[y], $MachinePrecision] * x + N[(N[(N[(N[(N[(N[(N[(N[(N[(-0.25 * y), $MachinePrecision] - 0.3333333333333333), $MachinePrecision] * y), $MachinePrecision] - 0.5), $MachinePrecision] * y), $MachinePrecision] - 1.0), $MachinePrecision] * y), $MachinePrecision] * z), $MachinePrecision] - t), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 88.1%

   \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto \left(x \cdot \log y + z \cdot \color{blue}{\left(y \cdot \left(y \cdot \left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) - 1\right)\right)}\right) - t \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(y \cdot \left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) - 1\right) \cdot \color{blue}{y}\right)\right) - t \]

   2. lower-*.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(y \cdot \left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) - 1\right) \cdot \color{blue}{y}\right)\right) - t \]

   3. lower--.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(y \cdot \left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) - 1\right) \cdot y\right)\right) - t \]

   4. *-commutative N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

   5. lower-*.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

   6. lower--.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

   7. *-commutative N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

   8. lower-*.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

   9. lower--.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

   10. lower-*.f64 99.7

       \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(\left(-0.25 \cdot y - 0.3333333333333333\right) \cdot y - 0.5\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

5. Applied rewrites 99.7%

   \[\leadsto \left(x \cdot \log y + z \cdot \color{blue}{\left(\left(\left(\left(-0.25 \cdot y - 0.3333333333333333\right) \cdot y - 0.5\right) \cdot y - 1\right) \cdot y\right)}\right) - t \]
6. Step-by-step derivation

   1. associate--l+ N/A

      \[\leadsto \color{blue}{x \cdot \log y + \left(z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right) - t\right)} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{\log y \cdot x} + \left(z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right) - t\right) \]

   3. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\log y, x, z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right) - t\right)} \]

   4. lower-log.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\log y}, x, z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right) - t\right) \]

   5. lower--.f64 N/A

      \[\leadsto \mathsf{fma}\left(\log y, x, \color{blue}{z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right) - t}\right) \]

7. Applied rewrites 99.7%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\log y, x, \left(\left(\left(\left(-0.25 \cdot y - 0.3333333333333333\right) \cdot y - 0.5\right) \cdot y - 1\right) \cdot y\right) \cdot z - t\right)} \]
8. Add Preprocessing

Alternative 3: 99.6% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot \log y + z \cdot \left(\left(\left(-0.3333333333333333 \cdot y - 0.5\right) \cdot y - 1\right) \cdot y\right)\right) - t \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (-
  (+ (* x (log y)) (* z (* (- (* (- (* -0.3333333333333333 y) 0.5) y) 1.0) y)))
  t))

C

double code(double x, double y, double z, double t) {
	return ((x * log(y)) + (z * (((((-0.3333333333333333 * y) - 0.5) * y) - 1.0) * y))) - t;
}

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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = ((x * log(y)) + (z * ((((((-0.3333333333333333d0) * y) - 0.5d0) * y) - 1.0d0) * y))) - t
end function

Java

public static double code(double x, double y, double z, double t) {
	return ((x * Math.log(y)) + (z * (((((-0.3333333333333333 * y) - 0.5) * y) - 1.0) * y))) - t;
}

Python

def code(x, y, z, t):
	return ((x * math.log(y)) + (z * (((((-0.3333333333333333 * y) - 0.5) * y) - 1.0) * y))) - t

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(x * log(y)) + Float64(z * Float64(Float64(Float64(Float64(Float64(-0.3333333333333333 * y) - 0.5) * y) - 1.0) * y))) - t)
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = ((x * log(y)) + (z * (((((-0.3333333333333333 * y) - 0.5) * y) - 1.0) * y))) - t;
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision] + N[(z * N[(N[(N[(N[(N[(-0.3333333333333333 * y), $MachinePrecision] - 0.5), $MachinePrecision] * y), $MachinePrecision] - 1.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision]

