Data.Number.Erf:$dmerfcx from erf-2.0.0.0

Percentage Accurate: 100.0% → 100.0%

Time: 5.1s

Alternatives: 11

Speedup: 1.0×

• 49.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x \cdot e^{y \cdot y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (exp (* y y))))

C

double code(double x, double y) {
	return x * exp((y * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * exp((y * y))
end function

Java

public static double code(double x, double y) {
	return x * Math.exp((y * y));
}

Python

def code(x, y):
	return x * math.exp((y * y))

Julia

function code(x, y)
	return Float64(x * exp(Float64(y * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = x * exp((y * y));
end

Wolfram

code[x_, y_] := N[(x * N[Exp[N[(y * y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot e^{y \cdot y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (exp (* y y))))

C

double code(double x, double y) {
	return x * exp((y * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * exp((y * y))
end function

Java

public static double code(double x, double y) {
	return x * Math.exp((y * y));
}

Python

def code(x, y):
	return x * math.exp((y * y))

Julia

function code(x, y)
	return Float64(x * exp(Float64(y * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = x * exp((y * y));
end

Wolfram

code[x_, y_] := N[(x * N[Exp[N[(y * y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ {\left(e^{y}\right)}^{y} \cdot x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (pow (exp y) y) x))

C

double code(double x, double y) {
	return pow(exp(y), y) * x;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (exp(y) ** y) * x
end function

Java

public static double code(double x, double y) {
	return Math.pow(Math.exp(y), y) * x;
}

Python

def code(x, y):
	return math.pow(math.exp(y), y) * x

Julia

function code(x, y)
	return Float64((exp(y) ^ y) * x)
end

MATLAB

function tmp = code(x, y)
	tmp = (exp(y) ^ y) * x;
end

Wolfram

code[x_, y_] := N[(N[Power[N[Exp[y], $MachinePrecision], y], $MachinePrecision] * x), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x \cdot e^{y \cdot y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot e^{y \cdot y}} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{e^{y \cdot y} \cdot x} \]

   3. lift-exp.f64 N/A

      \[\leadsto \color{blue}{e^{y \cdot y}} \cdot x \]

   4. lift-*.f64 N/A

      \[\leadsto e^{\color{blue}{y \cdot y}} \cdot x \]

   5. sqr-neg-rev N/A

      \[\leadsto e^{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot x \]

   6. pow2 N/A

      \[\leadsto e^{\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{2}}} \cdot x \]

   7. metadata-eval N/A

      \[\leadsto e^{{\left(\mathsf{neg}\left(y\right)\right)}^{\color{blue}{\left(1 + 1\right)}}} \cdot x \]

   8. metadata-eval N/A

      \[\leadsto e^{{\left(\mathsf{neg}\left(y\right)\right)}^{\left(\color{blue}{\frac{2}{2}} + 1\right)}} \cdot x \]

   9. pow-plus-rev N/A

      \[\leadsto e^{\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{\left(\frac{2}{2}\right)} \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot x \]

   10. sqrt-pow1 N/A

       \[\leadsto e^{\color{blue}{\sqrt{{\left(\mathsf{neg}\left(y\right)\right)}^{2}}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   11. pow2 N/A

       \[\leadsto e^{\sqrt{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   12. sqr-neg-rev N/A

       \[\leadsto e^{\sqrt{\color{blue}{y \cdot y}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   13. pow2 N/A

       \[\leadsto e^{\sqrt{\color{blue}{{y}^{2}}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   14. sqrt-pow1 N/A

       \[\leadsto e^{\color{blue}{{y}^{\left(\frac{2}{2}\right)}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   15. metadata-eval N/A

       \[\leadsto e^{{y}^{\color{blue}{1}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   16. unpow1 N/A

       \[\leadsto e^{\color{blue}{y} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

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

       \[\leadsto e^{\color{blue}{\mathsf{neg}\left(y \cdot y\right)}} \cdot x \]

   18. lift-*.f64 N/A

       \[\leadsto e^{\mathsf{neg}\left(\color{blue}{y \cdot y}\right)} \cdot x \]

