exp2 (problem 3.3.7)

Percentage Accurate: 53.9% → 99.9%

Time: 8.2s

Alternatives: 12

Speedup: 34.8×

Specification

\[\left|x\right| \leq 710\]

Math

\[\begin{array}{l} \\ \left(e^{x} - 2\right) + e^{-x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ (- (exp x) 2.0) (exp (- x))))

C

double code(double x) {
	return (exp(x) - 2.0) + exp(-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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    code = (exp(x) - 2.0d0) + exp(-x)
end function

Java

public static double code(double x) {
	return (Math.exp(x) - 2.0) + Math.exp(-x);
}

Python

def code(x):
	return (math.exp(x) - 2.0) + math.exp(-x)

Julia

function code(x)
	return Float64(Float64(exp(x) - 2.0) + exp(Float64(-x)))
end

MATLAB

function tmp = code(x)
	tmp = (exp(x) - 2.0) + exp(-x);
end

Wolfram

code[x_] := N[(N[(N[Exp[x], $MachinePrecision] - 2.0), $MachinePrecision] + N[Exp[(-x)], $MachinePrecision]), $MachinePrecision]

Initial Program: 53.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(e^{x} - 2\right) + e^{-x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ (- (exp x) 2.0) (exp (- x))))

C

double code(double x) {
	return (exp(x) - 2.0) + exp(-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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    code = (exp(x) - 2.0d0) + exp(-x)
end function

Java

public static double code(double x) {
	return (Math.exp(x) - 2.0) + Math.exp(-x);
}

Python

def code(x):
	return (math.exp(x) - 2.0) + math.exp(-x)

Julia

function code(x)
	return Float64(Float64(exp(x) - 2.0) + exp(Float64(-x)))
end

MATLAB

function tmp = code(x)
	tmp = (exp(x) - 2.0) + exp(-x);
end

Wolfram

code[x_] := N[(N[(N[Exp[x], $MachinePrecision] - 2.0), $MachinePrecision] + N[Exp[(-x)], $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{x} - 2\\ t_1 := e^{-x}\\ \mathbf{if}\;t\_0 + t\_1 \leq 5 \cdot 10^{-5}:\\ \;\;\;\;\mathsf{fma}\left(x, x, \left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot \left(x \cdot \left(x \cdot x\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(t\_0, t\_0, \left(-t\_1\right) \cdot t\_1\right)}{t\_0 - t\_1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (- (exp x) 2.0)) (t_1 (exp (- x))))
   (if (<= (+ t_0 t_1) 5e-5)
     (fma
      x
      x
      (*
       (* (fma 0.002777777777777778 (* x x) 0.08333333333333333) x)
       (* x (* x x))))
     (/ (fma t_0 t_0 (* (- t_1) t_1)) (- t_0 t_1)))))

C

double code(double x) {
	double t_0 = exp(x) - 2.0;
	double t_1 = exp(-x);
	double tmp;
	if ((t_0 + t_1) <= 5e-5) {
		tmp = fma(x, x, ((fma(0.002777777777777778, (x * x), 0.08333333333333333) * x) * (x * (x * x))));
	} else {
		tmp = fma(t_0, t_0, (-t_1 * t_1)) / (t_0 - t_1);
	}
	return tmp;
}

Julia

function code(x)
	t_0 = Float64(exp(x) - 2.0)
	t_1 = exp(Float64(-x))
	tmp = 0.0
	if (Float64(t_0 + t_1) <= 5e-5)
		tmp = fma(x, x, Float64(Float64(fma(0.002777777777777778, Float64(x * x), 0.08333333333333333) * x) * Float64(x * Float64(x * x))));
	else
		tmp = Float64(fma(t_0, t_0, Float64(Float64(-t_1) * t_1)) / Float64(t_0 - t_1));
	end
	return tmp
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[Exp[x], $MachinePrecision] - 2.0), $MachinePrecision]}, Block[{t$95$1 = N[Exp[(-x)], $MachinePrecision]}, If[LessEqual[N[(t$95$0 + t$95$1), $MachinePrecision], 5e-5], N[(x * x + N[(N[(N[(0.002777777777777778 * N[(x * x), $MachinePrecision] + 0.08333333333333333), $MachinePrecision] * x), $MachinePrecision] * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(t$95$0 * t$95$0 + N[((-t$95$1) * t$95$1), $MachinePrecision]), $MachinePrecision] / N[(t$95$0 - t$95$1), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (exp.f64 x) #s(literal 2 binary64)) (exp.f64 (neg.f64 x))) < 5.00000000000000024e-5

