Trigonometry A

Percentage Accurate: 99.8% → 99.8%

Time: 7.9s

Alternatives: 13

Speedup: 1.0×

Specification

\[0 \leq e \land e \leq 1\]

Math

\[\begin{array}{l} \\ \frac{e \cdot \sin v}{1 + e \cdot \cos v} \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (/ (* e (sin v)) (+ 1.0 (* e (cos v)))))

C

double code(double e, double v) {
	return (e * sin(v)) / (1.0 + (e * cos(v)));
}

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(e, v)
use fmin_fmax_functions
    real(8), intent (in) :: e
    real(8), intent (in) :: v
    code = (e * sin(v)) / (1.0d0 + (e * cos(v)))
end function

Java

public static double code(double e, double v) {
	return (e * Math.sin(v)) / (1.0 + (e * Math.cos(v)));
}

Python

def code(e, v):
	return (e * math.sin(v)) / (1.0 + (e * math.cos(v)))

Julia

function code(e, v)
	return Float64(Float64(e * sin(v)) / Float64(1.0 + Float64(e * cos(v))))
end

MATLAB

function tmp = code(e, v)
	tmp = (e * sin(v)) / (1.0 + (e * cos(v)));
end

Wolfram

code[e_, v_] := N[(N[(e * N[Sin[v], $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(e * N[Cos[v], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{e \cdot \sin v}{1 + e \cdot \cos v} \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (/ (* e (sin v)) (+ 1.0 (* e (cos v)))))

C

double code(double e, double v) {
	return (e * sin(v)) / (1.0 + (e * cos(v)));
}

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(e, v)
use fmin_fmax_functions
    real(8), intent (in) :: e
    real(8), intent (in) :: v
    code = (e * sin(v)) / (1.0d0 + (e * cos(v)))
end function

Java

public static double code(double e, double v) {
	return (e * Math.sin(v)) / (1.0 + (e * Math.cos(v)));
}

Python

def code(e, v):
	return (e * math.sin(v)) / (1.0 + (e * math.cos(v)))

Julia

function code(e, v)
	return Float64(Float64(e * sin(v)) / Float64(1.0 + Float64(e * cos(v))))
end

MATLAB

function tmp = code(e, v)
	tmp = (e * sin(v)) / (1.0 + (e * cos(v)));
end

Wolfram

code[e_, v_] := N[(N[(e * N[Sin[v], $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(e * N[Cos[v], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(\cos v \cdot e\right)}^{2}\\ \mathsf{fma}\left(\sin v \cdot \frac{e}{1 - t\_0}, 1, \left(\sin v \cdot \frac{e}{-1 + t\_0}\right) \cdot \left(e \cdot \cos v\right)\right) \end{array} \end{array} \]

FPCore

(FPCore (e v)
 :precision binary64
 (let* ((t_0 (pow (* (cos v) e) 2.0)))
   (fma
    (* (sin v) (/ e (- 1.0 t_0)))
    1.0
    (* (* (sin v) (/ e (+ -1.0 t_0))) (* e (cos v))))))

C

double code(double e, double v) {
	double t_0 = pow((cos(v) * e), 2.0);
	return fma((sin(v) * (e / (1.0 - t_0))), 1.0, ((sin(v) * (e / (-1.0 + t_0))) * (e * cos(v))));
}

Julia

function code(e, v)
	t_0 = Float64(cos(v) * e) ^ 2.0
	return fma(Float64(sin(v) * Float64(e / Float64(1.0 - t_0))), 1.0, Float64(Float64(sin(v) * Float64(e / Float64(-1.0 + t_0))) * Float64(e * cos(v))))
end

Wolfram

code[e_, v_] := Block[{t$95$0 = N[Power[N[(N[Cos[v], $MachinePrecision] * e), $MachinePrecision], 2.0], $MachinePrecision]}, N[(N[(N[Sin[v], $MachinePrecision] * N[(e / N[(1.0 - t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 1.0 + N[(N[(N[Sin[v], $MachinePrecision] * N[(e / N[(-1.0 + t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(e * N[Cos[v], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 99.8%

   \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{e \cdot \sin v}{1 + e \cdot \cos v}} \]

   2. lift-+.f64 N/A

      \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e \cdot \cos v}} \]

   3. flip-+ N/A

      \[\leadsto \frac{e \cdot \sin v}{\color{blue}{\frac{1 \cdot 1 - \left(e \cdot \cos v\right) \cdot \left(e \cdot \cos v\right)}{1 - e \cdot \cos v}}} \]

   4. associate-/r/ N/A

      \[\leadsto \color{blue}{\frac{e \cdot \sin v}{1 \cdot 1 - \left(e \cdot \cos v\right) \cdot \left(e \cdot \cos v\right)} \cdot \left(1 - e \cdot \cos v\right)} \]

