tan-example (used to crash)

Percentage Accurate: 79.5% → 99.7%

Time: 10.8s

Alternatives: 20

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: 79.5% 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, 0.1× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} t_0 := \tan y \cdot \tan z\\ x + \left(\left(\frac{\tan y}{\frac{1 - {\tan y}^{3} \cdot {\tan z}^{3}}{1 + \left({t\_0}^{2} + 1 \cdot t\_0\right)}} + \frac{\tan z}{1 - t\_0}\right) - \tan a\right) \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (* (tan y) (tan z))))
   (+
    x
    (-
     (+
      (/
       (tan y)
       (/
        (- 1.0 (* (pow (tan y) 3.0) (pow (tan z) 3.0)))
        (+ 1.0 (+ (pow t_0 2.0) (* 1.0 t_0)))))
      (/ (tan z) (- 1.0 t_0)))
     (tan a)))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double t_0 = tan(y) * tan(z);
	return x + (((tan(y) / ((1.0 - (pow(tan(y), 3.0) * pow(tan(z), 3.0))) / (1.0 + (pow(t_0, 2.0) + (1.0 * t_0))))) + (tan(z) / (1.0 - t_0))) - tan(a));
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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 = tan(y) * tan(z)
    code = x + (((tan(y) / ((1.0d0 - ((tan(y) ** 3.0d0) * (tan(z) ** 3.0d0))) / (1.0d0 + ((t_0 ** 2.0d0) + (1.0d0 * t_0))))) + (tan(z) / (1.0d0 - t_0))) - tan(a))
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	double t_0 = Math.tan(y) * Math.tan(z);
	return x + (((Math.tan(y) / ((1.0 - (Math.pow(Math.tan(y), 3.0) * Math.pow(Math.tan(z), 3.0))) / (1.0 + (Math.pow(t_0, 2.0) + (1.0 * t_0))))) + (Math.tan(z) / (1.0 - t_0))) - Math.tan(a));
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	t_0 = math.tan(y) * math.tan(z)
	return x + (((math.tan(y) / ((1.0 - (math.pow(math.tan(y), 3.0) * math.pow(math.tan(z), 3.0))) / (1.0 + (math.pow(t_0, 2.0) + (1.0 * t_0))))) + (math.tan(z) / (1.0 - t_0))) - math.tan(a))

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	t_0 = Float64(tan(y) * tan(z))
	return Float64(x + Float64(Float64(Float64(tan(y) / Float64(Float64(1.0 - Float64((tan(y) ^ 3.0) * (tan(z) ^ 3.0))) / Float64(1.0 + Float64((t_0 ^ 2.0) + Float64(1.0 * t_0))))) + Float64(tan(z) / Float64(1.0 - t_0))) - tan(a)))
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp = code(x, y, z, a)
	t_0 = tan(y) * tan(z);
	tmp = x + (((tan(y) / ((1.0 - ((tan(y) ^ 3.0) * (tan(z) ^ 3.0))) / (1.0 + ((t_0 ^ 2.0) + (1.0 * t_0))))) + (tan(z) / (1.0 - t_0))) - tan(a));
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := Block[{t$95$0 = N[(N[Tan[y], $MachinePrecision] * N[Tan[z], $MachinePrecision]), $MachinePrecision]}, N[(x + N[(N[(N[(N[Tan[y], $MachinePrecision] / N[(N[(1.0 - N[(N[Power[N[Tan[y], $MachinePrecision], 3.0], $MachinePrecision] * N[Power[N[Tan[z], $MachinePrecision], 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(N[Power[t$95$0, 2.0], $MachinePrecision] + N[(1.0 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[Tan[z], $MachinePrecision] / N[(1.0 - t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 79.5%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift-tan.f64 N/A

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

   3. tan-sum N/A

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

   4. quot-tan N/A

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

   5. quot-tan N/A

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

   6. div-add N/A

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

   7. lower-+.f64 N/A

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

3. Applied rewrites 99.7%

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

   1. lift--.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. lift-tan.f64 N/A

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

   4. lift-tan.f64 N/A

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

   5. flip3-- N/A

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

   6. lower-/.f64 N/A

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

   7. metadata-eval N/A

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

   8. lower--.f64 N/A

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

   9. lower-pow.f64 N/A

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

   10. lift-tan.f64 N/A

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

   11. lift-tan.f64 N/A

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

   12. lift-*.f64 N/A

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

   13. metadata-eval N/A

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

   14. lower-+.f64 N/A

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

5. Applied rewrites 99.7%

   \[\leadsto x + \left(\left(\frac{\tan y}{\color{blue}{\frac{1 - {\left(\tan y \cdot \tan z\right)}^{3}}{1 + \mathsf{fma}\left(\tan y \cdot \tan z, \tan y \cdot \tan z, 1 \cdot \left(\tan y \cdot \tan z\right)\right)}}} + \frac{\tan z}{1 - \tan y \cdot \tan z}\right) - \tan a\right) \]
6. Step-by-step derivation

   1. lift-pow.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. lift-tan.f64 N/A

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

   4. lift-tan.f64 N/A

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

   5. tan-quot N/A

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

   6. tan-quot N/A

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

   7. unpow-prod-down N/A

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

   8. cube-div-rev N/A

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

   9. cube-div-rev N/A

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

   10. lower-*.f64 N/A

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

   11. cube-div-rev N/A

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

   12. lower-pow.f64 N/A

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

   13. tan-quot N/A

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

   14. lift-tan.f64 N/A

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

   15. cube-div-rev N/A

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

   16. lower-pow.f64 N/A

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

   17. tan-quot N/A

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

   18. lift-tan.f64 99.7

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

7. Applied rewrites 99.7%

   \[\leadsto x + \left(\left(\frac{\tan y}{\frac{1 - \color{blue}{{\tan y}^{3} \cdot {\tan z}^{3}}}{1 + \mathsf{fma}\left(\tan y \cdot \tan z, \tan y \cdot \tan z, 1 \cdot \left(\tan y \cdot \tan z\right)\right)}} + \frac{\tan z}{1 - \tan y \cdot \tan z}\right) - \tan a\right) \]
8. Step-by-step derivation

   1. lift-fma.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. lift-tan.f64 N/A

