Statistics.Distribution.Beta:$centropy from math-functions-0.1.5.2

Percentage Accurate: 95.2% → 97.9%

Time: 15.1s

Alternatives: 25

Speedup: 0.5×

• 35.6% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* (- (+ y t) 2.0) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = ((x - ((y - 1.0d0) * z)) - ((t - 1.0d0) * a)) + (((y + t) - 2.0d0) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

Python

def code(x, y, z, t, a, b):
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x - Float64(Float64(y - 1.0) * z)) - Float64(Float64(t - 1.0) * a)) + Float64(Float64(Float64(y + t) - 2.0) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x - N[(N[(y - 1.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - N[(N[(t - 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Initial Program: 95.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* (- (+ y t) 2.0) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = ((x - ((y - 1.0d0) * z)) - ((t - 1.0d0) * a)) + (((y + t) - 2.0d0) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

Python

def code(x, y, z, t, a, b):
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x - Float64(Float64(y - 1.0) * z)) - Float64(Float64(t - 1.0) * a)) + Float64(Float64(Float64(y + t) - 2.0) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x - N[(N[(y - 1.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - N[(N[(t - 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Alternative 1: 97.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(x + z \cdot \left(1 - y\right)\right) + a \cdot \left(1 - t\right)\right) + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;t\_1 \leq \infty:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1
         (+ (+ (+ x (* z (- 1.0 y))) (* a (- 1.0 t))) (* b (- (+ y t) 2.0)))))
   (if (<= t_1 INFINITY) t_1 (* t (- b a)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (b * ((y + t) - 2.0));
	double tmp;
	if (t_1 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = t * (b - a);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (b * ((y + t) - 2.0));
	double tmp;
	if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else {
		tmp = t * (b - a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (b * ((y + t) - 2.0))
	tmp = 0
	if t_1 <= math.inf:
		tmp = t_1
	else:
		tmp = t * (b - a)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(x + Float64(z * Float64(1.0 - y))) + Float64(a * Float64(1.0 - t))) + Float64(b * Float64(Float64(y + t) - 2.0)))
	tmp = 0.0
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = Float64(t * Float64(b - a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (b * ((y + t) - 2.0));
	tmp = 0.0;
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = t * (b - a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(x + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, Infinity], t$95$1, N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (-.f64 x (*.f64 (-.f64 y #s(literal 1 binary64)) z)) (*.f64 (-.f64 t #s(literal 1 binary64)) a)) (*.f64 (-.f64 (+.f64 y t) #s(literal 2 binary64)) b)) < +inf.0

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   if +inf.0 < (+.f64 (-.f64 (-.f64 x (*.f64 (-.f64 y #s(literal 1 binary64)) z)) (*.f64 (-.f64 t #s(literal 1 binary64)) a)) (*.f64 (-.f64 (+.f64 y t) #s(literal 2 binary64)) b))

   1. Initial program 0.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 67.0%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(x + z \cdot \left(1 - y\right)\right) + a \cdot \left(1 - t\right)\right) + b \cdot \left(\left(y + t\right) - 2\right) \leq \infty:\\ \;\;\;\;\left(\left(x + z \cdot \left(1 - y\right)\right) + a \cdot \left(1 - t\right)\right) + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 97.3% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y + \left(t + -2\right), b, x - \mathsf{fma}\left(y + -1, z, a \cdot \left(t + -1\right)\right)\right) \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (fma (+ y (+ t -2.0)) b (- x (fma (+ y -1.0) z (* a (+ t -1.0))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return fma((y + (t + -2.0)), b, (x - fma((y + -1.0), z, (a * (t + -1.0)))));
}

Julia

function code(x, y, z, t, a, b)
	return fma(Float64(y + Float64(t + -2.0)), b, Float64(x - fma(Float64(y + -1.0), z, Float64(a * Float64(t + -1.0)))))
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(y + N[(t + -2.0), $MachinePrecision]), $MachinePrecision] * b + N[(x - N[(N[(y + -1.0), $MachinePrecision] * z + N[(a * N[(t + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 95.3%

   \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
2. Step-by-step derivation

   1. +-commutative 95.3%

      \[\leadsto \color{blue}{\left(\left(y + t\right) - 2\right) \cdot b + \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right)} \]

   2. fma-define 98.0%

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

   3. associate--l+ 98.0%

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

   4. sub-neg 98.0%

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

   5. metadata-eval 98.0%

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

   6. sub-neg 98.0%

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

   7. associate-+l- 98.0%

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

   8. fmm-def 98.4%

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

   9. sub-neg 98.4%

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

   10. metadata-eval 98.4%

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

   11. remove-double-neg 98.4%

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

   12. sub-neg 98.4%

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

   13. metadata-eval 98.4%

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

3. Simplified 98.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y + \left(t + -2\right), b, x - \mathsf{fma}\left(y + -1, z, \left(t + -1\right) \cdot a\right)\right)} \]
4. Add Preprocessing

5. Final simplification 98.4%

   \[\leadsto \mathsf{fma}\left(y + \left(t + -2\right), b, x - \mathsf{fma}\left(y + -1, z, a \cdot \left(t + -1\right)\right)\right) \]
6. Add Preprocessing

Alternative 3: 51.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + a \cdot \left(1 - t\right)\\ \mathbf{if}\;z \leq -1.45 \cdot 10^{+171}:\\ \;\;\;\;z \cdot \left(1 - y\right)\\ \mathbf{elif}\;z \leq -5.3 \cdot 10^{+101}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq -2.95 \cdot 10^{+56}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{elif}\;z \leq -1.15 \cdot 10^{-169}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 1.75 \cdot 10^{-276}:\\ \;\;\;\;b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{elif}\;z \leq 1.85 \cdot 10^{+73}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;z - y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* a (- 1.0 t)))))
   (if (<= z -1.45e+171)
     (* z (- 1.0 y))
     (if (<= z -5.3e+101)
       t_1
       (if (<= z -2.95e+56)
         (* y (- b z))
         (if (<= z -1.15e-169)
           t_1
           (if (<= z 1.75e-276)
             (* b (- (+ y t) 2.0))
             (if (<= z 1.85e+73) t_1 (- z (* y z))))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (a * (1.0 - t));
	double tmp;
	if (z <= -1.45e+171) {
		tmp = z * (1.0 - y);
	} else if (z <= -5.3e+101) {
		tmp = t_1;
	} else if (z <= -2.95e+56) {
		tmp = y * (b - z);
	} else if (z <= -1.15e-169) {
		tmp = t_1;
	} else if (z <= 1.75e-276) {
		tmp = b * ((y + t) - 2.0);
	} else if (z <= 1.85e+73) {
		tmp = t_1;
	} else {
		tmp = z - (y * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (a * (1.0d0 - t))
    if (z <= (-1.45d+171)) then
        tmp = z * (1.0d0 - y)
    else if (z <= (-5.3d+101)) then
        tmp = t_1
    else if (z <= (-2.95d+56)) then
        tmp = y * (b - z)
    else if (z <= (-1.15d-169)) then
        tmp = t_1
    else if (z <= 1.75d-276) then
        tmp = b * ((y + t) - 2.0d0)
    else if (z <= 1.85d+73) then
        tmp = t_1
    else
        tmp = z - (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (a * (1.0 - t));
	double tmp;
	if (z <= -1.45e+171) {
		tmp = z * (1.0 - y);
	} else if (z <= -5.3e+101) {
		tmp = t_1;
	} else if (z <= -2.95e+56) {
		tmp = y * (b - z);
	} else if (z <= -1.15e-169) {
		tmp = t_1;
	} else if (z <= 1.75e-276) {
		tmp = b * ((y + t) - 2.0);
	} else if (z <= 1.85e+73) {
		tmp = t_1;
	} else {
		tmp = z - (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (a * (1.0 - t))
	tmp = 0
	if z <= -1.45e+171:
		tmp = z * (1.0 - y)
	elif z <= -5.3e+101:
		tmp = t_1
	elif z <= -2.95e+56:
		tmp = y * (b - z)
	elif z <= -1.15e-169:
		tmp = t_1
	elif z <= 1.75e-276:
		tmp = b * ((y + t) - 2.0)
	elif z <= 1.85e+73:
		tmp = t_1
	else:
		tmp = z - (y * z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(a * Float64(1.0 - t)))
	tmp = 0.0
	if (z <= -1.45e+171)
		tmp = Float64(z * Float64(1.0 - y));
	elseif (z <= -5.3e+101)
		tmp = t_1;
	elseif (z <= -2.95e+56)
		tmp = Float64(y * Float64(b - z));
	elseif (z <= -1.15e-169)
		tmp = t_1;
	elseif (z <= 1.75e-276)
		tmp = Float64(b * Float64(Float64(y + t) - 2.0));
	elseif (z <= 1.85e+73)
		tmp = t_1;
	else
		tmp = Float64(z - Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (a * (1.0 - t));
	tmp = 0.0;
	if (z <= -1.45e+171)
		tmp = z * (1.0 - y);
	elseif (z <= -5.3e+101)
		tmp = t_1;
	elseif (z <= -2.95e+56)
		tmp = y * (b - z);
	elseif (z <= -1.15e-169)
		tmp = t_1;
	elseif (z <= 1.75e-276)
		tmp = b * ((y + t) - 2.0);
	elseif (z <= 1.85e+73)
		tmp = t_1;
	else
		tmp = z - (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.45e+171], N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -5.3e+101], t$95$1, If[LessEqual[z, -2.95e+56], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.15e-169], t$95$1, If[LessEqual[z, 1.75e-276], N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.85e+73], t$95$1, N[(z - N[(y * z), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 5 regimes

