Trigonometry B

Percentage Accurate: 99.5% → 100.0%

Time: 2.4s

Alternatives: 9

Speedup: 1.3×

Specification

Math

\[\begin{array}{l} t_0 := \tan x \cdot \tan x\\ \frac{1 - t\_0}{1 + t\_0} \end{array} \]

FPCore

(FPCore (x)
  :precision binary64
  (let* ((t_0 (* (tan x) (tan x)))) (/ (- 1 t_0) (+ 1 t_0))))

C

double code(double x) {
	double t_0 = tan(x) * tan(x);
	return (1.0 - t_0) / (1.0 + 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 = tan(x) * tan(x)
    code = (1.0d0 - t_0) / (1.0d0 + t_0)
end function

Java

public static double code(double x) {
	double t_0 = Math.tan(x) * Math.tan(x);
	return (1.0 - t_0) / (1.0 + t_0);
}

Python

def code(x):
	t_0 = math.tan(x) * math.tan(x)
	return (1.0 - t_0) / (1.0 + t_0)

Julia

function code(x)
	t_0 = Float64(tan(x) * tan(x))
	return Float64(Float64(1.0 - t_0) / Float64(1.0 + t_0))
end

MATLAB

function tmp = code(x)
	t_0 = tan(x) * tan(x);
	tmp = (1.0 - t_0) / (1.0 + t_0);
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[Tan[x], $MachinePrecision] * N[Tan[x], $MachinePrecision]), $MachinePrecision]}, N[(N[(1 - t$95$0), $MachinePrecision] / N[(1 + t$95$0), $MachinePrecision]), $MachinePrecision]]

Initial Program: 99.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} t_0 := \tan x \cdot \tan x\\ \frac{1 - t\_0}{1 + t\_0} \end{array} \]

FPCore

(FPCore (x)
  :precision binary64
  (let* ((t_0 (* (tan x) (tan x)))) (/ (- 1 t_0) (+ 1 t_0))))

C

double code(double x) {
	double t_0 = tan(x) * tan(x);
	return (1.0 - t_0) / (1.0 + 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 = tan(x) * tan(x)
    code = (1.0d0 - t_0) / (1.0d0 + t_0)
end function

Java

public static double code(double x) {
	double t_0 = Math.tan(x) * Math.tan(x);
	return (1.0 - t_0) / (1.0 + t_0);
}

Python

def code(x):
	t_0 = math.tan(x) * math.tan(x)
	return (1.0 - t_0) / (1.0 + t_0)

Julia

function code(x)
	t_0 = Float64(tan(x) * tan(x))
	return Float64(Float64(1.0 - t_0) / Float64(1.0 + t_0))
end

MATLAB

function tmp = code(x)
	t_0 = tan(x) * tan(x);
	tmp = (1.0 - t_0) / (1.0 + t_0);
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[Tan[x], $MachinePrecision] * N[Tan[x], $MachinePrecision]), $MachinePrecision]}, N[(N[(1 - t$95$0), $MachinePrecision] / N[(1 + t$95$0), $MachinePrecision]), $MachinePrecision]]

Alternative 1: 100.0% accurate, 1.3× speedup

Math

\[\begin{array}{l} t_0 := \cos \left(x + x\right)\\ \left(2 \cdot t\_0\right) \cdot \frac{\left(t\_0 - -1\right) \cdot \frac{-1}{2}}{-1 - t\_0} \end{array} \]

FPCore

(FPCore (x)
  :precision binary64
  (let* ((t_0 (cos (+ x x))))
  (* (* 2 t_0) (/ (* (- t_0 -1) -1/2) (- -1 t_0)))))

C

double code(double x) {
	double t_0 = cos((x + x));
	return (2.0 * t_0) * (((t_0 - -1.0) * -0.5) / (-1.0 - 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 = cos((x + x))
    code = (2.0d0 * t_0) * (((t_0 - (-1.0d0)) * (-0.5d0)) / ((-1.0d0) - t_0))
end function

Java

public static double code(double x) {
	double t_0 = Math.cos((x + x));
	return (2.0 * t_0) * (((t_0 - -1.0) * -0.5) / (-1.0 - t_0));
}

Python

def code(x):
	t_0 = math.cos((x + x))
	return (2.0 * t_0) * (((t_0 - -1.0) * -0.5) / (-1.0 - t_0))

Julia

function code(x)
	t_0 = cos(Float64(x + x))
	return Float64(Float64(2.0 * t_0) * Float64(Float64(Float64(t_0 - -1.0) * -0.5) / Float64(-1.0 - t_0)))
end

MATLAB

function tmp = code(x)
	t_0 = cos((x + x));
	tmp = (2.0 * t_0) * (((t_0 - -1.0) * -0.5) / (-1.0 - t_0));
end