   19. lower-*.f64 N/A

       \[\leadsto \color{blue}{e^{\mathsf{neg}\left(y \cdot y\right)} \cdot x} \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{{\left(e^{y}\right)}^{y} \cdot x} \]
5. Add Preprocessing

Alternative 2: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot e^{y \cdot y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (exp (* y y))))

C

double code(double x, double y) {
	return x * exp((y * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * exp((y * y))
end function

Java

public static double code(double x, double y) {
	return x * Math.exp((y * y));
}

Python

def code(x, y):
	return x * math.exp((y * y))

Julia

function code(x, y)
	return Float64(x * exp(Float64(y * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = x * exp((y * y));
end

Wolfram

code[x_, y_] := N[(x * N[Exp[N[(y * y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x \cdot e^{y \cdot y} \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 3: 75.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ {\left(y - -1\right)}^{y} \cdot x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (pow (- y -1.0) y) x))

C

double code(double x, double y) {
	return pow((y - -1.0), y) * x;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

public static double code(double x, double y) {
	return Math.pow((y - -1.0), y) * x;
}

Python

def code(x, y):
	return math.pow((y - -1.0), y) * x

Julia

function code(x, y)
	return Float64((Float64(y - -1.0) ^ y) * x)
end

MATLAB

function tmp = code(x, y)
	tmp = ((y - -1.0) ^ y) * x;
end

Wolfram

code[x_, y_] := N[(N[Power[N[(y - -1.0), $MachinePrecision], y], $MachinePrecision] * x), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x \cdot e^{y \cdot y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot e^{y \cdot y}} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{e^{y \cdot y} \cdot x} \]

   3. lift-exp.f64 N/A

      \[\leadsto \color{blue}{e^{y \cdot y}} \cdot x \]

   4. lift-*.f64 N/A

      \[\leadsto e^{\color{blue}{y \cdot y}} \cdot x \]

   5. sqr-neg-rev N/A

      \[\leadsto e^{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot x \]

   6. pow2 N/A

      \[\leadsto e^{\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{2}}} \cdot x \]

   7. metadata-eval N/A

      \[\leadsto e^{{\left(\mathsf{neg}\left(y\right)\right)}^{\color{blue}{\left(1 + 1\right)}}} \cdot x \]

   8. metadata-eval N/A

      \[\leadsto e^{{\left(\mathsf{neg}\left(y\right)\right)}^{\left(\color{blue}{\frac{2}{2}} + 1\right)}} \cdot x \]

   9. pow-plus-rev N/A

      \[\leadsto e^{\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{\left(\frac{2}{2}\right)} \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot x \]

   10. sqrt-pow1 N/A

       \[\leadsto e^{\color{blue}{\sqrt{{\left(\mathsf{neg}\left(y\right)\right)}^{2}}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   11. pow2 N/A

       \[\leadsto e^{\sqrt{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   12. sqr-neg-rev N/A

       \[\leadsto e^{\sqrt{\color{blue}{y \cdot y}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   13. pow2 N/A

       \[\leadsto e^{\sqrt{\color{blue}{{y}^{2}}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   14. sqrt-pow1 N/A

       \[\leadsto e^{\color{blue}{{y}^{\left(\frac{2}{2}\right)}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   15. metadata-eval N/A

       \[\leadsto e^{{y}^{\color{blue}{1}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   16. unpow1 N/A

       \[\leadsto e^{\color{blue}{y} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

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

       \[\leadsto e^{\color{blue}{\mathsf{neg}\left(y \cdot y\right)}} \cdot x \]

   18. lift-*.f64 N/A

       \[\leadsto e^{\mathsf{neg}\left(\color{blue}{y \cdot y}\right)} \cdot x \]

   19. lower-*.f64 N/A

       \[\leadsto \color{blue}{e^{\mathsf{neg}\left(y \cdot y\right)} \cdot x} \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{{\left(e^{y}\right)}^{y} \cdot x} \]