2. if z < -1.44999999999999992e171

   1. Initial program 90.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 67.2%

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

   if -1.44999999999999992e171 < z < -5.30000000000000006e101 or -2.9500000000000001e56 < z < -1.15e-169 or 1.74999999999999996e-276 < z < 1.84999999999999987e73

   1. Initial program 97.6%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 92.9%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in b around 0 62.8%

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

   if -5.30000000000000006e101 < z < -2.9500000000000001e56

   1. Initial program 80.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf 62.5%

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

   if -1.15e-169 < z < 1.74999999999999996e-276

   1. Initial program 94.1%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf 63.1%

      \[\leadsto \color{blue}{b \cdot \left(\left(t + y\right) - 2\right)} \]

   if 1.84999999999999987e73 < z

   1. Initial program 96.5%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 63.8%

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

   4. Taylor expanded in y around 0 63.8%

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

      1. mul-1-neg 63.8%

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

      2. unsub-neg 63.8%

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

   6. Applied egg-rr 63.8%

      \[\leadsto \color{blue}{z - y \cdot z} \]
3. Recombined 5 regimes into one program.

4. Final simplification 63.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.45 \cdot 10^{+171}:\\ \;\;\;\;z \cdot \left(1 - y\right)\\ \mathbf{elif}\;z \leq -5.3 \cdot 10^{+101}:\\ \;\;\;\;x + a \cdot \left(1 - t\right)\\ \mathbf{elif}\;z \leq -2.95 \cdot 10^{+56}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{elif}\;z \leq -1.15 \cdot 10^{-169}:\\ \;\;\;\;x + a \cdot \left(1 - t\right)\\ \mathbf{elif}\;z \leq 1.75 \cdot 10^{-276}:\\ \;\;\;\;b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{elif}\;z \leq 1.85 \cdot 10^{+73}:\\ \;\;\;\;x + a \cdot \left(1 - t\right)\\ \mathbf{else}:\\ \;\;\;\;z - y \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 60.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z + y \cdot \left(b - z\right)\\ t_2 := x + a \cdot \left(1 - t\right)\\ t_3 := x + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;a \leq -9.5 \cdot 10^{+174}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;a \leq -6.4 \cdot 10^{+106}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq -3.3 \cdot 10^{-131}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;a \leq -1.05 \cdot 10^{-163}:\\ \;\;\;\;x + z \cdot \left(1 - y\right)\\ \mathbf{elif}\;a \leq 1.5 \cdot 10^{-62}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;a \leq 2.35 \cdot 10^{+97}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ z (* y (- b z))))
        (t_2 (+ x (* a (- 1.0 t))))
        (t_3 (+ x (* b (- (+ y t) 2.0)))))
   (if (<= a -9.5e+174)
     t_2
     (if (<= a -6.4e+106)
       t_1
       (if (<= a -3.3e-131)
         t_3
         (if (<= a -1.05e-163)
           (+ x (* z (- 1.0 y)))
           (if (<= a 1.5e-62) t_3 (if (<= a 2.35e+97) t_1 t_2))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z + (y * (b - z));
	double t_2 = x + (a * (1.0 - t));
	double t_3 = x + (b * ((y + t) - 2.0));
	double tmp;
	if (a <= -9.5e+174) {
		tmp = t_2;
	} else if (a <= -6.4e+106) {
		tmp = t_1;
	} else if (a <= -3.3e-131) {
		tmp = t_3;
	} else if (a <= -1.05e-163) {
		tmp = x + (z * (1.0 - y));
	} else if (a <= 1.5e-62) {
		tmp = t_3;
	} else if (a <= 2.35e+97) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = z + (y * (b - z))
    t_2 = x + (a * (1.0d0 - t))
    t_3 = x + (b * ((y + t) - 2.0d0))
    if (a <= (-9.5d+174)) then
        tmp = t_2
    else if (a <= (-6.4d+106)) then
        tmp = t_1
    else if (a <= (-3.3d-131)) then
        tmp = t_3
    else if (a <= (-1.05d-163)) then
        tmp = x + (z * (1.0d0 - y))
    else if (a <= 1.5d-62) then
        tmp = t_3
    else if (a <= 2.35d+97) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z + (y * (b - z));
	double t_2 = x + (a * (1.0 - t));
	double t_3 = x + (b * ((y + t) - 2.0));
	double tmp;
	if (a <= -9.5e+174) {
		tmp = t_2;
	} else if (a <= -6.4e+106) {
		tmp = t_1;
	} else if (a <= -3.3e-131) {
		tmp = t_3;
	} else if (a <= -1.05e-163) {
		tmp = x + (z * (1.0 - y));
	} else if (a <= 1.5e-62) {
		tmp = t_3;
	} else if (a <= 2.35e+97) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z + (y * (b - z))
	t_2 = x + (a * (1.0 - t))
	t_3 = x + (b * ((y + t) - 2.0))
	tmp = 0
	if a <= -9.5e+174:
		tmp = t_2
	elif a <= -6.4e+106:
		tmp = t_1
	elif a <= -3.3e-131:
		tmp = t_3
	elif a <= -1.05e-163:
		tmp = x + (z * (1.0 - y))
	elif a <= 1.5e-62:
		tmp = t_3
	elif a <= 2.35e+97:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z + Float64(y * Float64(b - z)))
	t_2 = Float64(x + Float64(a * Float64(1.0 - t)))
	t_3 = Float64(x + Float64(b * Float64(Float64(y + t) - 2.0)))
	tmp = 0.0
	if (a <= -9.5e+174)
		tmp = t_2;
	elseif (a <= -6.4e+106)
		tmp = t_1;
	elseif (a <= -3.3e-131)
		tmp = t_3;
	elseif (a <= -1.05e-163)
		tmp = Float64(x + Float64(z * Float64(1.0 - y)));
	elseif (a <= 1.5e-62)
		tmp = t_3;
	elseif (a <= 2.35e+97)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z + (y * (b - z));
	t_2 = x + (a * (1.0 - t));
	t_3 = x + (b * ((y + t) - 2.0));
	tmp = 0.0;
	if (a <= -9.5e+174)
		tmp = t_2;
	elseif (a <= -6.4e+106)
		tmp = t_1;
	elseif (a <= -3.3e-131)
		tmp = t_3;
	elseif (a <= -1.05e-163)
		tmp = x + (z * (1.0 - y));
	elseif (a <= 1.5e-62)
		tmp = t_3;
	elseif (a <= 2.35e+97)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z + N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x + N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -9.5e+174], t$95$2, If[LessEqual[a, -6.4e+106], t$95$1, If[LessEqual[a, -3.3e-131], t$95$3, If[LessEqual[a, -1.05e-163], N[(x + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.5e-62], t$95$3, If[LessEqual[a, 2.35e+97], t$95$1, t$95$2]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if a < -9.4999999999999992e174 or 2.3499999999999999e97 < a

   1. Initial program 91.5%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 85.6%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in b around 0 80.1%

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

   if -9.4999999999999992e174 < a < -6.3999999999999996e106 or 1.5000000000000001e-62 < a < 2.3499999999999999e97

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 100.0%

      \[\leadsto \color{blue}{\left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \left(-1 \cdot z + a \cdot \left(t - 1\right)\right)} \]

   4. Taylor expanded in z around inf 82.4%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{-1 \cdot z} \]
   5. Step-by-step derivation

      1. mul-1-neg 82.4%

         \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   6. Simplified 82.4%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   7. Taylor expanded in y around inf 70.5%

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

   if -6.3999999999999996e106 < a < -3.3000000000000002e-131 or -1.04999999999999999e-163 < a < 1.5000000000000001e-62

