Graphics.Rendering.Plot.Render.Plot.Axis:tickPosition from plot-0.2.3.4

Percentage Accurate: 98.0% → 99.8%

Time: 2.0min

Alternatives: 10

Speedup: 0.8×

• 75.2% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[x + \left(y - x\right) \cdot \frac{z}{t} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (+ x (* (- y x) (/ z t))))

C

double code(double x, double y, double z, double t) {
	return x + ((y - x) * (z / t));
}

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

Java

public static double code(double x, double y, double z, double t) {
	return x + ((y - x) * (z / t));
}

Python

def code(x, y, z, t):
	return x + ((y - x) * (z / t))

Julia

function code(x, y, z, t)
	return Float64(x + Float64(Float64(y - x) * Float64(z / t)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x + ((y - x) * (z / t));
end

Wolfram

code[x_, y_, z_, t_] := N[(x + N[(N[(y - x), $MachinePrecision] * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 98.0% accurate, 1.0× speedup

Math

\[x + \left(y - x\right) \cdot \frac{z}{t} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (+ x (* (- y x) (/ z t))))

C

double code(double x, double y, double z, double t) {
	return x + ((y - x) * (z / t));
}

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

Java

public static double code(double x, double y, double z, double t) {
	return x + ((y - x) * (z / t));
}

Python

def code(x, y, z, t):
	return x + ((y - x) * (z / t))

Julia

function code(x, y, z, t)
	return Float64(x + Float64(Float64(y - x) * Float64(z / t)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x + ((y - x) * (z / t));
end

Wolfram

code[x_, y_, z_, t_] := N[(x + N[(N[(y - x), $MachinePrecision] * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 0.7× speedup

Math

\[\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \left(x - y\right), z\right) \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (134-z0z1z2z3z4 (/ 1 t) x t (- x y) z))

Derivation

1. Initial program 98.0%

   \[x + \left(y - x\right) \cdot \frac{z}{t} \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. lift-/.f64 N/A

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

   4. associate-*r/ N/A

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

   5. add-to-fraction N/A

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

   6. mult-flip N/A

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

   7. *-commutative N/A

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

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

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

   9. lift--.f64 N/A

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

   10. sub-negate-rev N/A

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

   11. lower-134-z0z1z2z3z4 N/A

       \[\leadsto \color{blue}{\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \left(x - y\right), z\right)} \]

   12. lower-/.f64 N/A

       \[\leadsto \mathsf{134\_z0z1z2z3z4}\left(\color{blue}{\left(\frac{1}{t}\right)}, x, t, \left(x - y\right), z\right) \]

   13. lower--.f64 99.8%

       \[\leadsto \mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \color{blue}{\left(x - y\right)}, z\right) \]

3. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \left(x - y\right), z\right)} \]
4. Add Preprocessing

Alternative 2: 98.0% accurate, 0.8× speedup

Math

\[x + \frac{y - x}{\frac{t}{z}} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (+ x (/ (- y x) (/ t z))))

C

double code(double x, double y, double z, double t) {
	return x + ((y - x) / (t / z));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

public static double code(double x, double y, double z, double t) {
	return x + ((y - x) / (t / z));
}

Python

def code(x, y, z, t):
	return x + ((y - x) / (t / z))

Julia

function code(x, y, z, t)
	return Float64(x + Float64(Float64(y - x) / Float64(t / z)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x + ((y - x) / (t / z));
end

Wolfram

code[x_, y_, z_, t_] := N[(x + N[(N[(y - x), $MachinePrecision] / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 98.0%

   \[x + \left(y - x\right) \cdot \frac{z}{t} \]
2. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. associate-*r/ N/A

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

   4. lower-/.f64 N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 92.8%

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

3. Applied rewrites 92.8%

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

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/l* N/A

      \[\leadsto x + \color{blue}{z \cdot \frac{y - x}{t}} \]

   4. *-commutative N/A

      \[\leadsto x + \color{blue}{\frac{y - x}{t} \cdot z} \]

   5. associate-/r/ N/A

      \[\leadsto x + \color{blue}{\frac{y - x}{\frac{t}{z}}} \]