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

   4. lift-tan.f64 N/A

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

   5. lift-*.f64 N/A

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

   6. lift-tan.f64 N/A

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

   7. lift-tan.f64 N/A

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

   8. lift-*.f64 N/A

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

   9. lift-*.f64 N/A

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

   10. lift-tan.f64 N/A

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

   11. lift-tan.f64 N/A

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

   12. lower-+.f64 N/A

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

9. Applied rewrites 99.7%

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

Alternative 2: 99.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} t_0 := \tan y \cdot \tan z\\ x + \left(\left(\frac{\tan y}{\frac{1 - {t\_0}^{3}}{1 + \left({t\_0}^{2} + 1 \cdot t\_0\right)}} + \frac{\tan z}{1 - t\_0}\right) - \tan a\right) \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (* (tan y) (tan z))))
   (+
    x
    (-
     (+
      (/
       (tan y)
       (/ (- 1.0 (pow t_0 3.0)) (+ 1.0 (+ (pow t_0 2.0) (* 1.0 t_0)))))
      (/ (tan z) (- 1.0 t_0)))
     (tan a)))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double t_0 = tan(y) * tan(z);
	return x + (((tan(y) / ((1.0 - pow(t_0, 3.0)) / (1.0 + (pow(t_0, 2.0) + (1.0 * t_0))))) + (tan(z) / (1.0 - t_0))) - tan(a));
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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 = tan(y) * tan(z)
    code = x + (((tan(y) / ((1.0d0 - (t_0 ** 3.0d0)) / (1.0d0 + ((t_0 ** 2.0d0) + (1.0d0 * t_0))))) + (tan(z) / (1.0d0 - t_0))) - tan(a))
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	double t_0 = Math.tan(y) * Math.tan(z);
	return x + (((Math.tan(y) / ((1.0 - Math.pow(t_0, 3.0)) / (1.0 + (Math.pow(t_0, 2.0) + (1.0 * t_0))))) + (Math.tan(z) / (1.0 - t_0))) - Math.tan(a));
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	t_0 = math.tan(y) * math.tan(z)
	return x + (((math.tan(y) / ((1.0 - math.pow(t_0, 3.0)) / (1.0 + (math.pow(t_0, 2.0) + (1.0 * t_0))))) + (math.tan(z) / (1.0 - t_0))) - math.tan(a))

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	t_0 = Float64(tan(y) * tan(z))
	return Float64(x + Float64(Float64(Float64(tan(y) / Float64(Float64(1.0 - (t_0 ^ 3.0)) / Float64(1.0 + Float64((t_0 ^ 2.0) + Float64(1.0 * t_0))))) + Float64(tan(z) / Float64(1.0 - t_0))) - tan(a)))
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp = code(x, y, z, a)
	t_0 = tan(y) * tan(z);
	tmp = x + (((tan(y) / ((1.0 - (t_0 ^ 3.0)) / (1.0 + ((t_0 ^ 2.0) + (1.0 * t_0))))) + (tan(z) / (1.0 - t_0))) - tan(a));
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := Block[{t$95$0 = N[(N[Tan[y], $MachinePrecision] * N[Tan[z], $MachinePrecision]), $MachinePrecision]}, N[(x + N[(N[(N[(N[Tan[y], $MachinePrecision] / N[(N[(1.0 - N[Power[t$95$0, 3.0], $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(N[Power[t$95$0, 2.0], $MachinePrecision] + N[(1.0 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[Tan[z], $MachinePrecision] / N[(1.0 - t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 79.5%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift-tan.f64 N/A

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

   3. tan-sum N/A

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

   4. quot-tan N/A

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

   5. quot-tan N/A

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

   6. div-add N/A

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

   7. lower-+.f64 N/A

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

3. Applied rewrites 99.7%

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

   1. lift--.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. lift-tan.f64 N/A

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

   4. lift-tan.f64 N/A

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

   5. flip3-- N/A

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

   6. lower-/.f64 N/A

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

   7. metadata-eval N/A

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

   8. lower--.f64 N/A

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

   9. lower-pow.f64 N/A

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

   10. lift-tan.f64 N/A

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

   11. lift-tan.f64 N/A

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

   12. lift-*.f64 N/A

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

   13. metadata-eval N/A

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

   14. lower-+.f64 N/A

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

5. Applied rewrites 99.7%

   \[\leadsto x + \left(\left(\frac{\tan y}{\color{blue}{\frac{1 - {\left(\tan y \cdot \tan z\right)}^{3}}{1 + \mathsf{fma}\left(\tan y \cdot \tan z, \tan y \cdot \tan z, 1 \cdot \left(\tan y \cdot \tan z\right)\right)}}} + \frac{\tan z}{1 - \tan y \cdot \tan z}\right) - \tan a\right) \]
6. Step-by-step derivation

   1. lift-fma.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. lift-tan.f64 N/A

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

   4. lift-tan.f64 N/A

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

   5. lift-*.f64 N/A

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

   6. lift-tan.f64 N/A

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

   7. lift-tan.f64 N/A

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

   8. lift-*.f64 N/A

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

   9. lift-*.f64 N/A

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

   10. lift-tan.f64 N/A

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

   11. lift-tan.f64 N/A

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

   12. lower-+.f64 N/A

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

7. Applied rewrites 99.7%

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

Alternative 3: 99.7% accurate, 0.2× speedup

Math

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

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (+
  x
  (-
   (+
    (/ (tan y) (- 1.0 (* (/ (sin y) (cos y)) (tan z))))
    (/ (tan z) (- 1.0 (* (tan y) (tan z)))))
   (tan a))))

C

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

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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) / (1.0d0 - ((sin(y) / cos(y)) * tan(z)))) + (tan(z) / (1.0d0 - (tan(y) * tan(z))))) - tan(a))
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	return x + (((Math.tan(y) / (1.0 - ((Math.sin(y) / Math.cos(y)) * Math.tan(z)))) + (Math.tan(z) / (1.0 - (Math.tan(y) * Math.tan(z))))) - Math.tan(a));
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	return x + (((math.tan(y) / (1.0 - ((math.sin(y) / math.cos(y)) * math.tan(z)))) + (math.tan(z) / (1.0 - (math.tan(y) * math.tan(z))))) - math.tan(a))

Julia

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

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp = code(x, y, z, a)
	tmp = x + (((tan(y) / (1.0 - ((sin(y) / cos(y)) * tan(z)))) + (tan(z) / (1.0 - (tan(y) * tan(z))))) - tan(a));
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := N[(x + N[(N[(N[(N[Tan[y], $MachinePrecision] / N[(1.0 - N[(N[(N[Sin[y], $MachinePrecision] / N[Cos[y], $MachinePrecision]), $MachinePrecision] * N[Tan[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[Tan[z], $MachinePrecision] / N[(1.0 - N[(N[Tan[y], $MachinePrecision] * N[Tan[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.5%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift-tan.f64 N/A

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

   3. tan-sum N/A

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

   4. quot-tan N/A

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

   5. quot-tan N/A

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

   6. div-add N/A

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

   7. lower-+.f64 N/A

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

3. Applied rewrites 99.7%

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

   1. lift-tan.f64 N/A

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

   2. tan-quot N/A

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

   3. lower-/.f64 N/A

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

   4. lower-sin.f64 N/A

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

   5. lower-cos.f64 99.7

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

5. Applied rewrites 99.7%

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

Alternative 4: 99.7% accurate, 0.2× speedup

Math

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

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (+
  x
  (-
   (+
    (/ (tan y) (- 1.0 (* (tan y) (tan z))))
    (/ (tan z) (- 1.0 (* (/ (sin y) (cos y)) (tan z)))))
   (tan a))))

C

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

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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) / (1.0d0 - (tan(y) * tan(z)))) + (tan(z) / (1.0d0 - ((sin(y) / cos(y)) * tan(z))))) - tan(a))
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	return x + (((Math.tan(y) / (1.0 - (Math.tan(y) * Math.tan(z)))) + (Math.tan(z) / (1.0 - ((Math.sin(y) / Math.cos(y)) * Math.tan(z))))) - Math.tan(a));
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	return x + (((math.tan(y) / (1.0 - (math.tan(y) * math.tan(z)))) + (math.tan(z) / (1.0 - ((math.sin(y) / math.cos(y)) * math.tan(z))))) - math.tan(a))