5. Taylor expanded in y around 0

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

   1. +-commutative N/A

      \[\leadsto {\left(y + \color{blue}{1}\right)}^{y} \cdot x \]

   2. rgt-mult-inverse N/A

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

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

      \[\leadsto {\left(y - \color{blue}{\left(\mathsf{neg}\left({y}^{2}\right)\right) \cdot \frac{1}{{y}^{2}}}\right)}^{y} \cdot x \]

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

      \[\leadsto {\left(y - \left(\mathsf{neg}\left({y}^{2} \cdot \frac{1}{{y}^{2}}\right)\right)\right)}^{y} \cdot x \]

   5. rgt-mult-inverse N/A

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

   6. metadata-eval N/A

      \[\leadsto {\left(y - -1\right)}^{y} \cdot x \]

   7. lower--.f64 72.6

      \[\leadsto {\left(y - \color{blue}{-1}\right)}^{y} \cdot x \]

7. Applied rewrites 72.6%

   \[\leadsto {\color{blue}{\left(y - -1\right)}}^{y} \cdot x \]
8. Add Preprocessing

Alternative 4: 91.0% accurate, 3.7× speedup

Math

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

FPCore

(FPCore (x y)
 :precision binary64
 (* (- x) (fma (* (fma -0.5 (* y y) -1.0) y) y -1.0)))

C

double code(double x, double y) {
	return -x * fma((fma(-0.5, (y * y), -1.0) * y), y, -1.0);
}

Julia

function code(x, y)
	return Float64(Float64(-x) * fma(Float64(fma(-0.5, Float64(y * y), -1.0) * y), y, -1.0))
end

Wolfram

code[x_, y_] := N[((-x) * N[(N[(N[(-0.5 * N[(y * y), $MachinePrecision] + -1.0), $MachinePrecision] * y), $MachinePrecision] * y + -1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x \cdot e^{y \cdot y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot e^{y \cdot y}} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{e^{y \cdot y} \cdot x} \]

   3. lift-exp.f64 N/A

      \[\leadsto \color{blue}{e^{y \cdot y}} \cdot x \]

   4. lift-*.f64 N/A

      \[\leadsto e^{\color{blue}{y \cdot y}} \cdot x \]

   5. sqr-neg-rev N/A

      \[\leadsto e^{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot x \]

   6. pow2 N/A

      \[\leadsto e^{\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{2}}} \cdot x \]

   7. metadata-eval N/A

      \[\leadsto e^{{\left(\mathsf{neg}\left(y\right)\right)}^{\color{blue}{\left(1 + 1\right)}}} \cdot x \]

   8. metadata-eval N/A

      \[\leadsto e^{{\left(\mathsf{neg}\left(y\right)\right)}^{\left(\color{blue}{\frac{2}{2}} + 1\right)}} \cdot x \]

   9. pow-plus-rev N/A

      \[\leadsto e^{\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{\left(\frac{2}{2}\right)} \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot x \]

   10. sqrt-pow1 N/A

       \[\leadsto e^{\color{blue}{\sqrt{{\left(\mathsf{neg}\left(y\right)\right)}^{2}}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   11. pow2 N/A

       \[\leadsto e^{\sqrt{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   12. sqr-neg-rev N/A

       \[\leadsto e^{\sqrt{\color{blue}{y \cdot y}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   13. pow2 N/A

       \[\leadsto e^{\sqrt{\color{blue}{{y}^{2}}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   14. sqrt-pow1 N/A

       \[\leadsto e^{\color{blue}{{y}^{\left(\frac{2}{2}\right)}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   15. metadata-eval N/A

       \[\leadsto e^{{y}^{\color{blue}{1}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   16. unpow1 N/A

       \[\leadsto e^{\color{blue}{y} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

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

       \[\leadsto e^{\color{blue}{\mathsf{neg}\left(y \cdot y\right)}} \cdot x \]

   18. lift-*.f64 N/A

       \[\leadsto e^{\mathsf{neg}\left(\color{blue}{y \cdot y}\right)} \cdot x \]