   6. lift-/.f64 N/A

      \[\leadsto x + \frac{y - x}{\color{blue}{\frac{t}{z}}} \]

   7. lower-/.f64 97.9%

      \[\leadsto x + \color{blue}{\frac{y - x}{\frac{t}{z}}} \]

5. Applied rewrites 97.9%

   \[\leadsto x + \color{blue}{\frac{y - x}{\frac{t}{z}}} \]
6. Add Preprocessing

Alternative 3: 86.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_1 := x + y \cdot \frac{z}{t}\\ \mathbf{if}\;y \leq \frac{-2274745500040835}{842498333348457493583344221469363458551160763204392890034487820288}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq \frac{1598517415830675}{91343852333181432387730302044767688728495783936}:\\ \;\;\;\;\left(1 - \frac{z}{t}\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (+ x (* y (/ z t)))))
  (if (<=
       y
       -2274745500040835/842498333348457493583344221469363458551160763204392890034487820288)
    t_1
    (if (<=
         y
         1598517415830675/91343852333181432387730302044767688728495783936)
      (* (- 1 (/ z t)) x)
      t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x + (y * (z / t));
	double tmp;
	if (y <= -2.7e-51) {
		tmp = t_1;
	} else if (y <= 1.75e-32) {
		tmp = (1.0 - (z / t)) * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (y * (z / t))
    if (y <= (-2.7d-51)) then
        tmp = t_1
    else if (y <= 1.75d-32) then
        tmp = (1.0d0 - (z / t)) * x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x + (y * (z / t));
	double tmp;
	if (y <= -2.7e-51) {
		tmp = t_1;
	} else if (y <= 1.75e-32) {
		tmp = (1.0 - (z / t)) * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x + (y * (z / t))
	tmp = 0
	if y <= -2.7e-51:
		tmp = t_1
	elif y <= 1.75e-32:
		tmp = (1.0 - (z / t)) * x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x + Float64(y * Float64(z / t)))
	tmp = 0.0
	if (y <= -2.7e-51)
		tmp = t_1;
	elseif (y <= 1.75e-32)
		tmp = Float64(Float64(1.0 - Float64(z / t)) * x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x + (y * (z / t));
	tmp = 0.0;
	if (y <= -2.7e-51)
		tmp = t_1;
	elseif (y <= 1.75e-32)
		tmp = (1.0 - (z / t)) * x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2274745500040835/842498333348457493583344221469363458551160763204392890034487820288], t$95$1, If[LessEqual[y, 1598517415830675/91343852333181432387730302044767688728495783936], N[(N[(1 - N[(z / t), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -2.6999999999999997e-51 or 1.7499999999999999e-32 < y

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]

   2. Taylor expanded in x around 0

      \[\leadsto x + \color{blue}{y} \cdot \frac{z}{t} \]
   3. Step-by-step derivation

   4. Applied rewrites 77.1%

      \[\leadsto x + \color{blue}{y} \cdot \frac{z}{t} \]

   if -2.6999999999999997e-51 < y < 1.7499999999999999e-32

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 65.9%

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

   4. Applied rewrites 65.9%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 65.9%

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

      4. lift-+.f64 N/A

         \[\leadsto \left(1 + -1 \cdot \frac{z}{t}\right) \cdot x \]

      5. lift-*.f64 N/A

         \[\leadsto \left(1 + -1 \cdot \frac{z}{t}\right) \cdot x \]

      6. mul-1-neg N/A

         \[\leadsto \left(1 + \left(\mathsf{neg}\left(\frac{z}{t}\right)\right)\right) \cdot x \]

      7. sub-flip-reverse N/A

         \[\leadsto \left(1 - \frac{z}{t}\right) \cdot x \]

      8. lower--.f64 65.9%

         \[\leadsto \left(1 - \frac{z}{t}\right) \cdot x \]