Derivation

1. Initial program 88.1%

   \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
2. Add Preprocessing

3. Taylor expanded in y around 0

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

   1. *-commutative N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(y \cdot \left(\frac{-1}{3} \cdot y - \frac{1}{2}\right) - 1\right) \cdot \color{blue}{y}\right)\right) - t \]

   2. lower-*.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(y \cdot \left(\frac{-1}{3} \cdot y - \frac{1}{2}\right) - 1\right) \cdot \color{blue}{y}\right)\right) - t \]

   3. lower--.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(y \cdot \left(\frac{-1}{3} \cdot y - \frac{1}{2}\right) - 1\right) \cdot y\right)\right) - t \]

   4. *-commutative N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(\frac{-1}{3} \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

   5. lower-*.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(\frac{-1}{3} \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

   6. lower--.f64 N/A

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(\frac{-1}{3} \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

   7. lower-*.f64 99.6

      \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(-0.3333333333333333 \cdot y - 0.5\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

5. Applied rewrites 99.6%

   \[\leadsto \left(x \cdot \log y + z \cdot \color{blue}{\left(\left(\left(-0.3333333333333333 \cdot y - 0.5\right) \cdot y - 1\right) \cdot y\right)}\right) - t \]
6. Add Preprocessing

Alternative 4: 99.5% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 88.1%

   \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
2. Add Preprocessing

3. Taylor expanded in y around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

   4. +-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 N/A

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

   8. mul-1-neg N/A

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

   9. lower-neg.f64 N/A

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

   10. *-commutative N/A

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

   11. lower-*.f64 N/A

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

   12. lower-log.f64 99.6

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

5. Applied rewrites 99.6%

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

Alternative 5: 90.2% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.2 \cdot 10^{-90} \lor \neg \left(x \leq 1.5 \cdot 10^{-38}\right):\\ \;\;\;\;\log y \cdot x - t\\ \mathbf{else}:\\ \;\;\;\;\left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot y - t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -4.2e-90) (not (<= x 1.5e-38)))
   (- (* (log y) x) t)
   (- (* (- (* (* z y) -0.5) z) y) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -4.2e-90) || !(x <= 1.5e-38)) {
		tmp = (log(y) * x) - t;
	} else {
		tmp = ((((z * y) * -0.5) - z) * y) - t;
	}
	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)
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) :: tmp
    if ((x <= (-4.2d-90)) .or. (.not. (x <= 1.5d-38))) then
        tmp = (log(y) * x) - t
    else
        tmp = ((((z * y) * (-0.5d0)) - z) * y) - t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -4.2e-90) || !(x <= 1.5e-38)) {
		tmp = (Math.log(y) * x) - t;
	} else {
		tmp = ((((z * y) * -0.5) - z) * y) - t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -4.2e-90) or not (x <= 1.5e-38):
		tmp = (math.log(y) * x) - t
	else:
		tmp = ((((z * y) * -0.5) - z) * y) - t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -4.2e-90) || !(x <= 1.5e-38))
		tmp = Float64(Float64(log(y) * x) - t);
	else
		tmp = Float64(Float64(Float64(Float64(Float64(z * y) * -0.5) - z) * y) - t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -4.2e-90) || ~((x <= 1.5e-38)))
		tmp = (log(y) * x) - t;
	else
		tmp = ((((z * y) * -0.5) - z) * y) - t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -4.2e-90], N[Not[LessEqual[x, 1.5e-38]], $MachinePrecision]], N[(N[(N[Log[y], $MachinePrecision] * x), $MachinePrecision] - t), $MachinePrecision], N[(N[(N[(N[(N[(z * y), $MachinePrecision] * -0.5), $MachinePrecision] - z), $MachinePrecision] * y), $MachinePrecision] - t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -4.1999999999999998e-90 or 1.49999999999999994e-38 < x