Wolfram

code[x_] := Block[{t$95$0 = N[Cos[N[(x + x), $MachinePrecision]], $MachinePrecision]}, N[(N[(2 * t$95$0), $MachinePrecision] * N[(N[(N[(t$95$0 - -1), $MachinePrecision] * -1/2), $MachinePrecision] / N[(-1 - t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 99.5%

   \[\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x} \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{1 + \tan x \cdot \tan x}} \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x \cdot \tan x}} \]

   3. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x} \cdot \tan x} \]

   4. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x}{\cos x}} \cdot \tan x} \]

   5. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\tan x}} \]

   6. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\frac{\sin x}{\cos x}}} \]

   7. frac-times N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   8. add-to-fraction N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   9. lower-/.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

3. Applied rewrites 98.9%

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

5. Applied rewrites 99.8%

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

6. Taylor expanded in x around inf

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

   1. lower-*.f64 N/A

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

   2. lower-/.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-cos.f64 N/A

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

   5. lower-*.f64 N/A

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

   6. lower-+.f64 N/A

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

   7. lower-*.f64 N/A

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

   8. lower-cos.f64 N/A

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

   9. lower-*.f64 N/A

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

   10. lower-+.f64 N/A

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

8. Applied rewrites 99.9%

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

10. Applied rewrites 100.0%

    \[\leadsto \left(2 \cdot \cos \left(x + x\right)\right) \cdot \color{blue}{\frac{\left(\cos \left(x + x\right) - -1\right) \cdot \frac{-1}{2}}{-1 - \cos \left(x + x\right)}} \]
11. Add Preprocessing

Alternative 2: 98.9% accurate, 1.3× speedup

Math

\[\left(\frac{-1}{2} \cdot \cos \left(x + x\right) - \frac{1}{2}\right) \cdot \left({\tan x}^{2} - 1\right) \]

FPCore

(FPCore (x)
  :precision binary64
  (* (- (* -1/2 (cos (+ x x))) 1/2) (- (pow (tan x) 2) 1)))

C

double code(double x) {
	return ((-0.5 * cos((x + x))) - 0.5) * (pow(tan(x), 2.0) - 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 = (((-0.5d0) * cos((x + x))) - 0.5d0) * ((tan(x) ** 2.0d0) - 1.0d0)
end function

Java

public static double code(double x) {
	return ((-0.5 * Math.cos((x + x))) - 0.5) * (Math.pow(Math.tan(x), 2.0) - 1.0);
}

Python

def code(x):
	return ((-0.5 * math.cos((x + x))) - 0.5) * (math.pow(math.tan(x), 2.0) - 1.0)

Julia

function code(x)
	return Float64(Float64(Float64(-0.5 * cos(Float64(x + x))) - 0.5) * Float64((tan(x) ^ 2.0) - 1.0))
end

MATLAB

function tmp = code(x)
	tmp = ((-0.5 * cos((x + x))) - 0.5) * ((tan(x) ^ 2.0) - 1.0);
end

Wolfram

code[x_] := N[(N[(N[(-1/2 * N[Cos[N[(x + x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - 1/2), $MachinePrecision] * N[(N[Power[N[Tan[x], $MachinePrecision], 2], $MachinePrecision] - 1), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.5%

   \[\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x}} \]

   2. frac-2neg N/A

      \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(\left(1 - \tan x \cdot \tan x\right)\right)}{\mathsf{neg}\left(\left(1 + \tan x \cdot \tan x\right)\right)}} \]

   3. mult-flip N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\left(1 - \tan x \cdot \tan x\right)\right)\right) \cdot \frac{1}{\mathsf{neg}\left(\left(1 + \tan x \cdot \tan x\right)\right)}} \]

   4. *-commutative N/A

      \[\leadsto \color{blue}{\frac{1}{\mathsf{neg}\left(\left(1 + \tan x \cdot \tan x\right)\right)} \cdot \left(\mathsf{neg}\left(\left(1 - \tan x \cdot \tan x\right)\right)\right)} \]

   5. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{1}{\mathsf{neg}\left(\left(1 + \tan x \cdot \tan x\right)\right)} \cdot \left(\mathsf{neg}\left(\left(1 - \tan x \cdot \tan x\right)\right)\right)} \]

   6. frac-2neg N/A

      \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(1\right)}{\mathsf{neg}\left(\left(\mathsf{neg}\left(\left(1 + \tan x \cdot \tan x\right)\right)\right)\right)}} \cdot \left(\mathsf{neg}\left(\left(1 - \tan x \cdot \tan x\right)\right)\right) \]