   6. Applied rewrites 65.9%

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

Alternative 4: 84.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_1 := x + \frac{y \cdot z}{t}\\ \mathbf{if}\;y \leq -650:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq \frac{1598517415830675}{91343852333181432387730302044767688728495783936}:\\ \;\;\;\;\left(1 - \frac{z}{t}\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (+ x (/ (* y z) t))))
  (if (<= y -650)
    t_1
    (if (<=
         y
         1598517415830675/91343852333181432387730302044767688728495783936)
      (* (- 1 (/ z t)) x)
      t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x + ((y * z) / t);
	double tmp;
	if (y <= -650.0) {
		tmp = t_1;
	} else if (y <= 1.75e-32) {
		tmp = (1.0 - (z / t)) * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + ((y * z) / t)
    if (y <= (-650.0d0)) then
        tmp = t_1
    else if (y <= 1.75d-32) then
        tmp = (1.0d0 - (z / t)) * x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x + ((y * z) / t);
	double tmp;
	if (y <= -650.0) {
		tmp = t_1;
	} else if (y <= 1.75e-32) {
		tmp = (1.0 - (z / t)) * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x + ((y * z) / t)
	tmp = 0
	if y <= -650.0:
		tmp = t_1
	elif y <= 1.75e-32:
		tmp = (1.0 - (z / t)) * x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x + Float64(Float64(y * z) / t))
	tmp = 0.0
	if (y <= -650.0)
		tmp = t_1;
	elseif (y <= 1.75e-32)
		tmp = Float64(Float64(1.0 - Float64(z / t)) * x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x + ((y * z) / t);
	tmp = 0.0;
	if (y <= -650.0)
		tmp = t_1;
	elseif (y <= 1.75e-32)
		tmp = (1.0 - (z / t)) * x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -650], t$95$1, If[LessEqual[y, 1598517415830675/91343852333181432387730302044767688728495783936], N[(N[(1 - N[(z / t), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -650 or 1.7499999999999999e-32 < y

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]

   2. Taylor expanded in x around 0

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto x + \frac{y \cdot z}{\color{blue}{t}} \]

      2. lower-*.f64 73.3%

         \[\leadsto x + \frac{y \cdot z}{t} \]

   4. Applied rewrites 73.3%

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]

   if -650 < y < 1.7499999999999999e-32

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 65.9%

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

   4. Applied rewrites 65.9%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 65.9%

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

      4. lift-+.f64 N/A

         \[\leadsto \left(1 + -1 \cdot \frac{z}{t}\right) \cdot x \]

      5. lift-*.f64 N/A

         \[\leadsto \left(1 + -1 \cdot \frac{z}{t}\right) \cdot x \]

      6. mul-1-neg N/A

         \[\leadsto \left(1 + \left(\mathsf{neg}\left(\frac{z}{t}\right)\right)\right) \cdot x \]

      7. sub-flip-reverse N/A

         \[\leadsto \left(1 - \frac{z}{t}\right) \cdot x \]

      8. lower--.f64 65.9%

         \[\leadsto \left(1 - \frac{z}{t}\right) \cdot x \]

   6. Applied rewrites 65.9%

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

Alternative 5: 83.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_1 := \left(1 - \frac{z}{t}\right) \cdot x\\ \mathbf{if}\;x \leq -54000000000000000000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 359999999999999991931072776714238961408413583406448582555808465872522433558245763113188495373317561016476904718336:\\ \;\;\;\;x + \frac{y}{t} \cdot z\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (* (- 1 (/ z t)) x)))
  (if (<= x -54000000000000000000)
    t_1
    (if (<=
         x
         359999999999999991931072776714238961408413583406448582555808465872522433558245763113188495373317561016476904718336)
      (+ x (* (/ y t) z))
      t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (1.0 - (z / t)) * x;
	double tmp;
	if (x <= -5.4e+19) {
		tmp = t_1;
	} else if (x <= 3.6e+113) {
		tmp = x + ((y / t) * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (1.0d0 - (z / t)) * x
    if (x <= (-5.4d+19)) then
        tmp = t_1
    else if (x <= 3.6d+113) then
        tmp = x + ((y / t) * z)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (1.0 - (z / t)) * x;
	double tmp;
	if (x <= -5.4e+19) {
		tmp = t_1;
	} else if (x <= 3.6e+113) {
		tmp = x + ((y / t) * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (1.0 - (z / t)) * x
	tmp = 0
	if x <= -5.4e+19:
		tmp = t_1
	elif x <= 3.6e+113:
		tmp = x + ((y / t) * z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(1.0 - Float64(z / t)) * x)
	tmp = 0.0
	if (x <= -5.4e+19)
		tmp = t_1;
	elseif (x <= 3.6e+113)
		tmp = Float64(x + Float64(Float64(y / t) * z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (1.0 - (z / t)) * x;
	tmp = 0.0;
	if (x <= -5.4e+19)
		tmp = t_1;
	elseif (x <= 3.6e+113)
		tmp = x + ((y / t) * z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(1 - N[(z / t), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -54000000000000000000], t$95$1, If[LessEqual[x, 359999999999999991931072776714238961408413583406448582555808465872522433558245763113188495373317561016476904718336], N[(x + N[(N[(y / t), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -5.4e19 or 3.5999999999999999e113 < x