   1. Initial program 93.7%

      \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \log y} - t \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \log y \cdot \color{blue}{x} - t \]

      2. lower-*.f64 N/A

         \[\leadsto \log y \cdot \color{blue}{x} - t \]

      3. lower-log.f64 93.5

         \[\leadsto \log y \cdot x - t \]

   5. Applied rewrites 93.5%

      \[\leadsto \color{blue}{\log y \cdot x} - t \]

   if -4.1999999999999998e-90 < x < 1.49999999999999994e-38

   1. Initial program 81.0%

      \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 N/A

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

      8. mul-1-neg N/A

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

      9. lower-neg.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. lower-log.f64 99.4

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

   5. Applied rewrites 99.4%

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

   6. Taylor expanded in x around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 89.9

         \[\leadsto \left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot y - t \]

   8. Applied rewrites 89.9%

      \[\leadsto \left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot \color{blue}{y} - t \]
3. Recombined 2 regimes into one program.

4. Final simplification 91.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.2 \cdot 10^{-90} \lor \neg \left(x \leq 1.5 \cdot 10^{-38}\right):\\ \;\;\;\;\log y \cdot x - t\\ \mathbf{else}:\\ \;\;\;\;\left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot y - t\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 90.2% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.2 \cdot 10^{-90}:\\ \;\;\;\;\mathsf{fma}\left(\log y, x, -t\right)\\ \mathbf{elif}\;x \leq 1.5 \cdot 10^{-38}:\\ \;\;\;\;\left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot y - t\\ \mathbf{else}:\\ \;\;\;\;\log y \cdot x - t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -4.2e-90)
   (fma (log y) x (- t))
   (if (<= x 1.5e-38) (- (* (- (* (* z y) -0.5) z) y) t) (- (* (log y) x) t))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -4.2e-90) {
		tmp = fma(log(y), x, -t);
	} else if (x <= 1.5e-38) {
		tmp = ((((z * y) * -0.5) - z) * y) - t;
	} else {
		tmp = (log(y) * x) - t;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -4.2e-90)
		tmp = fma(log(y), x, Float64(-t));
	elseif (x <= 1.5e-38)
		tmp = Float64(Float64(Float64(Float64(Float64(z * y) * -0.5) - z) * y) - t);
	else
		tmp = Float64(Float64(log(y) * x) - t);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -4.2e-90], N[(N[Log[y], $MachinePrecision] * x + (-t)), $MachinePrecision], If[LessEqual[x, 1.5e-38], N[(N[(N[(N[(N[(z * y), $MachinePrecision] * -0.5), $MachinePrecision] - z), $MachinePrecision] * y), $MachinePrecision] - t), $MachinePrecision], N[(N[(N[Log[y], $MachinePrecision] * x), $MachinePrecision] - t), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -4.1999999999999998e-90

   1. Initial program 94.1%

      \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \left(x \cdot \log y + z \cdot \color{blue}{\left(y \cdot \left(y \cdot \left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) - 1\right)\right)}\right) - t \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(y \cdot \left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) - 1\right) \cdot \color{blue}{y}\right)\right) - t \]

      2. lower-*.f64 N/A

         \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(y \cdot \left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) - 1\right) \cdot \color{blue}{y}\right)\right) - t \]

      3. lower--.f64 N/A

         \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(y \cdot \left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) - 1\right) \cdot y\right)\right) - t \]

      4. *-commutative N/A

         \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

      5. lower-*.f64 N/A

         \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

      6. lower--.f64 N/A

         \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(y \cdot \left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

      7. *-commutative N/A

         \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

      8. lower-*.f64 N/A

         \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

      9. lower--.f64 N/A

         \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

      10. lower-*.f64 99.7

          \[\leadsto \left(x \cdot \log y + z \cdot \left(\left(\left(\left(-0.25 \cdot y - 0.3333333333333333\right) \cdot y - 0.5\right) \cdot y - 1\right) \cdot y\right)\right) - t \]

