SynthBasics:oscSampleBasedAux from YampaSynth-0.2

Percentage Accurate: 100.0% → 100.0%

Time: 3.7s

Alternatives: 6

Speedup: 1.0×

• 20.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

2. Final simplification 100.0%

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

Alternative 2: 60.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(-x\right)\\ \mathbf{if}\;y \leq -2.7 \cdot 10^{+229}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -1.7 \cdot 10^{+207}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{+133}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -1.6 \cdot 10^{+57}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{+27}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -2.8 \cdot 10^{-49}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 7.8 \cdot 10^{-84}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 3.9 \cdot 10^{-41}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 2.45 \cdot 10^{-14}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* y (- x))))
   (if (<= y -2.7e+229)
     t_0
     (if (<= y -1.7e+207)
       (* y z)
       (if (<= y -1.15e+133)
         t_0
         (if (<= y -1.6e+57)
           (* y z)
           (if (<= y -1.15e+27)
             t_0
             (if (<= y -2.8e-49)
               (* y z)
               (if (<= y 7.8e-84)
                 x
                 (if (<= y 3.9e-41)
                   (* y z)
                   (if (<= y 2.45e-14) x (* y z))))))))))))

C

double code(double x, double y, double z) {
	double t_0 = y * -x;
	double tmp;
	if (y <= -2.7e+229) {
		tmp = t_0;
	} else if (y <= -1.7e+207) {
		tmp = y * z;
	} else if (y <= -1.15e+133) {
		tmp = t_0;
	} else if (y <= -1.6e+57) {
		tmp = y * z;
	} else if (y <= -1.15e+27) {
		tmp = t_0;
	} else if (y <= -2.8e-49) {
		tmp = y * z;
	} else if (y <= 7.8e-84) {
		tmp = x;
	} else if (y <= 3.9e-41) {
		tmp = y * z;
	} else if (y <= 2.45e-14) {
		tmp = x;
	} else {
		tmp = y * z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = y * -x
    if (y <= (-2.7d+229)) then
        tmp = t_0
    else if (y <= (-1.7d+207)) then
        tmp = y * z
    else if (y <= (-1.15d+133)) then
        tmp = t_0
    else if (y <= (-1.6d+57)) then
        tmp = y * z
    else if (y <= (-1.15d+27)) then
        tmp = t_0
    else if (y <= (-2.8d-49)) then
        tmp = y * z
    else if (y <= 7.8d-84) then
        tmp = x
    else if (y <= 3.9d-41) then
        tmp = y * z
    else if (y <= 2.45d-14) then
        tmp = x
    else
        tmp = y * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = y * -x;
	double tmp;
	if (y <= -2.7e+229) {
		tmp = t_0;
	} else if (y <= -1.7e+207) {
		tmp = y * z;
	} else if (y <= -1.15e+133) {
		tmp = t_0;
	} else if (y <= -1.6e+57) {
		tmp = y * z;
	} else if (y <= -1.15e+27) {
		tmp = t_0;
	} else if (y <= -2.8e-49) {
		tmp = y * z;
	} else if (y <= 7.8e-84) {
		tmp = x;
	} else if (y <= 3.9e-41) {
		tmp = y * z;
	} else if (y <= 2.45e-14) {
		tmp = x;
	} else {
		tmp = y * z;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = y * -x
	tmp = 0
	if y <= -2.7e+229:
		tmp = t_0
	elif y <= -1.7e+207:
		tmp = y * z
	elif y <= -1.15e+133:
		tmp = t_0
	elif y <= -1.6e+57:
		tmp = y * z
	elif y <= -1.15e+27:
		tmp = t_0
	elif y <= -2.8e-49:
		tmp = y * z
	elif y <= 7.8e-84:
		tmp = x
	elif y <= 3.9e-41:
		tmp = y * z
	elif y <= 2.45e-14:
		tmp = x
	else:
		tmp = y * z
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(y * Float64(-x))
	tmp = 0.0
	if (y <= -2.7e+229)
		tmp = t_0;
	elseif (y <= -1.7e+207)
		tmp = Float64(y * z);
	elseif (y <= -1.15e+133)
		tmp = t_0;
	elseif (y <= -1.6e+57)
		tmp = Float64(y * z);
	elseif (y <= -1.15e+27)
		tmp = t_0;
	elseif (y <= -2.8e-49)
		tmp = Float64(y * z);
	elseif (y <= 7.8e-84)
		tmp = x;
	elseif (y <= 3.9e-41)
		tmp = Float64(y * z);
	elseif (y <= 2.45e-14)
		tmp = x;
	else
		tmp = Float64(y * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = y * -x;
	tmp = 0.0;
	if (y <= -2.7e+229)
		tmp = t_0;
	elseif (y <= -1.7e+207)
		tmp = y * z;
	elseif (y <= -1.15e+133)
		tmp = t_0;
	elseif (y <= -1.6e+57)
		tmp = y * z;
	elseif (y <= -1.15e+27)
		tmp = t_0;
	elseif (y <= -2.8e-49)
		tmp = y * z;
	elseif (y <= 7.8e-84)
		tmp = x;
	elseif (y <= 3.9e-41)
		tmp = y * z;
	elseif (y <= 2.45e-14)
		tmp = x;
	else
		tmp = y * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(y * (-x)), $MachinePrecision]}, If[LessEqual[y, -2.7e+229], t$95$0, If[LessEqual[y, -1.7e+207], N[(y * z), $MachinePrecision], If[LessEqual[y, -1.15e+133], t$95$0, If[LessEqual[y, -1.6e+57], N[(y * z), $MachinePrecision], If[LessEqual[y, -1.15e+27], t$95$0, If[LessEqual[y, -2.8e-49], N[(y * z), $MachinePrecision], If[LessEqual[y, 7.8e-84], x, If[LessEqual[y, 3.9e-41], N[(y * z), $MachinePrecision], If[LessEqual[y, 2.45e-14], x, N[(y * z), $MachinePrecision]]]]]]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -2.7e229 or -1.6999999999999999e207 < y < -1.14999999999999995e133 or -1.60000000000000015e57 < y < -1.15e27