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 65.9%

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

   4. Applied rewrites 65.9%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 65.9%

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

      4. lift-+.f64 N/A

         \[\leadsto \left(1 + -1 \cdot \frac{z}{t}\right) \cdot x \]

      5. lift-*.f64 N/A

         \[\leadsto \left(1 + -1 \cdot \frac{z}{t}\right) \cdot x \]

      6. mul-1-neg N/A

         \[\leadsto \left(1 + \left(\mathsf{neg}\left(\frac{z}{t}\right)\right)\right) \cdot x \]

      7. sub-flip-reverse N/A

         \[\leadsto \left(1 - \frac{z}{t}\right) \cdot x \]

      8. lower--.f64 65.9%

         \[\leadsto \left(1 - \frac{z}{t}\right) \cdot x \]

   6. Applied rewrites 65.9%

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

   if -5.4e19 < x < 3.5999999999999999e113

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]

   2. Taylor expanded in x around 0

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto x + \frac{y \cdot z}{\color{blue}{t}} \]

      2. lower-*.f64 73.3%

         \[\leadsto x + \frac{y \cdot z}{t} \]

   4. Applied rewrites 73.3%

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto x + \frac{y \cdot z}{\color{blue}{t}} \]

      2. remove-double-neg N/A

         \[\leadsto x + \frac{\mathsf{neg}\left(\left(\mathsf{neg}\left(y \cdot z\right)\right)\right)}{t} \]

      3. lift-*.f64 N/A

         \[\leadsto x + \frac{\mathsf{neg}\left(\left(\mathsf{neg}\left(y \cdot z\right)\right)\right)}{t} \]

      4. *-commutative N/A

         \[\leadsto x + \frac{\mathsf{neg}\left(\left(\mathsf{neg}\left(z \cdot y\right)\right)\right)}{t} \]

      5. distribute-lft-neg-in N/A

         \[\leadsto x + \frac{\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right) \cdot y\right)}{t} \]

      6. distribute-lft-neg-in N/A

         \[\leadsto x + \frac{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \cdot y}{t} \]

      7. associate-/l* N/A

         \[\leadsto x + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \cdot \color{blue}{\frac{y}{t}} \]

      8. *-commutative N/A

         \[\leadsto x + \frac{y}{t} \cdot \color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right)} \]

      9. lower-*.f64 N/A

         \[\leadsto x + \frac{y}{t} \cdot \color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right)} \]

      10. lower-/.f64 N/A

          \[\leadsto x + \frac{y}{t} \cdot \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(z\right)\right)}\right)\right) \]

      11. mul-1-neg N/A

          \[\leadsto x + \frac{y}{t} \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

      12. *-commutative N/A

          \[\leadsto x + \frac{y}{t} \cdot \left(\mathsf{neg}\left(z \cdot -1\right)\right) \]

      13. distribute-rgt-neg-out N/A

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

      14. metadata-eval N/A

          \[\leadsto x + \frac{y}{t} \cdot \left(z \cdot 1\right) \]