   1. Initial program 96.3%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 72.3%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in a around 0 67.4%

      \[\leadsto \color{blue}{x + b \cdot \left(\left(t + y\right) - 2\right)} \]

   if -3.3000000000000002e-131 < a < -1.04999999999999999e-163

   1. Initial program 87.5%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 98.1%

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

   4. Taylor expanded in a around 0 86.5%

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

4. Final simplification 72.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -9.5 \cdot 10^{+174}:\\ \;\;\;\;x + a \cdot \left(1 - t\right)\\ \mathbf{elif}\;a \leq -6.4 \cdot 10^{+106}:\\ \;\;\;\;z + y \cdot \left(b - z\right)\\ \mathbf{elif}\;a \leq -3.3 \cdot 10^{-131}:\\ \;\;\;\;x + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{elif}\;a \leq -1.05 \cdot 10^{-163}:\\ \;\;\;\;x + z \cdot \left(1 - y\right)\\ \mathbf{elif}\;a \leq 1.5 \cdot 10^{-62}:\\ \;\;\;\;x + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{elif}\;a \leq 2.35 \cdot 10^{+97}:\\ \;\;\;\;z + y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;x + a \cdot \left(1 - t\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 81.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;b \leq -6 \cdot 10^{+88}:\\ \;\;\;\;t\_1 - y \cdot z\\ \mathbf{elif}\;b \leq 3.3 \cdot 10^{+26} \lor \neg \left(b \leq 2.55 \cdot 10^{+61}\right) \land b \leq 1.1 \cdot 10^{+166}:\\ \;\;\;\;x - \left(a \cdot \left(t + -1\right) + z \cdot \left(y + -1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* b (- (+ y t) 2.0))))
   (if (<= b -6e+88)
     (- t_1 (* y z))
     (if (or (<= b 3.3e+26) (and (not (<= b 2.55e+61)) (<= b 1.1e+166)))
       (- x (+ (* a (+ t -1.0)) (* z (+ y -1.0))))
       (+ x t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -6e+88) {
		tmp = t_1 - (y * z);
	} else if ((b <= 3.3e+26) || (!(b <= 2.55e+61) && (b <= 1.1e+166))) {
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	} else {
		tmp = x + t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = b * ((y + t) - 2.0d0)
    if (b <= (-6d+88)) then
        tmp = t_1 - (y * z)
    else if ((b <= 3.3d+26) .or. (.not. (b <= 2.55d+61)) .and. (b <= 1.1d+166)) then
        tmp = x - ((a * (t + (-1.0d0))) + (z * (y + (-1.0d0))))
    else
        tmp = x + t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -6e+88) {
		tmp = t_1 - (y * z);
	} else if ((b <= 3.3e+26) || (!(b <= 2.55e+61) && (b <= 1.1e+166))) {
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	} else {
		tmp = x + t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = b * ((y + t) - 2.0)
	tmp = 0
	if b <= -6e+88:
		tmp = t_1 - (y * z)
	elif (b <= 3.3e+26) or (not (b <= 2.55e+61) and (b <= 1.1e+166)):
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)))
	else:
		tmp = x + t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(b * Float64(Float64(y + t) - 2.0))
	tmp = 0.0
	if (b <= -6e+88)
		tmp = Float64(t_1 - Float64(y * z));
	elseif ((b <= 3.3e+26) || (!(b <= 2.55e+61) && (b <= 1.1e+166)))
		tmp = Float64(x - Float64(Float64(a * Float64(t + -1.0)) + Float64(z * Float64(y + -1.0))));
	else
		tmp = Float64(x + t_1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = b * ((y + t) - 2.0);
	tmp = 0.0;
	if (b <= -6e+88)
		tmp = t_1 - (y * z);
	elseif ((b <= 3.3e+26) || (~((b <= 2.55e+61)) && (b <= 1.1e+166)))
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	else
		tmp = x + t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -6e+88], N[(t$95$1 - N[(y * z), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[b, 3.3e+26], And[N[Not[LessEqual[b, 2.55e+61]], $MachinePrecision], LessEqual[b, 1.1e+166]]], N[(x - N[(N[(a * N[(t + -1.0), $MachinePrecision]), $MachinePrecision] + N[(z * N[(y + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + t$95$1), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if b < -6.00000000000000011e88

   1. Initial program 92.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf 82.4%

      \[\leadsto \color{blue}{-1 \cdot \left(y \cdot z\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   4. Step-by-step derivation

      1. mul-1-neg 82.4%

         \[\leadsto \color{blue}{\left(-y \cdot z\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. *-commutative 82.4%

         \[\leadsto \left(-\color{blue}{z \cdot y}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      3. distribute-rgt-neg-in 82.4%

         \[\leadsto \color{blue}{z \cdot \left(-y\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   5. Simplified 82.4%

      \[\leadsto \color{blue}{z \cdot \left(-y\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   if -6.00000000000000011e88 < b < 3.29999999999999993e26 or 2.55000000000000005e61 < b < 1.1e166

   1. Initial program 96.6%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 85.3%

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

   if 3.29999999999999993e26 < b < 2.55000000000000005e61 or 1.1e166 < b

   1. Initial program 93.1%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 96.6%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in a around 0 89.7%

      \[\leadsto \color{blue}{x + b \cdot \left(\left(t + y\right) - 2\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 85.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \leq -6 \cdot 10^{+88}:\\ \;\;\;\;b \cdot \left(\left(y + t\right) - 2\right) - y \cdot z\\ \mathbf{elif}\;b \leq 3.3 \cdot 10^{+26} \lor \neg \left(b \leq 2.55 \cdot 10^{+61}\right) \land b \leq 1.1 \cdot 10^{+166}:\\ \;\;\;\;x - \left(a \cdot \left(t + -1\right) + z \cdot \left(y + -1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + b \cdot \left(\left(y + t\right) - 2\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 75.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -3.5 \cdot 10^{+88} \lor \neg \left(b \leq 1.6 \cdot 10^{+27}\right) \land \left(b \leq 1.35 \cdot 10^{+104} \lor \neg \left(b \leq 1.1 \cdot 10^{+166}\right)\right):\\ \;\;\;\;x + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{else}:\\ \;\;\;\;x - \left(z \cdot \left(y + -1\right) + t \cdot a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= b -3.5e+88)
         (and (not (<= b 1.6e+27))
              (or (<= b 1.35e+104) (not (<= b 1.1e+166)))))
   (+ x (* b (- (+ y t) 2.0)))
   (- x (+ (* z (+ y -1.0)) (* t a)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -3.5e+88) || (!(b <= 1.6e+27) && ((b <= 1.35e+104) || !(b <= 1.1e+166)))) {
		tmp = x + (b * ((y + t) - 2.0));
	} else {
		tmp = x - ((z * (y + -1.0)) + (t * a));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((b <= (-3.5d+88)) .or. (.not. (b <= 1.6d+27)) .and. (b <= 1.35d+104) .or. (.not. (b <= 1.1d+166))) then
        tmp = x + (b * ((y + t) - 2.0d0))
    else
        tmp = x - ((z * (y + (-1.0d0))) + (t * a))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -3.5e+88) || (!(b <= 1.6e+27) && ((b <= 1.35e+104) || !(b <= 1.1e+166)))) {
		tmp = x + (b * ((y + t) - 2.0));
	} else {
		tmp = x - ((z * (y + -1.0)) + (t * a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (b <= -3.5e+88) or (not (b <= 1.6e+27) and ((b <= 1.35e+104) or not (b <= 1.1e+166))):
		tmp = x + (b * ((y + t) - 2.0))
	else:
		tmp = x - ((z * (y + -1.0)) + (t * a))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((b <= -3.5e+88) || (!(b <= 1.6e+27) && ((b <= 1.35e+104) || !(b <= 1.1e+166))))
		tmp = Float64(x + Float64(b * Float64(Float64(y + t) - 2.0)));
	else
		tmp = Float64(x - Float64(Float64(z * Float64(y + -1.0)) + Float64(t * a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((b <= -3.5e+88) || (~((b <= 1.6e+27)) && ((b <= 1.35e+104) || ~((b <= 1.1e+166)))))
		tmp = x + (b * ((y + t) - 2.0));
	else
		tmp = x - ((z * (y + -1.0)) + (t * a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[b, -3.5e+88], And[N[Not[LessEqual[b, 1.6e+27]], $MachinePrecision], Or[LessEqual[b, 1.35e+104], N[Not[LessEqual[b, 1.1e+166]], $MachinePrecision]]]], N[(x + N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(z * N[(y + -1.0), $MachinePrecision]), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if b < -3.4999999999999998e88 or 1.60000000000000008e27 < b < 1.34999999999999992e104 or 1.1e166 < b

   1. Initial program 93.1%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 87.7%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in a around 0 79.4%

      \[\leadsto \color{blue}{x + b \cdot \left(\left(t + y\right) - 2\right)} \]

   if -3.4999999999999998e88 < b < 1.60000000000000008e27 or 1.34999999999999992e104 < b < 1.1e166