   7. metadata-eval N/A

      \[\leadsto \frac{\color{blue}{-1}}{\mathsf{neg}\left(\left(\mathsf{neg}\left(\left(1 + \tan x \cdot \tan x\right)\right)\right)\right)} \cdot \left(\mathsf{neg}\left(\left(1 - \tan x \cdot \tan x\right)\right)\right) \]

   8. remove-double-neg N/A

      \[\leadsto \frac{-1}{\color{blue}{1 + \tan x \cdot \tan x}} \cdot \left(\mathsf{neg}\left(\left(1 - \tan x \cdot \tan x\right)\right)\right) \]

   9. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{-1}{1 + \tan x \cdot \tan x}} \cdot \left(\mathsf{neg}\left(\left(1 - \tan x \cdot \tan x\right)\right)\right) \]

   10. lift-+.f64 N/A

       \[\leadsto \frac{-1}{\color{blue}{1 + \tan x \cdot \tan x}} \cdot \left(\mathsf{neg}\left(\left(1 - \tan x \cdot \tan x\right)\right)\right) \]

   11. +-commutative N/A

       \[\leadsto \frac{-1}{\color{blue}{\tan x \cdot \tan x + 1}} \cdot \left(\mathsf{neg}\left(\left(1 - \tan x \cdot \tan x\right)\right)\right) \]

   12. add-flip N/A

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

   13. metadata-eval N/A

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

   14. lower--.f64 N/A

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

   15. lift-*.f64 N/A

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

   16. pow2 N/A

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

   17. lower-pow.f64 N/A

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

   18. lift--.f64 N/A

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

3. Applied rewrites 99.4%

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

5. Applied rewrites 98.9%

   \[\leadsto \color{blue}{\left(\frac{-1}{2} \cdot \cos \left(x + x\right) - \frac{1}{2}\right)} \cdot \left({\tan x}^{2} - 1\right) \]
6. Add Preprocessing

Alternative 3: 60.0% accurate, 1.8× speedup

Math

\[\begin{array}{l} t_0 := \cos \left(2 \cdot x\right)\\ 2 \cdot \frac{t\_0 \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot t\_0\right)}{2} \end{array} \]

FPCore

(FPCore (x)
  :precision binary64
  (let* ((t_0 (cos (* 2 x)))) (* 2 (/ (* t_0 (+ 1/2 (* 1/2 t_0))) 2))))

C

double code(double x) {
	double t_0 = cos((2.0 * x));
	return 2.0 * ((t_0 * (0.5 + (0.5 * t_0))) / 2.0);
}

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 = cos((2.0d0 * x))
    code = 2.0d0 * ((t_0 * (0.5d0 + (0.5d0 * t_0))) / 2.0d0)
end function

Java

public static double code(double x) {
	double t_0 = Math.cos((2.0 * x));
	return 2.0 * ((t_0 * (0.5 + (0.5 * t_0))) / 2.0);
}

Python

def code(x):
	t_0 = math.cos((2.0 * x))
	return 2.0 * ((t_0 * (0.5 + (0.5 * t_0))) / 2.0)

Julia

function code(x)
	t_0 = cos(Float64(2.0 * x))
	return Float64(2.0 * Float64(Float64(t_0 * Float64(0.5 + Float64(0.5 * t_0))) / 2.0))
end

MATLAB

function tmp = code(x)
	t_0 = cos((2.0 * x));
	tmp = 2.0 * ((t_0 * (0.5 + (0.5 * t_0))) / 2.0);
end

Wolfram

code[x_] := Block[{t$95$0 = N[Cos[N[(2 * x), $MachinePrecision]], $MachinePrecision]}, N[(2 * N[(N[(t$95$0 * N[(1/2 + N[(1/2 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 99.5%

   \[\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x} \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{1 + \tan x \cdot \tan x}} \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x \cdot \tan x}} \]

   3. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x} \cdot \tan x} \]

   4. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x}{\cos x}} \cdot \tan x} \]

   5. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\tan x}} \]

   6. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\frac{\sin x}{\cos x}}} \]

   7. frac-times N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   8. add-to-fraction N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   9. lower-/.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

3. Applied rewrites 98.9%

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

5. Applied rewrites 99.8%

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

6. Taylor expanded in x around inf

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

   1. lower-*.f64 N/A

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

   2. lower-/.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-cos.f64 N/A

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

   5. lower-*.f64 N/A

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

   6. lower-+.f64 N/A

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

   7. lower-*.f64 N/A

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

   8. lower-cos.f64 N/A

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

   9. lower-*.f64 N/A

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

   10. lower-+.f64 N/A

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

8. Applied rewrites 99.9%

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

9. Taylor expanded in x around 0

   \[\leadsto 2 \cdot \frac{\cos \left(2 \cdot x\right) \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot x\right)\right)}{2} \]
10. Step-by-step derivation