      15. *-rgt-identity 73.4%

          \[\leadsto x + \frac{y}{t} \cdot z \]

   6. Applied rewrites 73.4%

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

Alternative 6: 74.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_1 := \frac{z}{t} \cdot y\\ \mathbf{if}\;y \leq -9099999999999999804374895554563547679328607841149126257261365274968109668351902638222501663921318391549669595204323470895196107136050119658007584217618091607543523596757637974378193453879039885312:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 2549999999999999918481992149910411763438055036024249866455228495889500046429710570035755545772868446046768627271200998827750644861399728128:\\ \;\;\;\;\left(1 - \frac{z}{t}\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (* (/ z t) y)))
  (if (<=
       y
       -9099999999999999804374895554563547679328607841149126257261365274968109668351902638222501663921318391549669595204323470895196107136050119658007584217618091607543523596757637974378193453879039885312)
    t_1
    (if (<=
         y
         2549999999999999918481992149910411763438055036024249866455228495889500046429710570035755545772868446046768627271200998827750644861399728128)
      (* (- 1 (/ z t)) x)
      t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (z / t) * y;
	double tmp;
	if (y <= -9.1e+195) {
		tmp = t_1;
	} else if (y <= 2.55e+138) {
		tmp = (1.0 - (z / t)) * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z / t) * y
    if (y <= (-9.1d+195)) then
        tmp = t_1
    else if (y <= 2.55d+138) then
        tmp = (1.0d0 - (z / t)) * x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (z / t) * y;
	double tmp;
	if (y <= -9.1e+195) {
		tmp = t_1;
	} else if (y <= 2.55e+138) {
		tmp = (1.0 - (z / t)) * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (z / t) * y
	tmp = 0
	if y <= -9.1e+195:
		tmp = t_1
	elif y <= 2.55e+138:
		tmp = (1.0 - (z / t)) * x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(z / t) * y)
	tmp = 0.0
	if (y <= -9.1e+195)
		tmp = t_1;
	elseif (y <= 2.55e+138)
		tmp = Float64(Float64(1.0 - Float64(z / t)) * x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (z / t) * y;
	tmp = 0.0;
	if (y <= -9.1e+195)
		tmp = t_1;
	elseif (y <= 2.55e+138)
		tmp = (1.0 - (z / t)) * x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(z / t), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[y, -9099999999999999804374895554563547679328607841149126257261365274968109668351902638222501663921318391549669595204323470895196107136050119658007584217618091607543523596757637974378193453879039885312], t$95$1, If[LessEqual[y, 2549999999999999918481992149910411763438055036024249866455228495889500046429710570035755545772868446046768627271200998827750644861399728128], N[(N[(1 - N[(z / t), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -9.0999999999999998e195 or 2.5499999999999999e138 < y

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. associate-*r/ N/A

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

      5. add-to-fraction N/A

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

      6. mult-flip N/A

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

      7. *-commutative N/A

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

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

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

      9. lift--.f64 N/A

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

      10. sub-negate-rev N/A

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

      11. lower-134-z0z1z2z3z4 N/A

          \[\leadsto \color{blue}{\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \left(x - y\right), z\right)} \]

      12. lower-/.f64 N/A

          \[\leadsto \mathsf{134\_z0z1z2z3z4}\left(\color{blue}{\left(\frac{1}{t}\right)}, x, t, \left(x - y\right), z\right) \]

      13. lower--.f64 99.8%

          \[\leadsto \mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \color{blue}{\left(x - y\right)}, z\right) \]

   3. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \left(x - y\right), z\right)} \]

   4. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{y \cdot z}{t}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{t}} \]

      2. lower-*.f64 37.2%

         \[\leadsto \frac{y \cdot z}{t} \]

   6. Applied rewrites 37.2%

      \[\leadsto \color{blue}{\frac{y \cdot z}{t}} \]
   7. Step-by-step derivation

      1. *-commutative 37.2%

         \[\leadsto \frac{\color{blue}{y \cdot z}}{t} \]

      2. mult-flip-rev 37.2%

         \[\leadsto \frac{\color{blue}{y \cdot z}}{t} \]

