Numeric.Histogram:binBounds from Chart-1.5.3

Percentage Accurate: 92.3% → 97.7%

Time: 2.8s

Alternatives: 9

Speedup: 1.1×

• 74.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

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(Float64(y - x) * 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[(N[(y - x), $MachinePrecision] * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]

Initial Program: 92.3% accurate, 1.0× speedup

Math

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

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(Float64(y - x) * 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[(N[(y - x), $MachinePrecision] * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]

Alternative 1: 97.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \mathbf{if}\;z \leq 1.15 \cdot 10^{-204}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{t}, y - x, x\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y - x}{t}, z, x\right)\\ \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z 1.15e-204) (fma (/ z t) (- y x) x) (fma (/ (- y x) t) z x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= 1.15e-204) {
		tmp = fma((z / t), (y - x), x);
	} else {
		tmp = fma(((y - x) / t), z, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= 1.15e-204)
		tmp = fma(Float64(z / t), Float64(y - x), x);
	else
		tmp = fma(Float64(Float64(y - x) / t), z, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, 1.15e-204], N[(N[(z / t), $MachinePrecision] * N[(y - x), $MachinePrecision] + x), $MachinePrecision], N[(N[(N[(y - x), $MachinePrecision] / t), $MachinePrecision] * z + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 1.15e-204

   1. Initial program 92.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-/.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. associate-/l* N/A

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

      6. *-commutative N/A

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

      7. lower-fma.f64 N/A

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

      8. lower-/.f64 97.7%

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

   3. Applied rewrites 97.7%

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

   if 1.15e-204 < z

   1. Initial program 92.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. remove-double-neg N/A

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

      4. lift-/.f64 N/A

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

      5. mult-flip N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. associate-*l* N/A

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

      9. *-commutative N/A

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

      10. remove-double-neg N/A

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

      11. lower-fma.f64 N/A

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

      12. mult-flip-rev N/A

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

      13. lower-/.f64 92.7%

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

   3. Applied rewrites 92.7%

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

Alternative 2: 96.6% accurate, 1.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 92.3%

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

   1. lift-+.f64 N/A

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

   2. +-commutative N/A

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

   3. lift-/.f64 N/A

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

   4. lift-*.f64 N/A

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

   5. associate-/l* N/A

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

   6. *-commutative N/A

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

   7. lower-fma.f64 N/A

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

   8. lower-/.f64 97.7%

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

3. Applied rewrites 97.7%

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

Alternative 3: 84.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_1 := \mathsf{fma}\left(\frac{y}{t}, z, x\right)\\ \mathbf{if}\;y \leq -5800000000000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 1.5 \cdot 10^{-106}:\\ \;\;\;\;x - \frac{z}{t} \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (fma (/ y t) z x)))
   (if (<= y -5800000000000.0)
     t_1
     (if (<= y 1.5e-106) (- x (* (/ z t) x)) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = fma((y / t), z, x);
	double tmp;
	if (y <= -5800000000000.0) {
		tmp = t_1;
	} else if (y <= 1.5e-106) {
		tmp = x - ((z / t) * x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = fma(Float64(y / t), z, x)
	tmp = 0.0
	if (y <= -5800000000000.0)
		tmp = t_1;
	elseif (y <= 1.5e-106)
		tmp = Float64(x - Float64(Float64(z / t) * x));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y / t), $MachinePrecision] * z + x), $MachinePrecision]}, If[LessEqual[y, -5800000000000.0], t$95$1, If[LessEqual[y, 1.5e-106], N[(x - N[(N[(z / t), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -5.8e12 or 1.50000000000000009e-106 < y

   1. Initial program 92.3%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 73.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-/.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. associate-/l* N/A

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

      6. mult-flip N/A

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

      7. *-commutative N/A

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

      8. associate-*r* N/A

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

      9. lower-fma.f64 N/A

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

      10. mult-flip-rev N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{y}{t}}, z, x\right) \]

      11. lower-/.f64 73.2%

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{y}{t}}, z, x\right) \]