   1. Initial program 52.8%

      \[\left(e^{x} - 2\right) + e^{-x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right)} \]
   4. Step-by-step derivation

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right), x \cdot x\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 100.0%

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{x}, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot {x}^{4}\right) \]
   8. Step-by-step derivation

   9. Applied rewrites 100.0%

      \[\leadsto \mathsf{fma}\left(x, x, \left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot \left(x \cdot \left(x \cdot x\right)\right)\right) \]

   if 5.00000000000000024e-5 < (+.f64 (-.f64 (exp.f64 x) #s(literal 2 binary64)) (exp.f64 (neg.f64 x)))

   1. Initial program 99.2%

      \[\left(e^{x} - 2\right) + e^{-x} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(e^{x} - 2\right) + e^{-x}} \]

      2. flip-+ N/A

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

      3. lower-/.f64 N/A

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

      4. lower--.f64 N/A

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

      5. pow2 N/A

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

      6. lower-pow.f64 N/A

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

      7. pow2 N/A

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

      8. lower-pow.f64 N/A

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

      9. lower--.f64 99.0

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

   4. Applied rewrites 99.0%

      \[\leadsto \color{blue}{\frac{{\left(e^{x} - 2\right)}^{2} - {\left(e^{-x}\right)}^{2}}{\left(e^{x} - 2\right) - e^{-x}}} \]
   5. Step-by-step derivation

      1. lift--.f64 N/A

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

      2. lift-pow.f64 N/A

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

      3. unpow2 N/A

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

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

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

      5. lift-pow.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-fma.f64 N/A

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

      8. lower-*.f64 N/A

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

      9. lower-neg.f64 99.6

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

   6. Applied rewrites 99.6%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(e^{x} - 2, e^{x} - 2, \left(-e^{-x}\right) \cdot e^{-x}\right)}}{\left(e^{x} - 2\right) - e^{-x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 99.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(e^{x} - 2\right) + e^{-x}\\ \mathbf{if}\;t\_0 \leq 5 \cdot 10^{-5}:\\ \;\;\;\;\mathsf{fma}\left(x, x, \left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot \left(x \cdot \left(x \cdot x\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (+ (- (exp x) 2.0) (exp (- x)))))
   (if (<= t_0 5e-5)
     (fma
      x
      x
      (*
       (* (fma 0.002777777777777778 (* x x) 0.08333333333333333) x)
       (* x (* x x))))
     t_0)))

C

double code(double x) {
	double t_0 = (exp(x) - 2.0) + exp(-x);
	double tmp;
	if (t_0 <= 5e-5) {
		tmp = fma(x, x, ((fma(0.002777777777777778, (x * x), 0.08333333333333333) * x) * (x * (x * x))));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x)
	t_0 = Float64(Float64(exp(x) - 2.0) + exp(Float64(-x)))
	tmp = 0.0
	if (t_0 <= 5e-5)
		tmp = fma(x, x, Float64(Float64(fma(0.002777777777777778, Float64(x * x), 0.08333333333333333) * x) * Float64(x * Float64(x * x))));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[(N[Exp[x], $MachinePrecision] - 2.0), $MachinePrecision] + N[Exp[(-x)], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, 5e-5], N[(x * x + N[(N[(N[(0.002777777777777778 * N[(x * x), $MachinePrecision] + 0.08333333333333333), $MachinePrecision] * x), $MachinePrecision] * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (exp.f64 x) #s(literal 2 binary64)) (exp.f64 (neg.f64 x))) < 5.00000000000000024e-5

   1. Initial program 52.8%

      \[\left(e^{x} - 2\right) + e^{-x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right)} \]
   4. Step-by-step derivation

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right), x \cdot x\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 100.0%

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{x}, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot {x}^{4}\right) \]
   8. Step-by-step derivation

   9. Applied rewrites 100.0%

      \[\leadsto \mathsf{fma}\left(x, x, \left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot \left(x \cdot \left(x \cdot x\right)\right)\right) \]

   if 5.00000000000000024e-5 < (+.f64 (-.f64 (exp.f64 x) #s(literal 2 binary64)) (exp.f64 (neg.f64 x)))