   5. lift-*.f64 N/A

      \[\leadsto \frac{e \cdot \sin v}{1 \cdot 1 - \left(e \cdot \cos v\right) \cdot \left(e \cdot \cos v\right)} \cdot \left(1 - \color{blue}{e \cdot \cos v}\right) \]

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

      \[\leadsto \frac{e \cdot \sin v}{1 \cdot 1 - \left(e \cdot \cos v\right) \cdot \left(e \cdot \cos v\right)} \cdot \color{blue}{\left(1 + \left(\mathsf{neg}\left(e\right)\right) \cdot \cos v\right)} \]

   7. distribute-lft-in N/A

      \[\leadsto \color{blue}{\frac{e \cdot \sin v}{1 \cdot 1 - \left(e \cdot \cos v\right) \cdot \left(e \cdot \cos v\right)} \cdot 1 + \frac{e \cdot \sin v}{1 \cdot 1 - \left(e \cdot \cos v\right) \cdot \left(e \cdot \cos v\right)} \cdot \left(\left(\mathsf{neg}\left(e\right)\right) \cdot \cos v\right)} \]

   8. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{e \cdot \sin v}{1 \cdot 1 - \left(e \cdot \cos v\right) \cdot \left(e \cdot \cos v\right)}, 1, \frac{e \cdot \sin v}{1 \cdot 1 - \left(e \cdot \cos v\right) \cdot \left(e \cdot \cos v\right)} \cdot \left(\left(\mathsf{neg}\left(e\right)\right) \cdot \cos v\right)\right)} \]

4. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\sin v \cdot \frac{e}{1 - {\left(\cos v \cdot e\right)}^{2}}, 1, \left(\sin v \cdot \frac{e}{1 - {\left(\cos v \cdot e\right)}^{2}}\right) \cdot \left(\left(-e\right) \cdot \cos v\right)\right)} \]

5. Final simplification 99.8%

   \[\leadsto \mathsf{fma}\left(\sin v \cdot \frac{e}{1 - {\left(\cos v \cdot e\right)}^{2}}, 1, \left(\sin v \cdot \frac{e}{-1 + {\left(\cos v \cdot e\right)}^{2}}\right) \cdot \left(e \cdot \cos v\right)\right) \]
6. Add Preprocessing

Alternative 2: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\sin v \cdot e}{\mathsf{fma}\left(\cos v, e, 1\right)} \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (/ (* (sin v) e) (fma (cos v) e 1.0)))

C

double code(double e, double v) {
	return (sin(v) * e) / fma(cos(v), e, 1.0);
}

Julia

function code(e, v)
	return Float64(Float64(sin(v) * e) / fma(cos(v), e, 1.0))
end

Wolfram

code[e_, v_] := N[(N[(N[Sin[v], $MachinePrecision] * e), $MachinePrecision] / N[(N[Cos[v], $MachinePrecision] * e + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \frac{\color{blue}{e \cdot \sin v}}{1 + e \cdot \cos v} \]

   2. *-commutative N/A

      \[\leadsto \frac{\color{blue}{\sin v \cdot e}}{1 + e \cdot \cos v} \]

   3. lower-*.f64 99.8

      \[\leadsto \frac{\color{blue}{\sin v \cdot e}}{1 + e \cdot \cos v} \]

   4. lift-+.f64 N/A

      \[\leadsto \frac{\sin v \cdot e}{\color{blue}{1 + e \cdot \cos v}} \]

   5. +-commutative N/A

      \[\leadsto \frac{\sin v \cdot e}{\color{blue}{e \cdot \cos v + 1}} \]

   6. *-rgt-identity N/A

      \[\leadsto \frac{\sin v \cdot e}{\color{blue}{\left(e \cdot \cos v\right) \cdot 1} + 1} \]

   7. *-commutative N/A

      \[\leadsto \frac{\sin v \cdot e}{\color{blue}{1 \cdot \left(e \cdot \cos v\right)} + 1} \]

   8. *-lft-identity N/A

      \[\leadsto \frac{\sin v \cdot e}{\color{blue}{e \cdot \cos v} + 1} \]

   9. unpow1 N/A

      \[\leadsto \frac{\sin v \cdot e}{\color{blue}{{\left(e \cdot \cos v\right)}^{1}} + 1} \]

   10. metadata-eval N/A

       \[\leadsto \frac{\sin v \cdot e}{{\left(e \cdot \cos v\right)}^{\color{blue}{\left(\frac{2}{2}\right)}} + 1} \]

   11. sqrt-pow1 N/A

       \[\leadsto \frac{\sin v \cdot e}{\color{blue}{\sqrt{{\left(e \cdot \cos v\right)}^{2}}} + 1} \]

   12. pow2 N/A

       \[\leadsto \frac{\sin v \cdot e}{\sqrt{\color{blue}{\left(e \cdot \cos v\right) \cdot \left(e \cdot \cos v\right)}} + 1} \]