11. Applied rewrites 60.0%

    \[\leadsto 2 \cdot \frac{\cos \left(2 \cdot x\right) \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot x\right)\right)}{2} \]
12. Add Preprocessing

Alternative 4: 60.0% accurate, 1.9× speedup

Math

\[\frac{\left(1 - {\tan x}^{2}\right) \cdot \left(\frac{1}{2} - \frac{-1}{2}\right)}{\frac{3}{2} - \frac{1}{2}} \]

FPCore

(FPCore (x)
  :precision binary64
  (/ (* (- 1 (pow (tan x) 2)) (- 1/2 -1/2)) (- 3/2 1/2)))

C

double code(double x) {
	return ((1.0 - pow(tan(x), 2.0)) * (0.5 - -0.5)) / (1.5 - 0.5);
}

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 - (tan(x) ** 2.0d0)) * (0.5d0 - (-0.5d0))) / (1.5d0 - 0.5d0)
end function

Java

public static double code(double x) {
	return ((1.0 - Math.pow(Math.tan(x), 2.0)) * (0.5 - -0.5)) / (1.5 - 0.5);
}

Python

def code(x):
	return ((1.0 - math.pow(math.tan(x), 2.0)) * (0.5 - -0.5)) / (1.5 - 0.5)

Julia

function code(x)
	return Float64(Float64(Float64(1.0 - (tan(x) ^ 2.0)) * Float64(0.5 - -0.5)) / Float64(1.5 - 0.5))
end

MATLAB

function tmp = code(x)
	tmp = ((1.0 - (tan(x) ^ 2.0)) * (0.5 - -0.5)) / (1.5 - 0.5);
end

Wolfram

code[x_] := N[(N[(N[(1 - N[Power[N[Tan[x], $MachinePrecision], 2], $MachinePrecision]), $MachinePrecision] * N[(1/2 - -1/2), $MachinePrecision]), $MachinePrecision] / N[(3/2 - 1/2), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.5%

   \[\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x} \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{1 + \tan x \cdot \tan x}} \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x \cdot \tan x}} \]

   3. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x} \cdot \tan x} \]

   4. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x}{\cos x}} \cdot \tan x} \]

   5. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\tan x}} \]

   6. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\frac{\sin x}{\cos x}}} \]

   7. frac-times N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   8. add-to-fraction N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   9. lower-/.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

3. Applied rewrites 98.9%

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

4. Taylor expanded in x around 0

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

6. Applied rewrites 60.8%

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

7. Taylor expanded in x around 0

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

9. Applied rewrites 98.9%

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

10. Taylor expanded in x around 0

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

12. Applied rewrites 60.0%

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

14. Applied rewrites 60.0%

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

15. Taylor expanded in x around 0

    \[\leadsto \frac{\left(1 - {\tan x}^{2}\right) \cdot \left(\frac{1}{2} - \frac{-1}{2}\right)}{\color{blue}{\frac{3}{2}} - \frac{1}{2}} \]
16. Step-by-step derivation

17. Applied rewrites 60.0%

    \[\leadsto \frac{\left(1 - {\tan x}^{2}\right) \cdot \left(\frac{1}{2} - \frac{-1}{2}\right)}{\color{blue}{\frac{3}{2}} - \frac{1}{2}} \]
18. Add Preprocessing

Alternative 5: 58.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_0 := \tan x \cdot \tan x\\ \mathbf{if}\;\frac{1 - t\_0}{1 + t\_0} \leq \frac{-5764607523034235}{288230376151711744}:\\ \;\;\;\;\frac{1 - {x}^{2}}{1 + {x}^{2}}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - \left(\frac{1}{2} \cdot \left(1 - \cos \left(x + x\right)\right)\right) \cdot \frac{-2}{-2}}{\frac{1 \cdot \left(\frac{1}{2} + \frac{1}{2}\right) + \left(\frac{1}{2} - \frac{1}{2}\right)}{\frac{1}{2} + \frac{1}{2}}}\\ \end{array} \]

FPCore

(FPCore (x)
  :precision binary64
  (let* ((t_0 (* (tan x) (tan x))))
  (if (<=
       (/ (- 1 t_0) (+ 1 t_0))
       -5764607523034235/288230376151711744)
    (/ (- 1 (pow x 2)) (+ 1 (pow x 2)))
    (/
     (- 1 (* (* 1/2 (- 1 (cos (+ x x)))) (/ -2 -2)))
     (/ (+ (* 1 (+ 1/2 1/2)) (- 1/2 1/2)) (+ 1/2 1/2))))))