   1. Initial program 96.4%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 86.4%

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

   4. Taylor expanded in t around inf 76.0%

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

      1. *-commutative 76.0%

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

   6. Simplified 76.0%

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

4. Final simplification 77.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \leq -3.5 \cdot 10^{+88} \lor \neg \left(b \leq 1.6 \cdot 10^{+27}\right) \land \left(b \leq 1.35 \cdot 10^{+104} \lor \neg \left(b \leq 1.1 \cdot 10^{+166}\right)\right):\\ \;\;\;\;x + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{else}:\\ \;\;\;\;x - \left(z \cdot \left(y + -1\right) + t \cdot a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 73.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;b \leq -1 \cdot 10^{+89}:\\ \;\;\;\;t\_1 - t \cdot a\\ \mathbf{elif}\;b \leq 1.6 \cdot 10^{+27} \lor \neg \left(b \leq 5.5 \cdot 10^{+103}\right) \land b \leq 1.1 \cdot 10^{+166}:\\ \;\;\;\;x - \left(z \cdot \left(y + -1\right) + t \cdot a\right)\\ \mathbf{else}:\\ \;\;\;\;x + t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* b (- (+ y t) 2.0))))
   (if (<= b -1e+89)
     (- t_1 (* t a))
     (if (or (<= b 1.6e+27) (and (not (<= b 5.5e+103)) (<= b 1.1e+166)))
       (- x (+ (* z (+ y -1.0)) (* t a)))
       (+ x t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -1e+89) {
		tmp = t_1 - (t * a);
	} else if ((b <= 1.6e+27) || (!(b <= 5.5e+103) && (b <= 1.1e+166))) {
		tmp = x - ((z * (y + -1.0)) + (t * a));
	} else {
		tmp = x + t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = b * ((y + t) - 2.0d0)
    if (b <= (-1d+89)) then
        tmp = t_1 - (t * a)
    else if ((b <= 1.6d+27) .or. (.not. (b <= 5.5d+103)) .and. (b <= 1.1d+166)) then
        tmp = x - ((z * (y + (-1.0d0))) + (t * a))
    else
        tmp = x + t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -1e+89) {
		tmp = t_1 - (t * a);
	} else if ((b <= 1.6e+27) || (!(b <= 5.5e+103) && (b <= 1.1e+166))) {
		tmp = x - ((z * (y + -1.0)) + (t * a));
	} else {
		tmp = x + t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = b * ((y + t) - 2.0)
	tmp = 0
	if b <= -1e+89:
		tmp = t_1 - (t * a)
	elif (b <= 1.6e+27) or (not (b <= 5.5e+103) and (b <= 1.1e+166)):
		tmp = x - ((z * (y + -1.0)) + (t * a))
	else:
		tmp = x + t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(b * Float64(Float64(y + t) - 2.0))
	tmp = 0.0
	if (b <= -1e+89)
		tmp = Float64(t_1 - Float64(t * a));
	elseif ((b <= 1.6e+27) || (!(b <= 5.5e+103) && (b <= 1.1e+166)))
		tmp = Float64(x - Float64(Float64(z * Float64(y + -1.0)) + Float64(t * a)));
	else
		tmp = Float64(x + t_1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = b * ((y + t) - 2.0);
	tmp = 0.0;
	if (b <= -1e+89)
		tmp = t_1 - (t * a);
	elseif ((b <= 1.6e+27) || (~((b <= 5.5e+103)) && (b <= 1.1e+166)))
		tmp = x - ((z * (y + -1.0)) + (t * a));
	else
		tmp = x + t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -1e+89], N[(t$95$1 - N[(t * a), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[b, 1.6e+27], And[N[Not[LessEqual[b, 5.5e+103]], $MachinePrecision], LessEqual[b, 1.1e+166]]], N[(x - N[(N[(z * N[(y + -1.0), $MachinePrecision]), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + t$95$1), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if b < -9.99999999999999995e88

   1. Initial program 92.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 81.1%

      \[\leadsto \color{blue}{-1 \cdot \left(a \cdot t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   4. Step-by-step derivation

      1. mul-1-neg 81.1%

         \[\leadsto \color{blue}{\left(-a \cdot t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. distribute-rgt-neg-in 81.1%

         \[\leadsto \color{blue}{a \cdot \left(-t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   5. Simplified 81.1%

      \[\leadsto \color{blue}{a \cdot \left(-t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   if -9.99999999999999995e88 < b < 1.60000000000000008e27 or 5.50000000000000001e103 < b < 1.1e166

   1. Initial program 96.4%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 86.4%

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

   4. Taylor expanded in t around inf 76.0%

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

      1. *-commutative 76.0%

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

   6. Simplified 76.0%

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

   if 1.60000000000000008e27 < b < 5.50000000000000001e103 or 1.1e166 < b

   1. Initial program 94.6%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 91.2%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in a around 0 81.5%

      \[\leadsto \color{blue}{x + b \cdot \left(\left(t + y\right) - 2\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 77.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \leq -1 \cdot 10^{+89}:\\ \;\;\;\;b \cdot \left(\left(y + t\right) - 2\right) - t \cdot a\\ \mathbf{elif}\;b \leq 1.6 \cdot 10^{+27} \lor \neg \left(b \leq 5.5 \cdot 10^{+103}\right) \land b \leq 1.1 \cdot 10^{+166}:\\ \;\;\;\;x - \left(z \cdot \left(y + -1\right) + t \cdot a\right)\\ \mathbf{else}:\\ \;\;\;\;x + b \cdot \left(\left(y + t\right) - 2\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 73.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;b \leq -3.2 \cdot 10^{+88}:\\ \;\;\;\;t\_1 - y \cdot z\\ \mathbf{elif}\;b \leq 2.4 \cdot 10^{+27} \lor \neg \left(b \leq 3.8 \cdot 10^{+103}\right) \land b \leq 6.8 \cdot 10^{+169}:\\ \;\;\;\;x - \left(z \cdot \left(y + -1\right) + t \cdot a\right)\\ \mathbf{else}:\\ \;\;\;\;x + t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* b (- (+ y t) 2.0))))
   (if (<= b -3.2e+88)
     (- t_1 (* y z))
     (if (or (<= b 2.4e+27) (and (not (<= b 3.8e+103)) (<= b 6.8e+169)))
       (- x (+ (* z (+ y -1.0)) (* t a)))
       (+ x t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -3.2e+88) {
		tmp = t_1 - (y * z);
	} else if ((b <= 2.4e+27) || (!(b <= 3.8e+103) && (b <= 6.8e+169))) {
		tmp = x - ((z * (y + -1.0)) + (t * a));
	} else {
		tmp = x + t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = b * ((y + t) - 2.0d0)
    if (b <= (-3.2d+88)) then
        tmp = t_1 - (y * z)
    else if ((b <= 2.4d+27) .or. (.not. (b <= 3.8d+103)) .and. (b <= 6.8d+169)) then
        tmp = x - ((z * (y + (-1.0d0))) + (t * a))
    else
        tmp = x + t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -3.2e+88) {
		tmp = t_1 - (y * z);
	} else if ((b <= 2.4e+27) || (!(b <= 3.8e+103) && (b <= 6.8e+169))) {
		tmp = x - ((z * (y + -1.0)) + (t * a));
	} else {
		tmp = x + t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = b * ((y + t) - 2.0)
	tmp = 0
	if b <= -3.2e+88:
		tmp = t_1 - (y * z)
	elif (b <= 2.4e+27) or (not (b <= 3.8e+103) and (b <= 6.8e+169)):
		tmp = x - ((z * (y + -1.0)) + (t * a))
	else:
		tmp = x + t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(b * Float64(Float64(y + t) - 2.0))
	tmp = 0.0
	if (b <= -3.2e+88)
		tmp = Float64(t_1 - Float64(y * z));
	elseif ((b <= 2.4e+27) || (!(b <= 3.8e+103) && (b <= 6.8e+169)))
		tmp = Float64(x - Float64(Float64(z * Float64(y + -1.0)) + Float64(t * a)));
	else
		tmp = Float64(x + t_1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = b * ((y + t) - 2.0);
	tmp = 0.0;
	if (b <= -3.2e+88)
		tmp = t_1 - (y * z);
	elseif ((b <= 2.4e+27) || (~((b <= 3.8e+103)) && (b <= 6.8e+169)))
		tmp = x - ((z * (y + -1.0)) + (t * a));
	else
		tmp = x + t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -3.2e+88], N[(t$95$1 - N[(y * z), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[b, 2.4e+27], And[N[Not[LessEqual[b, 3.8e+103]], $MachinePrecision], LessEqual[b, 6.8e+169]]], N[(x - N[(N[(z * N[(y + -1.0), $MachinePrecision]), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + t$95$1), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if b < -3.1999999999999999e88

   1. Initial program 92.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf 82.4%

      \[\leadsto \color{blue}{-1 \cdot \left(y \cdot z\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   4. Step-by-step derivation

      1. mul-1-neg 82.4%

         \[\leadsto \color{blue}{\left(-y \cdot z\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. *-commutative 82.4%

         \[\leadsto \left(-\color{blue}{z \cdot y}\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