   19. lower-*.f64 N/A

       \[\leadsto \color{blue}{e^{\mathsf{neg}\left(y \cdot y\right)} \cdot x} \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{{\left(e^{y}\right)}^{y} \cdot x} \]

5. Taylor expanded in y around 0

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

   1. exp-prod N/A

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

   2. *-commutative N/A

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

   3. sinh-+-cosh-rev N/A

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

   4. +-commutative N/A

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

   5. +-commutative N/A

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

   6. *-commutative N/A

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

   7. lower-fma.f64 N/A

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

   8. +-commutative N/A

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

   9. *-commutative N/A

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

   10. lower-fma.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot {y}^{2}, \frac{1}{2}, x\right), {\color{blue}{y}}^{2}, x\right) \]

   11. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   12. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   13. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   14. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   15. unpow2 N/A

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

   16. lower-*.f64 86.4

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

7. Applied rewrites 86.4%

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

8. Taylor expanded in x around -inf

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

10. Applied rewrites 89.0%

    \[\leadsto \left(-x\right) \cdot \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(-0.5, y \cdot y, -1\right), y \cdot y, -1\right)} \]
11. Step-by-step derivation

    1. lift-*.f64 N/A

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

    2. lift-fma.f64 N/A

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

    3. associate-*r* N/A

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

    4. lower-fma.f64 N/A

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

    5. lower-*.f64 89.0

       \[\leadsto \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(-0.5, y \cdot y, -1\right) \cdot y, y, -1\right) \]

12. Applied rewrites 89.0%

    \[\leadsto \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(-0.5, y \cdot y, -1\right) \cdot y, y, -1\right) \]

13. Final simplification 89.0%

    \[\leadsto \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(-0.5, y \cdot y, -1\right) \cdot y, y, -1\right) \]
14. Add Preprocessing

Alternative 5: 88.1% accurate, 4.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (fma (* (fma 0.5 (* (* x y) y) x) y) y x))

C

double code(double x, double y) {
	return fma((fma(0.5, ((x * y) * y), x) * y), y, x);
}

Julia

function code(x, y)
	return fma(Float64(fma(0.5, Float64(Float64(x * y) * y), x) * y), y, x)
end

Wolfram

code[x_, y_] := N[(N[(N[(0.5 * N[(N[(x * y), $MachinePrecision] * y), $MachinePrecision] + x), $MachinePrecision] * y), $MachinePrecision] * y + x), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x \cdot e^{y \cdot y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot e^{y \cdot y}} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{e^{y \cdot y} \cdot x} \]

   3. lift-exp.f64 N/A

      \[\leadsto \color{blue}{e^{y \cdot y}} \cdot x \]

   4. lift-*.f64 N/A

      \[\leadsto e^{\color{blue}{y \cdot y}} \cdot x \]

   5. sqr-neg-rev N/A

      \[\leadsto e^{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot x \]

   6. pow2 N/A

      \[\leadsto e^{\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{2}}} \cdot x \]

   7. metadata-eval N/A

      \[\leadsto e^{{\left(\mathsf{neg}\left(y\right)\right)}^{\color{blue}{\left(1 + 1\right)}}} \cdot x \]

   8. metadata-eval N/A

      \[\leadsto e^{{\left(\mathsf{neg}\left(y\right)\right)}^{\left(\color{blue}{\frac{2}{2}} + 1\right)}} \cdot x \]

   9. pow-plus-rev N/A

      \[\leadsto e^{\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{\left(\frac{2}{2}\right)} \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot x \]

   10. sqrt-pow1 N/A

       \[\leadsto e^{\color{blue}{\sqrt{{\left(\mathsf{neg}\left(y\right)\right)}^{2}}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   11. pow2 N/A

       \[\leadsto e^{\sqrt{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   12. sqr-neg-rev N/A

       \[\leadsto e^{\sqrt{\color{blue}{y \cdot y}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   13. pow2 N/A

       \[\leadsto e^{\sqrt{\color{blue}{{y}^{2}}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   14. sqrt-pow1 N/A

       \[\leadsto e^{\color{blue}{{y}^{\left(\frac{2}{2}\right)}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   15. metadata-eval N/A

       \[\leadsto e^{{y}^{\color{blue}{1}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   16. unpow1 N/A

       \[\leadsto e^{\color{blue}{y} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

