tan-example (used to crash)

Percentage Accurate: 79.4% → 99.7%

Time: 22.0s

Alternatives: 16

Speedup: 1.0×

Specification

\[\left(\left(\left(x = 0 \lor 0.5884142 \leq x \land x \leq 505.5909\right) \land \left(-1.796658 \cdot 10^{+308} \leq y \land y \leq -9.425585 \cdot 10^{-310} \lor 1.284938 \cdot 10^{-309} \leq y \land y \leq 1.751224 \cdot 10^{+308}\right)\right) \land \left(-1.776707 \cdot 10^{+308} \leq z \land z \leq -8.599796 \cdot 10^{-310} \lor 3.293145 \cdot 10^{-311} \leq z \land z \leq 1.725154 \cdot 10^{+308}\right)\right) \land \left(-1.796658 \cdot 10^{+308} \leq a \land a \leq -9.425585 \cdot 10^{-310} \lor 1.284938 \cdot 10^{-309} \leq a \land a \leq 1.751224 \cdot 10^{+308}\right)\]

Math

\[\begin{array}{l} \\ x + \left(\tan \left(y + z\right) - \tan a\right) \end{array} \]

FPCore

(FPCore (x y z a) :precision binary64 (+ x (- (tan (+ y z)) (tan a))))

C

double code(double x, double y, double z, double a) {
	return x + (tan((y + z)) - tan(a));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

public static double code(double x, double y, double z, double a) {
	return x + (Math.tan((y + z)) - Math.tan(a));
}

Python

def code(x, y, z, a):
	return x + (math.tan((y + z)) - math.tan(a))

Julia

function code(x, y, z, a)
	return Float64(x + Float64(tan(Float64(y + z)) - tan(a)))
end

MATLAB

function tmp = code(x, y, z, a)
	tmp = x + (tan((y + z)) - tan(a));
end

Wolfram

code[x_, y_, z_, a_] := N[(x + N[(N[Tan[N[(y + z), $MachinePrecision]], $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 79.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \left(\tan \left(y + z\right) - \tan a\right) \end{array} \]

FPCore

(FPCore (x y z a) :precision binary64 (+ x (- (tan (+ y z)) (tan a))))

C

double code(double x, double y, double z, double a) {
	return x + (tan((y + z)) - tan(a));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

public static double code(double x, double y, double z, double a) {
	return x + (Math.tan((y + z)) - Math.tan(a));
}

Python

def code(x, y, z, a):
	return x + (math.tan((y + z)) - math.tan(a))

Julia

function code(x, y, z, a)
	return Float64(x + Float64(tan(Float64(y + z)) - tan(a)))
end

MATLAB

function tmp = code(x, y, z, a)
	tmp = x + (tan((y + z)) - tan(a));
end

Wolfram

code[x_, y_, z_, a_] := N[(x + N[(N[Tan[N[(y + z), $MachinePrecision]], $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \cos y \cdot \cos z - \sin y \cdot \sin z\\ x + \left(\left(\frac{\cos z \cdot \sin y}{t\_0} + \frac{\cos y \cdot \sin z}{t\_0}\right) - \tan a\right) \end{array} \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (- (* (cos y) (cos z)) (* (sin y) (sin z)))))
   (+
    x
    (- (+ (/ (* (cos z) (sin y)) t_0) (/ (* (cos y) (sin z)) t_0)) (tan a)))))

C

double code(double x, double y, double z, double a) {
	double t_0 = (cos(y) * cos(z)) - (sin(y) * sin(z));
	return x + ((((cos(z) * sin(y)) / t_0) + ((cos(y) * sin(z)) / t_0)) - tan(a));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: a
    real(8) :: t_0
    t_0 = (cos(y) * cos(z)) - (sin(y) * sin(z))
    code = x + ((((cos(z) * sin(y)) / t_0) + ((cos(y) * sin(z)) / t_0)) - tan(a))
end function

Java

public static double code(double x, double y, double z, double a) {
	double t_0 = (Math.cos(y) * Math.cos(z)) - (Math.sin(y) * Math.sin(z));
	return x + ((((Math.cos(z) * Math.sin(y)) / t_0) + ((Math.cos(y) * Math.sin(z)) / t_0)) - Math.tan(a));
}

Python

def code(x, y, z, a):
	t_0 = (math.cos(y) * math.cos(z)) - (math.sin(y) * math.sin(z))
	return x + ((((math.cos(z) * math.sin(y)) / t_0) + ((math.cos(y) * math.sin(z)) / t_0)) - math.tan(a))

Julia

function code(x, y, z, a)
	t_0 = Float64(Float64(cos(y) * cos(z)) - Float64(sin(y) * sin(z)))
	return Float64(x + Float64(Float64(Float64(Float64(cos(z) * sin(y)) / t_0) + Float64(Float64(cos(y) * sin(z)) / t_0)) - tan(a)))
end

MATLAB

function tmp = code(x, y, z, a)
	t_0 = (cos(y) * cos(z)) - (sin(y) * sin(z));
	tmp = x + ((((cos(z) * sin(y)) / t_0) + ((cos(y) * sin(z)) / t_0)) - tan(a));
end

Wolfram

code[x_, y_, z_, a_] := Block[{t$95$0 = N[(N[(N[Cos[y], $MachinePrecision] * N[Cos[z], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(x + N[(N[(N[(N[(N[Cos[z], $MachinePrecision] * N[Sin[y], $MachinePrecision]), $MachinePrecision] / t$95$0), $MachinePrecision] + N[(N[(N[Cos[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 79.4%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

3. Applied rewrites 79.8%

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

5. Applied rewrites 80.7%

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

7. Applied rewrites 99.7%

   \[\leadsto x + \left(\left(\frac{\cos z \cdot \sin y}{\cos y \cdot \cos z - \sin y \cdot \sin z} + \frac{\cos y \cdot \sin z}{\color{blue}{\cos y \cdot \cos z - \sin y \cdot \sin z}}\right) - \tan a\right) \]
8. Add Preprocessing