   1. Initial program 99.2%

      \[\left(e^{x} - 2\right) + e^{-x} \]
   2. Add Preprocessing
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 99.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(e^{x} - 2\right) + e^{-x} \leq 5 \cdot 10^{-5}:\\ \;\;\;\;\mathsf{fma}\left(x, x, \left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot \left(x \cdot \left(x \cdot x\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\cosh x - \left(2 - \cosh x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (+ (- (exp x) 2.0) (exp (- x))) 5e-5)
   (fma
    x
    x
    (*
     (* (fma 0.002777777777777778 (* x x) 0.08333333333333333) x)
     (* x (* x x))))
   (- (cosh x) (- 2.0 (cosh x)))))

C

double code(double x) {
	double tmp;
	if (((exp(x) - 2.0) + exp(-x)) <= 5e-5) {
		tmp = fma(x, x, ((fma(0.002777777777777778, (x * x), 0.08333333333333333) * x) * (x * (x * x))));
	} else {
		tmp = cosh(x) - (2.0 - cosh(x));
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (Float64(Float64(exp(x) - 2.0) + exp(Float64(-x))) <= 5e-5)
		tmp = fma(x, x, Float64(Float64(fma(0.002777777777777778, Float64(x * x), 0.08333333333333333) * x) * Float64(x * Float64(x * x))));
	else
		tmp = Float64(cosh(x) - Float64(2.0 - cosh(x)));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[N[(N[(N[Exp[x], $MachinePrecision] - 2.0), $MachinePrecision] + N[Exp[(-x)], $MachinePrecision]), $MachinePrecision], 5e-5], N[(x * x + N[(N[(N[(0.002777777777777778 * N[(x * x), $MachinePrecision] + 0.08333333333333333), $MachinePrecision] * x), $MachinePrecision] * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Cosh[x], $MachinePrecision] - N[(2.0 - N[Cosh[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (exp.f64 x) #s(literal 2 binary64)) (exp.f64 (neg.f64 x))) < 5.00000000000000024e-5

   1. Initial program 52.8%

      \[\left(e^{x} - 2\right) + e^{-x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right)} \]
   4. Step-by-step derivation

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right), x \cdot x\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 100.0%

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{x}, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot {x}^{4}\right) \]
   8. Step-by-step derivation

   9. Applied rewrites 100.0%

      \[\leadsto \mathsf{fma}\left(x, x, \left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot \left(x \cdot \left(x \cdot x\right)\right)\right) \]

   if 5.00000000000000024e-5 < (+.f64 (-.f64 (exp.f64 x) #s(literal 2 binary64)) (exp.f64 (neg.f64 x)))

   1. Initial program 99.2%

      \[\left(e^{x} - 2\right) + e^{-x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\left(e^{x} + e^{\mathsf{neg}\left(x\right)}\right) - 2} \]
   4. Step-by-step derivation

   5. Applied rewrites 98.4%

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

Alternative 4: 99.0% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot x\\ \frac{t\_0 \cdot t\_0 - 1}{t\_0 - 1} \cdot \left(x \cdot x\right) \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0
         (*
          (*
           (fma
            (fma (* x x) 4.96031746031746e-5 0.002777777777777778)
            (* x x)
            0.08333333333333333)
           x)
          x)))
   (* (/ (- (* t_0 t_0) 1.0) (- t_0 1.0)) (* x x))))

C

double code(double x) {
	double t_0 = (fma(fma((x * x), 4.96031746031746e-5, 0.002777777777777778), (x * x), 0.08333333333333333) * x) * x;
	return (((t_0 * t_0) - 1.0) / (t_0 - 1.0)) * (x * x);
}

Julia

function code(x)
	t_0 = Float64(Float64(fma(fma(Float64(x * x), 4.96031746031746e-5, 0.002777777777777778), Float64(x * x), 0.08333333333333333) * x) * x)
	return Float64(Float64(Float64(Float64(t_0 * t_0) - 1.0) / Float64(t_0 - 1.0)) * Float64(x * x))
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[(N[(N[(N[(x * x), $MachinePrecision] * 4.96031746031746e-5 + 0.002777777777777778), $MachinePrecision] * N[(x * x), $MachinePrecision] + 0.08333333333333333), $MachinePrecision] * x), $MachinePrecision] * x), $MachinePrecision]}, N[(N[(N[(N[(t$95$0 * t$95$0), $MachinePrecision] - 1.0), $MachinePrecision] / N[(t$95$0 - 1.0), $MachinePrecision]), $MachinePrecision] * N[(x * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 54.5%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + {x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]
4. Step-by-step derivation