   6. Applied rewrites 73.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{y}{t}, z, x\right)} \]

   if -5.8e12 < y < 1.50000000000000009e-106

   1. Initial program 92.3%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. lower--.f64 N/A

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

      4. lift-/.f64 N/A

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

      5. distribute-neg-frac2 N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. associate-/l* N/A

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

      9. *-commutative N/A

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

      10. lower-*.f64 N/A

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

      11. lift--.f64 N/A

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

      12. sub-negate-rev N/A

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

      13. frac-2neg-rev N/A

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

      14. lower-/.f64 N/A

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

      15. lower--.f64 92.7%

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

   3. Applied rewrites 92.7%

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

   4. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 60.3%

         \[\leadsto x - \frac{x \cdot z}{t} \]

   6. Applied rewrites 60.3%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

         \[\leadsto x - \frac{x \cdot z}{t} \]

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 64.9%

         \[\leadsto x - \frac{z}{t} \cdot x \]

   8. Applied rewrites 64.9%

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

Alternative 4: 83.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_1 := \mathsf{fma}\left(\frac{y}{t}, z, x\right)\\ \mathbf{if}\;y \leq -5800000000000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 1.5 \cdot 10^{-106}:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (fma (/ y t) z x)))
   (if (<= y -5800000000000.0)
     t_1
     (if (<= y 1.5e-106) (* x (- 1.0 (/ z t))) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = fma((y / t), z, x);
	double tmp;
	if (y <= -5800000000000.0) {
		tmp = t_1;
	} else if (y <= 1.5e-106) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = fma(Float64(y / t), z, x)
	tmp = 0.0
	if (y <= -5800000000000.0)
		tmp = t_1;
	elseif (y <= 1.5e-106)
		tmp = Float64(x * Float64(1.0 - Float64(z / t)));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y / t), $MachinePrecision] * z + x), $MachinePrecision]}, If[LessEqual[y, -5800000000000.0], t$95$1, If[LessEqual[y, 1.5e-106], N[(x * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -5.8e12 or 1.50000000000000009e-106 < y

   1. Initial program 92.3%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 73.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-/.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. associate-/l* N/A

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

      6. mult-flip N/A

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

      7. *-commutative N/A

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

      8. associate-*r* N/A

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

      9. lower-fma.f64 N/A

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

      10. mult-flip-rev N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{y}{t}}, z, x\right) \]

      11. lower-/.f64 73.2%

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{y}{t}}, z, x\right) \]

   6. Applied rewrites 73.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{y}{t}, z, x\right)} \]

   if -5.8e12 < y < 1.50000000000000009e-106

   1. Initial program 92.3%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. lower--.f64 N/A

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

      4. lift-/.f64 N/A

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

      5. distribute-neg-frac2 N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. associate-/l* N/A

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

      9. *-commutative N/A

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

      10. lower-*.f64 N/A

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

      11. lift--.f64 N/A

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

      12. sub-negate-rev N/A

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

      13. frac-2neg-rev N/A

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

      14. lower-/.f64 N/A

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

      15. lower--.f64 92.7%

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

   3. Applied rewrites 92.7%

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

   4. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-/.f64 64.9%

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

   6. Applied rewrites 64.9%

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

Alternative 5: 83.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_1 := \mathsf{fma}\left(\frac{y}{t}, z, x\right)\\ \mathbf{if}\;t \leq -0.00085:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 3.25 \cdot 10^{-108}:\\ \;\;\;\;\frac{z \cdot \left(y - x\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (fma (/ y t) z x)))
   (if (<= t -0.00085) t_1 (if (<= t 3.25e-108) (/ (* z (- y x)) t) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = fma((y / t), z, x);
	double tmp;
	if (t <= -0.00085) {
		tmp = t_1;
	} else if (t <= 3.25e-108) {
		tmp = (z * (y - x)) / t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = fma(Float64(y / t), z, x)
	tmp = 0.0
	if (t <= -0.00085)
		tmp = t_1;
	elseif (t <= 3.25e-108)
		tmp = Float64(Float64(z * Float64(y - x)) / t);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y / t), $MachinePrecision] * z + x), $MachinePrecision]}, If[LessEqual[t, -0.00085], t$95$1, If[LessEqual[t, 3.25e-108], N[(N[(z * N[(y - x), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if t < -8.49999999999999953e-4 or 3.2500000000000001e-108 < t