C

double code(double x) {
	double t_0 = tan(x) * tan(x);
	double tmp;
	if (((1.0 - t_0) / (1.0 + t_0)) <= -0.02) {
		tmp = (1.0 - pow(x, 2.0)) / (1.0 + pow(x, 2.0));
	} else {
		tmp = (1.0 - ((0.5 * (1.0 - cos((x + x)))) * (-2.0 / -2.0))) / (((1.0 * (0.5 + 0.5)) + (0.5 - 0.5)) / (0.5 + 0.5));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = tan(x) * tan(x)
    if (((1.0d0 - t_0) / (1.0d0 + t_0)) <= (-0.02d0)) then
        tmp = (1.0d0 - (x ** 2.0d0)) / (1.0d0 + (x ** 2.0d0))
    else
        tmp = (1.0d0 - ((0.5d0 * (1.0d0 - cos((x + x)))) * ((-2.0d0) / (-2.0d0)))) / (((1.0d0 * (0.5d0 + 0.5d0)) + (0.5d0 - 0.5d0)) / (0.5d0 + 0.5d0))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = Math.tan(x) * Math.tan(x);
	double tmp;
	if (((1.0 - t_0) / (1.0 + t_0)) <= -0.02) {
		tmp = (1.0 - Math.pow(x, 2.0)) / (1.0 + Math.pow(x, 2.0));
	} else {
		tmp = (1.0 - ((0.5 * (1.0 - Math.cos((x + x)))) * (-2.0 / -2.0))) / (((1.0 * (0.5 + 0.5)) + (0.5 - 0.5)) / (0.5 + 0.5));
	}
	return tmp;
}

Python

def code(x):
	t_0 = math.tan(x) * math.tan(x)
	tmp = 0
	if ((1.0 - t_0) / (1.0 + t_0)) <= -0.02:
		tmp = (1.0 - math.pow(x, 2.0)) / (1.0 + math.pow(x, 2.0))
	else:
		tmp = (1.0 - ((0.5 * (1.0 - math.cos((x + x)))) * (-2.0 / -2.0))) / (((1.0 * (0.5 + 0.5)) + (0.5 - 0.5)) / (0.5 + 0.5))
	return tmp

Julia

function code(x)
	t_0 = Float64(tan(x) * tan(x))
	tmp = 0.0
	if (Float64(Float64(1.0 - t_0) / Float64(1.0 + t_0)) <= -0.02)
		tmp = Float64(Float64(1.0 - (x ^ 2.0)) / Float64(1.0 + (x ^ 2.0)));
	else
		tmp = Float64(Float64(1.0 - Float64(Float64(0.5 * Float64(1.0 - cos(Float64(x + x)))) * Float64(-2.0 / -2.0))) / Float64(Float64(Float64(1.0 * Float64(0.5 + 0.5)) + Float64(0.5 - 0.5)) / Float64(0.5 + 0.5)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = tan(x) * tan(x);
	tmp = 0.0;
	if (((1.0 - t_0) / (1.0 + t_0)) <= -0.02)
		tmp = (1.0 - (x ^ 2.0)) / (1.0 + (x ^ 2.0));
	else
		tmp = (1.0 - ((0.5 * (1.0 - cos((x + x)))) * (-2.0 / -2.0))) / (((1.0 * (0.5 + 0.5)) + (0.5 - 0.5)) / (0.5 + 0.5));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[Tan[x], $MachinePrecision] * N[Tan[x], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(1 - t$95$0), $MachinePrecision] / N[(1 + t$95$0), $MachinePrecision]), $MachinePrecision], -5764607523034235/288230376151711744], N[(N[(1 - N[Power[x, 2], $MachinePrecision]), $MachinePrecision] / N[(1 + N[Power[x, 2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(1 - N[(N[(1/2 * N[(1 - N[Cos[N[(x + x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(-2 / -2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(1 * N[(1/2 + 1/2), $MachinePrecision]), $MachinePrecision] + N[(1/2 - 1/2), $MachinePrecision]), $MachinePrecision] / N[(1/2 + 1/2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 #s(literal 1 binary64) (*.f64 (tan.f64 x) (tan.f64 x))) (+.f64 #s(literal 1 binary64) (*.f64 (tan.f64 x) (tan.f64 x)))) < -0.02

   1. Initial program 99.5%

      \[\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{1 - \color{blue}{{x}^{2}}}{1 + \tan x \cdot \tan x} \]
   3. Step-by-step derivation

      1. lower-pow.f64 51.9%

         \[\leadsto \frac{1 - {x}^{\color{blue}{2}}}{1 + \tan x \cdot \tan x} \]