   13. rem-sqrt-square-rev N/A

       \[\leadsto \frac{\sin v \cdot e}{\color{blue}{\left|e \cdot \cos v\right|} + 1} \]

   14. rem-sqrt-square-rev N/A

       \[\leadsto \frac{\sin v \cdot e}{\color{blue}{\sqrt{\left(e \cdot \cos v\right) \cdot \left(e \cdot \cos v\right)}} + 1} \]

   15. pow2 N/A

       \[\leadsto \frac{\sin v \cdot e}{\sqrt{\color{blue}{{\left(e \cdot \cos v\right)}^{2}}} + 1} \]

   16. sqrt-pow1 N/A

       \[\leadsto \frac{\sin v \cdot e}{\color{blue}{{\left(e \cdot \cos v\right)}^{\left(\frac{2}{2}\right)}} + 1} \]

   17. metadata-eval N/A

       \[\leadsto \frac{\sin v \cdot e}{{\left(e \cdot \cos v\right)}^{\color{blue}{1}} + 1} \]

   18. unpow1 N/A

       \[\leadsto \frac{\sin v \cdot e}{\color{blue}{e \cdot \cos v} + 1} \]

   19. lift-*.f64 N/A

       \[\leadsto \frac{\sin v \cdot e}{\color{blue}{e \cdot \cos v} + 1} \]

   20. *-commutative N/A

       \[\leadsto \frac{\sin v \cdot e}{\color{blue}{\cos v \cdot e} + 1} \]

   21. lower-fma.f64 99.8

       \[\leadsto \frac{\sin v \cdot e}{\color{blue}{\mathsf{fma}\left(\cos v, e, 1\right)}} \]

4. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\frac{\sin v \cdot e}{\mathsf{fma}\left(\cos v, e, 1\right)}} \]
5. Add Preprocessing

Alternative 3: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{e}{\mathsf{fma}\left(\cos v, e, 1\right)} \cdot \sin v \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (* (/ e (fma (cos v) e 1.0)) (sin v)))

C

double code(double e, double v) {
	return (e / fma(cos(v), e, 1.0)) * sin(v);
}

Julia

function code(e, v)
	return Float64(Float64(e / fma(cos(v), e, 1.0)) * sin(v))
end

Wolfram

code[e_, v_] := N[(N[(e / N[(N[Cos[v], $MachinePrecision] * e + 1.0), $MachinePrecision]), $MachinePrecision] * N[Sin[v], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{e \cdot \sin v}{1 + e \cdot \cos v}} \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{\color{blue}{e \cdot \sin v}}{1 + e \cdot \cos v} \]

   3. *-commutative N/A

      \[\leadsto \frac{\color{blue}{\sin v \cdot e}}{1 + e \cdot \cos v} \]

   4. associate-/l* N/A

      \[\leadsto \color{blue}{\sin v \cdot \frac{e}{1 + e \cdot \cos v}} \]

   5. *-commutative N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e \cdot \cos v} \cdot \sin v} \]

   6. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e \cdot \cos v} \cdot \sin v} \]

4. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\frac{e}{\mathsf{fma}\left(\cos v, e, 1\right)} \cdot \sin v} \]
5. Add Preprocessing

Alternative 4: 98.7% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \frac{e \cdot \sin v}{1 + e} \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (/ (* e (sin v)) (+ 1.0 e)))

C

double code(double e, double v) {
	return (e * sin(v)) / (1.0 + e);
}

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(e, v)
use fmin_fmax_functions
    real(8), intent (in) :: e
    real(8), intent (in) :: v
    code = (e * sin(v)) / (1.0d0 + e)
end function

Java

public static double code(double e, double v) {
	return (e * Math.sin(v)) / (1.0 + e);
}

Python

def code(e, v):
	return (e * math.sin(v)) / (1.0 + e)

Julia

function code(e, v)
	return Float64(Float64(e * sin(v)) / Float64(1.0 + e))
end

MATLAB

function tmp = code(e, v)
	tmp = (e * sin(v)) / (1.0 + e);
end

Wolfram

code[e_, v_] := N[(N[(e * N[Sin[v], $MachinePrecision]), $MachinePrecision] / N[(1.0 + e), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
2. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]
4. Step-by-step derivation

   1. lower-+.f64 98.5

      \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]

5. Applied rewrites 98.5%

   \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]
6. Add Preprocessing

Alternative 5: 98.7% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \frac{\sin v}{1 + e} \cdot e \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (* (/ (sin v) (+ 1.0 e)) e))

C

double code(double e, double v) {
	return (sin(v) / (1.0 + e)) * e;
}

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(e, v)
use fmin_fmax_functions
    real(8), intent (in) :: e
    real(8), intent (in) :: v
    code = (sin(v) / (1.0d0 + e)) * e
end function

Java

public static double code(double e, double v) {
	return (Math.sin(v) / (1.0 + e)) * e;
}