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

       \[\leadsto e^{\color{blue}{\mathsf{neg}\left(y \cdot y\right)}} \cdot x \]

   18. lift-*.f64 N/A

       \[\leadsto e^{\mathsf{neg}\left(\color{blue}{y \cdot y}\right)} \cdot x \]

   19. lower-*.f64 N/A

       \[\leadsto \color{blue}{e^{\mathsf{neg}\left(y \cdot y\right)} \cdot x} \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{{\left(e^{y}\right)}^{y} \cdot x} \]

5. Taylor expanded in y around 0

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

   1. exp-prod N/A

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

   2. *-commutative N/A

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

   3. sinh-+-cosh-rev N/A

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

   4. +-commutative N/A

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

   5. +-commutative N/A

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

   6. *-commutative N/A

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

   7. lower-fma.f64 N/A

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

   8. +-commutative N/A

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

   9. *-commutative N/A

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

   10. lower-fma.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot {y}^{2}, \frac{1}{2}, x\right), {\color{blue}{y}}^{2}, x\right) \]

   11. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   12. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   13. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   14. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   15. unpow2 N/A

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

   16. lower-*.f64 86.4

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

7. Applied rewrites 86.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\left(y \cdot y\right) \cdot x, 0.5, x\right), y \cdot y, x\right)} \]
8. Step-by-step derivation

   1. lift-fma.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-*r* N/A

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

   4. lower-fma.f64 N/A

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

   5. lower-*.f64 86.4

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

   6. lift-fma.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-fma.f64 86.4

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

   9. lift-*.f64 N/A

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

   10. *-commutative N/A

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

   11. lift-*.f64 N/A

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

   12. associate-*l* N/A

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

   13. lift-*.f64 N/A

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

   14. lower-*.f64 86.4

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

9. Applied rewrites 86.4%

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

10. Final simplification 86.4%

    \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.5, \left(x \cdot y\right) \cdot y, x\right) \cdot y, y, x\right) \]
11. Add Preprocessing

Alternative 6: 87.7% accurate, 4.1× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (fma (* (* (* x y) y) 0.5) (* y y) x))

C

double code(double x, double y) {
	return fma((((x * y) * y) * 0.5), (y * y), x);
}

Julia

function code(x, y)
	return fma(Float64(Float64(Float64(x * y) * y) * 0.5), Float64(y * y), x)
end

Wolfram

code[x_, y_] := N[(N[(N[(N[(x * y), $MachinePrecision] * y), $MachinePrecision] * 0.5), $MachinePrecision] * N[(y * y), $MachinePrecision] + x), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x \cdot e^{y \cdot y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot e^{y \cdot y}} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{e^{y \cdot y} \cdot x} \]

   3. lift-exp.f64 N/A

      \[\leadsto \color{blue}{e^{y \cdot y}} \cdot x \]

   4. lift-*.f64 N/A

      \[\leadsto e^{\color{blue}{y \cdot y}} \cdot x \]

   5. sqr-neg-rev N/A

      \[\leadsto e^{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot x \]

   6. pow2 N/A

      \[\leadsto e^{\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{2}}} \cdot x \]

   7. metadata-eval N/A

      \[\leadsto e^{{\left(\mathsf{neg}\left(y\right)\right)}^{\color{blue}{\left(1 + 1\right)}}} \cdot x \]

   8. metadata-eval N/A

      \[\leadsto e^{{\left(\mathsf{neg}\left(y\right)\right)}^{\left(\color{blue}{\frac{2}{2}} + 1\right)}} \cdot x \]

   9. pow-plus-rev N/A

      \[\leadsto e^{\color{blue}{{\left(\mathsf{neg}\left(y\right)\right)}^{\left(\frac{2}{2}\right)} \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot x \]