   4. Applied rewrites 51.9%

      \[\leadsto \frac{1 - \color{blue}{{x}^{2}}}{1 + \tan x \cdot \tan x} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{1 - {x}^{2}}{1 + \color{blue}{{x}^{2}}} \]
   6. Step-by-step derivation

      1. lower-pow.f64 53.4%

         \[\leadsto \frac{1 - {x}^{2}}{1 + {x}^{\color{blue}{2}}} \]

   7. Applied rewrites 53.4%

      \[\leadsto \frac{1 - {x}^{2}}{1 + \color{blue}{{x}^{2}}} \]

   if -0.02 < (/.f64 (-.f64 #s(literal 1 binary64) (*.f64 (tan.f64 x) (tan.f64 x))) (+.f64 #s(literal 1 binary64) (*.f64 (tan.f64 x) (tan.f64 x))))

   1. Initial program 99.5%

      \[\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{1 + \tan x \cdot \tan x}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x \cdot \tan x}} \]

      3. lift-tan.f64 N/A

         \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x} \cdot \tan x} \]

      4. tan-quot N/A

         \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x}{\cos x}} \cdot \tan x} \]

      5. lift-tan.f64 N/A

         \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\tan x}} \]

      6. tan-quot N/A

         \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\frac{\sin x}{\cos x}}} \]

      7. frac-times N/A

         \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

      8. add-to-fraction N/A

         \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   3. Applied rewrites 98.9%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 60.8%

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

   7. Taylor expanded in x around 0

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

   9. Applied rewrites 98.9%

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

   10. Taylor expanded in x around 0

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

   12. Applied rewrites 60.0%

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

   14. Applied rewrites 60.0%

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

   15. Taylor expanded in x around 0

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

   17. Applied rewrites 56.2%

       \[\leadsto \frac{1 - \left(\frac{1}{2} \cdot \left(1 - \cos \left(x + x\right)\right)\right) \cdot \frac{-2}{\color{blue}{-2}}}{\frac{1 \cdot \left(\frac{1}{2} + \frac{1}{2}\right) + \left(\frac{1}{2} - \frac{1}{2}\right)}{\frac{1}{2} + \frac{1}{2}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 56.2% accurate, 2.5× speedup

Math

\[\frac{1 - \left(\frac{1}{2} \cdot \left(1 - \cos \left(x + x\right)\right)\right) \cdot \frac{-2}{-2}}{\frac{1 \cdot \left(\frac{1}{2} + \frac{1}{2}\right) + \left(\frac{1}{2} - \frac{1}{2}\right)}{\frac{1}{2} + \frac{1}{2}}} \]

FPCore

(FPCore (x)
  :precision binary64
  (/
 (- 1 (* (* 1/2 (- 1 (cos (+ x x)))) (/ -2 -2)))
 (/ (+ (* 1 (+ 1/2 1/2)) (- 1/2 1/2)) (+ 1/2 1/2))))

C

double code(double x) {
	return (1.0 - ((0.5 * (1.0 - cos((x + x)))) * (-2.0 / -2.0))) / (((1.0 * (0.5 + 0.5)) + (0.5 - 0.5)) / (0.5 + 0.5));
}

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 - ((0.5d0 * (1.0d0 - cos((x + x)))) * ((-2.0d0) / (-2.0d0)))) / (((1.0d0 * (0.5d0 + 0.5d0)) + (0.5d0 - 0.5d0)) / (0.5d0 + 0.5d0))
end function

Java

public static double code(double x) {
	return (1.0 - ((0.5 * (1.0 - Math.cos((x + x)))) * (-2.0 / -2.0))) / (((1.0 * (0.5 + 0.5)) + (0.5 - 0.5)) / (0.5 + 0.5));
}

Python

def code(x):
	return (1.0 - ((0.5 * (1.0 - math.cos((x + x)))) * (-2.0 / -2.0))) / (((1.0 * (0.5 + 0.5)) + (0.5 - 0.5)) / (0.5 + 0.5))

Julia

function code(x)
	return Float64(Float64(1.0 - Float64(Float64(0.5 * Float64(1.0 - cos(Float64(x + x)))) * Float64(-2.0 / -2.0))) / Float64(Float64(Float64(1.0 * Float64(0.5 + 0.5)) + Float64(0.5 - 0.5)) / Float64(0.5 + 0.5)))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 - ((0.5 * (1.0 - cos((x + x)))) * (-2.0 / -2.0))) / (((1.0 * (0.5 + 0.5)) + (0.5 - 0.5)) / (0.5 + 0.5));
end