Python

def code(e, v):
	return (math.sin(v) / (1.0 + e)) * e

Julia

function code(e, v)
	return Float64(Float64(sin(v) / Float64(1.0 + e)) * e)
end

MATLAB

function tmp = code(e, v)
	tmp = (sin(v) / (1.0 + e)) * e;
end

Wolfram

code[e_, v_] := N[(N[(N[Sin[v], $MachinePrecision] / N[(1.0 + e), $MachinePrecision]), $MachinePrecision] * e), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
2. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]
4. Step-by-step derivation

   1. lower-+.f64 98.5

      \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]

5. Applied rewrites 98.5%

   \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]
6. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{e \cdot \sin v}{1 + e}} \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{\color{blue}{e \cdot \sin v}}{1 + e} \]

   3. associate-/l* N/A

      \[\leadsto \color{blue}{e \cdot \frac{\sin v}{1 + e}} \]

   4. *-commutative N/A

      \[\leadsto \color{blue}{\frac{\sin v}{1 + e} \cdot e} \]

   5. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{\sin v}{1 + e} \cdot e} \]

   6. lower-/.f64 98.5

      \[\leadsto \color{blue}{\frac{\sin v}{1 + e}} \cdot e \]

7. Applied rewrites 98.5%

   \[\leadsto \color{blue}{\frac{\sin v}{1 + e} \cdot e} \]
8. Add Preprocessing

Alternative 6: 97.6% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \sin v \cdot e \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (* (sin v) e))

C

double code(double e, double v) {
	return sin(v) * e;
}

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(e, v)
use fmin_fmax_functions
    real(8), intent (in) :: e
    real(8), intent (in) :: v
    code = sin(v) * e
end function

Java

public static double code(double e, double v) {
	return Math.sin(v) * e;
}

Python

def code(e, v):
	return math.sin(v) * e

Julia

function code(e, v)
	return Float64(sin(v) * e)
end

MATLAB

function tmp = code(e, v)
	tmp = sin(v) * e;
end

Wolfram

code[e_, v_] := N[(N[Sin[v], $MachinePrecision] * e), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
2. Add Preprocessing

3. Taylor expanded in e around 0

   \[\leadsto \color{blue}{e \cdot \sin v} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\sin v \cdot e} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\sin v \cdot e} \]

   3. lower-sin.f64 97.3

      \[\leadsto \color{blue}{\sin v} \cdot e \]

5. Applied rewrites 97.3%

   \[\leadsto \color{blue}{\sin v \cdot e} \]
6. Add Preprocessing

Alternative 7: 52.1% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;v \leq 3.2:\\ \;\;\;\;\frac{\mathsf{fma}\left(v, e, \left(\left(\mathsf{fma}\left(0.008333333333333333, \left(v \cdot v\right) \cdot e, -0.16666666666666666 \cdot e\right) \cdot v\right) \cdot v\right) \cdot v\right)}{1 + e}\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-e\right) \cdot v\right) \cdot e\\ \end{array} \end{array} \]

FPCore

(FPCore (e v)
 :precision binary64
 (if (<= v 3.2)
   (/
    (fma
     v
     e
     (*
      (*
       (*
        (fma 0.008333333333333333 (* (* v v) e) (* -0.16666666666666666 e))
        v)
       v)
      v))
    (+ 1.0 e))
   (* (* (- e) v) e)))

C

double code(double e, double v) {
	double tmp;
	if (v <= 3.2) {
		tmp = fma(v, e, (((fma(0.008333333333333333, ((v * v) * e), (-0.16666666666666666 * e)) * v) * v) * v)) / (1.0 + e);
	} else {
		tmp = (-e * v) * e;
	}
	return tmp;
}

Julia

function code(e, v)
	tmp = 0.0
	if (v <= 3.2)
		tmp = Float64(fma(v, e, Float64(Float64(Float64(fma(0.008333333333333333, Float64(Float64(v * v) * e), Float64(-0.16666666666666666 * e)) * v) * v) * v)) / Float64(1.0 + e));
	else
		tmp = Float64(Float64(Float64(-e) * v) * e);
	end
	return tmp
end

Wolfram

code[e_, v_] := If[LessEqual[v, 3.2], N[(N[(v * e + N[(N[(N[(N[(0.008333333333333333 * N[(N[(v * v), $MachinePrecision] * e), $MachinePrecision] + N[(-0.16666666666666666 * e), $MachinePrecision]), $MachinePrecision] * v), $MachinePrecision] * v), $MachinePrecision] * v), $MachinePrecision]), $MachinePrecision] / N[(1.0 + e), $MachinePrecision]), $MachinePrecision], N[(N[((-e) * v), $MachinePrecision] * e), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if v < 3.2000000000000002

   1. Initial program 99.8%

      \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
   2. Add Preprocessing

   3. Taylor expanded in v around 0

      \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]
   4. Step-by-step derivation

      1. lower-+.f64 99.2

         \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]

   5. Applied rewrites 99.2%

      \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]