Julia

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

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp = code(x, y, z, a)
	tmp = x + (((tan(y) / (1.0 - (tan(y) * tan(z)))) + (tan(z) / (1.0 - ((sin(y) / cos(y)) * tan(z))))) - tan(a));
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := N[(x + N[(N[(N[(N[Tan[y], $MachinePrecision] / N[(1.0 - N[(N[Tan[y], $MachinePrecision] * N[Tan[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[Tan[z], $MachinePrecision] / N[(1.0 - N[(N[(N[Sin[y], $MachinePrecision] / N[Cos[y], $MachinePrecision]), $MachinePrecision] * N[Tan[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.5%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift-tan.f64 N/A

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

   3. tan-sum N/A

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

   4. quot-tan N/A

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

   5. quot-tan N/A

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

   6. div-add N/A

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

   7. lower-+.f64 N/A

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

3. Applied rewrites 99.7%

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

   1. lift-tan.f64 N/A

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

   2. tan-quot N/A

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

   3. lower-/.f64 N/A

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

   4. lower-sin.f64 N/A

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

   5. lower-cos.f64 99.7

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

5. Applied rewrites 99.7%

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

Alternative 5: 99.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} t_0 := 1 - \tan y \cdot \tan z\\ x + \left(\left(\frac{\tan y}{t\_0} + \frac{\tan z}{t\_0}\right) - \tan a\right) \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (- 1.0 (* (tan y) (tan z)))))
   (+ x (- (+ (/ (tan y) t_0) (/ (tan z) t_0)) (tan a)))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double t_0 = 1.0 - (tan(y) * tan(z));
	return x + (((tan(y) / t_0) + (tan(z) / t_0)) - tan(a));
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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 = 1.0d0 - (tan(y) * tan(z))
    code = x + (((tan(y) / t_0) + (tan(z) / t_0)) - tan(a))
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	double t_0 = 1.0 - (Math.tan(y) * Math.tan(z));
	return x + (((Math.tan(y) / t_0) + (Math.tan(z) / t_0)) - Math.tan(a));
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	t_0 = 1.0 - (math.tan(y) * math.tan(z))
	return x + (((math.tan(y) / t_0) + (math.tan(z) / t_0)) - math.tan(a))

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	t_0 = Float64(1.0 - Float64(tan(y) * tan(z)))
	return Float64(x + Float64(Float64(Float64(tan(y) / t_0) + Float64(tan(z) / t_0)) - tan(a)))
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp = code(x, y, z, a)
	t_0 = 1.0 - (tan(y) * tan(z));
	tmp = x + (((tan(y) / t_0) + (tan(z) / t_0)) - tan(a));
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := Block[{t$95$0 = N[(1.0 - N[(N[Tan[y], $MachinePrecision] * N[Tan[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(x + N[(N[(N[(N[Tan[y], $MachinePrecision] / t$95$0), $MachinePrecision] + N[(N[Tan[z], $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 79.5%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift-tan.f64 N/A

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

   3. tan-sum N/A

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

   4. quot-tan N/A

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

   5. quot-tan N/A

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

   6. div-add N/A

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

   7. lower-+.f64 N/A

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

3. Applied rewrites 99.7%

   \[\leadsto x + \left(\color{blue}{\left(\frac{\tan y}{1 - \tan y \cdot \tan z} + \frac{\tan z}{1 - \tan y \cdot \tan z}\right)} - \tan a\right) \]
4. Add Preprocessing

Alternative 6: 99.7% accurate, 0.4× speedup

Math

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

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (+ x (- (/ (+ (tan z) (tan y)) (- 1.0 (* (tan z) (tan y)))) (tan a))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	return x + (((tan(z) + tan(y)) / (1.0 - (tan(z) * tan(y)))) - tan(a));
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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(z) + tan(y)) / (1.0d0 - (tan(z) * tan(y)))) - tan(a))
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	return x + (((Math.tan(z) + Math.tan(y)) / (1.0 - (Math.tan(z) * Math.tan(y)))) - Math.tan(a));
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	return x + (((math.tan(z) + math.tan(y)) / (1.0 - (math.tan(z) * math.tan(y)))) - math.tan(a))

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	return Float64(x + Float64(Float64(Float64(tan(z) + tan(y)) / Float64(1.0 - Float64(tan(z) * tan(y)))) - tan(a)))
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp = code(x, y, z, a)
	tmp = x + (((tan(z) + tan(y)) / (1.0 - (tan(z) * tan(y)))) - tan(a));
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := N[(x + N[(N[(N[(N[Tan[z], $MachinePrecision] + N[Tan[y], $MachinePrecision]), $MachinePrecision] / N[(1.0 - N[(N[Tan[z], $MachinePrecision] * N[Tan[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.5%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift-tan.f64 N/A

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

   3. +-commutative N/A

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

   4. tan-sum N/A

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

   5. lower-/.f64 N/A

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

   6. quot-tan N/A

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

   7. quot-tan N/A

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

   8. lower-+.f64 N/A

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

   9. quot-tan N/A

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

   10. lower-tan.f64 N/A

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

   11. quot-tan N/A

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

   12. lower-tan.f64 N/A

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

   13. lower--.f64 N/A

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

   14. quot-tan N/A

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

   15. quot-tan N/A

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

   16. lower-*.f64 N/A

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

   17. quot-tan N/A

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

   18. lower-tan.f64 N/A

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

   19. quot-tan N/A

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

   20. lower-tan.f64 99.7

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

3. Applied rewrites 99.7%

   \[\leadsto x + \left(\color{blue}{\frac{\tan z + \tan y}{1 - \tan z \cdot \tan y}} - \tan a\right) \]
4. Add Preprocessing

Alternative 7: 59.9% accurate, 0.6× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} t_0 := x - \tan a\\ \mathbf{if}\;\tan a \leq -0.04:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;\tan a \leq 0.0001:\\ \;\;\;\;x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(0.3333333333333333, a \cdot a, 1\right) \cdot a\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (- x (tan a))))
   (if (<= (tan a) -0.04)
     t_0
     (if (<= (tan a) 0.0001)
       (+ x (- (tan (+ y z)) (* (fma 0.3333333333333333 (* a a) 1.0) a)))
       t_0))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double t_0 = x - tan(a);
	double tmp;
	if (tan(a) <= -0.04) {
		tmp = t_0;
	} else if (tan(a) <= 0.0001) {
		tmp = x + (tan((y + z)) - (fma(0.3333333333333333, (a * a), 1.0) * a));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	t_0 = Float64(x - tan(a))
	tmp = 0.0
	if (tan(a) <= -0.04)
		tmp = t_0;
	elseif (tan(a) <= 0.0001)
		tmp = Float64(x + Float64(tan(Float64(y + z)) - Float64(fma(0.3333333333333333, Float64(a * a), 1.0) * a)));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := Block[{t$95$0 = N[(x - N[Tan[a], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Tan[a], $MachinePrecision], -0.04], t$95$0, If[LessEqual[N[Tan[a], $MachinePrecision], 0.0001], N[(x + N[(N[Tan[N[(y + z), $MachinePrecision]], $MachinePrecision] - N[(N[(0.3333333333333333 * N[(a * a), $MachinePrecision] + 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if (tan.f64 a) < -0.0400000000000000008 or 1.00000000000000005e-4 < (tan.f64 a)

   1. Initial program 79.9%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

   4. Applied rewrites 51.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(1 - \left(-{\tan z}^{2}\right), y, \tan z\right) + x\right) - \tan a} \]