Wolfram

code[x_] := N[(N[(1 - N[(N[(1/2 * N[(1 - N[Cos[N[(x + x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(-2 / -2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(1 * N[(1/2 + 1/2), $MachinePrecision]), $MachinePrecision] + N[(1/2 - 1/2), $MachinePrecision]), $MachinePrecision] / N[(1/2 + 1/2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.5%

   \[\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x} \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{1 + \tan x \cdot \tan x}} \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x \cdot \tan x}} \]

   3. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x} \cdot \tan x} \]

   4. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x}{\cos x}} \cdot \tan x} \]

   5. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\tan x}} \]

   6. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\frac{\sin x}{\cos x}}} \]

   7. frac-times N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   8. add-to-fraction N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   9. lower-/.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

3. Applied rewrites 98.9%

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

4. Taylor expanded in x around 0

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

6. Applied rewrites 60.8%

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

7. Taylor expanded in x around 0

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

9. Applied rewrites 98.9%

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

10. Taylor expanded in x around 0

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

12. Applied rewrites 60.0%

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

14. Applied rewrites 60.0%

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

15. Taylor expanded in x around 0

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

17. Applied rewrites 56.2%

    \[\leadsto \frac{1 - \left(\frac{1}{2} \cdot \left(1 - \cos \left(x + x\right)\right)\right) \cdot \frac{-2}{\color{blue}{-2}}}{\frac{1 \cdot \left(\frac{1}{2} + \frac{1}{2}\right) + \left(\frac{1}{2} - \frac{1}{2}\right)}{\frac{1}{2} + \frac{1}{2}}} \]
18. Add Preprocessing

Alternative 7: 56.2% accurate, 3.1× speedup

Math

\[\frac{1}{\frac{1}{\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot x\right)}} \]

FPCore

(FPCore (x)
  :precision binary64
  (/ 1 (/ 1 (+ 1/2 (* 1/2 (cos (* 2 x)))))))

C

double code(double x) {
	return 1.0 / (1.0 / (0.5 + (0.5 * cos((2.0 * 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 = 1.0d0 / (1.0d0 / (0.5d0 + (0.5d0 * cos((2.0d0 * x)))))
end function

Java

public static double code(double x) {
	return 1.0 / (1.0 / (0.5 + (0.5 * Math.cos((2.0 * x)))));
}

Python

def code(x):
	return 1.0 / (1.0 / (0.5 + (0.5 * math.cos((2.0 * x)))))

Julia

function code(x)
	return Float64(1.0 / Float64(1.0 / Float64(0.5 + Float64(0.5 * cos(Float64(2.0 * x))))))
end

MATLAB

function tmp = code(x)
	tmp = 1.0 / (1.0 / (0.5 + (0.5 * cos((2.0 * x)))));
end

Wolfram

code[x_] := N[(1 / N[(1 / N[(1/2 + N[(1/2 * N[Cos[N[(2 * x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.5%

   \[\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x} \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{1 + \tan x \cdot \tan x}} \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x \cdot \tan x}} \]

   3. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x} \cdot \tan x} \]

   4. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x}{\cos x}} \cdot \tan x} \]

   5. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\tan x}} \]

   6. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\frac{\sin x}{\cos x}}} \]

   7. frac-times N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   8. add-to-fraction N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   9. lower-/.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

3. Applied rewrites 98.9%

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

4. Taylor expanded in x around 0

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

6. Applied rewrites 60.8%

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

7. Taylor expanded in x around 0

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

9. Applied rewrites 98.9%

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

10. Taylor expanded in x around 0

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

12. Applied rewrites 60.0%

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

13. Taylor expanded in x around 0

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

15. Applied rewrites 55.8%

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

16. Taylor expanded in x around inf

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

    1. lower-/.f64 N/A

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

    2. lower-+.f64 N/A

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

    3. lower-*.f64 N/A

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

    4. lower-cos.f64 N/A

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

    5. lower-*.f64 56.2%

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

18. Applied rewrites 56.2%

    \[\leadsto \frac{1}{\color{blue}{\frac{1}{\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot x\right)}}} \]
19. Add Preprocessing

Alternative 8: 55.8% accurate, 16.5× speedup

Math

\[\frac{1 \cdot \left(\frac{1}{2} - \frac{-1}{2}\right)}{\left(\frac{1}{2} - -1\right) - \frac{1}{2}} \]

FPCore

(FPCore (x)
  :precision binary64
  (/ (* 1 (- 1/2 -1/2)) (- (- 1/2 -1) 1/2)))

C

double code(double x) {
	return (1.0 * (0.5 - -0.5)) / ((0.5 - -1.0) - 0.5);
}

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 * (0.5d0 - (-0.5d0))) / ((0.5d0 - (-1.0d0)) - 0.5d0)
end function