Alternative 2: 89.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \cos y \cdot \cos z - \sin y \cdot \sin z\\ t_1 := \frac{\cos z \cdot \sin y}{t\_0}\\ t_2 := \cos y \cdot \sin z\\ t_3 := x + \left(\left(t\_1 + \frac{t\_2}{\cos \left(y + z\right)}\right) - \tan a\right)\\ \mathbf{if}\;a \leq -0.0225:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;a \leq 0.0076:\\ \;\;\;\;x + \mathsf{fma}\left(a, -0.3333333333333333 \cdot {a}^{2} - 1, \frac{t\_2}{t\_0} + t\_1\right)\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (- (* (cos y) (cos z)) (* (sin y) (sin z))))
        (t_1 (/ (* (cos z) (sin y)) t_0))
        (t_2 (* (cos y) (sin z)))
        (t_3 (+ x (- (+ t_1 (/ t_2 (cos (+ y z)))) (tan a)))))
   (if (<= a -0.0225)
     t_3
     (if (<= a 0.0076)
       (+
        x
        (fma
         a
         (- (* -0.3333333333333333 (pow a 2.0)) 1.0)
         (+ (/ t_2 t_0) t_1)))
       t_3))))

C

double code(double x, double y, double z, double a) {
	double t_0 = (cos(y) * cos(z)) - (sin(y) * sin(z));
	double t_1 = (cos(z) * sin(y)) / t_0;
	double t_2 = cos(y) * sin(z);
	double t_3 = x + ((t_1 + (t_2 / cos((y + z)))) - tan(a));
	double tmp;
	if (a <= -0.0225) {
		tmp = t_3;
	} else if (a <= 0.0076) {
		tmp = x + fma(a, ((-0.3333333333333333 * pow(a, 2.0)) - 1.0), ((t_2 / t_0) + t_1));
	} else {
		tmp = t_3;
	}
	return tmp;
}

Julia

function code(x, y, z, a)
	t_0 = Float64(Float64(cos(y) * cos(z)) - Float64(sin(y) * sin(z)))
	t_1 = Float64(Float64(cos(z) * sin(y)) / t_0)
	t_2 = Float64(cos(y) * sin(z))
	t_3 = Float64(x + Float64(Float64(t_1 + Float64(t_2 / cos(Float64(y + z)))) - tan(a)))
	tmp = 0.0
	if (a <= -0.0225)
		tmp = t_3;
	elseif (a <= 0.0076)
		tmp = Float64(x + fma(a, Float64(Float64(-0.3333333333333333 * (a ^ 2.0)) - 1.0), Float64(Float64(t_2 / t_0) + t_1)));
	else
		tmp = t_3;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, a_] := Block[{t$95$0 = N[(N[(N[Cos[y], $MachinePrecision] * N[Cos[z], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[Cos[z], $MachinePrecision] * N[Sin[y], $MachinePrecision]), $MachinePrecision] / t$95$0), $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x + N[(N[(t$95$1 + N[(t$95$2 / N[Cos[N[(y + z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -0.0225], t$95$3, If[LessEqual[a, 0.0076], N[(x + N[(a * N[(N[(-0.3333333333333333 * N[Power[a, 2.0], $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision] + N[(N[(t$95$2 / t$95$0), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$3]]]]]]

Derivation

1. Split input into 2 regimes

2. if a < -0.022499999999999999 or 0.00759999999999999998 < a

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   3. Applied rewrites 79.8%

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

   5. Applied rewrites 80.7%

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

   7. Applied rewrites 99.7%

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

   9. Applied rewrites 80.7%

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

   if -0.022499999999999999 < a < 0.00759999999999999998

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   3. Applied rewrites 79.8%

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

   5. Applied rewrites 80.7%

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

   7. Applied rewrites 99.7%

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

   8. Taylor expanded in a around 0

      \[\leadsto x + \color{blue}{\left(a \cdot \left(\frac{-1}{3} \cdot {a}^{2} - 1\right) + \left(\frac{\cos y \cdot \sin z}{\cos y \cdot \cos z - \sin y \cdot \sin z} + \frac{\cos z \cdot \sin y}{\cos y \cdot \cos z - \sin y \cdot \sin z}\right)\right)} \]

   10. Applied rewrites 49.7%

       \[\leadsto x + \color{blue}{\mathsf{fma}\left(a, -0.3333333333333333 \cdot {a}^{2} - 1, \frac{\cos y \cdot \sin z}{\cos y \cdot \cos z - \sin y \cdot \sin z} + \frac{\cos z \cdot \sin y}{\cos y \cdot \cos z - \sin y \cdot \sin z}\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 89.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \cos y \cdot \cos z - \sin y \cdot \sin z\\ t_1 := \frac{\cos z \cdot \sin y}{t\_0}\\ t_2 := \cos y \cdot \sin z\\ t_3 := x + \left(\left(t\_1 + \frac{t\_2}{\cos \left(y + z\right)}\right) - \tan a\right)\\ \mathbf{if}\;\tan a \leq -0.02:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;\tan a \leq 0.0002:\\ \;\;\;\;x + \left(\left(t\_1 + \frac{t\_2}{t\_0}\right) - a\right)\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (- (* (cos y) (cos z)) (* (sin y) (sin z))))
        (t_1 (/ (* (cos z) (sin y)) t_0))
        (t_2 (* (cos y) (sin z)))
        (t_3 (+ x (- (+ t_1 (/ t_2 (cos (+ y z)))) (tan a)))))
   (if (<= (tan a) -0.02)
     t_3
     (if (<= (tan a) 0.0002) (+ x (- (+ t_1 (/ t_2 t_0)) a)) t_3))))