   1. Initial program 92.3%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 73.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-/.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. associate-/l* N/A

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

      6. mult-flip N/A

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

      7. *-commutative N/A

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

      8. associate-*r* N/A

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

      9. lower-fma.f64 N/A

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

      10. mult-flip-rev N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{y}{t}}, z, x\right) \]

      11. lower-/.f64 73.2%

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{y}{t}}, z, x\right) \]

   6. Applied rewrites 73.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{y}{t}, z, x\right)} \]

   if -8.49999999999999953e-4 < t < 3.2500000000000001e-108

   1. Initial program 92.3%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 38.2%

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

   4. Applied rewrites 38.2%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. associate-/l* N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 38.0%

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

   6. Applied rewrites 38.0%

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

   7. Taylor expanded in z around -inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 58.4%

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

   9. Applied rewrites 58.4%

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

Alternative 6: 73.2% accurate, 1.4× speedup

Math

\[\mathsf{fma}\left(\frac{y}{t}, z, x\right) \]

FPCore

(FPCore (x y z t) :precision binary64 (fma (/ y t) z x))

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 92.3%

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

2. Taylor expanded in x around 0

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

4. Applied rewrites 73.3%

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

   1. lift-+.f64 N/A

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

   2. +-commutative N/A

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

   3. lift-/.f64 N/A

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

   4. lift-*.f64 N/A

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

   5. associate-/l* N/A

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

   6. mult-flip N/A

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

   7. *-commutative N/A

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

   8. associate-*r* N/A

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

   9. lower-fma.f64 N/A

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

   10. mult-flip-rev N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{y}{t}}, z, x\right) \]

   11. lower-/.f64 73.2%

       \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{y}{t}}, z, x\right) \]

6. Applied rewrites 73.2%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{y}{t}, z, x\right)} \]
7. Add Preprocessing

Alternative 7: 41.4% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

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 = z / (t / y)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 92.3%

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

2. Taylor expanded in x around 0

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

   1. lower-/.f64 N/A

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

   2. lower-*.f64 38.2%

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

4. Applied rewrites 38.2%

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

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-/l* N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

   7. lower-/.f64 38.0%

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

6. Applied rewrites 38.0%

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

   1. lift-*.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. div-flip-rev N/A

      \[\leadsto \frac{1}{\frac{t}{y}} \cdot z \]

   4. associate-/r/ N/A

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{t}{y}}{z}}} \]

   5. div-flip-rev N/A

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

   6. lower-/.f64 N/A

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

   7. lower-/.f64 38.2%

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

8. Applied rewrites 38.2%

   \[\leadsto \frac{z}{\color{blue}{\frac{t}{y}}} \]
9. Add Preprocessing

Alternative 8: 38.2% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

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 = (z / t) * y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 92.3%

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

2. Taylor expanded in x around 0

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

   1. lower-/.f64 N/A

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

   2. lower-*.f64 38.2%

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

4. Applied rewrites 38.2%

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

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/l* N/A

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

   4. lift-/.f64 N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 41.4%

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

6. Applied rewrites 41.4%

   \[\leadsto \frac{z}{t} \cdot \color{blue}{y} \]
7. Add Preprocessing

Alternative 9: 38.0% accurate, 1.7× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t) {
	return (y / 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 = (y / t) * z
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 92.3%

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

2. Taylor expanded in x around 0

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

   1. lower-/.f64 N/A

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

   2. lower-*.f64 38.2%

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

4. Applied rewrites 38.2%

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

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-/l* N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

   7. lower-/.f64 38.0%

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

6. Applied rewrites 38.0%

   \[\leadsto \frac{y}{t} \cdot \color{blue}{z} \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2025183 
(FPCore (x y z t)
  :name "Numeric.Histogram:binBounds from Chart-1.5.3"
  :precision binary64
  (+ x (/ (* (- y x) z) t)))