   5. Applied rewrites 99.7%

      \[\leadsto \left(x \cdot \log y + z \cdot \color{blue}{\left(\left(\left(\left(-0.25 \cdot y - 0.3333333333333333\right) \cdot y - 0.5\right) \cdot y - 1\right) \cdot y\right)}\right) - t \]
   6. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto \color{blue}{x \cdot \log y + \left(z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right) - t\right)} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\log y \cdot x} + \left(z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right) - t\right) \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\log y, x, z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right) - t\right)} \]

      4. lower-log.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\log y}, x, z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right) - t\right) \]

      5. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(\log y, x, \color{blue}{z \cdot \left(\left(\left(\left(\frac{-1}{4} \cdot y - \frac{1}{3}\right) \cdot y - \frac{1}{2}\right) \cdot y - 1\right) \cdot y\right) - t}\right) \]

   7. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\log y, x, \left(\left(\left(\left(-0.25 \cdot y - 0.3333333333333333\right) \cdot y - 0.5\right) \cdot y - 1\right) \cdot y\right) \cdot z - t\right)} \]

   8. Taylor expanded in y around 0

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

      1. mul-1-neg N/A

         \[\leadsto \mathsf{fma}\left(\log y, x, \mathsf{neg}\left(t\right)\right) \]

      2. lower-neg.f64 93.7

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

   10. Applied rewrites 93.7%

       \[\leadsto \mathsf{fma}\left(\log y, x, \color{blue}{-t}\right) \]

   if -4.1999999999999998e-90 < x < 1.49999999999999994e-38

   1. Initial program 81.0%

      \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 N/A

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

      8. mul-1-neg N/A

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

      9. lower-neg.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. lower-log.f64 99.4

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

   5. Applied rewrites 99.4%

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

   6. Taylor expanded in x around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 89.9

         \[\leadsto \left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot y - t \]

   8. Applied rewrites 89.9%

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

   if 1.49999999999999994e-38 < x

   1. Initial program 93.3%

      \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \log y} - t \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \log y \cdot \color{blue}{x} - t \]

      2. lower-*.f64 N/A

         \[\leadsto \log y \cdot \color{blue}{x} - t \]

      3. lower-log.f64 93.3

         \[\leadsto \log y \cdot x - t \]

   5. Applied rewrites 93.3%

      \[\leadsto \color{blue}{\log y \cdot x} - t \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 7: 76.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.1 \cdot 10^{+130} \lor \neg \left(x \leq 4.3 \cdot 10^{-38}\right):\\ \;\;\;\;\log y \cdot x\\ \mathbf{else}:\\ \;\;\;\;\left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot y - t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -4.1e+130) (not (<= x 4.3e-38)))
   (* (log y) x)
   (- (* (- (* (* z y) -0.5) z) y) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -4.1e+130) || !(x <= 4.3e-38)) {
		tmp = log(y) * x;
	} else {
		tmp = ((((z * y) * -0.5) - z) * y) - t;
	}
	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)
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) :: tmp
    if ((x <= (-4.1d+130)) .or. (.not. (x <= 4.3d-38))) then
        tmp = log(y) * x
    else
        tmp = ((((z * y) * (-0.5d0)) - z) * y) - t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -4.1e+130) || !(x <= 4.3e-38)) {
		tmp = Math.log(y) * x;
	} else {
		tmp = ((((z * y) * -0.5) - z) * y) - t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -4.1e+130) or not (x <= 4.3e-38):
		tmp = math.log(y) * x
	else:
		tmp = ((((z * y) * -0.5) - z) * y) - t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -4.1e+130) || !(x <= 4.3e-38))
		tmp = Float64(log(y) * x);
	else
		tmp = Float64(Float64(Float64(Float64(Float64(z * y) * -0.5) - z) * y) - t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -4.1e+130) || ~((x <= 4.3e-38)))
		tmp = log(y) * x;
	else
		tmp = ((((z * y) * -0.5) - z) * y) - t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -4.1e+130], N[Not[LessEqual[x, 4.3e-38]], $MachinePrecision]], N[(N[Log[y], $MachinePrecision] * x), $MachinePrecision], N[(N[(N[(N[(N[(z * y), $MachinePrecision] * -0.5), $MachinePrecision] - z), $MachinePrecision] * y), $MachinePrecision] - t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -4.09999999999999978e130 or 4.3000000000000002e-38 < x