   1. Initial program 100.0%

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

   2. Taylor expanded in z around 0 79.0%

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

      1. mul-1-neg 79.0%

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

      2. distribute-lft-neg-out 79.0%

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

      3. *-commutative 79.0%

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

   4. Simplified 79.0%

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

   5. Taylor expanded in y around inf 79.0%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot y\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 79.0%

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

      2. mul-1-neg 79.0%

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

   7. Simplified 79.0%

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

   if -2.7e229 < y < -1.6999999999999999e207 or -1.14999999999999995e133 < y < -1.60000000000000015e57 or -1.15e27 < y < -2.79999999999999997e-49 or 7.80000000000000045e-84 < y < 3.89999999999999991e-41 or 2.44999999999999997e-14 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in z around inf 77.6%

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

   3. Taylor expanded in x around 0 73.1%

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

   if -2.79999999999999997e-49 < y < 7.80000000000000045e-84 or 3.89999999999999991e-41 < y < 2.44999999999999997e-14

   1. Initial program 100.0%

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

   2. Taylor expanded in z around inf 99.9%

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

   3. Taylor expanded in x around inf 76.3%

      \[\leadsto \color{blue}{x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 75.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.7 \cdot 10^{+229}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \mathbf{elif}\;y \leq -1.7 \cdot 10^{+207}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{+133}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \mathbf{elif}\;y \leq -1.6 \cdot 10^{+57}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{+27}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \mathbf{elif}\;y \leq -2.8 \cdot 10^{-49}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 7.8 \cdot 10^{-84}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 3.9 \cdot 10^{-41}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 2.45 \cdot 10^{-14}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \]

Alternative 3: 60.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.95 \cdot 10^{-49}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 8 \cdot 10^{-84}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 8.2 \cdot 10^{-42}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 7.9 \cdot 10^{-13}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -2.95e-49)
   (* y z)
   (if (<= y 8e-84)
     x
     (if (<= y 8.2e-42) (* y z) (if (<= y 7.9e-13) x (* y z))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -2.95e-49) {
		tmp = y * z;
	} else if (y <= 8e-84) {
		tmp = x;
	} else if (y <= 8.2e-42) {
		tmp = y * z;
	} else if (y <= 7.9e-13) {
		tmp = x;
	} else {
		tmp = y * z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-2.95d-49)) then
        tmp = y * z
    else if (y <= 8d-84) then
        tmp = x
    else if (y <= 8.2d-42) then
        tmp = y * z
    else if (y <= 7.9d-13) then
        tmp = x
    else
        tmp = y * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -2.95e-49) {
		tmp = y * z;
	} else if (y <= 8e-84) {
		tmp = x;
	} else if (y <= 8.2e-42) {
		tmp = y * z;
	} else if (y <= 7.9e-13) {
		tmp = x;
	} else {
		tmp = y * z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -2.95e-49:
		tmp = y * z
	elif y <= 8e-84:
		tmp = x
	elif y <= 8.2e-42:
		tmp = y * z
	elif y <= 7.9e-13:
		tmp = x
	else:
		tmp = y * z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -2.95e-49)
		tmp = Float64(y * z);
	elseif (y <= 8e-84)
		tmp = x;
	elseif (y <= 8.2e-42)
		tmp = Float64(y * z);
	elseif (y <= 7.9e-13)
		tmp = x;
	else
		tmp = Float64(y * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -2.95e-49)
		tmp = y * z;
	elseif (y <= 8e-84)
		tmp = x;
	elseif (y <= 8.2e-42)
		tmp = y * z;
	elseif (y <= 7.9e-13)
		tmp = x;
	else
		tmp = y * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -2.95e-49], N[(y * z), $MachinePrecision], If[LessEqual[y, 8e-84], x, If[LessEqual[y, 8.2e-42], N[(y * z), $MachinePrecision], If[LessEqual[y, 7.9e-13], x, N[(y * z), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if y < -2.95000000000000018e-49 or 8.0000000000000003e-84 < y < 8.2000000000000003e-42 or 7.89999999999999966e-13 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in z around inf 66.2%