      3. fp-cancel-sub-sign-inv 37.2%

         \[\leadsto \frac{\color{blue}{y} \cdot z}{t} \]

      4. add-to-fraction 37.2%

         \[\leadsto \frac{\color{blue}{y \cdot z}}{t} \]

      5. sub-negate-rev 37.2%

         \[\leadsto \frac{y \cdot z}{t} \]

      6. associate-*r/ 37.2%

         \[\leadsto \frac{y \cdot \color{blue}{z}}{t} \]

      7. lift-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{t}} \]

      8. lift-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{t} \]

      9. associate-/l* N/A

         \[\leadsto y \cdot \color{blue}{\frac{z}{t}} \]

      10. lift-/.f64 N/A

          \[\leadsto y \cdot \frac{z}{\color{blue}{t}} \]

      11. *-commutative N/A

          \[\leadsto \frac{z}{t} \cdot \color{blue}{y} \]

      12. lift-*.f64 40.5%

          \[\leadsto \frac{z}{t} \cdot \color{blue}{y} \]

   8. Applied rewrites 40.5%

      \[\leadsto \color{blue}{\frac{z}{t} \cdot y} \]

   if -9.0999999999999998e195 < y < 2.5499999999999999e138

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 65.9%

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

   4. Applied rewrites 65.9%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 65.9%

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

      4. lift-+.f64 N/A

         \[\leadsto \left(1 + -1 \cdot \frac{z}{t}\right) \cdot x \]

      5. lift-*.f64 N/A

         \[\leadsto \left(1 + -1 \cdot \frac{z}{t}\right) \cdot x \]

      6. mul-1-neg N/A

         \[\leadsto \left(1 + \left(\mathsf{neg}\left(\frac{z}{t}\right)\right)\right) \cdot x \]

      7. sub-flip-reverse N/A

         \[\leadsto \left(1 - \frac{z}{t}\right) \cdot x \]

      8. lower--.f64 65.9%

         \[\leadsto \left(1 - \frac{z}{t}\right) \cdot x \]

   6. Applied rewrites 65.9%

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

Alternative 7: 64.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} t_1 := \frac{z}{t} \cdot y\\ \mathbf{if}\;\frac{z}{t} \leq \frac{-546812681195753}{273406340597876490546562778389702670669146178861651554553221325801244124899921990402939147127881728}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;\frac{z}{t} \leq \frac{5092589940836215}{509258994083621521567111422102344540262867098416484062659035112338595324940834176545849344}:\\ \;\;\;\;x \cdot 1\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (* (/ z t) y)))
  (if (<=
       (/ z t)
       -546812681195753/273406340597876490546562778389702670669146178861651554553221325801244124899921990402939147127881728)
    t_1
    (if (<=
         (/ z t)
         5092589940836215/509258994083621521567111422102344540262867098416484062659035112338595324940834176545849344)
      (* x 1)
      t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (z / t) * y;
	double tmp;
	if ((z / t) <= -2e-84) {
		tmp = t_1;
	} else if ((z / t) <= 1e-74) {
		tmp = x * 1.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z / t) * y
    if ((z / t) <= (-2d-84)) then
        tmp = t_1
    else if ((z / t) <= 1d-74) then
        tmp = x * 1.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (z / t) * y;
	double tmp;
	if ((z / t) <= -2e-84) {
		tmp = t_1;
	} else if ((z / t) <= 1e-74) {
		tmp = x * 1.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (z / t) * y
	tmp = 0
	if (z / t) <= -2e-84:
		tmp = t_1
	elif (z / t) <= 1e-74:
		tmp = x * 1.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(z / t) * y)
	tmp = 0.0
	if (Float64(z / t) <= -2e-84)
		tmp = t_1;
	elseif (Float64(z / t) <= 1e-74)
		tmp = Float64(x * 1.0);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (z / t) * y;
	tmp = 0.0;
	if ((z / t) <= -2e-84)
		tmp = t_1;
	elseif ((z / t) <= 1e-74)
		tmp = x * 1.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(z / t), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[N[(z / t), $MachinePrecision], -546812681195753/273406340597876490546562778389702670669146178861651554553221325801244124899921990402939147127881728], t$95$1, If[LessEqual[N[(z / t), $MachinePrecision], 5092589940836215/509258994083621521567111422102344540262867098416484062659035112338595324940834176545849344], N[(x * 1), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 z t) < -2.0000000000000001e-84 or 9.9999999999999996e-75 < (/.f64 z t)