   5. Taylor expanded in x around inf

      \[\leadsto x - \tan \color{blue}{a} \]
   6. Step-by-step derivation

   7. Applied rewrites 42.2%

      \[\leadsto x - \tan \color{blue}{a} \]

   if -0.0400000000000000008 < (tan.f64 a) < 1.00000000000000005e-4

   1. Initial program 79.1%

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

   2. Taylor expanded in a around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. unpow2 N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\frac{1}{3}, a \cdot a, 1\right) \cdot a\right) \]

      6. lower-*.f64 78.0

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(0.3333333333333333, a \cdot a, 1\right) \cdot a\right) \]

   4. Applied rewrites 78.0%

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

Alternative 8: 59.9% accurate, 0.6× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} t_0 := x - \tan a\\ \mathbf{if}\;\tan a \leq -0.04:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;\tan a \leq 0.0001:\\ \;\;\;\;\left(x + \left(-a\right)\right) + \tan \left(z + y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (- x (tan a))))
   (if (<= (tan a) -0.04)
     t_0
     (if (<= (tan a) 0.0001) (+ (+ x (- a)) (tan (+ z y))) t_0))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double t_0 = x - tan(a);
	double tmp;
	if (tan(a) <= -0.04) {
		tmp = t_0;
	} else if (tan(a) <= 0.0001) {
		tmp = (x + -a) + tan((z + y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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 = x - tan(a)
    if (tan(a) <= (-0.04d0)) then
        tmp = t_0
    else if (tan(a) <= 0.0001d0) then
        tmp = (x + -a) + tan((z + y))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	double t_0 = x - Math.tan(a);
	double tmp;
	if (Math.tan(a) <= -0.04) {
		tmp = t_0;
	} else if (Math.tan(a) <= 0.0001) {
		tmp = (x + -a) + Math.tan((z + y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	t_0 = x - math.tan(a)
	tmp = 0
	if math.tan(a) <= -0.04:
		tmp = t_0
	elif math.tan(a) <= 0.0001:
		tmp = (x + -a) + math.tan((z + y))
	else:
		tmp = t_0
	return tmp

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	t_0 = Float64(x - tan(a))
	tmp = 0.0
	if (tan(a) <= -0.04)
		tmp = t_0;
	elseif (tan(a) <= 0.0001)
		tmp = Float64(Float64(x + Float64(-a)) + tan(Float64(z + y)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp_2 = code(x, y, z, a)
	t_0 = x - tan(a);
	tmp = 0.0;
	if (tan(a) <= -0.04)
		tmp = t_0;
	elseif (tan(a) <= 0.0001)
		tmp = (x + -a) + tan((z + y));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := Block[{t$95$0 = N[(x - N[Tan[a], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Tan[a], $MachinePrecision], -0.04], t$95$0, If[LessEqual[N[Tan[a], $MachinePrecision], 0.0001], N[(N[(x + (-a)), $MachinePrecision] + N[Tan[N[(z + y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if (tan.f64 a) < -0.0400000000000000008 or 1.00000000000000005e-4 < (tan.f64 a)

   1. Initial program 79.9%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

   4. Applied rewrites 51.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(1 - \left(-{\tan z}^{2}\right), y, \tan z\right) + x\right) - \tan a} \]

   5. Taylor expanded in x around inf

      \[\leadsto x - \tan \color{blue}{a} \]
   6. Step-by-step derivation

   7. Applied rewrites 42.2%

      \[\leadsto x - \tan \color{blue}{a} \]

   if -0.0400000000000000008 < (tan.f64 a) < 1.00000000000000005e-4

   1. Initial program 79.1%

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

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{x + \left(-1 \cdot a + \frac{\sin \left(y + z\right)}{\cos \left(y + z\right)}\right)} \]
   3. Step-by-step derivation

      1. tan-quot N/A

         \[\leadsto x + \left(-1 \cdot a + \tan \left(y + z\right)\right) \]

      2. associate-+r+ N/A

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

      3. lower-+.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. mul-1-neg N/A

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

      6. lower-neg.f64 N/A

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

      7. lift-tan.f64 N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 77.9

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

   4. Applied rewrites 77.9%

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

Alternative 9: 59.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} t_0 := x - \tan a\\ \mathbf{if}\;\tan a \leq -0.04:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;\tan a \leq 0.0001:\\ \;\;\;\;x + \left(\tan \left(y + z\right) - a\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (- x (tan a))))
   (if (<= (tan a) -0.04)
     t_0
     (if (<= (tan a) 0.0001) (+ x (- (tan (+ y z)) a)) t_0))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double t_0 = x - tan(a);
	double tmp;
	if (tan(a) <= -0.04) {
		tmp = t_0;
	} else if (tan(a) <= 0.0001) {
		tmp = x + (tan((y + z)) - a);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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 = x - tan(a)
    if (tan(a) <= (-0.04d0)) then
        tmp = t_0
    else if (tan(a) <= 0.0001d0) then
        tmp = x + (tan((y + z)) - a)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	double t_0 = x - Math.tan(a);
	double tmp;
	if (Math.tan(a) <= -0.04) {
		tmp = t_0;
	} else if (Math.tan(a) <= 0.0001) {
		tmp = x + (Math.tan((y + z)) - a);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	t_0 = x - math.tan(a)
	tmp = 0
	if math.tan(a) <= -0.04:
		tmp = t_0
	elif math.tan(a) <= 0.0001:
		tmp = x + (math.tan((y + z)) - a)
	else:
		tmp = t_0
	return tmp

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	t_0 = Float64(x - tan(a))
	tmp = 0.0
	if (tan(a) <= -0.04)
		tmp = t_0;
	elseif (tan(a) <= 0.0001)
		tmp = Float64(x + Float64(tan(Float64(y + z)) - a));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp_2 = code(x, y, z, a)
	t_0 = x - tan(a);
	tmp = 0.0;
	if (tan(a) <= -0.04)
		tmp = t_0;
	elseif (tan(a) <= 0.0001)
		tmp = x + (tan((y + z)) - a);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := Block[{t$95$0 = N[(x - N[Tan[a], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Tan[a], $MachinePrecision], -0.04], t$95$0, If[LessEqual[N[Tan[a], $MachinePrecision], 0.0001], 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) < -0.0400000000000000008 or 1.00000000000000005e-4 < (tan.f64 a)

   1. Initial program 79.9%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

   4. Applied rewrites 51.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(1 - \left(-{\tan z}^{2}\right), y, \tan z\right) + x\right) - \tan a} \]

   5. Taylor expanded in x around inf

      \[\leadsto x - \tan \color{blue}{a} \]
   6. Step-by-step derivation

   7. Applied rewrites 42.2%

      \[\leadsto x - \tan \color{blue}{a} \]

   if -0.0400000000000000008 < (tan.f64 a) < 1.00000000000000005e-4

   1. Initial program 79.1%

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

   2. Taylor expanded in a around 0

      \[\leadsto x + \left(\tan \left(y + z\right) - \color{blue}{a}\right) \]
   3. Step-by-step derivation