   10. sqrt-pow1 N/A

       \[\leadsto e^{\color{blue}{\sqrt{{\left(\mathsf{neg}\left(y\right)\right)}^{2}}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   11. pow2 N/A

       \[\leadsto e^{\sqrt{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot \left(\mathsf{neg}\left(y\right)\right)}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   12. sqr-neg-rev N/A

       \[\leadsto e^{\sqrt{\color{blue}{y \cdot y}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   13. pow2 N/A

       \[\leadsto e^{\sqrt{\color{blue}{{y}^{2}}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   14. sqrt-pow1 N/A

       \[\leadsto e^{\color{blue}{{y}^{\left(\frac{2}{2}\right)}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   15. metadata-eval N/A

       \[\leadsto e^{{y}^{\color{blue}{1}} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

   16. unpow1 N/A

       \[\leadsto e^{\color{blue}{y} \cdot \left(\mathsf{neg}\left(y\right)\right)} \cdot x \]

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

       \[\leadsto e^{\color{blue}{\mathsf{neg}\left(y \cdot y\right)}} \cdot x \]

   18. lift-*.f64 N/A

       \[\leadsto e^{\mathsf{neg}\left(\color{blue}{y \cdot y}\right)} \cdot x \]

   19. lower-*.f64 N/A

       \[\leadsto \color{blue}{e^{\mathsf{neg}\left(y \cdot y\right)} \cdot x} \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{{\left(e^{y}\right)}^{y} \cdot x} \]

5. Taylor expanded in y around 0

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

   1. exp-prod N/A

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

   2. *-commutative N/A

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

   3. sinh-+-cosh-rev N/A

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

   4. +-commutative N/A

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

   5. +-commutative N/A

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

   6. *-commutative N/A

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

   7. lower-fma.f64 N/A

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

   8. +-commutative N/A

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

   9. *-commutative N/A

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

   10. lower-fma.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot {y}^{2}, \frac{1}{2}, x\right), {\color{blue}{y}}^{2}, x\right) \]

   11. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   12. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   13. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   14. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \frac{1}{2}, x\right), {y}^{2}, x\right) \]

   15. unpow2 N/A

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

   16. lower-*.f64 86.4

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

7. Applied rewrites 86.4%

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

8. Taylor expanded in y around inf

   \[\leadsto \mathsf{fma}\left(\frac{1}{2} \cdot \left(x \cdot {y}^{2}\right), \color{blue}{y} \cdot y, x\right) \]
9. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(\left(x \cdot {y}^{2}\right) \cdot \frac{1}{2}, y \cdot y, x\right) \]

   2. lower-*.f64 N/A

      \[\leadsto \mathsf{fma}\left(\left(x \cdot {y}^{2}\right) \cdot \frac{1}{2}, y \cdot y, x\right) \]

   3. unpow2 N/A

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

   4. associate-*l* N/A

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

   5. lower-*.f64 N/A

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

   6. lower-*.f64 86.0

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

10. Applied rewrites 86.0%

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

11. Final simplification 86.0%

    \[\leadsto \mathsf{fma}\left(\left(\left(x \cdot y\right) \cdot y\right) \cdot 0.5, y \cdot y, x\right) \]
12. Add Preprocessing

Alternative 7: 79.4% accurate, 6.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 10^{+35}:\\ \;\;\;\;\mathsf{fma}\left(x \cdot y, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y \cdot y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y 1e+35) (fma (* x y) y x) (* x (* y y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 1e+35) {
		tmp = fma((x * y), y, x);
	} else {
		tmp = x * (y * y);
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 1e+35)
		tmp = fma(Float64(x * y), y, x);
	else
		tmp = Float64(x * Float64(y * y));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[y, 1e+35], N[(N[(x * y), $MachinePrecision] * y + x), $MachinePrecision], N[(x * N[(y * y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 9.9999999999999997e34