5. Applied rewrites 97.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, \mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right), x \cdot x\right)} \]
6. Step-by-step derivation

7. Applied rewrites 97.8%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot \left(x \cdot x\right), \color{blue}{x \cdot x}, x \cdot x\right) \]
8. Step-by-step derivation

9. Applied rewrites 97.8%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right), x \cdot x, 1\right) \cdot \color{blue}{\left(x \cdot x\right)} \]
10. Step-by-step derivation

11. Applied rewrites 97.8%

    \[\leadsto \frac{\left(\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot x\right) \cdot \left(\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot x\right) - 1}{\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot x - 1} \cdot \left(\color{blue}{x} \cdot x\right) \]
12. Add Preprocessing

Alternative 5: 99.0% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right), x \cdot x, 1\right) \cdot \left(x \cdot x\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  (fma
   (fma
    (fma 4.96031746031746e-5 (* x x) 0.002777777777777778)
    (* x x)
    0.08333333333333333)
   (* x x)
   1.0)
  (* x x)))

C

double code(double x) {
	return fma(fma(fma(4.96031746031746e-5, (x * x), 0.002777777777777778), (x * x), 0.08333333333333333), (x * x), 1.0) * (x * x);
}

Julia

function code(x)
	return Float64(fma(fma(fma(4.96031746031746e-5, Float64(x * x), 0.002777777777777778), Float64(x * x), 0.08333333333333333), Float64(x * x), 1.0) * Float64(x * x))
end

Wolfram

code[x_] := N[(N[(N[(N[(4.96031746031746e-5 * N[(x * x), $MachinePrecision] + 0.002777777777777778), $MachinePrecision] * N[(x * x), $MachinePrecision] + 0.08333333333333333), $MachinePrecision] * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * N[(x * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 54.5%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + {x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]
4. Step-by-step derivation

5. Applied rewrites 97.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, \mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right), x \cdot x\right)} \]
6. Step-by-step derivation

7. Applied rewrites 97.8%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot \left(x \cdot x\right), \color{blue}{x \cdot x}, x \cdot x\right) \]
8. Step-by-step derivation

9. Applied rewrites 97.8%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right), x \cdot x, 1\right) \cdot \color{blue}{\left(x \cdot x\right)} \]
10. Add Preprocessing

Alternative 6: 99.0% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ x \cdot \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot x, x \cdot x, x\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  x
  (fma
   (*
    (fma
     (fma 4.96031746031746e-5 (* x x) 0.002777777777777778)
     (* x x)
     0.08333333333333333)
    x)
   (* x x)
   x)))

C

double code(double x) {
	return x * fma((fma(fma(4.96031746031746e-5, (x * x), 0.002777777777777778), (x * x), 0.08333333333333333) * x), (x * x), x);
}

Julia

function code(x)
	return Float64(x * fma(Float64(fma(fma(4.96031746031746e-5, Float64(x * x), 0.002777777777777778), Float64(x * x), 0.08333333333333333) * x), Float64(x * x), x))
end

Wolfram

code[x_] := N[(x * N[(N[(N[(N[(4.96031746031746e-5 * N[(x * x), $MachinePrecision] + 0.002777777777777778), $MachinePrecision] * N[(x * x), $MachinePrecision] + 0.08333333333333333), $MachinePrecision] * x), $MachinePrecision] * N[(x * x), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 54.5%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + {x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]
4. Step-by-step derivation

5. Applied rewrites 97.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, \mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right), x \cdot x\right)} \]
6. Step-by-step derivation

7. Applied rewrites 97.8%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot \left(x \cdot x\right), \color{blue}{x \cdot x}, x \cdot x\right) \]
8. Step-by-step derivation

9. Applied rewrites 97.8%

   \[\leadsto x \cdot \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot x, x \cdot x, x\right)} \]
10. Add Preprocessing