   4. Applied rewrites 77.9%

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

Alternative 10: 69.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} \mathbf{if}\;y + z \leq 10^{-13}:\\ \;\;\;\;\left(\tan y + x\right) - \tan a\\ \mathbf{else}:\\ \;\;\;\;x + \tan \left(z + y\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (if (<= (+ y z) 1e-13) (- (+ (tan y) x) (tan a)) (+ x (tan (+ z y)))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double tmp;
	if ((y + z) <= 1e-13) {
		tmp = (tan(y) + x) - tan(a);
	} else {
		tmp = x + tan((z + y));
	}
	return tmp;
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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 + z) <= 1d-13) then
        tmp = (tan(y) + x) - tan(a)
    else
        tmp = x + tan((z + y))
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	double tmp;
	if ((y + z) <= 1e-13) {
		tmp = (Math.tan(y) + x) - Math.tan(a);
	} else {
		tmp = x + Math.tan((z + y));
	}
	return tmp;
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	tmp = 0
	if (y + z) <= 1e-13:
		tmp = (math.tan(y) + x) - math.tan(a)
	else:
		tmp = x + math.tan((z + y))
	return tmp

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	tmp = 0.0
	if (Float64(y + z) <= 1e-13)
		tmp = Float64(Float64(tan(y) + x) - tan(a));
	else
		tmp = Float64(x + tan(Float64(z + y)));
	end
	return tmp
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp_2 = code(x, y, z, a)
	tmp = 0.0;
	if ((y + z) <= 1e-13)
		tmp = (tan(y) + x) - tan(a);
	else
		tmp = x + tan((z + y));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := If[LessEqual[N[(y + z), $MachinePrecision], 1e-13], N[(N[(N[Tan[y], $MachinePrecision] + x), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision], N[(x + N[Tan[N[(z + y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 y z) < 1e-13

   1. Initial program 83.4%

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

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{\left(x + \frac{\sin y}{\cos y}\right) - \frac{\sin a}{\cos a}} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. quot-tan N/A

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

      5. lower-tan.f64 N/A

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

      6. tan-quot N/A

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

      7. lift-tan.f64 83.2

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

   4. Applied rewrites 83.2%

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

   if 1e-13 < (+.f64 y z)

   1. Initial program 73.3%

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

   2. Taylor expanded in a around 0

      \[\leadsto x + \color{blue}{\frac{\sin \left(y + z\right)}{\cos \left(y + z\right)}} \]
   3. Step-by-step derivation

      1. tan-quot N/A

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

      2. lift-tan.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 46.2

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

   4. Applied rewrites 46.2%

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

Alternative 11: 79.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} \mathbf{if}\;z \leq 1.15 \cdot 10^{-6}:\\ \;\;\;\;x + \left(\tan y - \tan a\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(\tan z - \tan a\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (if (<= z 1.15e-6) (+ x (- (tan y) (tan a))) (+ x (- (tan z) (tan a)))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double tmp;
	if (z <= 1.15e-6) {
		tmp = x + (tan(y) - tan(a));
	} else {
		tmp = x + (tan(z) - tan(a));
	}
	return tmp;
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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 (z <= 1.15d-6) then
        tmp = x + (tan(y) - tan(a))
    else
        tmp = x + (tan(z) - tan(a))
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	double tmp;
	if (z <= 1.15e-6) {
		tmp = x + (Math.tan(y) - Math.tan(a));
	} else {
		tmp = x + (Math.tan(z) - Math.tan(a));
	}
	return tmp;
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	tmp = 0
	if z <= 1.15e-6:
		tmp = x + (math.tan(y) - math.tan(a))
	else:
		tmp = x + (math.tan(z) - math.tan(a))
	return tmp

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	tmp = 0.0
	if (z <= 1.15e-6)
		tmp = Float64(x + Float64(tan(y) - tan(a)));
	else
		tmp = Float64(x + Float64(tan(z) - tan(a)));
	end
	return tmp
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp_2 = code(x, y, z, a)
	tmp = 0.0;
	if (z <= 1.15e-6)
		tmp = x + (tan(y) - tan(a));
	else
		tmp = x + (tan(z) - tan(a));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := If[LessEqual[z, 1.15e-6], N[(x + N[(N[Tan[y], $MachinePrecision] - N[Tan[a], $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 z < 1.15e-6

   1. Initial program 90.5%

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

   2. Taylor expanded in y around inf

      \[\leadsto x + \left(\tan \color{blue}{y} - \tan a\right) \]
   3. Step-by-step derivation

   4. Applied rewrites 90.2%

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

   if 1.15e-6 < z

   1. Initial program 65.7%

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

   2. Taylor expanded in y around 0

      \[\leadsto x + \left(\tan \color{blue}{z} - \tan a\right) \]
   3. Step-by-step derivation

   4. Applied rewrites 65.4%

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

Alternative 12: 79.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} \mathbf{if}\;z \leq 1.15 \cdot 10^{-6}:\\ \;\;\;\;x + \left(\tan y - \tan a\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\tan z + x\right) - \tan a\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (if (<= z 1.15e-6) (+ x (- (tan y) (tan a))) (- (+ (tan z) x) (tan a))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double tmp;
	if (z <= 1.15e-6) {
		tmp = x + (tan(y) - tan(a));
	} else {
		tmp = (tan(z) + x) - tan(a);
	}
	return tmp;
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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 (z <= 1.15d-6) then
        tmp = x + (tan(y) - tan(a))
    else
        tmp = (tan(z) + x) - tan(a)
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	double tmp;
	if (z <= 1.15e-6) {
		tmp = x + (Math.tan(y) - Math.tan(a));
	} else {
		tmp = (Math.tan(z) + x) - Math.tan(a);
	}
	return tmp;
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	tmp = 0
	if z <= 1.15e-6:
		tmp = x + (math.tan(y) - math.tan(a))
	else:
		tmp = (math.tan(z) + x) - math.tan(a)
	return tmp

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	tmp = 0.0
	if (z <= 1.15e-6)
		tmp = Float64(x + Float64(tan(y) - tan(a)));
	else
		tmp = Float64(Float64(tan(z) + x) - tan(a));
	end
	return tmp
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp_2 = code(x, y, z, a)
	tmp = 0.0;
	if (z <= 1.15e-6)
		tmp = x + (tan(y) - tan(a));
	else
		tmp = (tan(z) + x) - tan(a);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := If[LessEqual[z, 1.15e-6], N[(x + N[(N[Tan[y], $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[Tan[z], $MachinePrecision] + x), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 1.15e-6

   1. Initial program 90.5%

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

   2. Taylor expanded in y around inf

      \[\leadsto x + \left(\tan \color{blue}{y} - \tan a\right) \]
   3. Step-by-step derivation

   4. Applied rewrites 90.2%

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

   if 1.15e-6 < z

   1. Initial program 65.7%

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

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{\left(x + \frac{\sin z}{\cos z}\right) - \frac{\sin a}{\cos a}} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. quot-tan N/A

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

      5. lower-tan.f64 N/A

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

      6. tan-quot N/A

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

      7. lift-tan.f64 65.3

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

   4. Applied rewrites 65.3%

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

Alternative 13: 79.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} \mathbf{if}\;z \leq 1.15 \cdot 10^{-6}:\\ \;\;\;\;\left(\tan y + x\right) - \tan a\\ \mathbf{else}:\\ \;\;\;\;\left(\tan z + x\right) - \tan a\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (if (<= z 1.15e-6) (- (+ (tan y) x) (tan a)) (- (+ (tan z) x) (tan a))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double tmp;
	if (z <= 1.15e-6) {
		tmp = (tan(y) + x) - tan(a);
	} else {
		tmp = (tan(z) + x) - tan(a);
	}
	return tmp;
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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 (z <= 1.15d-6) then
        tmp = (tan(y) + x) - tan(a)
    else
        tmp = (tan(z) + x) - tan(a)
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	double tmp;
	if (z <= 1.15e-6) {
		tmp = (Math.tan(y) + x) - Math.tan(a);
	} else {
		tmp = (Math.tan(z) + x) - Math.tan(a);
	}
	return tmp;
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	tmp = 0
	if z <= 1.15e-6:
		tmp = (math.tan(y) + x) - math.tan(a)
	else:
		tmp = (math.tan(z) + x) - math.tan(a)
	return tmp