   1. Initial program 94.3%

      \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \log y} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \log y \cdot \color{blue}{x} \]

      2. lower-*.f64 N/A

         \[\leadsto \log y \cdot \color{blue}{x} \]

      3. lower-log.f64 78.3

         \[\leadsto \log y \cdot x \]

   5. Applied rewrites 78.3%

      \[\leadsto \color{blue}{\log y \cdot x} \]

   if -4.09999999999999978e130 < x < 4.3000000000000002e-38

   1. Initial program 83.6%

      \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 N/A

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

      8. mul-1-neg N/A

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

      9. lower-neg.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. lower-log.f64 99.5

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

   5. Applied rewrites 99.5%

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

   6. Taylor expanded in x around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 84.0

         \[\leadsto \left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot y - t \]

   8. Applied rewrites 84.0%

      \[\leadsto \left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot \color{blue}{y} - t \]
3. Recombined 2 regimes into one program.

4. Final simplification 81.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.1 \cdot 10^{+130} \lor \neg \left(x \leq 4.3 \cdot 10^{-38}\right):\\ \;\;\;\;\log y \cdot x\\ \mathbf{else}:\\ \;\;\;\;\left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot y - t\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 99.1% accurate, 1.9× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 88.1%

   \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
2. Add Preprocessing

3. Taylor expanded in y around 0

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

   1. associate--l+ N/A

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

   2. associate-*r* N/A

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

   3. mul-1-neg N/A

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

   4. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{neg}\left(y\right), \color{blue}{z}, x \cdot \log y - t\right) \]

   5. lower-neg.f64 N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. lower-log.f64 99.4

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

5. Applied rewrites 99.4%

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

Alternative 9: 57.8% accurate, 10.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot y - t \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (- (* (- (* (* z y) -0.5) z) y) t))

C

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

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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = ((((z * y) * (-0.5d0)) - z) * y) - t
end function

Java

public static double code(double x, double y, double z, double t) {
	return ((((z * y) * -0.5) - z) * y) - t;
}

Python

def code(x, y, z, t):
	return ((((z * y) * -0.5) - z) * y) - t

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(Float64(Float64(z * y) * -0.5) - z) * y) - t)
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(N[(N[(z * y), $MachinePrecision] * -0.5), $MachinePrecision] - z), $MachinePrecision] * y), $MachinePrecision] - t), $MachinePrecision]

Derivation

1. Initial program 88.1%

   \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
2. Add Preprocessing

3. Taylor expanded in y around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

   4. +-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 N/A

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

   8. mul-1-neg N/A

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

   9. lower-neg.f64 N/A

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

   10. *-commutative N/A

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

   11. lower-*.f64 N/A

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

   12. lower-log.f64 99.6

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

5. Applied rewrites 99.6%

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

6. Taylor expanded in x around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. lower--.f64 N/A

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

   4. *-commutative N/A

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

   5. lower-*.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 58.2

      \[\leadsto \left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot y - t \]

8. Applied rewrites 58.2%

   \[\leadsto \left(\left(z \cdot y\right) \cdot -0.5 - z\right) \cdot \color{blue}{y} - t \]
9. Add Preprocessing

Alternative 10: 43.8% accurate, 11.6× speedup

Math

\[\begin{array}{l} \\ \left(\left(y \cdot y\right) \cdot z\right) \cdot -0.5 - t \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (- (* (* (* y y) z) -0.5) t))