   6. Taylor expanded in v around 0

      \[\leadsto \frac{\color{blue}{v \cdot \left(e + {v}^{2} \cdot \left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right)\right)\right)}}{1 + e} \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\color{blue}{\left(e + {v}^{2} \cdot \left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right)\right)\right) \cdot v}}{1 + e} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\color{blue}{\left(e + {v}^{2} \cdot \left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right)\right)\right) \cdot v}}{1 + e} \]

      3. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\left({v}^{2} \cdot \left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right)\right) + e\right)} \cdot v}{1 + e} \]

      4. *-commutative N/A

         \[\leadsto \frac{\left(\color{blue}{\left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right)\right) \cdot {v}^{2}} + e\right) \cdot v}{1 + e} \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right), {v}^{2}, e\right)} \cdot v}{1 + e} \]

      6. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\frac{1}{120} \cdot \left(e \cdot {v}^{2}\right) + \frac{-1}{6} \cdot e}, {v}^{2}, e\right) \cdot v}{1 + e} \]

      7. *-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\left(e \cdot {v}^{2}\right) \cdot \frac{1}{120}} + \frac{-1}{6} \cdot e, {v}^{2}, e\right) \cdot v}{1 + e} \]

      8. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(e \cdot {v}^{2}, \frac{1}{120}, \frac{-1}{6} \cdot e\right)}, {v}^{2}, e\right) \cdot v}{1 + e} \]

      9. *-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{{v}^{2} \cdot e}, \frac{1}{120}, \frac{-1}{6} \cdot e\right), {v}^{2}, e\right) \cdot v}{1 + e} \]

      10. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{{v}^{2} \cdot e}, \frac{1}{120}, \frac{-1}{6} \cdot e\right), {v}^{2}, e\right) \cdot v}{1 + e} \]

      11. unpow2 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\left(v \cdot v\right)} \cdot e, \frac{1}{120}, \frac{-1}{6} \cdot e\right), {v}^{2}, e\right) \cdot v}{1 + e} \]

      12. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\left(v \cdot v\right)} \cdot e, \frac{1}{120}, \frac{-1}{6} \cdot e\right), {v}^{2}, e\right) \cdot v}{1 + e} \]

      13. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot e, \frac{1}{120}, \color{blue}{\frac{-1}{6} \cdot e}\right), {v}^{2}, e\right) \cdot v}{1 + e} \]

      14. unpow2 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot e, \frac{1}{120}, \frac{-1}{6} \cdot e\right), \color{blue}{v \cdot v}, e\right) \cdot v}{1 + e} \]

      15. lower-*.f64 62.3

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot e, 0.008333333333333333, -0.16666666666666666 \cdot e\right), \color{blue}{v \cdot v}, e\right) \cdot v}{1 + e} \]

   8. Applied rewrites 62.3%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot e, 0.008333333333333333, -0.16666666666666666 \cdot e\right), v \cdot v, e\right) \cdot v}}{1 + e} \]
   9. Step-by-step derivation

   10. Applied rewrites 62.3%

       \[\leadsto \frac{\mathsf{fma}\left(v, \color{blue}{e}, \left(\left(\mathsf{fma}\left(0.008333333333333333, \left(v \cdot v\right) \cdot e, -0.16666666666666666 \cdot e\right) \cdot v\right) \cdot v\right) \cdot v\right)}{1 + e} \]

   if 3.2000000000000002 < v

   1. Initial program 99.7%

      \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
   2. Add Preprocessing

   3. Taylor expanded in v around 0

      \[\leadsto \color{blue}{\frac{e \cdot v}{1 + e}} \]
   4. Step-by-step derivation

      1. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

      3. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{e}{1 + e}} \cdot v \]

      4. lower-+.f64 3.7

         \[\leadsto \frac{e}{\color{blue}{1 + e}} \cdot v \]

   5. Applied rewrites 3.7%

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   6. Taylor expanded in e around 0

      \[\leadsto e \cdot \color{blue}{v} \]
   7. Step-by-step derivation

   8. Applied rewrites 3.7%

      \[\leadsto e \cdot \color{blue}{v} \]

   9. Taylor expanded in e around 0

      \[\leadsto e \cdot \color{blue}{\left(v + -1 \cdot \left(e \cdot v\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 3.7%

       \[\leadsto \left(\left(1 - e\right) \cdot v\right) \cdot \color{blue}{e} \]

   12. Taylor expanded in e around inf

       \[\leadsto \left(-1 \cdot \left(e \cdot v\right)\right) \cdot e \]
   13. Step-by-step derivation

   14. Applied rewrites 5.9%

       \[\leadsto \left(\left(-e\right) \cdot v\right) \cdot e \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 52.1% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;v \leq 3.2:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(\left(v \cdot v\right) \cdot 0.008333333333333333 - 0.16666666666666666\right) \cdot e, v \cdot v, e\right) \cdot v}{1 + e}\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-e\right) \cdot v\right) \cdot e\\ \end{array} \end{array} \]