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	tmp = 0.0
	if (z <= 1.15e-6)
		tmp = Float64(Float64(tan(y) + x) - tan(a));
	else
		tmp = Float64(Float64(tan(z) + x) - tan(a));
	end
	return tmp
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp_2 = code(x, y, z, a)
	tmp = 0.0;
	if (z <= 1.15e-6)
		tmp = (tan(y) + x) - tan(a);
	else
		tmp = (tan(z) + x) - tan(a);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := If[LessEqual[z, 1.15e-6], N[(N[(N[Tan[y], $MachinePrecision] + x), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision], N[(N[(N[Tan[z], $MachinePrecision] + x), $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 1.15e-6

   1. Initial program 90.5%

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

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{\left(x + \frac{\sin y}{\cos y}\right) - \frac{\sin a}{\cos a}} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. quot-tan N/A

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

      5. lower-tan.f64 N/A

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

      6. tan-quot N/A

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

      7. lift-tan.f64 90.2

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

   4. Applied rewrites 90.2%

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

   if 1.15e-6 < z

   1. Initial program 65.7%

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

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{\left(x + \frac{\sin z}{\cos z}\right) - \frac{\sin a}{\cos a}} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. quot-tan N/A

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

      5. lower-tan.f64 N/A

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

      6. tan-quot N/A

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

      7. lift-tan.f64 65.3

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

   4. Applied rewrites 65.3%

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

Alternative 14: 79.5% accurate, 1.0× speedup

Math

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

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a) :precision binary64 (+ x (- (tan (+ y z)) (tan a))))

C

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

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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

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

Python

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

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	return Float64(x + Float64(tan(Float64(y + z)) - tan(a)))
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp = code(x, y, z, a)
	tmp = x + (tan((y + z)) - tan(a));
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := N[(x + N[(N[Tan[N[(y + z), $MachinePrecision]], $MachinePrecision] - N[Tan[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.5%

   \[x + \left(\tan \left(y + z\right) - \tan a\right) \]
2. Add Preprocessing

Alternative 15: 60.3% accurate, 1.3× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} t_0 := x - \tan a\\ \mathbf{if}\;a \leq -0.55:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;a \leq 0.45:\\ \;\;\;\;x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.05396825396825397, a \cdot a, 0.13333333333333333\right), a \cdot a, 0.3333333333333333\right), a \cdot a, 1\right) \cdot a\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (- x (tan a))))
   (if (<= a -0.55)
     t_0
     (if (<= a 0.45)
       (+
        x
        (-
         (tan (+ y z))
         (*
          (fma
           (fma
            (fma 0.05396825396825397 (* a a) 0.13333333333333333)
            (* a a)
            0.3333333333333333)
           (* a a)
           1.0)
          a)))
       t_0))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double t_0 = x - tan(a);
	double tmp;
	if (a <= -0.55) {
		tmp = t_0;
	} else if (a <= 0.45) {
		tmp = x + (tan((y + z)) - (fma(fma(fma(0.05396825396825397, (a * a), 0.13333333333333333), (a * a), 0.3333333333333333), (a * a), 1.0) * a));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	t_0 = Float64(x - tan(a))
	tmp = 0.0
	if (a <= -0.55)
		tmp = t_0;
	elseif (a <= 0.45)
		tmp = Float64(x + Float64(tan(Float64(y + z)) - Float64(fma(fma(fma(0.05396825396825397, Float64(a * a), 0.13333333333333333), Float64(a * a), 0.3333333333333333), Float64(a * a), 1.0) * a)));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := Block[{t$95$0 = N[(x - N[Tan[a], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -0.55], t$95$0, If[LessEqual[a, 0.45], N[(x + N[(N[Tan[N[(y + z), $MachinePrecision]], $MachinePrecision] - N[(N[(N[(N[(0.05396825396825397 * N[(a * a), $MachinePrecision] + 0.13333333333333333), $MachinePrecision] * N[(a * a), $MachinePrecision] + 0.3333333333333333), $MachinePrecision] * N[(a * a), $MachinePrecision] + 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if a < -0.55000000000000004 or 0.450000000000000011 < a

   1. Initial program 79.9%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

   4. Applied rewrites 51.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(1 - \left(-{\tan z}^{2}\right), y, \tan z\right) + x\right) - \tan a} \]

   5. Taylor expanded in x around inf

      \[\leadsto x - \tan \color{blue}{a} \]
   6. Step-by-step derivation

   7. Applied rewrites 42.2%

      \[\leadsto x - \tan \color{blue}{a} \]

   if -0.55000000000000004 < a < 0.450000000000000011

   1. Initial program 79.2%

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

   2. Taylor expanded in a around 0

      \[\leadsto x + \left(\tan \left(y + z\right) - \color{blue}{a \cdot \left(1 + {a}^{2} \cdot \left(\frac{1}{3} + {a}^{2} \cdot \left(\frac{2}{15} + \frac{17}{315} \cdot {a}^{2}\right)\right)\right)}\right) \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \left(1 + {a}^{2} \cdot \left(\frac{1}{3} + {a}^{2} \cdot \left(\frac{2}{15} + \frac{17}{315} \cdot {a}^{2}\right)\right)\right) \cdot \color{blue}{a}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \left(1 + {a}^{2} \cdot \left(\frac{1}{3} + {a}^{2} \cdot \left(\frac{2}{15} + \frac{17}{315} \cdot {a}^{2}\right)\right)\right) \cdot \color{blue}{a}\right) \]

      3. +-commutative N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \left({a}^{2} \cdot \left(\frac{1}{3} + {a}^{2} \cdot \left(\frac{2}{15} + \frac{17}{315} \cdot {a}^{2}\right)\right) + 1\right) \cdot a\right) \]

      4. *-commutative N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \left(\left(\frac{1}{3} + {a}^{2} \cdot \left(\frac{2}{15} + \frac{17}{315} \cdot {a}^{2}\right)\right) \cdot {a}^{2} + 1\right) \cdot a\right) \]

      5. lower-fma.f64 N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\frac{1}{3} + {a}^{2} \cdot \left(\frac{2}{15} + \frac{17}{315} \cdot {a}^{2}\right), {a}^{2}, 1\right) \cdot a\right) \]

      6. +-commutative N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left({a}^{2} \cdot \left(\frac{2}{15} + \frac{17}{315} \cdot {a}^{2}\right) + \frac{1}{3}, {a}^{2}, 1\right) \cdot a\right) \]

      7. *-commutative N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\left(\frac{2}{15} + \frac{17}{315} \cdot {a}^{2}\right) \cdot {a}^{2} + \frac{1}{3}, {a}^{2}, 1\right) \cdot a\right) \]

      8. lower-fma.f64 N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\frac{2}{15} + \frac{17}{315} \cdot {a}^{2}, {a}^{2}, \frac{1}{3}\right), {a}^{2}, 1\right) \cdot a\right) \]