C

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

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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (((y * y) * z) * (-0.5d0)) - t
end function

Java

public static double code(double x, double y, double z, double t) {
	return (((y * y) * z) * -0.5) - t;
}

Python

def code(x, y, z, t):
	return (((y * y) * z) * -0.5) - t

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(Float64(y * y) * z) * -0.5) - t)
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(N[(y * y), $MachinePrecision] * z), $MachinePrecision] * -0.5), $MachinePrecision] - t), $MachinePrecision]

Derivation

1. Initial program 88.1%

   \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
2. Add Preprocessing

3. Taylor expanded in y around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

   4. +-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 N/A

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

   8. mul-1-neg N/A

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

   9. lower-neg.f64 N/A

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

   10. *-commutative N/A

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

   11. lower-*.f64 N/A

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

   12. lower-log.f64 99.6

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

5. Applied rewrites 99.6%

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

6. Taylor expanded in y around inf

   \[\leadsto \frac{-1}{2} \cdot \color{blue}{\left({y}^{2} \cdot z\right)} - t \]
7. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \left({y}^{2} \cdot z\right) \cdot \frac{-1}{2} - t \]

   2. lower-*.f64 N/A

      \[\leadsto \left({y}^{2} \cdot z\right) \cdot \frac{-1}{2} - t \]

   3. lower-*.f64 N/A

      \[\leadsto \left({y}^{2} \cdot z\right) \cdot \frac{-1}{2} - t \]

   4. pow2 N/A

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

   5. lower-*.f64 46.5

      \[\leadsto \left(\left(y \cdot y\right) \cdot z\right) \cdot -0.5 - t \]

8. Applied rewrites 46.5%

   \[\leadsto \left(\left(y \cdot y\right) \cdot z\right) \cdot \color{blue}{-0.5} - t \]
9. Add Preprocessing

Alternative 11: 43.8% accurate, 11.6× speedup

Math

\[\begin{array}{l} \\ \left(y \cdot \left(z \cdot y\right)\right) \cdot -0.5 - t \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (- (* (* y (* z y)) -0.5) t))

C

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

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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = ((y * (z * y)) * (-0.5d0)) - t
end function

Java

public static double code(double x, double y, double z, double t) {
	return ((y * (z * y)) * -0.5) - t;
}

Python

def code(x, y, z, t):
	return ((y * (z * y)) * -0.5) - t

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(y * Float64(z * y)) * -0.5) - t)
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(y * N[(z * y), $MachinePrecision]), $MachinePrecision] * -0.5), $MachinePrecision] - t), $MachinePrecision]

Derivation

1. Initial program 88.1%

   \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
2. Add Preprocessing

3. Taylor expanded in y around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

   4. +-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 N/A

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

   8. mul-1-neg N/A

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

   9. lower-neg.f64 N/A

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

   10. *-commutative N/A

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

   11. lower-*.f64 N/A

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

   12. lower-log.f64 99.6

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

5. Applied rewrites 99.6%

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

6. Taylor expanded in y around inf

   \[\leadsto \frac{-1}{2} \cdot \color{blue}{\left({y}^{2} \cdot z\right)} - t \]
7. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \left({y}^{2} \cdot z\right) \cdot \frac{-1}{2} - t \]

   2. lower-*.f64 N/A

      \[\leadsto \left({y}^{2} \cdot z\right) \cdot \frac{-1}{2} - t \]

   3. lower-*.f64 N/A

      \[\leadsto \left({y}^{2} \cdot z\right) \cdot \frac{-1}{2} - t \]

   4. pow2 N/A

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

   5. lower-*.f64 46.5

      \[\leadsto \left(\left(y \cdot y\right) \cdot z\right) \cdot -0.5 - t \]

8. Applied rewrites 46.5%

   \[\leadsto \left(\left(y \cdot y\right) \cdot z\right) \cdot \color{blue}{-0.5} - t \]
9. Step-by-step derivation