C

double code(double x, double y, double z, double a) {
	double t_0 = (cos(y) * cos(z)) - (sin(y) * sin(z));
	double t_1 = (cos(z) * sin(y)) / t_0;
	double t_2 = cos(y) * sin(z);
	double t_3 = x + ((t_1 + (t_2 / cos((y + z)))) - tan(a));
	double tmp;
	if (tan(a) <= -0.02) {
		tmp = t_3;
	} else if (tan(a) <= 0.0002) {
		tmp = x + ((t_1 + (t_2 / t_0)) - a);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: a
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_0 = (cos(y) * cos(z)) - (sin(y) * sin(z))
    t_1 = (cos(z) * sin(y)) / t_0
    t_2 = cos(y) * sin(z)
    t_3 = x + ((t_1 + (t_2 / cos((y + z)))) - tan(a))
    if (tan(a) <= (-0.02d0)) then
        tmp = t_3
    else if (tan(a) <= 0.0002d0) then
        tmp = x + ((t_1 + (t_2 / t_0)) - a)
    else
        tmp = t_3
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double a) {
	double t_0 = (Math.cos(y) * Math.cos(z)) - (Math.sin(y) * Math.sin(z));
	double t_1 = (Math.cos(z) * Math.sin(y)) / t_0;
	double t_2 = Math.cos(y) * Math.sin(z);
	double t_3 = x + ((t_1 + (t_2 / Math.cos((y + z)))) - Math.tan(a));
	double tmp;
	if (Math.tan(a) <= -0.02) {
		tmp = t_3;
	} else if (Math.tan(a) <= 0.0002) {
		tmp = x + ((t_1 + (t_2 / t_0)) - a);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, a):
	t_0 = (math.cos(y) * math.cos(z)) - (math.sin(y) * math.sin(z))
	t_1 = (math.cos(z) * math.sin(y)) / t_0
	t_2 = math.cos(y) * math.sin(z)
	t_3 = x + ((t_1 + (t_2 / math.cos((y + z)))) - math.tan(a))
	tmp = 0
	if math.tan(a) <= -0.02:
		tmp = t_3
	elif math.tan(a) <= 0.0002:
		tmp = x + ((t_1 + (t_2 / t_0)) - a)
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, a)
	t_0 = Float64(Float64(cos(y) * cos(z)) - Float64(sin(y) * sin(z)))
	t_1 = Float64(Float64(cos(z) * sin(y)) / t_0)
	t_2 = Float64(cos(y) * sin(z))
	t_3 = Float64(x + Float64(Float64(t_1 + Float64(t_2 / cos(Float64(y + z)))) - tan(a)))
	tmp = 0.0
	if (tan(a) <= -0.02)
		tmp = t_3;
	elseif (tan(a) <= 0.0002)
		tmp = Float64(x + Float64(Float64(t_1 + Float64(t_2 / t_0)) - a));
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, a)
	t_0 = (cos(y) * cos(z)) - (sin(y) * sin(z));
	t_1 = (cos(z) * sin(y)) / t_0;
	t_2 = cos(y) * sin(z);
	t_3 = x + ((t_1 + (t_2 / cos((y + z)))) - tan(a));
	tmp = 0.0;
	if (tan(a) <= -0.02)
		tmp = t_3;
	elseif (tan(a) <= 0.0002)
		tmp = x + ((t_1 + (t_2 / t_0)) - a);
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, a_] := Block[{t$95$0 = N[(N[(N[Cos[y], $MachinePrecision] * N[Cos[z], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[Cos[z], $MachinePrecision] * N[Sin[y], $MachinePrecision]), $MachinePrecision] / t$95$0), $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x + N[(N[(t$95$1 + N[(t$95$2 / N[Cos[N[(y + z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Tan[a], $MachinePrecision], -0.02], t$95$3, If[LessEqual[N[Tan[a], $MachinePrecision], 0.0002], N[(x + N[(N[(t$95$1 + N[(t$95$2 / t$95$0), $MachinePrecision]), $MachinePrecision] - a), $MachinePrecision]), $MachinePrecision], t$95$3]]]]]]

Derivation

1. Split input into 2 regimes

2. if (tan.f64 a) < -0.0200000000000000004 or 2.0000000000000001e-4 < (tan.f64 a)

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   3. Applied rewrites 79.8%

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

   5. Applied rewrites 80.7%

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

   7. Applied rewrites 99.7%

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

   9. Applied rewrites 80.7%

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

   if -0.0200000000000000004 < (tan.f64 a) < 2.0000000000000001e-4

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   3. Applied rewrites 79.8%

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

   5. Applied rewrites 80.7%

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

   7. Applied rewrites 99.7%

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

   8. Taylor expanded in a around 0

      \[\leadsto x + \left(\left(\frac{\cos z \cdot \sin y}{\cos y \cdot \cos z - \sin y \cdot \sin z} + \frac{\cos y \cdot \sin z}{\cos y \cdot \cos z - \sin y \cdot \sin z}\right) - \color{blue}{a}\right) \]

   10. Applied rewrites 50.1%

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

Alternative 4: 89.2% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \cos y \cdot \cos z - \sin y \cdot \sin z\\ t_1 := \frac{\cos z \cdot \sin y}{t\_0}\\ t_2 := \cos y \cdot \sin z\\ t_3 := x + \left(\left(t\_1 + \frac{t\_2}{\cos \left(y + z\right)}\right) - \tan a\right)\\ \mathbf{if}\;\tan a \leq -0.02:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;\tan a \leq 10^{-20}:\\ \;\;\;\;x + \left(\frac{t\_2}{t\_0} + t\_1\right)\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (- (* (cos y) (cos z)) (* (sin y) (sin z))))
        (t_1 (/ (* (cos z) (sin y)) t_0))
        (t_2 (* (cos y) (sin z)))
        (t_3 (+ x (- (+ t_1 (/ t_2 (cos (+ y z)))) (tan a)))))
   (if (<= (tan a) -0.02)
     t_3
     (if (<= (tan a) 1e-20) (+ x (+ (/ t_2 t_0) t_1)) t_3))))