      9. +-commutative N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\frac{17}{315} \cdot {a}^{2} + \frac{2}{15}, {a}^{2}, \frac{1}{3}\right), {a}^{2}, 1\right) \cdot a\right) \]

      10. lower-fma.f64 N/A

          \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{17}{315}, {a}^{2}, \frac{2}{15}\right), {a}^{2}, \frac{1}{3}\right), {a}^{2}, 1\right) \cdot a\right) \]

      11. unpow2 N/A

          \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{17}{315}, a \cdot a, \frac{2}{15}\right), {a}^{2}, \frac{1}{3}\right), {a}^{2}, 1\right) \cdot a\right) \]

      12. lower-*.f64 N/A

          \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{17}{315}, a \cdot a, \frac{2}{15}\right), {a}^{2}, \frac{1}{3}\right), {a}^{2}, 1\right) \cdot a\right) \]

      13. unpow2 N/A

          \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{17}{315}, a \cdot a, \frac{2}{15}\right), a \cdot a, \frac{1}{3}\right), {a}^{2}, 1\right) \cdot a\right) \]

      14. lower-*.f64 N/A

          \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{17}{315}, a \cdot a, \frac{2}{15}\right), a \cdot a, \frac{1}{3}\right), {a}^{2}, 1\right) \cdot a\right) \]

      15. unpow2 N/A

          \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{17}{315}, a \cdot a, \frac{2}{15}\right), a \cdot a, \frac{1}{3}\right), a \cdot a, 1\right) \cdot a\right) \]

      16. lower-*.f64 79.0

          \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.05396825396825397, a \cdot a, 0.13333333333333333\right), a \cdot a, 0.3333333333333333\right), a \cdot a, 1\right) \cdot a\right) \]

   4. Applied rewrites 79.0%

      \[\leadsto x + \left(\tan \left(y + z\right) - \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.05396825396825397, a \cdot a, 0.13333333333333333\right), a \cdot a, 0.3333333333333333\right), a \cdot a, 1\right) \cdot a}\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 16: 60.3% accurate, 1.4× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} t_0 := x - \tan a\\ \mathbf{if}\;a \leq -0.55:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;a \leq 0.36:\\ \;\;\;\;x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(0.13333333333333333, a \cdot a, 0.3333333333333333\right), a \cdot a, 1\right) \cdot a\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (- x (tan a))))
   (if (<= a -0.55)
     t_0
     (if (<= a 0.36)
       (+
        x
        (-
         (tan (+ y z))
         (*
          (fma
           (fma 0.13333333333333333 (* a a) 0.3333333333333333)
           (* a a)
           1.0)
          a)))
       t_0))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double t_0 = x - tan(a);
	double tmp;
	if (a <= -0.55) {
		tmp = t_0;
	} else if (a <= 0.36) {
		tmp = x + (tan((y + z)) - (fma(fma(0.13333333333333333, (a * a), 0.3333333333333333), (a * a), 1.0) * a));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	t_0 = Float64(x - tan(a))
	tmp = 0.0
	if (a <= -0.55)
		tmp = t_0;
	elseif (a <= 0.36)
		tmp = Float64(x + Float64(tan(Float64(y + z)) - Float64(fma(fma(0.13333333333333333, Float64(a * a), 0.3333333333333333), Float64(a * a), 1.0) * a)));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := Block[{t$95$0 = N[(x - N[Tan[a], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -0.55], t$95$0, If[LessEqual[a, 0.36], N[(x + N[(N[Tan[N[(y + z), $MachinePrecision]], $MachinePrecision] - N[(N[(N[(0.13333333333333333 * N[(a * a), $MachinePrecision] + 0.3333333333333333), $MachinePrecision] * N[(a * a), $MachinePrecision] + 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if a < -0.55000000000000004 or 0.35999999999999999 < a

   1. Initial program 79.9%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

   4. Applied rewrites 51.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(1 - \left(-{\tan z}^{2}\right), y, \tan z\right) + x\right) - \tan a} \]

   5. Taylor expanded in x around inf

      \[\leadsto x - \tan \color{blue}{a} \]
   6. Step-by-step derivation

   7. Applied rewrites 42.1%

      \[\leadsto x - \tan \color{blue}{a} \]

   if -0.55000000000000004 < a < 0.35999999999999999

   1. Initial program 79.1%

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

   2. Taylor expanded in a around 0

      \[\leadsto x + \left(\tan \left(y + z\right) - \color{blue}{a \cdot \left(1 + {a}^{2} \cdot \left(\frac{1}{3} + \frac{2}{15} \cdot {a}^{2}\right)\right)}\right) \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \left(1 + {a}^{2} \cdot \left(\frac{1}{3} + \frac{2}{15} \cdot {a}^{2}\right)\right) \cdot \color{blue}{a}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \left(1 + {a}^{2} \cdot \left(\frac{1}{3} + \frac{2}{15} \cdot {a}^{2}\right)\right) \cdot \color{blue}{a}\right) \]

      3. +-commutative N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \left({a}^{2} \cdot \left(\frac{1}{3} + \frac{2}{15} \cdot {a}^{2}\right) + 1\right) \cdot a\right) \]

      4. *-commutative N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \left(\left(\frac{1}{3} + \frac{2}{15} \cdot {a}^{2}\right) \cdot {a}^{2} + 1\right) \cdot a\right) \]

      5. lower-fma.f64 N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\frac{1}{3} + \frac{2}{15} \cdot {a}^{2}, {a}^{2}, 1\right) \cdot a\right) \]

      6. +-commutative N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\frac{2}{15} \cdot {a}^{2} + \frac{1}{3}, {a}^{2}, 1\right) \cdot a\right) \]

      7. lower-fma.f64 N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\frac{2}{15}, {a}^{2}, \frac{1}{3}\right), {a}^{2}, 1\right) \cdot a\right) \]

      8. unpow2 N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\frac{2}{15}, a \cdot a, \frac{1}{3}\right), {a}^{2}, 1\right) \cdot a\right) \]

      9. lower-*.f64 N/A

         \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\frac{2}{15}, a \cdot a, \frac{1}{3}\right), {a}^{2}, 1\right) \cdot a\right) \]

      10. unpow2 N/A

          \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(\frac{2}{15}, a \cdot a, \frac{1}{3}\right), a \cdot a, 1\right) \cdot a\right) \]

      11. lower-*.f64 79.0

          \[\leadsto x + \left(\tan \left(y + z\right) - \mathsf{fma}\left(\mathsf{fma}\left(0.13333333333333333, a \cdot a, 0.3333333333333333\right), a \cdot a, 1\right) \cdot a\right) \]