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. associate-*r/ N/A

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

      5. add-to-fraction N/A

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

      6. mult-flip N/A

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

      7. *-commutative N/A

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

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

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

      9. lift--.f64 N/A

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

      10. sub-negate-rev N/A

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

      11. lower-134-z0z1z2z3z4 N/A

          \[\leadsto \color{blue}{\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \left(x - y\right), z\right)} \]

      12. lower-/.f64 N/A

          \[\leadsto \mathsf{134\_z0z1z2z3z4}\left(\color{blue}{\left(\frac{1}{t}\right)}, x, t, \left(x - y\right), z\right) \]

      13. lower--.f64 99.8%

          \[\leadsto \mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \color{blue}{\left(x - y\right)}, z\right) \]

   3. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \left(x - y\right), z\right)} \]

   4. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{y \cdot z}{t}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{t}} \]

      2. lower-*.f64 37.2%

         \[\leadsto \frac{y \cdot z}{t} \]

   6. Applied rewrites 37.2%

      \[\leadsto \color{blue}{\frac{y \cdot z}{t}} \]
   7. Step-by-step derivation

      1. *-commutative 37.2%

         \[\leadsto \frac{\color{blue}{y \cdot z}}{t} \]

      2. mult-flip-rev 37.2%

         \[\leadsto \frac{\color{blue}{y \cdot z}}{t} \]

      3. fp-cancel-sub-sign-inv 37.2%

         \[\leadsto \frac{\color{blue}{y} \cdot z}{t} \]

      4. add-to-fraction 37.2%

         \[\leadsto \frac{\color{blue}{y \cdot z}}{t} \]

      5. sub-negate-rev 37.2%

         \[\leadsto \frac{y \cdot z}{t} \]

      6. associate-*r/ 37.2%

         \[\leadsto \frac{y \cdot \color{blue}{z}}{t} \]

      7. lift-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{t}} \]

      8. lift-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{t} \]

      9. associate-/l* N/A

         \[\leadsto y \cdot \color{blue}{\frac{z}{t}} \]

      10. lift-/.f64 N/A

          \[\leadsto y \cdot \frac{z}{\color{blue}{t}} \]

      11. *-commutative N/A

          \[\leadsto \frac{z}{t} \cdot \color{blue}{y} \]

      12. lift-*.f64 40.5%

          \[\leadsto \frac{z}{t} \cdot \color{blue}{y} \]

   8. Applied rewrites 40.5%

      \[\leadsto \color{blue}{\frac{z}{t} \cdot y} \]