   1. associate-*l* N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. lower-*.f64 46.5

      \[\leadsto \left(y \cdot \left(z \cdot y\right)\right) \cdot -0.5 - t \]

10. Applied rewrites 46.5%

    \[\leadsto \left(y \cdot \left(z \cdot y\right)\right) \cdot -0.5 - t \]
11. Add Preprocessing

Alternative 12: 43.3% accurate, 73.3× speedup

Math

\[\begin{array}{l} \\ -t \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (- t))

C

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

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

Java

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

Python

def code(x, y, z, t):
	return -t

Julia

function code(x, y, z, t)
	return Float64(-t)
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := (-t)

Derivation

1. Initial program 88.1%

   \[\left(x \cdot \log y + z \cdot \log \left(1 - y\right)\right) - t \]
2. Add Preprocessing

3. Taylor expanded in t around inf

   \[\leadsto \color{blue}{-1 \cdot t} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \mathsf{neg}\left(t\right) \]

   2. lower-neg.f64 46.1

      \[\leadsto -t \]

5. Applied rewrites 46.1%

   \[\leadsto \color{blue}{-t} \]
6. Add Preprocessing

Developer Target 1: 99.6% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \left(-z\right) \cdot \left(\left(0.5 \cdot \left(y \cdot y\right) + y\right) + \frac{0.3333333333333333}{1 \cdot \left(1 \cdot 1\right)} \cdot \left(y \cdot \left(y \cdot y\right)\right)\right) - \left(t - x \cdot \log y\right) \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (-
  (*
   (- z)
   (+
    (+ (* 0.5 (* y y)) y)
    (* (/ 0.3333333333333333 (* 1.0 (* 1.0 1.0))) (* y (* y y)))))
  (- t (* x (log y)))))

C

double code(double x, double y, double z, double t) {
	return (-z * (((0.5 * (y * y)) + y) + ((0.3333333333333333 / (1.0 * (1.0 * 1.0))) * (y * (y * y))))) - (t - (x * log(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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (-z * (((0.5d0 * (y * y)) + y) + ((0.3333333333333333d0 / (1.0d0 * (1.0d0 * 1.0d0))) * (y * (y * y))))) - (t - (x * log(y)))
end function

Java

public static double code(double x, double y, double z, double t) {
	return (-z * (((0.5 * (y * y)) + y) + ((0.3333333333333333 / (1.0 * (1.0 * 1.0))) * (y * (y * y))))) - (t - (x * Math.log(y)));
}

Python

def code(x, y, z, t):
	return (-z * (((0.5 * (y * y)) + y) + ((0.3333333333333333 / (1.0 * (1.0 * 1.0))) * (y * (y * y))))) - (t - (x * math.log(y)))

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(-z) * Float64(Float64(Float64(0.5 * Float64(y * y)) + y) + Float64(Float64(0.3333333333333333 / Float64(1.0 * Float64(1.0 * 1.0))) * Float64(y * Float64(y * y))))) - Float64(t - Float64(x * log(y))))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (-z * (((0.5 * (y * y)) + y) + ((0.3333333333333333 / (1.0 * (1.0 * 1.0))) * (y * (y * y))))) - (t - (x * log(y)));
end

Wolfram

code[x_, y_, z_, t_] := N[(N[((-z) * N[(N[(N[(0.5 * N[(y * y), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] + N[(N[(0.3333333333333333 / N[(1.0 * N[(1.0 * 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(y * N[(y * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(t - N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2025044 
(FPCore (x y z t)
  :name "Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, B"
  :precision binary64

  :alt
  (! :herbie-platform default (- (* (- z) (+ (+ (* 1/2 (* y y)) y) (* (/ 1/3 (* 1 (* 1 1))) (* y (* y y))))) (- t (* x (log y)))))

  (- (+ (* x (log y)) (* z (log (- 1.0 y)))) t))