      3. distribute-rgt-neg-in 82.4%

         \[\leadsto \color{blue}{z \cdot \left(-y\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   5. Simplified 82.4%

      \[\leadsto \color{blue}{z \cdot \left(-y\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   if -3.1999999999999999e88 < b < 2.39999999999999998e27 or 3.7999999999999997e103 < b < 6.80000000000000056e169

   1. Initial program 96.4%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 86.4%

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

   4. Taylor expanded in t around inf 76.0%

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

      1. *-commutative 76.0%

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

   6. Simplified 76.0%

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

   if 2.39999999999999998e27 < b < 3.7999999999999997e103 or 6.80000000000000056e169 < b

   1. Initial program 94.6%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 91.2%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in a around 0 81.5%

      \[\leadsto \color{blue}{x + b \cdot \left(\left(t + y\right) - 2\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 78.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \leq -3.2 \cdot 10^{+88}:\\ \;\;\;\;b \cdot \left(\left(y + t\right) - 2\right) - y \cdot z\\ \mathbf{elif}\;b \leq 2.4 \cdot 10^{+27} \lor \neg \left(b \leq 3.8 \cdot 10^{+103}\right) \land b \leq 6.8 \cdot 10^{+169}:\\ \;\;\;\;x - \left(z \cdot \left(y + -1\right) + t \cdot a\right)\\ \mathbf{else}:\\ \;\;\;\;x + b \cdot \left(\left(y + t\right) - 2\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 49.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -1.35 \cdot 10^{+33}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq -3.8 \cdot 10^{-27}:\\ \;\;\;\;z \cdot \left(1 - y\right)\\ \mathbf{elif}\;t \leq -2.95 \cdot 10^{-151}:\\ \;\;\;\;b \cdot \left(y - 2\right)\\ \mathbf{elif}\;t \leq 1.75 \cdot 10^{-127}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 1500000:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* t (- b a))))
   (if (<= t -1.35e+33)
     t_1
     (if (<= t -3.8e-27)
       (* z (- 1.0 y))
       (if (<= t -2.95e-151)
         (* b (- y 2.0))
         (if (<= t 1.75e-127)
           (+ x a)
           (if (<= t 1500000.0) (* y (- b z)) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -1.35e+33) {
		tmp = t_1;
	} else if (t <= -3.8e-27) {
		tmp = z * (1.0 - y);
	} else if (t <= -2.95e-151) {
		tmp = b * (y - 2.0);
	} else if (t <= 1.75e-127) {
		tmp = x + a;
	} else if (t <= 1500000.0) {
		tmp = y * (b - z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t * (b - a)
    if (t <= (-1.35d+33)) then
        tmp = t_1
    else if (t <= (-3.8d-27)) then
        tmp = z * (1.0d0 - y)
    else if (t <= (-2.95d-151)) then
        tmp = b * (y - 2.0d0)
    else if (t <= 1.75d-127) then
        tmp = x + a
    else if (t <= 1500000.0d0) then
        tmp = y * (b - 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 a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -1.35e+33) {
		tmp = t_1;
	} else if (t <= -3.8e-27) {
		tmp = z * (1.0 - y);
	} else if (t <= -2.95e-151) {
		tmp = b * (y - 2.0);
	} else if (t <= 1.75e-127) {
		tmp = x + a;
	} else if (t <= 1500000.0) {
		tmp = y * (b - z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t * (b - a)
	tmp = 0
	if t <= -1.35e+33:
		tmp = t_1
	elif t <= -3.8e-27:
		tmp = z * (1.0 - y)
	elif t <= -2.95e-151:
		tmp = b * (y - 2.0)
	elif t <= 1.75e-127:
		tmp = x + a
	elif t <= 1500000.0:
		tmp = y * (b - z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -1.35e+33)
		tmp = t_1;
	elseif (t <= -3.8e-27)
		tmp = Float64(z * Float64(1.0 - y));
	elseif (t <= -2.95e-151)
		tmp = Float64(b * Float64(y - 2.0));
	elseif (t <= 1.75e-127)
		tmp = Float64(x + a);
	elseif (t <= 1500000.0)
		tmp = Float64(y * Float64(b - z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t * (b - a);
	tmp = 0.0;
	if (t <= -1.35e+33)
		tmp = t_1;
	elseif (t <= -3.8e-27)
		tmp = z * (1.0 - y);
	elseif (t <= -2.95e-151)
		tmp = b * (y - 2.0);
	elseif (t <= 1.75e-127)
		tmp = x + a;
	elseif (t <= 1500000.0)
		tmp = y * (b - z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.35e+33], t$95$1, If[LessEqual[t, -3.8e-27], N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -2.95e-151], N[(b * N[(y - 2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.75e-127], N[(x + a), $MachinePrecision], If[LessEqual[t, 1500000.0], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 5 regimes

2. if t < -1.34999999999999996e33 or 1.5e6 < t

   1. Initial program 91.9%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 70.5%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   if -1.34999999999999996e33 < t < -3.8e-27

   1. Initial program 87.5%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 62.2%

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

   if -3.8e-27 < t < -2.95e-151

   1. Initial program 99.9%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 52.6%

      \[\leadsto \color{blue}{-1 \cdot \left(a \cdot t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   4. Step-by-step derivation

      1. mul-1-neg 52.6%

         \[\leadsto \color{blue}{\left(-a \cdot t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. distribute-rgt-neg-in 52.6%

         \[\leadsto \color{blue}{a \cdot \left(-t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   5. Simplified 52.6%

      \[\leadsto \color{blue}{a \cdot \left(-t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   6. Taylor expanded in t around 0 52.4%

      \[\leadsto \color{blue}{b \cdot \left(y - 2\right)} \]

   if -2.95e-151 < t < 1.74999999999999995e-127

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 74.1%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in b around 0 55.2%

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

   5. Taylor expanded in t around 0 55.2%

      \[\leadsto x - \color{blue}{-1 \cdot a} \]
   6. Step-by-step derivation

      1. neg-mul-1 55.2%

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

   7. Simplified 55.2%

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

   if 1.74999999999999995e-127 < t < 1.5e6

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf 47.0%

      \[\leadsto \color{blue}{y \cdot \left(b - z\right)} \]
3. Recombined 5 regimes into one program.

4. Final simplification 61.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1.35 \cdot 10^{+33}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \mathbf{elif}\;t \leq -3.8 \cdot 10^{-27}:\\ \;\;\;\;z \cdot \left(1 - y\right)\\ \mathbf{elif}\;t \leq -2.95 \cdot 10^{-151}:\\ \;\;\;\;b \cdot \left(y - 2\right)\\ \mathbf{elif}\;t \leq 1.75 \cdot 10^{-127}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 1500000:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 27.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(-a\right)\\ \mathbf{if}\;t \leq -1.55 \cdot 10^{+34}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq -8 \cdot 10^{-49}:\\ \;\;\;\;z\\ \mathbf{elif}\;t \leq -4.4 \cdot 10^{-154}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;t \leq 5 \cdot 10^{-182}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq 7.8 \cdot 10^{-10}:\\ \;\;\;\;a\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* t (- a))))
   (if (<= t -1.55e+34)
     t_1
     (if (<= t -8e-49)
       z
       (if (<= t -4.4e-154)
         (* y b)
         (if (<= t 5e-182) x (if (<= t 7.8e-10) a t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * -a;
	double tmp;
	if (t <= -1.55e+34) {
		tmp = t_1;
	} else if (t <= -8e-49) {
		tmp = z;
	} else if (t <= -4.4e-154) {
		tmp = y * b;
	} else if (t <= 5e-182) {
		tmp = x;
	} else if (t <= 7.8e-10) {
		tmp = a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t * -a
    if (t <= (-1.55d+34)) then
        tmp = t_1
    else if (t <= (-8d-49)) then
        tmp = z
    else if (t <= (-4.4d-154)) then
        tmp = y * b
    else if (t <= 5d-182) then
        tmp = x
    else if (t <= 7.8d-10) then
        tmp = a
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * -a;
	double tmp;
	if (t <= -1.55e+34) {
		tmp = t_1;
	} else if (t <= -8e-49) {
		tmp = z;
	} else if (t <= -4.4e-154) {
		tmp = y * b;
	} else if (t <= 5e-182) {
		tmp = x;
	} else if (t <= 7.8e-10) {
		tmp = a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t * -a
	tmp = 0
	if t <= -1.55e+34:
		tmp = t_1
	elif t <= -8e-49:
		tmp = z
	elif t <= -4.4e-154:
		tmp = y * b
	elif t <= 5e-182:
		tmp = x
	elif t <= 7.8e-10:
		tmp = a
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t * Float64(-a))
	tmp = 0.0
	if (t <= -1.55e+34)
		tmp = t_1;
	elseif (t <= -8e-49)
		tmp = z;
	elseif (t <= -4.4e-154)
		tmp = Float64(y * b);
	elseif (t <= 5e-182)
		tmp = x;
	elseif (t <= 7.8e-10)
		tmp = a;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t * -a;
	tmp = 0.0;
	if (t <= -1.55e+34)
		tmp = t_1;
	elseif (t <= -8e-49)
		tmp = z;
	elseif (t <= -4.4e-154)
		tmp = y * b;
	elseif (t <= 5e-182)
		tmp = x;
	elseif (t <= 7.8e-10)
		tmp = a;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t * (-a)), $MachinePrecision]}, If[LessEqual[t, -1.55e+34], t$95$1, If[LessEqual[t, -8e-49], z, If[LessEqual[t, -4.4e-154], N[(y * b), $MachinePrecision], If[LessEqual[t, 5e-182], x, If[LessEqual[t, 7.8e-10], a, t$95$1]]]]]]