   1. Initial program 100.0%

      \[x \cdot e^{y \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{x + x \cdot {y}^{2}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot {y}^{2} + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto {y}^{2} \cdot x + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left({y}^{2}, \color{blue}{x}, x\right) \]

      4. unpow2 N/A

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

      5. lower-*.f64 86.1

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

   5. Applied rewrites 86.1%

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

      1. lift-fma.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 82.4

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

   7. Applied rewrites 82.4%

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

   if 9.9999999999999997e34 < y

   1. Initial program 100.0%

      \[x \cdot e^{y \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. metadata-eval N/A

         \[\leadsto x \cdot \left({y}^{2} + \left(0 + \color{blue}{1}\right)\right) \]

      3. associate-+r+ N/A

         \[\leadsto x \cdot \left(\left({y}^{2} + 0\right) + \color{blue}{1}\right) \]

      4. +-commutative N/A

         \[\leadsto x \cdot \left(\left(0 + {y}^{2}\right) + 1\right) \]

      5. +-lft-identity N/A

         \[\leadsto x \cdot \left({y}^{2} + 1\right) \]

      6. unpow2 N/A

         \[\leadsto x \cdot \left(y \cdot y + 1\right) \]

      7. lower-fma.f64 62.6

         \[\leadsto x \cdot \mathsf{fma}\left(y, \color{blue}{y}, 1\right) \]

   5. Applied rewrites 62.6%

      \[\leadsto x \cdot \color{blue}{\mathsf{fma}\left(y, y, 1\right)} \]

   6. Taylor expanded in y around inf

      \[\leadsto x \cdot {y}^{\color{blue}{2}} \]
   7. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto x \cdot \left(y \cdot y\right) \]

      2. lower-*.f64 62.6

         \[\leadsto x \cdot \left(y \cdot y\right) \]

   8. Applied rewrites 62.6%

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

Alternative 8: 67.1% accurate, 6.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y \cdot y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 1.0) x (* x (* y y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 1.0) {
		tmp = x;
	} else {
		tmp = x * (y * y);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 1.0d0) then
        tmp = x
    else
        tmp = x * (y * y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 1.0) {
		tmp = x;
	} else {
		tmp = x * (y * y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 1.0:
		tmp = x
	else:
		tmp = x * (y * y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 1.0)
		tmp = x;
	else
		tmp = Float64(x * Float64(y * y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 1.0)
		tmp = x;
	else
		tmp = x * (y * y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 1.0], x, N[(x * N[(y * y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1

   1. Initial program 100.0%

      \[x \cdot e^{y \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 64.7%

      \[\leadsto \color{blue}{x} \]

   if 1 < y

   1. Initial program 100.0%

      \[x \cdot e^{y \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. metadata-eval N/A

         \[\leadsto x \cdot \left({y}^{2} + \left(0 + \color{blue}{1}\right)\right) \]

      3. associate-+r+ N/A

         \[\leadsto x \cdot \left(\left({y}^{2} + 0\right) + \color{blue}{1}\right) \]

      4. +-commutative N/A

         \[\leadsto x \cdot \left(\left(0 + {y}^{2}\right) + 1\right) \]

      5. +-lft-identity N/A

         \[\leadsto x \cdot \left({y}^{2} + 1\right) \]

      6. unpow2 N/A

         \[\leadsto x \cdot \left(y \cdot y + 1\right) \]

      7. lower-fma.f64 56.2

         \[\leadsto x \cdot \mathsf{fma}\left(y, \color{blue}{y}, 1\right) \]

   5. Applied rewrites 56.2%

      \[\leadsto x \cdot \color{blue}{\mathsf{fma}\left(y, y, 1\right)} \]

   6. Taylor expanded in y around inf

      \[\leadsto x \cdot {y}^{\color{blue}{2}} \]
   7. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto x \cdot \left(y \cdot y\right) \]

      2. lower-*.f64 56.2

         \[\leadsto x \cdot \left(y \cdot y\right) \]

   8. Applied rewrites 56.2%

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

Alternative 9: 64.3% accurate, 6.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\left(x \cdot y\right) \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 1.0) x (* (* x y) y)))