FPCore

(FPCore (e v)
 :precision binary64
 (if (<= v 3.2)
   (/
    (*
     (fma
      (* (- (* (* v v) 0.008333333333333333) 0.16666666666666666) e)
      (* v v)
      e)
     v)
    (+ 1.0 e))
   (* (* (- e) v) e)))

C

double code(double e, double v) {
	double tmp;
	if (v <= 3.2) {
		tmp = (fma(((((v * v) * 0.008333333333333333) - 0.16666666666666666) * e), (v * v), e) * v) / (1.0 + e);
	} else {
		tmp = (-e * v) * e;
	}
	return tmp;
}

Julia

function code(e, v)
	tmp = 0.0
	if (v <= 3.2)
		tmp = Float64(Float64(fma(Float64(Float64(Float64(Float64(v * v) * 0.008333333333333333) - 0.16666666666666666) * e), Float64(v * v), e) * v) / Float64(1.0 + e));
	else
		tmp = Float64(Float64(Float64(-e) * v) * e);
	end
	return tmp
end

Wolfram

code[e_, v_] := If[LessEqual[v, 3.2], N[(N[(N[(N[(N[(N[(N[(v * v), $MachinePrecision] * 0.008333333333333333), $MachinePrecision] - 0.16666666666666666), $MachinePrecision] * e), $MachinePrecision] * N[(v * v), $MachinePrecision] + e), $MachinePrecision] * v), $MachinePrecision] / N[(1.0 + e), $MachinePrecision]), $MachinePrecision], N[(N[((-e) * v), $MachinePrecision] * e), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if v < 3.2000000000000002

   1. Initial program 99.8%

      \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
   2. Add Preprocessing

   3. Taylor expanded in v around 0

      \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]
   4. Step-by-step derivation

      1. lower-+.f64 99.2

         \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]

   5. Applied rewrites 99.2%

      \[\leadsto \frac{e \cdot \sin v}{\color{blue}{1 + e}} \]

   6. Taylor expanded in v around 0

      \[\leadsto \frac{\color{blue}{v \cdot \left(e + {v}^{2} \cdot \left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right)\right)\right)}}{1 + e} \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\color{blue}{\left(e + {v}^{2} \cdot \left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right)\right)\right) \cdot v}}{1 + e} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\color{blue}{\left(e + {v}^{2} \cdot \left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right)\right)\right) \cdot v}}{1 + e} \]

      3. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\left({v}^{2} \cdot \left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right)\right) + e\right)} \cdot v}{1 + e} \]

      4. *-commutative N/A

         \[\leadsto \frac{\left(\color{blue}{\left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right)\right) \cdot {v}^{2}} + e\right) \cdot v}{1 + e} \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\frac{-1}{6} \cdot e + \frac{1}{120} \cdot \left(e \cdot {v}^{2}\right), {v}^{2}, e\right)} \cdot v}{1 + e} \]

      6. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\frac{1}{120} \cdot \left(e \cdot {v}^{2}\right) + \frac{-1}{6} \cdot e}, {v}^{2}, e\right) \cdot v}{1 + e} \]

      7. *-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\left(e \cdot {v}^{2}\right) \cdot \frac{1}{120}} + \frac{-1}{6} \cdot e, {v}^{2}, e\right) \cdot v}{1 + e} \]

      8. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(e \cdot {v}^{2}, \frac{1}{120}, \frac{-1}{6} \cdot e\right)}, {v}^{2}, e\right) \cdot v}{1 + e} \]

      9. *-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{{v}^{2} \cdot e}, \frac{1}{120}, \frac{-1}{6} \cdot e\right), {v}^{2}, e\right) \cdot v}{1 + e} \]

      10. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{{v}^{2} \cdot e}, \frac{1}{120}, \frac{-1}{6} \cdot e\right), {v}^{2}, e\right) \cdot v}{1 + e} \]

      11. unpow2 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\left(v \cdot v\right)} \cdot e, \frac{1}{120}, \frac{-1}{6} \cdot e\right), {v}^{2}, e\right) \cdot v}{1 + e} \]

      12. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\left(v \cdot v\right)} \cdot e, \frac{1}{120}, \frac{-1}{6} \cdot e\right), {v}^{2}, e\right) \cdot v}{1 + e} \]

      13. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot e, \frac{1}{120}, \color{blue}{\frac{-1}{6} \cdot e}\right), {v}^{2}, e\right) \cdot v}{1 + e} \]

      14. unpow2 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot e, \frac{1}{120}, \frac{-1}{6} \cdot e\right), \color{blue}{v \cdot v}, e\right) \cdot v}{1 + e} \]

      15. lower-*.f64 62.3

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot e, 0.008333333333333333, -0.16666666666666666 \cdot e\right), \color{blue}{v \cdot v}, e\right) \cdot v}{1 + e} \]