Derivation

1. Split input into 5 regimes

2. if t < -1.54999999999999989e34 or 7.7999999999999999e-10 < t

   1. Initial program 92.1%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 68.6%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   4. Taylor expanded in b around 0 40.3%

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

      1. associate-*r* 40.3%

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

      2. mul-1-neg 40.3%

         \[\leadsto \color{blue}{\left(-a\right)} \cdot t \]

   6. Simplified 40.3%

      \[\leadsto \color{blue}{\left(-a\right) \cdot t} \]

   if -1.54999999999999989e34 < t < -7.99999999999999949e-49

   1. Initial program 91.3%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 52.4%

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

   4. Taylor expanded in y around 0 31.6%

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

   if -7.99999999999999949e-49 < t < -4.40000000000000015e-154

   1. Initial program 99.9%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 78.3%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in y around inf 44.0%

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

   if -4.40000000000000015e-154 < t < 5.00000000000000024e-182

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 39.9%

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

   if 5.00000000000000024e-182 < t < 7.7999999999999999e-10

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 29.9%

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

   4. Taylor expanded in t around 0 29.9%

      \[\leadsto \color{blue}{a} \]
3. Recombined 5 regimes into one program.

4. Final simplification 38.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1.55 \cdot 10^{+34}:\\ \;\;\;\;t \cdot \left(-a\right)\\ \mathbf{elif}\;t \leq -8 \cdot 10^{-49}:\\ \;\;\;\;z\\ \mathbf{elif}\;t \leq -4.4 \cdot 10^{-154}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;t \leq 5 \cdot 10^{-182}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq 7.8 \cdot 10^{-10}:\\ \;\;\;\;a\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(-a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 35.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(-a\right)\\ \mathbf{if}\;t \leq -7.2 \cdot 10^{+38}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq -2.7 \cdot 10^{-69}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;t \leq -1.75 \cdot 10^{-149}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;t \leq -1.55 \cdot 10^{-199}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;t \leq 0.055:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* t (- a))))
   (if (<= t -7.2e+38)
     t_1
     (if (<= t -2.7e-69)
       (+ x z)
       (if (<= t -1.75e-149)
         (* y b)
         (if (<= t -1.55e-199) (+ x z) (if (<= t 0.055) (+ x a) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * -a;
	double tmp;
	if (t <= -7.2e+38) {
		tmp = t_1;
	} else if (t <= -2.7e-69) {
		tmp = x + z;
	} else if (t <= -1.75e-149) {
		tmp = y * b;
	} else if (t <= -1.55e-199) {
		tmp = x + z;
	} else if (t <= 0.055) {
		tmp = x + a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t * -a
    if (t <= (-7.2d+38)) then
        tmp = t_1
    else if (t <= (-2.7d-69)) then
        tmp = x + z
    else if (t <= (-1.75d-149)) then
        tmp = y * b
    else if (t <= (-1.55d-199)) then
        tmp = x + z
    else if (t <= 0.055d0) then
        tmp = x + a
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * -a;
	double tmp;
	if (t <= -7.2e+38) {
		tmp = t_1;
	} else if (t <= -2.7e-69) {
		tmp = x + z;
	} else if (t <= -1.75e-149) {
		tmp = y * b;
	} else if (t <= -1.55e-199) {
		tmp = x + z;
	} else if (t <= 0.055) {
		tmp = x + a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t * -a
	tmp = 0
	if t <= -7.2e+38:
		tmp = t_1
	elif t <= -2.7e-69:
		tmp = x + z
	elif t <= -1.75e-149:
		tmp = y * b
	elif t <= -1.55e-199:
		tmp = x + z
	elif t <= 0.055:
		tmp = x + a
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t * Float64(-a))
	tmp = 0.0
	if (t <= -7.2e+38)
		tmp = t_1;
	elseif (t <= -2.7e-69)
		tmp = Float64(x + z);
	elseif (t <= -1.75e-149)
		tmp = Float64(y * b);
	elseif (t <= -1.55e-199)
		tmp = Float64(x + z);
	elseif (t <= 0.055)
		tmp = Float64(x + a);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t * -a;
	tmp = 0.0;
	if (t <= -7.2e+38)
		tmp = t_1;
	elseif (t <= -2.7e-69)
		tmp = x + z;
	elseif (t <= -1.75e-149)
		tmp = y * b;
	elseif (t <= -1.55e-199)
		tmp = x + z;
	elseif (t <= 0.055)
		tmp = x + a;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t * (-a)), $MachinePrecision]}, If[LessEqual[t, -7.2e+38], t$95$1, If[LessEqual[t, -2.7e-69], N[(x + z), $MachinePrecision], If[LessEqual[t, -1.75e-149], N[(y * b), $MachinePrecision], If[LessEqual[t, -1.55e-199], N[(x + z), $MachinePrecision], If[LessEqual[t, 0.055], N[(x + a), $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 4 regimes

2. if t < -7.19999999999999938e38 or 0.0550000000000000003 < t

   1. Initial program 92.1%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 68.6%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   4. Taylor expanded in b around 0 40.3%

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

      1. associate-*r* 40.3%

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

      2. mul-1-neg 40.3%

         \[\leadsto \color{blue}{\left(-a\right)} \cdot t \]

   6. Simplified 40.3%

      \[\leadsto \color{blue}{\left(-a\right) \cdot t} \]

   if -7.19999999999999938e38 < t < -2.6999999999999997e-69 or -1.75e-149 < t < -1.55000000000000006e-199

   1. Initial program 94.1%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 100.0%

      \[\leadsto \color{blue}{\left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \left(-1 \cdot z + a \cdot \left(t - 1\right)\right)} \]

   4. Taylor expanded in z around inf 91.7%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{-1 \cdot z} \]
   5. Step-by-step derivation

      1. mul-1-neg 91.7%

         \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   6. Simplified 91.7%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   7. Taylor expanded in x around inf 51.9%

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

   if -2.6999999999999997e-69 < t < -1.75e-149

   1. Initial program 99.9%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 79.1%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in y around inf 42.3%

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

   if -1.55000000000000006e-199 < t < 0.0550000000000000003

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 73.3%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in b around 0 47.2%