C

double code(double x, double y, double z, double a) {
	double t_0 = (cos(y) * cos(z)) - (sin(y) * sin(z));
	double t_1 = (cos(z) * sin(y)) / t_0;
	double t_2 = cos(y) * sin(z);
	double t_3 = x + ((t_1 + (t_2 / cos((y + z)))) - tan(a));
	double tmp;
	if (tan(a) <= -0.02) {
		tmp = t_3;
	} else if (tan(a) <= 1e-20) {
		tmp = x + ((t_2 / t_0) + t_1);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: a
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_0 = (cos(y) * cos(z)) - (sin(y) * sin(z))
    t_1 = (cos(z) * sin(y)) / t_0
    t_2 = cos(y) * sin(z)
    t_3 = x + ((t_1 + (t_2 / cos((y + z)))) - tan(a))
    if (tan(a) <= (-0.02d0)) then
        tmp = t_3
    else if (tan(a) <= 1d-20) then
        tmp = x + ((t_2 / t_0) + t_1)
    else
        tmp = t_3
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double a) {
	double t_0 = (Math.cos(y) * Math.cos(z)) - (Math.sin(y) * Math.sin(z));
	double t_1 = (Math.cos(z) * Math.sin(y)) / t_0;
	double t_2 = Math.cos(y) * Math.sin(z);
	double t_3 = x + ((t_1 + (t_2 / Math.cos((y + z)))) - Math.tan(a));
	double tmp;
	if (Math.tan(a) <= -0.02) {
		tmp = t_3;
	} else if (Math.tan(a) <= 1e-20) {
		tmp = x + ((t_2 / t_0) + t_1);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, a):
	t_0 = (math.cos(y) * math.cos(z)) - (math.sin(y) * math.sin(z))
	t_1 = (math.cos(z) * math.sin(y)) / t_0
	t_2 = math.cos(y) * math.sin(z)
	t_3 = x + ((t_1 + (t_2 / math.cos((y + z)))) - math.tan(a))
	tmp = 0
	if math.tan(a) <= -0.02:
		tmp = t_3
	elif math.tan(a) <= 1e-20:
		tmp = x + ((t_2 / t_0) + t_1)
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, a)
	t_0 = Float64(Float64(cos(y) * cos(z)) - Float64(sin(y) * sin(z)))
	t_1 = Float64(Float64(cos(z) * sin(y)) / t_0)
	t_2 = Float64(cos(y) * sin(z))
	t_3 = Float64(x + Float64(Float64(t_1 + Float64(t_2 / cos(Float64(y + z)))) - tan(a)))
	tmp = 0.0
	if (tan(a) <= -0.02)
		tmp = t_3;
	elseif (tan(a) <= 1e-20)
		tmp = Float64(x + Float64(Float64(t_2 / t_0) + t_1));
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, a)
	t_0 = (cos(y) * cos(z)) - (sin(y) * sin(z));
	t_1 = (cos(z) * sin(y)) / t_0;
	t_2 = cos(y) * sin(z);
	t_3 = x + ((t_1 + (t_2 / cos((y + z)))) - tan(a));
	tmp = 0.0;
	if (tan(a) <= -0.02)
		tmp = t_3;
	elseif (tan(a) <= 1e-20)
		tmp = x + ((t_2 / t_0) + t_1);
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, a_] := Block[{t$95$0 = N[(N[(N[Cos[y], $MachinePrecision] * N[Cos[z], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[Cos[z], $MachinePrecision] * N[Sin[y], $MachinePrecision]), $MachinePrecision] / t$95$0), $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x + N[(N[(t$95$1 + N[(t$95$2 / N[Cos[N[(y + z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Tan[a], $MachinePrecision], -0.02], t$95$3, If[LessEqual[N[Tan[a], $MachinePrecision], 1e-20], N[(x + N[(N[(t$95$2 / t$95$0), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision], t$95$3]]]]]]

Derivation

1. Split input into 2 regimes

2. if (tan.f64 a) < -0.0200000000000000004 or 9.99999999999999945e-21 < (tan.f64 a)

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   3. Applied rewrites 79.8%

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

   5. Applied rewrites 80.7%

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

   7. Applied rewrites 99.7%

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

   9. Applied rewrites 80.7%

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

   if -0.0200000000000000004 < (tan.f64 a) < 9.99999999999999945e-21

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   3. Applied rewrites 79.8%

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

   5. Applied rewrites 80.7%

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

   7. Applied rewrites 99.7%

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

   8. Taylor expanded in a around 0

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

   10. Applied rewrites 60.0%

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

Alternative 5: 80.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ x + \left(\left(\frac{\cos z \cdot \sin y}{\cos y \cdot \cos z - \sin y \cdot \sin z} + \frac{\cos y \cdot \sin z}{\cos \left(y + z\right)}\right) - \tan a\right) \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (+
  x
  (-
   (+
    (/ (* (cos z) (sin y)) (- (* (cos y) (cos z)) (* (sin y) (sin z))))
    (/ (* (cos y) (sin z)) (cos (+ y z))))
   (tan a))))

C

double code(double x, double y, double z, double a) {
	return x + ((((cos(z) * sin(y)) / ((cos(y) * cos(z)) - (sin(y) * sin(z)))) + ((cos(y) * sin(z)) / cos((y + z)))) - tan(a));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: a
    code = x + ((((cos(z) * sin(y)) / ((cos(y) * cos(z)) - (sin(y) * sin(z)))) + ((cos(y) * sin(z)) / cos((y + z)))) - tan(a))
end function

Java

public static double code(double x, double y, double z, double a) {
	return x + ((((Math.cos(z) * Math.sin(y)) / ((Math.cos(y) * Math.cos(z)) - (Math.sin(y) * Math.sin(z)))) + ((Math.cos(y) * Math.sin(z)) / Math.cos((y + z)))) - Math.tan(a));
}

Python

def code(x, y, z, a):
	return x + ((((math.cos(z) * math.sin(y)) / ((math.cos(y) * math.cos(z)) - (math.sin(y) * math.sin(z)))) + ((math.cos(y) * math.sin(z)) / math.cos((y + z)))) - math.tan(a))

Julia

function code(x, y, z, a)
	return Float64(x + Float64(Float64(Float64(Float64(cos(z) * sin(y)) / Float64(Float64(cos(y) * cos(z)) - Float64(sin(y) * sin(z)))) + Float64(Float64(cos(y) * sin(z)) / cos(Float64(y + z)))) - tan(a)))
end

MATLAB

function tmp = code(x, y, z, a)
	tmp = x + ((((cos(z) * sin(y)) / ((cos(y) * cos(z)) - (sin(y) * sin(z)))) + ((cos(y) * sin(z)) / cos((y + z)))) - tan(a));
end