C

double code(double x, double y) {
	double tmp;
	if (y <= 1.0) {
		tmp = x;
	} else {
		tmp = (x * y) * y;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 1.0d0) then
        tmp = x
    else
        tmp = (x * y) * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 1.0) {
		tmp = x;
	} else {
		tmp = (x * y) * y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 1.0:
		tmp = x
	else:
		tmp = (x * y) * y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 1.0)
		tmp = x;
	else
		tmp = Float64(Float64(x * y) * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 1.0)
		tmp = x;
	else
		tmp = (x * y) * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 1.0], x, N[(N[(x * y), $MachinePrecision] * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1

   1. Initial program 100.0%

      \[x \cdot e^{y \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 64.7%

      \[\leadsto \color{blue}{x} \]

   if 1 < y

   1. Initial program 100.0%

      \[x \cdot e^{y \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{x + x \cdot {y}^{2}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot {y}^{2} + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto {y}^{2} \cdot x + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left({y}^{2}, \color{blue}{x}, x\right) \]

      4. unpow2 N/A

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

      5. lower-*.f64 56.2

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

   5. Applied rewrites 56.2%

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

      1. lift-fma.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 50.3

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

   7. Applied rewrites 50.3%

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

   8. Taylor expanded in y around inf

      \[\leadsto x \cdot \color{blue}{{y}^{2}} \]
   9. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto x \cdot \left(y \cdot y\right) \]

      2. associate-*l* N/A

         \[\leadsto \left(x \cdot y\right) \cdot y \]

      3. lower-*.f64 N/A

         \[\leadsto \left(x \cdot y\right) \cdot y \]

      4. lower-*.f64 50.3

         \[\leadsto \left(x \cdot y\right) \cdot y \]

   10. Applied rewrites 50.3%

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

Alternative 10: 82.2% accurate, 9.3× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (fma (* y y) x x))

C

double code(double x, double y) {
	return fma((y * y), x, x);
}

Julia

function code(x, y)
	return fma(Float64(y * y), x, x)
end

Wolfram

code[x_, y_] := N[(N[(y * y), $MachinePrecision] * x + x), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x \cdot e^{y \cdot y} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto \color{blue}{x + x \cdot {y}^{2}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto x \cdot {y}^{2} + \color{blue}{x} \]

   2. *-commutative N/A

      \[\leadsto {y}^{2} \cdot x + x \]

   3. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left({y}^{2}, \color{blue}{x}, x\right) \]

   4. unpow2 N/A

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

   5. lower-*.f64 80.9

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

5. Applied rewrites 80.9%

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

Alternative 11: 51.9% accurate, 111.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 x)

C

double code(double x, double y) {
	return x;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

def code(x, y):
	return x

Julia

function code(x, y)
	return x
end

MATLAB

function tmp = code(x, y)
	tmp = x;
end

Wolfram

code[x_, y_] := x

Derivation

1. Initial program 100.0%

   \[x \cdot e^{y \cdot y} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

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

5. Applied rewrites 49.8%

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

Developer Target 1: 100.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ x \cdot {\left(e^{y}\right)}^{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (pow (exp y) y)))

C

double code(double x, double y) {
	return x * pow(exp(y), 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (exp(y) ** y)
end function

Java

public static double code(double x, double y) {
	return x * Math.pow(Math.exp(y), y);
}

Python

def code(x, y):
	return x * math.pow(math.exp(y), y)

Julia

function code(x, y)
	return Float64(x * (exp(y) ^ y))
end

MATLAB

function tmp = code(x, y)
	tmp = x * (exp(y) ^ y);
end

Wolfram

code[x_, y_] := N[(x * N[Power[N[Exp[y], $MachinePrecision], y], $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2025026 
(FPCore (x y)
  :name "Data.Number.Erf:$dmerfcx from erf-2.0.0.0"
  :precision binary64

  :alt
  (! :herbie-platform default (* x (pow (exp y) y)))

  (* x (exp (* y y))))