   4. Applied rewrites 79.0%

      \[\leadsto x + \left(\tan \left(y + z\right) - \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.13333333333333333, a \cdot a, 0.3333333333333333\right), a \cdot a, 1\right) \cdot a}\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 17: 59.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} t_0 := x - \tan a\\ \mathbf{if}\;a \leq -475000000000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;a \leq 2.3 \cdot 10^{-10}:\\ \;\;\;\;x + \tan \left(z + y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (let* ((t_0 (- x (tan a))))
   (if (<= a -475000000000.0)
     t_0
     (if (<= a 2.3e-10) (+ x (tan (+ z y))) t_0))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double t_0 = x - tan(a);
	double tmp;
	if (a <= -475000000000.0) {
		tmp = t_0;
	} else if (a <= 2.3e-10) {
		tmp = x + tan((z + y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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 = x - tan(a)
    if (a <= (-475000000000.0d0)) then
        tmp = t_0
    else if (a <= 2.3d-10) then
        tmp = x + tan((z + y))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	double t_0 = x - Math.tan(a);
	double tmp;
	if (a <= -475000000000.0) {
		tmp = t_0;
	} else if (a <= 2.3e-10) {
		tmp = x + Math.tan((z + y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	t_0 = x - math.tan(a)
	tmp = 0
	if a <= -475000000000.0:
		tmp = t_0
	elif a <= 2.3e-10:
		tmp = x + math.tan((z + y))
	else:
		tmp = t_0
	return tmp

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	t_0 = Float64(x - tan(a))
	tmp = 0.0
	if (a <= -475000000000.0)
		tmp = t_0;
	elseif (a <= 2.3e-10)
		tmp = Float64(x + tan(Float64(z + y)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp_2 = code(x, y, z, a)
	t_0 = x - tan(a);
	tmp = 0.0;
	if (a <= -475000000000.0)
		tmp = t_0;
	elseif (a <= 2.3e-10)
		tmp = x + tan((z + y));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := Block[{t$95$0 = N[(x - N[Tan[a], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -475000000000.0], t$95$0, If[LessEqual[a, 2.3e-10], N[(x + N[Tan[N[(z + y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if a < -4.75e11 or 2.30000000000000007e-10 < a

   1. Initial program 79.9%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

   4. Applied rewrites 51.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(1 - \left(-{\tan z}^{2}\right), y, \tan z\right) + x\right) - \tan a} \]

   5. Taylor expanded in x around inf

      \[\leadsto x - \tan \color{blue}{a} \]
   6. Step-by-step derivation

   7. Applied rewrites 42.0%

      \[\leadsto x - \tan \color{blue}{a} \]

   if -4.75e11 < a < 2.30000000000000007e-10

   1. Initial program 79.1%

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

   2. Taylor expanded in a around 0

      \[\leadsto x + \color{blue}{\frac{\sin \left(y + z\right)}{\cos \left(y + z\right)}} \]
   3. Step-by-step derivation

      1. tan-quot N/A

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

      2. lift-tan.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 77.3

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

   4. Applied rewrites 77.3%

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

Alternative 18: 50.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ \begin{array}{l} \mathbf{if}\;y + z \leq 10^{-13}:\\ \;\;\;\;x - \tan a\\ \mathbf{else}:\\ \;\;\;\;x + \tan z\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a)
 :precision binary64
 (if (<= (+ y z) 1e-13) (- x (tan a)) (+ x (tan z))))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	double tmp;
	if ((y + z) <= 1e-13) {
		tmp = x - tan(a);
	} else {
		tmp = x + tan(z);
	}
	return tmp;
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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 + z) <= 1d-13) then
        tmp = x - tan(a)
    else
        tmp = x + tan(z)
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	double tmp;
	if ((y + z) <= 1e-13) {
		tmp = x - Math.tan(a);
	} else {
		tmp = x + Math.tan(z);
	}
	return tmp;
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	tmp = 0
	if (y + z) <= 1e-13:
		tmp = x - math.tan(a)
	else:
		tmp = x + math.tan(z)
	return tmp

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	tmp = 0.0
	if (Float64(y + z) <= 1e-13)
		tmp = Float64(x - tan(a));
	else
		tmp = Float64(x + tan(z));
	end
	return tmp
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp_2 = code(x, y, z, a)
	tmp = 0.0;
	if ((y + z) <= 1e-13)
		tmp = x - tan(a);
	else
		tmp = x + tan(z);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := If[LessEqual[N[(y + z), $MachinePrecision], 1e-13], N[(x - N[Tan[a], $MachinePrecision]), $MachinePrecision], N[(x + N[Tan[z], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 y z) < 1e-13

   1. Initial program 83.4%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

   4. Applied rewrites 39.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(1 - \left(-{\tan z}^{2}\right), y, \tan z\right) + x\right) - \tan a} \]

   5. Taylor expanded in x around inf

      \[\leadsto x - \tan \color{blue}{a} \]
   6. Step-by-step derivation

   7. Applied rewrites 52.3%

      \[\leadsto x - \tan \color{blue}{a} \]

   if 1e-13 < (+.f64 y z)

   1. Initial program 73.3%

      \[x + \left(\tan \left(y + z\right) - \tan a\right) \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-tan.f64 N/A

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

      3. tan-sum N/A

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

      4. quot-tan N/A

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

      5. quot-tan N/A

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

      6. div-add N/A

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

      7. lower-+.f64 N/A

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

   3. Applied rewrites 99.6%

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

   4. Taylor expanded in y around 0

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

   6. Applied rewrites 73.1%

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

   7. Taylor expanded in a around 0

      \[\leadsto x + \color{blue}{\frac{\sin z}{\cos z}} \]
   8. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto x + \frac{\sin z}{\color{blue}{\cos z}} \]

      2. tan-quot N/A

         \[\leadsto x + \tan z \]

      3. lift-tan.f64 46.2

         \[\leadsto x + \tan z \]

   9. Applied rewrites 46.2%

      \[\leadsto x + \color{blue}{\tan z} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 19: 41.7% accurate, 2.0× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ x - \tan a \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a) :precision binary64 (- x (tan a)))

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	return x - tan(a);
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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)
end function

Java

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

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	return x - math.tan(a)

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	return Float64(x - tan(a))
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp = code(x, y, z, a)
	tmp = x - tan(a);
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := N[(x - N[Tan[a], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.5%

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

2. Taylor expanded in y around 0

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

   1. lower--.f64 N/A

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

4. Applied rewrites 50.2%

   \[\leadsto \color{blue}{\left(\mathsf{fma}\left(1 - \left(-{\tan z}^{2}\right), y, \tan z\right) + x\right) - \tan a} \]

5. Taylor expanded in x around inf

   \[\leadsto x - \tan \color{blue}{a} \]
6. Step-by-step derivation

7. Applied rewrites 41.7%

   \[\leadsto x - \tan \color{blue}{a} \]
8. Add Preprocessing

Alternative 20: 31.3% accurate, 210.0× speedup

Math

\[\begin{array}{l} [x, y, z, a] = \mathsf{sort}([x, y, z, a])\\ \\ x \end{array} \]

FPCore

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
(FPCore (x y z a) :precision binary64 x)

C

assert(x < y && y < z && z < a);
double code(double x, double y, double z, double a) {
	return x;
}

Fortran

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
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
end function

Java

assert x < y && y < z && z < a;
public static double code(double x, double y, double z, double a) {
	return x;
}

Python

[x, y, z, a] = sort([x, y, z, a])
def code(x, y, z, a):
	return x

Julia

x, y, z, a = sort([x, y, z, a])
function code(x, y, z, a)
	return x
end

MATLAB

x, y, z, a = num2cell(sort([x, y, z, a])){:}
function tmp = code(x, y, z, a)
	tmp = x;
end

Wolfram

NOTE: x, y, z, and a should be sorted in increasing order before calling this function.
code[x_, y_, z_, a_] := x

Derivation

1. Initial program 79.5%

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

2. Taylor expanded in x around inf

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

4. Applied rewrites 31.3%

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

Reproduce

herbie shell --seed 2025107 
(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))))