Wolfram

code[x_, y_, z_, a_] := N[(x + N[(N[(N[(N[(N[Cos[z], $MachinePrecision] * N[Sin[y], $MachinePrecision]), $MachinePrecision] / N[(N[(N[Cos[y], $MachinePrecision] * N[Cos[z], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[Cos[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision] / N[Cos[N[(y + z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.4%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

3. Applied rewrites 79.8%

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

5. Applied rewrites 80.7%

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

7. Applied rewrites 99.7%

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

9. Applied rewrites 80.7%

   \[\leadsto x + \left(\left(\frac{\cos z \cdot \sin y}{\cos y \cdot \cos z - \sin y \cdot \sin z} + \color{blue}{\frac{\cos y \cdot \sin z}{\cos \left(y + z\right)}}\right) - \tan a\right) \]
10. Add Preprocessing

Alternative 6: 80.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ x + \left(\left(\frac{\cos z \cdot \sin y}{\cos \left(y + z\right)} + \frac{\cos y \cdot \sin z}{\cos y \cdot \cos z - \sin y \cdot \sin z}\right) - \tan a\right) \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (+
  x
  (-
   (+
    (/ (* (cos z) (sin y)) (cos (+ y z)))
    (/ (* (cos y) (sin z)) (- (* (cos y) (cos z)) (* (sin y) (sin z)))))
   (tan a))))

C

double code(double x, double y, double z, double a) {
	return x + ((((cos(z) * sin(y)) / cos((y + z))) + ((cos(y) * sin(z)) / ((cos(y) * cos(z)) - (sin(y) * sin(z))))) - tan(a));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: a
    code = x + ((((cos(z) * sin(y)) / cos((y + z))) + ((cos(y) * sin(z)) / ((cos(y) * cos(z)) - (sin(y) * sin(z))))) - tan(a))
end function

Java

public static double code(double x, double y, double z, double a) {
	return x + ((((Math.cos(z) * Math.sin(y)) / Math.cos((y + z))) + ((Math.cos(y) * Math.sin(z)) / ((Math.cos(y) * Math.cos(z)) - (Math.sin(y) * Math.sin(z))))) - Math.tan(a));
}

Python

def code(x, y, z, a):
	return x + ((((math.cos(z) * math.sin(y)) / math.cos((y + z))) + ((math.cos(y) * math.sin(z)) / ((math.cos(y) * math.cos(z)) - (math.sin(y) * math.sin(z))))) - math.tan(a))

Julia

function code(x, y, z, a)
	return Float64(x + Float64(Float64(Float64(Float64(cos(z) * sin(y)) / cos(Float64(y + z))) + Float64(Float64(cos(y) * sin(z)) / Float64(Float64(cos(y) * cos(z)) - Float64(sin(y) * sin(z))))) - tan(a)))
end

MATLAB

function tmp = code(x, y, z, a)
	tmp = x + ((((cos(z) * sin(y)) / cos((y + z))) + ((cos(y) * sin(z)) / ((cos(y) * cos(z)) - (sin(y) * sin(z))))) - tan(a));
end

Wolfram

code[x_, y_, z_, a_] := N[(x + N[(N[(N[(N[(N[Cos[z], $MachinePrecision] * N[Sin[y], $MachinePrecision]), $MachinePrecision] / N[Cos[N[(y + z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + N[(N[(N[Cos[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision] / N[(N[(N[Cos[y], $MachinePrecision] * N[Cos[z], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.4%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

3. Applied rewrites 79.8%

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

5. Applied rewrites 80.7%

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

7. Applied rewrites 99.7%

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

9. Applied rewrites 80.6%

   \[\leadsto x + \left(\left(\color{blue}{\frac{\cos z \cdot \sin y}{\cos \left(y + z\right)}} + \frac{\cos y \cdot \sin z}{\cos y \cdot \cos z - \sin y \cdot \sin z}\right) - \tan a\right) \]
10. Add Preprocessing

Alternative 7: 79.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\frac{1}{\mathsf{fma}\left(\cos z, \cos y, \sin y \cdot \sin \left(-z\right)\right)}, \sin \left(y + z\right), x - \tan a\right) \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (fma
  (/ 1.0 (fma (cos z) (cos y) (* (sin y) (sin (- z)))))
  (sin (+ y z))
  (- x (tan a))))

C

double code(double x, double y, double z, double a) {
	return fma((1.0 / fma(cos(z), cos(y), (sin(y) * sin(-z)))), sin((y + z)), (x - tan(a)));
}

Julia

function code(x, y, z, a)
	return fma(Float64(1.0 / fma(cos(z), cos(y), Float64(sin(y) * sin(Float64(-z))))), sin(Float64(y + z)), Float64(x - tan(a)))
end

Wolfram

code[x_, y_, z_, a_] := N[(N[(1.0 / N[(N[Cos[z], $MachinePrecision] * N[Cos[y], $MachinePrecision] + N[(N[Sin[y], $MachinePrecision] * N[Sin[(-z)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Sin[N[(y + z), $MachinePrecision]], $MachinePrecision] + N[(x - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.4%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

3. Applied rewrites 79.3%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{1}{\cos \left(y + z\right)}, \sin \left(y + z\right), x - \tan a\right)} \]

5. Applied rewrites 79.9%

   \[\leadsto \mathsf{fma}\left(\frac{1}{\color{blue}{\mathsf{fma}\left(\cos z, \cos y, \sin y \cdot \sin \left(-z\right)\right)}}, \sin \left(y + z\right), x - \tan a\right) \]
6. Add Preprocessing

Alternative 8: 79.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\frac{1}{\cos y \cdot \cos z - \sin y \cdot \sin z}, \sin \left(y + z\right), x - \tan a\right) \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (fma
  (/ 1.0 (- (* (cos y) (cos z)) (* (sin y) (sin z))))
  (sin (+ y z))
  (- x (tan a))))

C

double code(double x, double y, double z, double a) {
	return fma((1.0 / ((cos(y) * cos(z)) - (sin(y) * sin(z)))), sin((y + z)), (x - tan(a)));
}

Julia

function code(x, y, z, a)
	return fma(Float64(1.0 / Float64(Float64(cos(y) * cos(z)) - Float64(sin(y) * sin(z)))), sin(Float64(y + z)), Float64(x - tan(a)))
end