   if -2.0000000000000001e-84 < (/.f64 z t) < 9.9999999999999996e-75

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 65.9%

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

   4. Applied rewrites 65.9%

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

   5. Taylor expanded in z around 0

      \[\leadsto x \cdot 1 \]
   6. Step-by-step derivation

   7. Applied rewrites 39.0%

      \[\leadsto x \cdot 1 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 61.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} t_1 := \frac{y \cdot z}{t}\\ \mathbf{if}\;\frac{z}{t} \leq \frac{-546812681195753}{273406340597876490546562778389702670669146178861651554553221325801244124899921990402939147127881728}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;\frac{z}{t} \leq \frac{5092589940836215}{509258994083621521567111422102344540262867098416484062659035112338595324940834176545849344}:\\ \;\;\;\;x \cdot 1\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (/ (* y z) t)))
  (if (<=
       (/ z t)
       -546812681195753/273406340597876490546562778389702670669146178861651554553221325801244124899921990402939147127881728)
    t_1
    (if (<=
         (/ z t)
         5092589940836215/509258994083621521567111422102344540262867098416484062659035112338595324940834176545849344)
      (* x 1)
      t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (y * z) / t;
	double tmp;
	if ((z / t) <= -2e-84) {
		tmp = t_1;
	} else if ((z / t) <= 1e-74) {
		tmp = x * 1.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (y * z) / t
    if ((z / t) <= (-2d-84)) then
        tmp = t_1
    else if ((z / t) <= 1d-74) then
        tmp = x * 1.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (y * z) / t;
	double tmp;
	if ((z / t) <= -2e-84) {
		tmp = t_1;
	} else if ((z / t) <= 1e-74) {
		tmp = x * 1.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (y * z) / t
	tmp = 0
	if (z / t) <= -2e-84:
		tmp = t_1
	elif (z / t) <= 1e-74:
		tmp = x * 1.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(y * z) / t)
	tmp = 0.0
	if (Float64(z / t) <= -2e-84)
		tmp = t_1;
	elseif (Float64(z / t) <= 1e-74)
		tmp = Float64(x * 1.0);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (y * z) / t;
	tmp = 0.0;
	if ((z / t) <= -2e-84)
		tmp = t_1;
	elseif ((z / t) <= 1e-74)
		tmp = x * 1.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]}, If[LessEqual[N[(z / t), $MachinePrecision], -546812681195753/273406340597876490546562778389702670669146178861651554553221325801244124899921990402939147127881728], t$95$1, If[LessEqual[N[(z / t), $MachinePrecision], 5092589940836215/509258994083621521567111422102344540262867098416484062659035112338595324940834176545849344], N[(x * 1), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 z t) < -2.0000000000000001e-84 or 9.9999999999999996e-75 < (/.f64 z t)

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. associate-*r/ N/A

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

      5. add-to-fraction N/A

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

      6. mult-flip N/A

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

      7. *-commutative N/A

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

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

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

      9. lift--.f64 N/A

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

      10. sub-negate-rev N/A

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

      11. lower-134-z0z1z2z3z4 N/A

          \[\leadsto \color{blue}{\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \left(x - y\right), z\right)} \]

      12. lower-/.f64 N/A

          \[\leadsto \mathsf{134\_z0z1z2z3z4}\left(\color{blue}{\left(\frac{1}{t}\right)}, x, t, \left(x - y\right), z\right) \]

      13. lower--.f64 99.8%

          \[\leadsto \mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \color{blue}{\left(x - y\right)}, z\right) \]

   3. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{t}\right), x, t, \left(x - y\right), z\right)} \]

   4. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{y \cdot z}{t}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{t}} \]

      2. lower-*.f64 37.2%

         \[\leadsto \frac{y \cdot z}{t} \]

   6. Applied rewrites 37.2%

      \[\leadsto \color{blue}{\frac{y \cdot z}{t}} \]

   if -2.0000000000000001e-84 < (/.f64 z t) < 9.9999999999999996e-75

   1. Initial program 98.0%

      \[x + \left(y - x\right) \cdot \frac{z}{t} \]

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 65.9%

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

   4. Applied rewrites 65.9%

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

   5. Taylor expanded in z around 0

      \[\leadsto x \cdot 1 \]
   6. Step-by-step derivation

   7. Applied rewrites 39.0%

      \[\leadsto x \cdot 1 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 39.0% accurate, 3.8× speedup

Math

\[x \cdot 1 \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (* x 1))

C

double code(double x, double y, double z, double t) {
	return x * 1.0;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x * 1.0d0
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := N[(x * 1), $MachinePrecision]

Derivation

1. Initial program 98.0%

   \[x + \left(y - x\right) \cdot \frac{z}{t} \]

2. Taylor expanded in x around inf

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

   1. lower-*.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-/.f64 65.9%

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

4. Applied rewrites 65.9%

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

5. Taylor expanded in z around 0

   \[\leadsto x \cdot 1 \]
6. Step-by-step derivation

7. Applied rewrites 39.0%

   \[\leadsto x \cdot 1 \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025271 -o generate:evaluate
(FPCore (x y z t)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:tickPosition from plot-0.2.3.4"
  :precision binary64
  (+ x (* (- y x) (/ z t))))