   8. Applied rewrites 62.3%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot e, 0.008333333333333333, -0.16666666666666666 \cdot e\right), v \cdot v, e\right) \cdot v}}{1 + e} \]

   9. Taylor expanded in e around 0

      \[\leadsto \frac{\mathsf{fma}\left(e \cdot \left(\frac{1}{120} \cdot {v}^{2} - \frac{1}{6}\right), v \cdot v, e\right) \cdot v}{1 + e} \]
   10. Step-by-step derivation

   11. Applied rewrites 62.3%

       \[\leadsto \frac{\mathsf{fma}\left(\left(\left(v \cdot v\right) \cdot 0.008333333333333333 - 0.16666666666666666\right) \cdot e, v \cdot v, e\right) \cdot v}{1 + e} \]

   if 3.2000000000000002 < v

   1. Initial program 99.7%

      \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
   2. Add Preprocessing

   3. Taylor expanded in v around 0

      \[\leadsto \color{blue}{\frac{e \cdot v}{1 + e}} \]
   4. Step-by-step derivation

      1. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

      3. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{e}{1 + e}} \cdot v \]

      4. lower-+.f64 3.7

         \[\leadsto \frac{e}{\color{blue}{1 + e}} \cdot v \]

   5. Applied rewrites 3.7%

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   6. Taylor expanded in e around 0

      \[\leadsto e \cdot \color{blue}{v} \]
   7. Step-by-step derivation

   8. Applied rewrites 3.7%

      \[\leadsto e \cdot \color{blue}{v} \]

   9. Taylor expanded in e around 0

      \[\leadsto e \cdot \color{blue}{\left(v + -1 \cdot \left(e \cdot v\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 3.7%

       \[\leadsto \left(\left(1 - e\right) \cdot v\right) \cdot \color{blue}{e} \]

   12. Taylor expanded in e around inf

       \[\leadsto \left(-1 \cdot \left(e \cdot v\right)\right) \cdot e \]
   13. Step-by-step derivation

   14. Applied rewrites 5.9%

       \[\leadsto \left(\left(-e\right) \cdot v\right) \cdot e \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 51.7% accurate, 11.3× speedup

Math

\[\begin{array}{l} \\ \frac{e \cdot v}{1 + e} \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (/ (* e v) (+ 1.0 e)))

C

double code(double e, double v) {
	return (e * v) / (1.0 + e);
}

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(e, v)
use fmin_fmax_functions
    real(8), intent (in) :: e
    real(8), intent (in) :: v
    code = (e * v) / (1.0d0 + e)
end function

Java

public static double code(double e, double v) {
	return (e * v) / (1.0 + e);
}

Python

def code(e, v):
	return (e * v) / (1.0 + e)

Julia

function code(e, v)
	return Float64(Float64(e * v) / Float64(1.0 + e))
end

MATLAB

function tmp = code(e, v)
	tmp = (e * v) / (1.0 + e);
end

Wolfram

code[e_, v_] := N[(N[(e * v), $MachinePrecision] / N[(1.0 + e), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
2. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \color{blue}{\frac{e \cdot v}{1 + e}} \]
4. Step-by-step derivation

   1. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   3. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e}} \cdot v \]

   4. lower-+.f64 47.6

      \[\leadsto \frac{e}{\color{blue}{1 + e}} \cdot v \]

5. Applied rewrites 47.6%

   \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]
6. Step-by-step derivation

7. Applied rewrites 47.6%

   \[\leadsto \frac{e \cdot v}{\color{blue}{1 + e}} \]
8. Add Preprocessing

Alternative 10: 51.7% accurate, 11.3× speedup

Math

\[\begin{array}{l} \\ \frac{e}{1 + e} \cdot v \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (* (/ e (+ 1.0 e)) v))

C

double code(double e, double v) {
	return (e / (1.0 + e)) * v;
}

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(e, v)
use fmin_fmax_functions
    real(8), intent (in) :: e
    real(8), intent (in) :: v
    code = (e / (1.0d0 + e)) * v
end function

Java

public static double code(double e, double v) {
	return (e / (1.0 + e)) * v;
}

Python

def code(e, v):
	return (e / (1.0 + e)) * v

Julia

function code(e, v)
	return Float64(Float64(e / Float64(1.0 + e)) * v)
end

MATLAB

function tmp = code(e, v)
	tmp = (e / (1.0 + e)) * v;
end

Wolfram

code[e_, v_] := N[(N[(e / N[(1.0 + e), $MachinePrecision]), $MachinePrecision] * v), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
2. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \color{blue}{\frac{e \cdot v}{1 + e}} \]
4. Step-by-step derivation

   1. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   3. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e}} \cdot v \]

   4. lower-+.f64 47.6

      \[\leadsto \frac{e}{\color{blue}{1 + e}} \cdot v \]