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

   5. Taylor expanded in t around 0 47.2%

      \[\leadsto x - \color{blue}{-1 \cdot a} \]
   6. Step-by-step derivation

      1. neg-mul-1 47.2%

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

   7. Simplified 47.2%

      \[\leadsto x - \color{blue}{\left(-a\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 44.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -7.2 \cdot 10^{+38}:\\ \;\;\;\;t \cdot \left(-a\right)\\ \mathbf{elif}\;t \leq -2.7 \cdot 10^{-69}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;t \leq -1.75 \cdot 10^{-149}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;t \leq -1.55 \cdot 10^{-199}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;t \leq 0.055:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(-a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 57.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + z \cdot \left(1 - y\right)\\ t_2 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -1.3 \cdot 10^{+34}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t \leq -3.4 \cdot 10^{-110}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq -4.8 \cdot 10^{-150}:\\ \;\;\;\;b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{elif}\;t \leq 1750000000000:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* z (- 1.0 y)))) (t_2 (* t (- b a))))
   (if (<= t -1.3e+34)
     t_2
     (if (<= t -3.4e-110)
       t_1
       (if (<= t -4.8e-150)
         (* b (- (+ y t) 2.0))
         (if (<= t 1750000000000.0) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (z * (1.0 - y));
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -1.3e+34) {
		tmp = t_2;
	} else if (t <= -3.4e-110) {
		tmp = t_1;
	} else if (t <= -4.8e-150) {
		tmp = b * ((y + t) - 2.0);
	} else if (t <= 1750000000000.0) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = x + (z * (1.0d0 - y))
    t_2 = t * (b - a)
    if (t <= (-1.3d+34)) then
        tmp = t_2
    else if (t <= (-3.4d-110)) then
        tmp = t_1
    else if (t <= (-4.8d-150)) then
        tmp = b * ((y + t) - 2.0d0)
    else if (t <= 1750000000000.0d0) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (z * (1.0 - y));
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -1.3e+34) {
		tmp = t_2;
	} else if (t <= -3.4e-110) {
		tmp = t_1;
	} else if (t <= -4.8e-150) {
		tmp = b * ((y + t) - 2.0);
	} else if (t <= 1750000000000.0) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (z * (1.0 - y))
	t_2 = t * (b - a)
	tmp = 0
	if t <= -1.3e+34:
		tmp = t_2
	elif t <= -3.4e-110:
		tmp = t_1
	elif t <= -4.8e-150:
		tmp = b * ((y + t) - 2.0)
	elif t <= 1750000000000.0:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(z * Float64(1.0 - y)))
	t_2 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -1.3e+34)
		tmp = t_2;
	elseif (t <= -3.4e-110)
		tmp = t_1;
	elseif (t <= -4.8e-150)
		tmp = Float64(b * Float64(Float64(y + t) - 2.0));
	elseif (t <= 1750000000000.0)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (z * (1.0 - y));
	t_2 = t * (b - a);
	tmp = 0.0;
	if (t <= -1.3e+34)
		tmp = t_2;
	elseif (t <= -3.4e-110)
		tmp = t_1;
	elseif (t <= -4.8e-150)
		tmp = b * ((y + t) - 2.0);
	elseif (t <= 1750000000000.0)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.3e+34], t$95$2, If[LessEqual[t, -3.4e-110], t$95$1, If[LessEqual[t, -4.8e-150], N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1750000000000.0], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if t < -1.29999999999999999e34 or 1.75e12 < t

   1. Initial program 91.9%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 70.5%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   if -1.29999999999999999e34 < t < -3.4000000000000001e-110 or -4.8e-150 < t < 1.75e12

   1. Initial program 98.4%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 74.7%

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

   4. Taylor expanded in a around 0 56.0%

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

   if -3.4000000000000001e-110 < t < -4.8e-150

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf 84.0%

      \[\leadsto \color{blue}{b \cdot \left(\left(t + y\right) - 2\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 64.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1.3 \cdot 10^{+34}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \mathbf{elif}\;t \leq -3.4 \cdot 10^{-110}:\\ \;\;\;\;x + z \cdot \left(1 - y\right)\\ \mathbf{elif}\;t \leq -4.8 \cdot 10^{-150}:\\ \;\;\;\;b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{elif}\;t \leq 1750000000000:\\ \;\;\;\;x + z \cdot \left(1 - y\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 57.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z + y \cdot \left(b - z\right)\\ t_2 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -3 \cdot 10^{+112}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t \leq -7.2 \cdot 10^{-145}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 1.35 \cdot 10^{-132}:\\ \;\;\;\;x + z \cdot \left(1 - y\right)\\ \mathbf{elif}\;t \leq 1750000000000:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ z (* y (- b z)))) (t_2 (* t (- b a))))
   (if (<= t -3e+112)
     t_2
     (if (<= t -7.2e-145)
       t_1
       (if (<= t 1.35e-132)
         (+ x (* z (- 1.0 y)))
         (if (<= t 1750000000000.0) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z + (y * (b - z));
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -3e+112) {
		tmp = t_2;
	} else if (t <= -7.2e-145) {
		tmp = t_1;
	} else if (t <= 1.35e-132) {
		tmp = x + (z * (1.0 - y));
	} else if (t <= 1750000000000.0) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = z + (y * (b - z))
    t_2 = t * (b - a)
    if (t <= (-3d+112)) then
        tmp = t_2
    else if (t <= (-7.2d-145)) then
        tmp = t_1
    else if (t <= 1.35d-132) then
        tmp = x + (z * (1.0d0 - y))
    else if (t <= 1750000000000.0d0) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z + (y * (b - z));
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -3e+112) {
		tmp = t_2;
	} else if (t <= -7.2e-145) {
		tmp = t_1;
	} else if (t <= 1.35e-132) {
		tmp = x + (z * (1.0 - y));
	} else if (t <= 1750000000000.0) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z + (y * (b - z))
	t_2 = t * (b - a)
	tmp = 0
	if t <= -3e+112:
		tmp = t_2
	elif t <= -7.2e-145:
		tmp = t_1
	elif t <= 1.35e-132:
		tmp = x + (z * (1.0 - y))
	elif t <= 1750000000000.0:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z + Float64(y * Float64(b - z)))
	t_2 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -3e+112)
		tmp = t_2;
	elseif (t <= -7.2e-145)
		tmp = t_1;
	elseif (t <= 1.35e-132)
		tmp = Float64(x + Float64(z * Float64(1.0 - y)));
	elseif (t <= 1750000000000.0)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z + (y * (b - z));
	t_2 = t * (b - a);
	tmp = 0.0;
	if (t <= -3e+112)
		tmp = t_2;
	elseif (t <= -7.2e-145)
		tmp = t_1;
	elseif (t <= 1.35e-132)
		tmp = x + (z * (1.0 - y));
	elseif (t <= 1750000000000.0)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z + N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3e+112], t$95$2, If[LessEqual[t, -7.2e-145], t$95$1, If[LessEqual[t, 1.35e-132], N[(x + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1750000000000.0], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if t < -2.99999999999999979e112 or 1.75e12 < t

   1. Initial program 91.7%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 73.7%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   if -2.99999999999999979e112 < t < -7.2000000000000001e-145 or 1.34999999999999995e-132 < t < 1.75e12

   1. Initial program 96.6%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 97.8%

      \[\leadsto \color{blue}{\left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \left(-1 \cdot z + a \cdot \left(t - 1\right)\right)} \]

   4. Taylor expanded in z around inf 79.3%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{-1 \cdot z} \]
   5. Step-by-step derivation

      1. mul-1-neg 79.3%

         \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   6. Simplified 79.3%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   7. Taylor expanded in y around inf 62.2%

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

   if -7.2000000000000001e-145 < t < 1.34999999999999995e-132

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 77.8%

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

   4. Taylor expanded in a around 0 59.7%

      \[\leadsto x - \color{blue}{z \cdot \left(y - 1\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 66.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -3 \cdot 10^{+112}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \mathbf{elif}\;t \leq -7.2 \cdot 10^{-145}:\\ \;\;\;\;z + y \cdot \left(b - z\right)\\ \mathbf{elif}\;t \leq 1.35 \cdot 10^{-132}:\\ \;\;\;\;x + z \cdot \left(1 - y\right)\\ \mathbf{elif}\;t \leq 1750000000000:\\ \;\;\;\;z + y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 41.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(1 - t\right)\\ \mathbf{if}\;a \leq -2.7 \cdot 10^{+68}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 6.1 \cdot 10^{-55}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;a \leq 3.9 \cdot 10^{+30}:\\ \;\;\;\;y \cdot \left(-z\right)\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+98}:\\ \;\;\;\;x + z\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (- 1.0 t))))
   (if (<= a -2.7e+68)
     t_1
     (if (<= a 6.1e-55)
       (+ x z)
       (if (<= a 3.9e+30) (* y (- z)) (if (<= a 1.02e+98) (+ x z) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (1.0 - t);
	double tmp;
	if (a <= -2.7e+68) {
		tmp = t_1;
	} else if (a <= 6.1e-55) {
		tmp = x + z;
	} else if (a <= 3.9e+30) {
		tmp = y * -z;
	} else if (a <= 1.02e+98) {
		tmp = x + z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a * (1.0d0 - t)
    if (a <= (-2.7d+68)) then
        tmp = t_1
    else if (a <= 6.1d-55) then
        tmp = x + z
    else if (a <= 3.9d+30) then
        tmp = y * -z
    else if (a <= 1.02d+98) then
        tmp = x + 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 a, double b) {
	double t_1 = a * (1.0 - t);
	double tmp;
	if (a <= -2.7e+68) {
		tmp = t_1;
	} else if (a <= 6.1e-55) {
		tmp = x + z;
	} else if (a <= 3.9e+30) {
		tmp = y * -z;
	} else if (a <= 1.02e+98) {
		tmp = x + z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (1.0 - t)
	tmp = 0
	if a <= -2.7e+68:
		tmp = t_1
	elif a <= 6.1e-55:
		tmp = x + z
	elif a <= 3.9e+30:
		tmp = y * -z
	elif a <= 1.02e+98:
		tmp = x + z
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(1.0 - t))
	tmp = 0.0
	if (a <= -2.7e+68)
		tmp = t_1;
	elseif (a <= 6.1e-55)
		tmp = Float64(x + z);
	elseif (a <= 3.9e+30)
		tmp = Float64(y * Float64(-z));
	elseif (a <= 1.02e+98)
		tmp = Float64(x + z);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (1.0 - t);
	tmp = 0.0;
	if (a <= -2.7e+68)
		tmp = t_1;
	elseif (a <= 6.1e-55)
		tmp = x + z;
	elseif (a <= 3.9e+30)
		tmp = y * -z;
	elseif (a <= 1.02e+98)
		tmp = x + z;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.7e+68], t$95$1, If[LessEqual[a, 6.1e-55], N[(x + z), $MachinePrecision], If[LessEqual[a, 3.9e+30], N[(y * (-z)), $MachinePrecision], If[LessEqual[a, 1.02e+98], N[(x + z), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 3 regimes