Alternative 7: 98.9% accurate, 5.5× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, x, \left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot \left(x \cdot \left(x \cdot x\right)\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (fma
  x
  x
  (*
   (* (fma 0.002777777777777778 (* x x) 0.08333333333333333) x)
   (* x (* x x)))))

C

double code(double x) {
	return fma(x, x, ((fma(0.002777777777777778, (x * x), 0.08333333333333333) * x) * (x * (x * x))));
}

Julia

function code(x)
	return fma(x, x, Float64(Float64(fma(0.002777777777777778, Float64(x * x), 0.08333333333333333) * x) * Float64(x * Float64(x * x))))
end

Wolfram

code[x_] := N[(x * x + N[(N[(N[(0.002777777777777778 * N[(x * x), $MachinePrecision] + 0.08333333333333333), $MachinePrecision] * x), $MachinePrecision] * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 54.5%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right)} \]
4. Step-by-step derivation

5. Applied rewrites 97.5%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right), x \cdot x\right)} \]
6. Step-by-step derivation

7. Applied rewrites 97.6%

   \[\leadsto \mathsf{fma}\left(x, \color{blue}{x}, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot {x}^{4}\right) \]
8. Step-by-step derivation

9. Applied rewrites 97.6%

   \[\leadsto \mathsf{fma}\left(x, x, \left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot x\right) \cdot \left(x \cdot \left(x \cdot x\right)\right)\right) \]
10. Add Preprocessing

Alternative 8: 98.9% accurate, 6.3× speedup

Math

\[\begin{array}{l} \\ x \cdot \mathsf{fma}\left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot x, x \cdot x, x\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  x
  (fma
   (* (fma 0.002777777777777778 (* x x) 0.08333333333333333) x)
   (* x x)
   x)))

C

double code(double x) {
	return x * fma((fma(0.002777777777777778, (x * x), 0.08333333333333333) * x), (x * x), x);
}

Julia

function code(x)
	return Float64(x * fma(Float64(fma(0.002777777777777778, Float64(x * x), 0.08333333333333333) * x), Float64(x * x), x))
end

Wolfram

code[x_] := N[(x * N[(N[(N[(0.002777777777777778 * N[(x * x), $MachinePrecision] + 0.08333333333333333), $MachinePrecision] * x), $MachinePrecision] * N[(x * x), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 54.5%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right)} \]
4. Step-by-step derivation

5. Applied rewrites 97.5%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right), x \cdot x\right)} \]
6. Step-by-step derivation

7. Applied rewrites 97.5%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot \left(x \cdot x\right), \color{blue}{x \cdot x}, x \cdot x\right) \]
8. Step-by-step derivation

9. Applied rewrites 97.5%

   \[\leadsto x \cdot \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot x, x \cdot x, x\right)} \]
10. Add Preprocessing

Alternative 9: 98.6% accurate, 9.5× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(0.08333333333333333, x \cdot x, 1\right) \cdot \left(x \cdot x\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* (fma 0.08333333333333333 (* x x) 1.0) (* x x)))

C

double code(double x) {
	return fma(0.08333333333333333, (x * x), 1.0) * (x * x);
}

Julia

function code(x)
	return Float64(fma(0.08333333333333333, Float64(x * x), 1.0) * Float64(x * x))
end

Wolfram

code[x_] := N[(N[(0.08333333333333333 * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * N[(x * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 54.5%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + {x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]
4. Step-by-step derivation

5. Applied rewrites 97.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, \mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right), x \cdot x\right)} \]
6. Step-by-step derivation

7. Applied rewrites 97.8%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot \left(x \cdot x\right), \color{blue}{x \cdot x}, x \cdot x\right) \]
8. Step-by-step derivation

9. Applied rewrites 97.8%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right), x \cdot x, 1\right) \cdot \color{blue}{\left(x \cdot x\right)} \]

10. Taylor expanded in x around 0

    \[\leadsto \mathsf{fma}\left(\frac{1}{12}, x \cdot x, 1\right) \cdot \left(x \cdot x\right) \]
11. Step-by-step derivation

12. Applied rewrites 97.3%

    \[\leadsto \mathsf{fma}\left(0.08333333333333333, x \cdot x, 1\right) \cdot \left(x \cdot x\right) \]
13. Add Preprocessing