Wolfram

code[x_, y_, z_, a_] := N[(N[(1.0 / N[(N[(N[Cos[y], $MachinePrecision] * N[Cos[z], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[y], $MachinePrecision] * N[Sin[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Sin[N[(y + z), $MachinePrecision]], $MachinePrecision] + N[(x - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.4%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

3. Applied rewrites 79.3%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{1}{\cos \left(y + z\right)}, \sin \left(y + z\right), x - \tan a\right)} \]

5. Applied rewrites 79.9%

   \[\leadsto \mathsf{fma}\left(\frac{1}{\color{blue}{\cos y \cdot \cos z - \sin y \cdot \sin z}}, \sin \left(y + z\right), x - \tan a\right) \]
6. Add Preprocessing

Alternative 9: 79.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\frac{1}{\cos \left(y + z\right)}, \mathsf{fma}\left(\sin z, \cos y, \cos z \cdot \sin y\right), x - \tan a\right) \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (fma
  (/ 1.0 (cos (+ y z)))
  (fma (sin z) (cos y) (* (cos z) (sin y)))
  (- x (tan a))))

C

double code(double x, double y, double z, double a) {
	return fma((1.0 / cos((y + z))), fma(sin(z), cos(y), (cos(z) * sin(y))), (x - tan(a)));
}

Julia

function code(x, y, z, a)
	return fma(Float64(1.0 / cos(Float64(y + z))), fma(sin(z), cos(y), Float64(cos(z) * sin(y))), Float64(x - tan(a)))
end

Wolfram

code[x_, y_, z_, a_] := N[(N[(1.0 / N[Cos[N[(y + z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[(N[Sin[z], $MachinePrecision] * N[Cos[y], $MachinePrecision] + N[(N[Cos[z], $MachinePrecision] * N[Sin[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.4%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

3. Applied rewrites 79.3%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{1}{\cos \left(y + z\right)}, \sin \left(y + z\right), x - \tan a\right)} \]

5. Applied rewrites 79.8%

   \[\leadsto \mathsf{fma}\left(\frac{1}{\cos \left(y + z\right)}, \color{blue}{\mathsf{fma}\left(\sin z, \cos y, \cos z \cdot \sin y\right)}, x - \tan a\right) \]
6. Add Preprocessing

Alternative 10: 79.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x - \left(\tan a - \tan \left(y + z\right)\right) \end{array} \]

FPCore

(FPCore (x y z a) :precision binary64 (- x (- (tan a) (tan (+ y z)))))

C

double code(double x, double y, double z, double a) {
	return x - (tan(a) - tan((y + z)));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

public static double code(double x, double y, double z, double a) {
	return x - (Math.tan(a) - Math.tan((y + z)));
}

Python

def code(x, y, z, a):
	return x - (math.tan(a) - math.tan((y + z)))

Julia

function code(x, y, z, a)
	return Float64(x - Float64(tan(a) - tan(Float64(y + z))))
end

MATLAB

function tmp = code(x, y, z, a)
	tmp = x - (tan(a) - tan((y + z)));
end

Wolfram

code[x_, y_, z_, a_] := N[(x - N[(N[Tan[a], $MachinePrecision] - N[Tan[N[(y + z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.4%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

3. Applied rewrites 79.4%

   \[\leadsto \color{blue}{x - \left(\tan a - \tan \left(y + z\right)\right)} \]
4. Add Preprocessing

Alternative 11: 64.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -0.0225:\\ \;\;\;\;x + \frac{\sin \left(y + z\right)}{\cos \left(y + z\right)}\\ \mathbf{else}:\\ \;\;\;\;x + \left(\tan z - \tan a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (if (<= y -0.0225)
   (+ x (/ (sin (+ y z)) (cos (+ y z))))
   (+ x (- (tan z) (tan a)))))

C

double code(double x, double y, double z, double a) {
	double tmp;
	if (y <= -0.0225) {
		tmp = x + (sin((y + z)) / cos((y + z)));
	} else {
		tmp = x + (tan(z) - tan(a));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: a
    real(8) :: tmp
    if (y <= (-0.0225d0)) then
        tmp = x + (sin((y + z)) / cos((y + z)))
    else
        tmp = x + (tan(z) - tan(a))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double a) {
	double tmp;
	if (y <= -0.0225) {
		tmp = x + (Math.sin((y + z)) / Math.cos((y + z)));
	} else {
		tmp = x + (Math.tan(z) - Math.tan(a));
	}
	return tmp;
}

Python

def code(x, y, z, a):
	tmp = 0
	if y <= -0.0225:
		tmp = x + (math.sin((y + z)) / math.cos((y + z)))
	else:
		tmp = x + (math.tan(z) - math.tan(a))
	return tmp

Julia

function code(x, y, z, a)
	tmp = 0.0
	if (y <= -0.0225)
		tmp = Float64(x + Float64(sin(Float64(y + z)) / cos(Float64(y + z))));
	else
		tmp = Float64(x + Float64(tan(z) - tan(a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, a)
	tmp = 0.0;
	if (y <= -0.0225)
		tmp = x + (sin((y + z)) / cos((y + z)));
	else
		tmp = x + (tan(z) - tan(a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, a_] := If[LessEqual[y, -0.0225], N[(x + N[(N[Sin[N[(y + z), $MachinePrecision]], $MachinePrecision] / N[Cos[N[(y + z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[Tan[z], $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -0.022499999999999999

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{x + \frac{\sin \left(y + z\right)}{\cos \left(y + z\right)}} \]

   4. Applied rewrites 49.8%

      \[\leadsto \color{blue}{x + \frac{\sin \left(y + z\right)}{\cos \left(y + z\right)}} \]

   if -0.022499999999999999 < y

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   2. Taylor expanded in y around 0

      \[\leadsto x + \left(\tan \color{blue}{z} - \tan a\right) \]

   4. Applied rewrites 60.2%

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

Alternative 12: 49.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{\sin \left(y + z\right)}{\cos \left(y + z\right)} \end{array} \]