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

   3. Taylor expanded in x around 0 62.5%

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

   if -2.95000000000000018e-49 < y < 8.0000000000000003e-84 or 8.2000000000000003e-42 < y < 7.89999999999999966e-13

   1. Initial program 100.0%

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

   2. Taylor expanded in z around inf 99.9%

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

   3. Taylor expanded in x around inf 76.3%

      \[\leadsto \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 67.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.95 \cdot 10^{-49}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 8 \cdot 10^{-84}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 8.2 \cdot 10^{-42}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 7.9 \cdot 10^{-13}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \]

Alternative 4: 86.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.45 \cdot 10^{+111} \lor \neg \left(x \leq 7.5 \cdot 10^{+15}\right):\\ \;\;\;\;x - x \cdot y\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -1.45e+111) (not (<= x 7.5e+15))) (- x (* x y)) (+ x (* y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.45e+111) || !(x <= 7.5e+15)) {
		tmp = x - (x * y);
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-1.45d+111)) .or. (.not. (x <= 7.5d+15))) then
        tmp = x - (x * y)
    else
        tmp = x + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.45e+111) || !(x <= 7.5e+15)) {
		tmp = x - (x * y);
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -1.45e+111) or not (x <= 7.5e+15):
		tmp = x - (x * y)
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -1.45e+111) || !(x <= 7.5e+15))
		tmp = Float64(x - Float64(x * y));
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -1.45e+111) || ~((x <= 7.5e+15)))
		tmp = x - (x * y);
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -1.45e+111], N[Not[LessEqual[x, 7.5e+15]], $MachinePrecision]], N[(x - N[(x * y), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.45e111 or 7.5e15 < x

   1. Initial program 100.0%

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

   2. Taylor expanded in z around 0 91.9%

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

      1. mul-1-neg 91.9%

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

      2. distribute-lft-neg-out 91.9%

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

      3. *-commutative 91.9%

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

   4. Simplified 91.9%

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

   5. Taylor expanded in x around 0 91.9%

      \[\leadsto \color{blue}{x \cdot \left(1 + -1 \cdot y\right)} \]
   6. Step-by-step derivation

      1. neg-mul-1 91.9%

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

      2. distribute-rgt-in 91.9%

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

      3. *-lft-identity 91.9%

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

      4. cancel-sign-sub-inv 91.9%

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

   7. Simplified 91.9%

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

   if -1.45e111 < x < 7.5e15

   1. Initial program 100.0%

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

   2. Taylor expanded in z around inf 89.7%

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

4. Final simplification 90.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.45 \cdot 10^{+111} \lor \neg \left(x \leq 7.5 \cdot 10^{+15}\right):\\ \;\;\;\;x - x \cdot y\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \]

Alternative 5: 75.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ x + y \cdot z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ x (* y z)))

C

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

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x + (y * z)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

2. Taylor expanded in z around inf 79.1%

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

3. Final simplification 79.1%

   \[\leadsto x + y \cdot z \]

Alternative 6: 36.6% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ x \end{array} \]

FPCore

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

C

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

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x
end function

Java

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

Python

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

Julia

function code(x, y, z)
	return x
end

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 100.0%

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

2. Taylor expanded in z around inf 79.1%

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

3. Taylor expanded in x around inf 33.4%

   \[\leadsto \color{blue}{x} \]

4. Final simplification 33.4%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023290 
(FPCore (x y z)
  :name "SynthBasics:oscSampleBasedAux from YampaSynth-0.2"
  :precision binary64
  (+ x (* y (- z x))))