Java

public static double code(double x) {
	return (1.0 * (0.5 - -0.5)) / ((0.5 - -1.0) - 0.5);
}

Python

def code(x):
	return (1.0 * (0.5 - -0.5)) / ((0.5 - -1.0) - 0.5)

Julia

function code(x)
	return Float64(Float64(1.0 * Float64(0.5 - -0.5)) / Float64(Float64(0.5 - -1.0) - 0.5))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 * (0.5 - -0.5)) / ((0.5 - -1.0) - 0.5);
end

Wolfram

code[x_] := N[(N[(1 * N[(1/2 - -1/2), $MachinePrecision]), $MachinePrecision] / N[(N[(1/2 - -1), $MachinePrecision] - 1/2), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.5%

   \[\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x} \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{1 + \tan x \cdot \tan x}} \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x \cdot \tan x}} \]

   3. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\tan x} \cdot \tan x} \]

   4. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x}{\cos x}} \cdot \tan x} \]

   5. lift-tan.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\tan x}} \]

   6. tan-quot N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \frac{\sin x}{\cos x} \cdot \color{blue}{\frac{\sin x}{\cos x}}} \]

   7. frac-times N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{1 + \color{blue}{\frac{\sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   8. add-to-fraction N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

   9. lower-/.f64 N/A

      \[\leadsto \frac{1 - \tan x \cdot \tan x}{\color{blue}{\frac{1 \cdot \left(\cos x \cdot \cos x\right) + \sin x \cdot \sin x}{\cos x \cdot \cos x}}} \]

3. Applied rewrites 98.9%

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

4. Taylor expanded in x around 0

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

6. Applied rewrites 60.8%

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

7. Taylor expanded in x around 0

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

9. Applied rewrites 98.9%

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

10. Taylor expanded in x around 0

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

12. Applied rewrites 60.0%

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

13. Taylor expanded in x around 0

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

15. Applied rewrites 55.8%

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

    1. lift-/.f64 N/A

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

    2. lift-/.f64 N/A

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

    3. *-lft-identity N/A

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

    4. lift-*.f64 N/A

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

    5. associate-/r/ N/A

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

    6. associate-*l/ N/A

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

17. Applied rewrites 55.8%

    \[\leadsto \color{blue}{\frac{1 \cdot \left(\frac{1}{2} - \frac{-1}{2}\right)}{\left(\frac{1}{2} - -1\right) - \frac{1}{2}}} \]
18. Add Preprocessing

Alternative 9: 51.9% accurate, 35.7× speedup

Math

\[1 - \left(x + x\right) \cdot x \]

FPCore

(FPCore (x)
  :precision binary64
  (- 1 (* (+ x x) x)))

C

double code(double x) {
	return 1.0 - ((x + 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 = 1.0d0 - ((x + x) * x)
end function

Java

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

Python

def code(x):
	return 1.0 - ((x + x) * x)

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(1 - N[(N[(x + x), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.5%

   \[\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x} \]

2. Taylor expanded in x around 0

   \[\leadsto \color{blue}{1 + -2 \cdot {x}^{2}} \]
3. Step-by-step derivation

   1. lower-+.f64 N/A

      \[\leadsto 1 + \color{blue}{-2 \cdot {x}^{2}} \]

   2. lower-*.f64 N/A

      \[\leadsto 1 + -2 \cdot \color{blue}{{x}^{2}} \]

   3. lower-pow.f64 51.9%

      \[\leadsto 1 + -2 \cdot {x}^{\color{blue}{2}} \]

4. Applied rewrites 51.9%

   \[\leadsto \color{blue}{1 + -2 \cdot {x}^{2}} \]
5. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto 1 + \color{blue}{-2 \cdot {x}^{2}} \]

   2. lift-*.f64 N/A

      \[\leadsto 1 + -2 \cdot \color{blue}{{x}^{2}} \]

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

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

   4. lower--.f64 N/A

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

   5. metadata-eval N/A

      \[\leadsto 1 - 2 \cdot {\color{blue}{x}}^{2} \]

   6. lift-pow.f64 N/A

      \[\leadsto 1 - 2 \cdot {x}^{\color{blue}{2}} \]

   7. unpow2 N/A

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

   8. associate-*r* N/A

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

   9. count-2 N/A

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

   10. lift-+.f64 N/A

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

   11. lower-*.f64 51.9%

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

6. Applied rewrites 51.9%

   \[\leadsto 1 - \color{blue}{\left(x + x\right) \cdot x} \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2025285 -o generate:evaluate
(FPCore (x)
  :name "Trigonometry B"
  :precision binary64
  (/ (- 1 (* (tan x) (tan x))) (+ 1 (* (tan x) (tan x)))))