FPCore

(FPCore (x y z a) :precision binary64 (+ x (/ (sin (+ y z)) (cos (+ y z)))))

C

double code(double x, double y, double z, double a) {
	return x + (sin((y + z)) / cos((y + z)));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: a
    code = x + (sin((y + z)) / cos((y + z)))
end function

Java

public static double code(double x, double y, double z, double a) {
	return x + (Math.sin((y + z)) / Math.cos((y + z)));
}

Python

def code(x, y, z, a):
	return x + (math.sin((y + z)) / math.cos((y + z)))

Julia

function code(x, y, z, a)
	return Float64(x + Float64(sin(Float64(y + z)) / cos(Float64(y + z))))
end

MATLAB

function tmp = code(x, y, z, a)
	tmp = x + (sin((y + z)) / cos((y + z)));
end

Wolfram

code[x_, y_, z_, a_] := N[(x + N[(N[Sin[N[(y + z), $MachinePrecision]], $MachinePrecision] / N[Cos[N[(y + z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.4%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

2. Taylor expanded in a around 0

   \[\leadsto \color{blue}{x + \frac{\sin \left(y + z\right)}{\cos \left(y + z\right)}} \]

4. Applied rewrites 49.8%

   \[\leadsto \color{blue}{x + \frac{\sin \left(y + z\right)}{\cos \left(y + z\right)}} \]
5. Add Preprocessing

Alternative 13: 49.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -1 \cdot \left(x \cdot -1\right)\\ \mathbf{if}\;\tan a \leq -0.02:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;\tan a \leq 0.05:\\ \;\;\;\;x + \left(\tan \left(y + z\right) - a \cdot \left(1 + 0.3333333333333333 \cdot {a}^{2}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (* -1.0 (* x -1.0))))
   (if (<= (tan a) -0.02)
     t_0
     (if (<= (tan a) 0.05)
       (+ x (- (tan (+ y z)) (* a (+ 1.0 (* 0.3333333333333333 (pow a 2.0))))))
       t_0))))

C

double code(double x, double y, double z, double a) {
	double t_0 = -1.0 * (x * -1.0);
	double tmp;
	if (tan(a) <= -0.02) {
		tmp = t_0;
	} else if (tan(a) <= 0.05) {
		tmp = x + (tan((y + z)) - (a * (1.0 + (0.3333333333333333 * pow(a, 2.0)))));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: a
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (-1.0d0) * (x * (-1.0d0))
    if (tan(a) <= (-0.02d0)) then
        tmp = t_0
    else if (tan(a) <= 0.05d0) then
        tmp = x + (tan((y + z)) - (a * (1.0d0 + (0.3333333333333333d0 * (a ** 2.0d0)))))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double a) {
	double t_0 = -1.0 * (x * -1.0);
	double tmp;
	if (Math.tan(a) <= -0.02) {
		tmp = t_0;
	} else if (Math.tan(a) <= 0.05) {
		tmp = x + (Math.tan((y + z)) - (a * (1.0 + (0.3333333333333333 * Math.pow(a, 2.0)))));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z, a):
	t_0 = -1.0 * (x * -1.0)
	tmp = 0
	if math.tan(a) <= -0.02:
		tmp = t_0
	elif math.tan(a) <= 0.05:
		tmp = x + (math.tan((y + z)) - (a * (1.0 + (0.3333333333333333 * math.pow(a, 2.0)))))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z, a)
	t_0 = Float64(-1.0 * Float64(x * -1.0))
	tmp = 0.0
	if (tan(a) <= -0.02)
		tmp = t_0;
	elseif (tan(a) <= 0.05)
		tmp = Float64(x + Float64(tan(Float64(y + z)) - Float64(a * Float64(1.0 + Float64(0.3333333333333333 * (a ^ 2.0))))));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, a)
	t_0 = -1.0 * (x * -1.0);
	tmp = 0.0;
	if (tan(a) <= -0.02)
		tmp = t_0;
	elseif (tan(a) <= 0.05)
		tmp = x + (tan((y + z)) - (a * (1.0 + (0.3333333333333333 * (a ^ 2.0)))));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, a_] := Block[{t$95$0 = N[(-1.0 * N[(x * -1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Tan[a], $MachinePrecision], -0.02], t$95$0, If[LessEqual[N[Tan[a], $MachinePrecision], 0.05], N[(x + N[(N[Tan[N[(y + z), $MachinePrecision]], $MachinePrecision] - N[(a * N[(1.0 + N[(0.3333333333333333 * N[Power[a, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if (tan.f64 a) < -0.0200000000000000004 or 0.050000000000000003 < (tan.f64 a)

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   2. Taylor expanded in x around -inf

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

   4. Applied rewrites 79.2%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 31.7%

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

   if -0.0200000000000000004 < (tan.f64 a) < 0.050000000000000003

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   2. Taylor expanded in a around 0

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

   4. Applied rewrites 39.8%

      \[\leadsto x + \left(\tan \left(y + z\right) - \color{blue}{a \cdot \left(1 + 0.3333333333333333 \cdot {a}^{2}\right)}\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 49.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -1 \cdot \left(x \cdot -1\right)\\ \mathbf{if}\;\tan a \leq -0.02:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;\tan a \leq 0.05:\\ \;\;\;\;x + \left(\tan \left(y + z\right) - a\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (* -1.0 (* x -1.0))))
   (if (<= (tan a) -0.02)
     t_0
     (if (<= (tan a) 0.05) (+ x (- (tan (+ y z)) a)) t_0))))