5. Applied rewrites 47.6%

   \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]
6. Add Preprocessing

Alternative 11: 51.4% accurate, 11.3× speedup

Math

\[\begin{array}{l} \\ \left(\mathsf{fma}\left(e - 1, e, 1\right) \cdot e\right) \cdot v \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (* (* (fma (- e 1.0) e 1.0) e) v))

C

double code(double e, double v) {
	return (fma((e - 1.0), e, 1.0) * e) * v;
}

Julia

function code(e, v)
	return Float64(Float64(fma(Float64(e - 1.0), e, 1.0) * e) * v)
end

Wolfram

code[e_, v_] := N[(N[(N[(N[(e - 1.0), $MachinePrecision] * e + 1.0), $MachinePrecision] * e), $MachinePrecision] * v), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
2. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \color{blue}{\frac{e \cdot v}{1 + e}} \]
4. Step-by-step derivation

   1. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   3. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e}} \cdot v \]

   4. lower-+.f64 47.6

      \[\leadsto \frac{e}{\color{blue}{1 + e}} \cdot v \]

5. Applied rewrites 47.6%

   \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

6. Taylor expanded in e around 0

   \[\leadsto \left(e \cdot \left(1 + e \cdot \left(e - 1\right)\right)\right) \cdot v \]
7. Step-by-step derivation

8. Applied rewrites 46.9%

   \[\leadsto \left(\mathsf{fma}\left(e - 1, e, 1\right) \cdot e\right) \cdot v \]
9. Add Preprocessing

Alternative 12: 51.2% accurate, 16.1× speedup

Math

\[\begin{array}{l} \\ \left(\left(1 - e\right) \cdot v\right) \cdot e \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (* (* (- 1.0 e) v) e))

C

double code(double e, double v) {
	return ((1.0 - e) * v) * e;
}

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

Java

public static double code(double e, double v) {
	return ((1.0 - e) * v) * e;
}

Python

def code(e, v):
	return ((1.0 - e) * v) * e

Julia

function code(e, v)
	return Float64(Float64(Float64(1.0 - e) * v) * e)
end

MATLAB

function tmp = code(e, v)
	tmp = ((1.0 - e) * v) * e;
end

Wolfram

code[e_, v_] := N[(N[(N[(1.0 - e), $MachinePrecision] * v), $MachinePrecision] * e), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
2. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \color{blue}{\frac{e \cdot v}{1 + e}} \]
4. Step-by-step derivation

   1. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   3. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e}} \cdot v \]

   4. lower-+.f64 47.6

      \[\leadsto \frac{e}{\color{blue}{1 + e}} \cdot v \]

5. Applied rewrites 47.6%

   \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

6. Taylor expanded in e around 0

   \[\leadsto e \cdot \color{blue}{v} \]
7. Step-by-step derivation

8. Applied rewrites 46.4%

   \[\leadsto e \cdot \color{blue}{v} \]

9. Taylor expanded in e around 0

   \[\leadsto e \cdot \color{blue}{\left(v + -1 \cdot \left(e \cdot v\right)\right)} \]
10. Step-by-step derivation

11. Applied rewrites 46.7%

    \[\leadsto \left(\left(1 - e\right) \cdot v\right) \cdot \color{blue}{e} \]
12. Add Preprocessing

Alternative 13: 50.6% accurate, 37.5× speedup

Math

\[\begin{array}{l} \\ e \cdot v \end{array} \]

FPCore

(FPCore (e v) :precision binary64 (* e v))

C

double code(double e, double v) {
	return e * v;
}

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(e, v)
use fmin_fmax_functions
    real(8), intent (in) :: e
    real(8), intent (in) :: v
    code = e * v
end function

Java

public static double code(double e, double v) {
	return e * v;
}

Python

def code(e, v):
	return e * v

Julia

function code(e, v)
	return Float64(e * v)
end

MATLAB

function tmp = code(e, v)
	tmp = e * v;
end

Wolfram

code[e_, v_] := N[(e * v), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{e \cdot \sin v}{1 + e \cdot \cos v} \]
2. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \color{blue}{\frac{e \cdot v}{1 + e}} \]
4. Step-by-step derivation

   1. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

   3. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{e}{1 + e}} \cdot v \]

   4. lower-+.f64 47.6

      \[\leadsto \frac{e}{\color{blue}{1 + e}} \cdot v \]

5. Applied rewrites 47.6%

   \[\leadsto \color{blue}{\frac{e}{1 + e} \cdot v} \]

6. Taylor expanded in e around 0

   \[\leadsto e \cdot \color{blue}{v} \]
7. Step-by-step derivation

8. Applied rewrites 46.4%

   \[\leadsto e \cdot \color{blue}{v} \]
9. Add Preprocessing

Reproduce

herbie shell --seed 2024359 
(FPCore (e v)
  :name "Trigonometry A"
  :precision binary64
  :pre (and (<= 0.0 e) (<= e 1.0))
  (/ (* e (sin v)) (+ 1.0 (* e (cos v)))))