Alternative 10: 98.7% accurate, 9.5× speedup

Math

\[\begin{array}{l} \\ x \cdot \mathsf{fma}\left(0.08333333333333333 \cdot x, x \cdot x, x\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* x (fma (* 0.08333333333333333 x) (* x x) x)))

C

double code(double x) {
	return x * fma((0.08333333333333333 * x), (x * x), x);
}

Julia

function code(x)
	return Float64(x * fma(Float64(0.08333333333333333 * x), Float64(x * x), x))
end

Wolfram

code[x_] := N[(x * N[(N[(0.08333333333333333 * x), $MachinePrecision] * N[(x * x), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 54.5%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + {x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]
4. Step-by-step derivation

5. Applied rewrites 97.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, \mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right), x \cdot x\right)} \]
6. Step-by-step derivation

7. Applied rewrites 97.8%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot \left(x \cdot x\right), \color{blue}{x \cdot x}, x \cdot x\right) \]
8. Step-by-step derivation

9. Applied rewrites 97.8%

   \[\leadsto x \cdot \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right) \cdot x, x \cdot x, x\right)} \]

10. Taylor expanded in x around 0

    \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{12} \cdot x, x \cdot x, x\right) \]
11. Step-by-step derivation

12. Applied rewrites 97.2%

    \[\leadsto x \cdot \mathsf{fma}\left(0.08333333333333333 \cdot x, x \cdot x, x\right) \]
13. Add Preprocessing

Alternative 11: 98.0% accurate, 34.8× speedup

Math

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

FPCore

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

C

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

Java

public static double code(double x) {
	return x * x;
}

Python

def code(x):
	return x * x

Julia

function code(x)
	return Float64(x * x)
end

MATLAB

function tmp = code(x)
	tmp = x * x;
end

Wolfram

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

Derivation

1. Initial program 54.5%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 96.4%

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

Alternative 12: 51.0% accurate, 52.3× speedup

Math

\[\begin{array}{l} \\ -1 + 1 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ -1.0 1.0))

C

double code(double x) {
	return -1.0 + 1.0;
}

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

Java

public static double code(double x) {
	return -1.0 + 1.0;
}

Python

def code(x):
	return -1.0 + 1.0

Julia

function code(x)
	return Float64(-1.0 + 1.0)
end

MATLAB

function tmp = code(x)
	tmp = -1.0 + 1.0;
end

Wolfram

code[x_] := N[(-1.0 + 1.0), $MachinePrecision]

Derivation

1. Initial program 54.5%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 50.2%

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

6. Taylor expanded in x around 0

   \[\leadsto \color{blue}{-1} + 1 \]
7. Step-by-step derivation

8. Applied rewrites 49.6%

   \[\leadsto \color{blue}{-1} + 1 \]
9. Add Preprocessing

Developer Target 1: 99.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \sinh \left(\frac{x}{2}\right)\\ 4 \cdot \left(t\_0 \cdot t\_0\right) \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (sinh (/ x 2.0)))) (* 4.0 (* t_0 t_0))))

C

double code(double x) {
	double t_0 = sinh((x / 2.0));
	return 4.0 * (t_0 * t_0);
}

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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8) :: t_0
    t_0 = sinh((x / 2.0d0))
    code = 4.0d0 * (t_0 * t_0)
end function

Java

public static double code(double x) {
	double t_0 = Math.sinh((x / 2.0));
	return 4.0 * (t_0 * t_0);
}

Python

def code(x):
	t_0 = math.sinh((x / 2.0))
	return 4.0 * (t_0 * t_0)

Julia

function code(x)
	t_0 = sinh(Float64(x / 2.0))
	return Float64(4.0 * Float64(t_0 * t_0))
end

MATLAB

function tmp = code(x)
	t_0 = sinh((x / 2.0));
	tmp = 4.0 * (t_0 * t_0);
end

Wolfram

code[x_] := Block[{t$95$0 = N[Sinh[N[(x / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(4.0 * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]]

Reproduce

herbie shell --seed 2025019 
(FPCore (x)
  :name "exp2 (problem 3.3.7)"
  :precision binary64
  :pre (<= (fabs x) 710.0)

  :alt
  (! :herbie-platform default (* 4 (* (sinh (/ x 2)) (sinh (/ x 2)))))

  (+ (- (exp x) 2.0) (exp (- x))))