C

double code(double x, double y, double z, double a) {
	double t_0 = -1.0 * (x * -1.0);
	double tmp;
	if (tan(a) <= -0.02) {
		tmp = t_0;
	} else if (tan(a) <= 0.05) {
		tmp = x + (tan((y + z)) - a);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: a
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (-1.0d0) * (x * (-1.0d0))
    if (tan(a) <= (-0.02d0)) then
        tmp = t_0
    else if (tan(a) <= 0.05d0) then
        tmp = x + (tan((y + z)) - a)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double a) {
	double t_0 = -1.0 * (x * -1.0);
	double tmp;
	if (Math.tan(a) <= -0.02) {
		tmp = t_0;
	} else if (Math.tan(a) <= 0.05) {
		tmp = x + (Math.tan((y + z)) - a);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z, a):
	t_0 = -1.0 * (x * -1.0)
	tmp = 0
	if math.tan(a) <= -0.02:
		tmp = t_0
	elif math.tan(a) <= 0.05:
		tmp = x + (math.tan((y + z)) - a)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z, a)
	t_0 = Float64(-1.0 * Float64(x * -1.0))
	tmp = 0.0
	if (tan(a) <= -0.02)
		tmp = t_0;
	elseif (tan(a) <= 0.05)
		tmp = Float64(x + Float64(tan(Float64(y + z)) - a));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, a)
	t_0 = -1.0 * (x * -1.0);
	tmp = 0.0;
	if (tan(a) <= -0.02)
		tmp = t_0;
	elseif (tan(a) <= 0.05)
		tmp = x + (tan((y + z)) - a);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, a_] := Block[{t$95$0 = N[(-1.0 * N[(x * -1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Tan[a], $MachinePrecision], -0.02], t$95$0, If[LessEqual[N[Tan[a], $MachinePrecision], 0.05], N[(x + N[(N[Tan[N[(y + z), $MachinePrecision]], $MachinePrecision] - a), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if (tan.f64 a) < -0.0200000000000000004 or 0.050000000000000003 < (tan.f64 a)

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   2. Taylor expanded in x around -inf

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

   4. Applied rewrites 79.2%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 31.7%

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

   if -0.0200000000000000004 < (tan.f64 a) < 0.050000000000000003

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   2. Taylor expanded in a around 0

      \[\leadsto x + \left(\tan \left(y + z\right) - \color{blue}{a}\right) \]

   4. Applied rewrites 40.2%

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

Alternative 15: 40.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -1 \cdot \left(x \cdot -1\right)\\ \mathbf{if}\;\tan a \leq -0.02:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;\tan a \leq 0.05:\\ \;\;\;\;x + \left(\tan z - a\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (* -1.0 (* x -1.0))))
   (if (<= (tan a) -0.02) t_0 (if (<= (tan a) 0.05) (+ x (- (tan z) a)) t_0))))

C

double code(double x, double y, double z, double a) {
	double t_0 = -1.0 * (x * -1.0);
	double tmp;
	if (tan(a) <= -0.02) {
		tmp = t_0;
	} else if (tan(a) <= 0.05) {
		tmp = x + (tan(z) - a);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: a
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (-1.0d0) * (x * (-1.0d0))
    if (tan(a) <= (-0.02d0)) then
        tmp = t_0
    else if (tan(a) <= 0.05d0) then
        tmp = x + (tan(z) - a)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double a) {
	double t_0 = -1.0 * (x * -1.0);
	double tmp;
	if (Math.tan(a) <= -0.02) {
		tmp = t_0;
	} else if (Math.tan(a) <= 0.05) {
		tmp = x + (Math.tan(z) - a);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z, a):
	t_0 = -1.0 * (x * -1.0)
	tmp = 0
	if math.tan(a) <= -0.02:
		tmp = t_0
	elif math.tan(a) <= 0.05:
		tmp = x + (math.tan(z) - a)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z, a)
	t_0 = Float64(-1.0 * Float64(x * -1.0))
	tmp = 0.0
	if (tan(a) <= -0.02)
		tmp = t_0;
	elseif (tan(a) <= 0.05)
		tmp = Float64(x + Float64(tan(z) - a));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, a)
	t_0 = -1.0 * (x * -1.0);
	tmp = 0.0;
	if (tan(a) <= -0.02)
		tmp = t_0;
	elseif (tan(a) <= 0.05)
		tmp = x + (tan(z) - a);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, a_] := Block[{t$95$0 = N[(-1.0 * N[(x * -1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Tan[a], $MachinePrecision], -0.02], t$95$0, If[LessEqual[N[Tan[a], $MachinePrecision], 0.05], N[(x + N[(N[Tan[z], $MachinePrecision] - a), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if (tan.f64 a) < -0.0200000000000000004 or 0.050000000000000003 < (tan.f64 a)

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   2. Taylor expanded in x around -inf

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

   4. Applied rewrites 79.2%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 31.7%

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

   if -0.0200000000000000004 < (tan.f64 a) < 0.050000000000000003

   1. Initial program 79.4%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

   2. Taylor expanded in a around 0

      \[\leadsto x + \left(\tan \left(y + z\right) - \color{blue}{a}\right) \]

   4. Applied rewrites 40.2%

      \[\leadsto x + \left(\tan \left(y + z\right) - \color{blue}{a}\right) \]

   5. Taylor expanded in y around 0

      \[\leadsto x + \left(\tan \color{blue}{z} - a\right) \]

   7. Applied rewrites 31.0%

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

Alternative 16: 31.7% accurate, 11.5× speedup

Math

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

FPCore

(FPCore (x y z a) :precision binary64 (* -1.0 (* x -1.0)))

C

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

Java

public static double code(double x, double y, double z, double a) {
	return -1.0 * (x * -1.0);
}

Python

def code(x, y, z, a):
	return -1.0 * (x * -1.0)

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 79.4%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]

2. Taylor expanded in x around -inf

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

4. Applied rewrites 79.2%

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

5. Taylor expanded in x around inf

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

7. Applied rewrites 31.7%

   \[\leadsto -1 \cdot \left(x \cdot -1\right) \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025153 
(FPCore (x y z a)
  :name "tan-example (used to crash)"
  :precision binary64
  :pre (and (and (and (or (== x 0.0) (and (<= 0.5884142 x) (<= x 505.5909))) (or (and (<= -1.796658e+308 y) (<= y -9.425585e-310)) (and (<= 1.284938e-309 y) (<= y 1.751224e+308)))) (or (and (<= -1.776707e+308 z) (<= z -8.599796e-310)) (and (<= 3.293145e-311 z) (<= z 1.725154e+308)))) (or (and (<= -1.796658e+308 a) (<= a -9.425585e-310)) (and (<= 1.284938e-309 a) (<= a 1.751224e+308))))
  (+ x (- (tan (+ y z)) (tan a))))