2. if a < -2.69999999999999991e68 or 1.02000000000000007e98 < a

   1. Initial program 91.8%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 62.3%

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

   if -2.69999999999999991e68 < a < 6.1000000000000001e-55 or 3.90000000000000011e30 < a < 1.02000000000000007e98

   1. Initial program 97.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 98.6%

      \[\leadsto \color{blue}{\left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \left(-1 \cdot z + a \cdot \left(t - 1\right)\right)} \]

   4. Taylor expanded in z around inf 90.8%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{-1 \cdot z} \]
   5. Step-by-step derivation

      1. mul-1-neg 90.8%

         \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   6. Simplified 90.8%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   7. Taylor expanded in x around inf 36.7%

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

   if 6.1000000000000001e-55 < a < 3.90000000000000011e30

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 65.6%

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

   4. Taylor expanded in y around 0 65.6%

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

   5. Taylor expanded in y around inf 51.8%

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

      1. associate-*r* 51.8%

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

      2. neg-mul-1 51.8%

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

   7. Simplified 51.8%

      \[\leadsto \color{blue}{\left(-y\right) \cdot z} \]
3. Recombined 3 regimes into one program.

4. Final simplification 47.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -2.7 \cdot 10^{+68}:\\ \;\;\;\;a \cdot \left(1 - t\right)\\ \mathbf{elif}\;a \leq 6.1 \cdot 10^{-55}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;a \leq 3.9 \cdot 10^{+30}:\\ \;\;\;\;y \cdot \left(-z\right)\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+98}:\\ \;\;\;\;x + z\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(1 - t\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 15: 49.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -1.3 \cdot 10^{+33}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq -3.5 \cdot 10^{-28}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;t \leq -1.7 \cdot 10^{-151}:\\ \;\;\;\;b \cdot \left(y - 2\right)\\ \mathbf{elif}\;t \leq 0.055:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* t (- b a))))
   (if (<= t -1.3e+33)
     t_1
     (if (<= t -3.5e-28)
       (+ x z)
       (if (<= t -1.7e-151) (* b (- y 2.0)) (if (<= t 0.055) (+ x a) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -1.3e+33) {
		tmp = t_1;
	} else if (t <= -3.5e-28) {
		tmp = x + z;
	} else if (t <= -1.7e-151) {
		tmp = b * (y - 2.0);
	} else if (t <= 0.055) {
		tmp = x + a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t * (b - a)
    if (t <= (-1.3d+33)) then
        tmp = t_1
    else if (t <= (-3.5d-28)) then
        tmp = x + z
    else if (t <= (-1.7d-151)) then
        tmp = b * (y - 2.0d0)
    else if (t <= 0.055d0) then
        tmp = x + a
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -1.3e+33) {
		tmp = t_1;
	} else if (t <= -3.5e-28) {
		tmp = x + z;
	} else if (t <= -1.7e-151) {
		tmp = b * (y - 2.0);
	} else if (t <= 0.055) {
		tmp = x + a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t * (b - a)
	tmp = 0
	if t <= -1.3e+33:
		tmp = t_1
	elif t <= -3.5e-28:
		tmp = x + z
	elif t <= -1.7e-151:
		tmp = b * (y - 2.0)
	elif t <= 0.055:
		tmp = x + a
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -1.3e+33)
		tmp = t_1;
	elseif (t <= -3.5e-28)
		tmp = Float64(x + z);
	elseif (t <= -1.7e-151)
		tmp = Float64(b * Float64(y - 2.0));
	elseif (t <= 0.055)
		tmp = Float64(x + a);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t * (b - a);
	tmp = 0.0;
	if (t <= -1.3e+33)
		tmp = t_1;
	elseif (t <= -3.5e-28)
		tmp = x + z;
	elseif (t <= -1.7e-151)
		tmp = b * (y - 2.0);
	elseif (t <= 0.055)
		tmp = x + a;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.3e+33], t$95$1, If[LessEqual[t, -3.5e-28], N[(x + z), $MachinePrecision], If[LessEqual[t, -1.7e-151], N[(b * N[(y - 2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.055], N[(x + a), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if t < -1.2999999999999999e33 or 0.0550000000000000003 < t

   1. Initial program 92.1%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 68.6%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   if -1.2999999999999999e33 < t < -3.5e-28

   1. Initial program 87.5%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 100.0%

      \[\leadsto \color{blue}{\left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \left(-1 \cdot z + a \cdot \left(t - 1\right)\right)} \]

   4. Taylor expanded in z around inf 94.3%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{-1 \cdot z} \]
   5. Step-by-step derivation

      1. mul-1-neg 94.3%

         \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   6. Simplified 94.3%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   7. Taylor expanded in x around inf 52.2%

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

   if -3.5e-28 < t < -1.7000000000000001e-151

   1. Initial program 99.9%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 52.6%

      \[\leadsto \color{blue}{-1 \cdot \left(a \cdot t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]
   4. Step-by-step derivation

      1. mul-1-neg 52.6%

         \[\leadsto \color{blue}{\left(-a \cdot t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

      2. distribute-rgt-neg-in 52.6%

         \[\leadsto \color{blue}{a \cdot \left(-t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   5. Simplified 52.6%

      \[\leadsto \color{blue}{a \cdot \left(-t\right)} + \left(\left(y + t\right) - 2\right) \cdot b \]

   6. Taylor expanded in t around 0 52.4%

      \[\leadsto \color{blue}{b \cdot \left(y - 2\right)} \]

   if -1.7000000000000001e-151 < t < 0.0550000000000000003

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 72.0%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in b around 0 48.1%

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

   5. Taylor expanded in t around 0 48.1%

      \[\leadsto x - \color{blue}{-1 \cdot a} \]
   6. Step-by-step derivation

      1. neg-mul-1 48.1%

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

   7. Simplified 48.1%

      \[\leadsto x - \color{blue}{\left(-a\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 59.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1.3 \cdot 10^{+33}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \mathbf{elif}\;t \leq -3.5 \cdot 10^{-28}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;t \leq -1.7 \cdot 10^{-151}:\\ \;\;\;\;b \cdot \left(y - 2\right)\\ \mathbf{elif}\;t \leq 0.055:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 16: 50.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(b - z\right)\\ t_2 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -3.6 \cdot 10^{+28}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t \leq -1.26 \cdot 10^{-146}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 3.4 \cdot 10^{-131}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 2200000000000:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* y (- b z))) (t_2 (* t (- b a))))
   (if (<= t -3.6e+28)
     t_2
     (if (<= t -1.26e-146)
       t_1
       (if (<= t 3.4e-131) (+ x a) (if (<= t 2200000000000.0) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y * (b - z);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -3.6e+28) {
		tmp = t_2;
	} else if (t <= -1.26e-146) {
		tmp = t_1;
	} else if (t <= 3.4e-131) {
		tmp = x + a;
	} else if (t <= 2200000000000.0) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = y * (b - z)
    t_2 = t * (b - a)
    if (t <= (-3.6d+28)) then
        tmp = t_2
    else if (t <= (-1.26d-146)) then
        tmp = t_1
    else if (t <= 3.4d-131) then
        tmp = x + a
    else if (t <= 2200000000000.0d0) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y * (b - z);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -3.6e+28) {
		tmp = t_2;
	} else if (t <= -1.26e-146) {
		tmp = t_1;
	} else if (t <= 3.4e-131) {
		tmp = x + a;
	} else if (t <= 2200000000000.0) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y * (b - z)
	t_2 = t * (b - a)
	tmp = 0
	if t <= -3.6e+28:
		tmp = t_2
	elif t <= -1.26e-146:
		tmp = t_1
	elif t <= 3.4e-131:
		tmp = x + a
	elif t <= 2200000000000.0:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y * Float64(b - z))
	t_2 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -3.6e+28)
		tmp = t_2;
	elseif (t <= -1.26e-146)
		tmp = t_1;
	elseif (t <= 3.4e-131)
		tmp = Float64(x + a);
	elseif (t <= 2200000000000.0)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y * (b - z);
	t_2 = t * (b - a);
	tmp = 0.0;
	if (t <= -3.6e+28)
		tmp = t_2;
	elseif (t <= -1.26e-146)
		tmp = t_1;
	elseif (t <= 3.4e-131)
		tmp = x + a;
	elseif (t <= 2200000000000.0)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3.6e+28], t$95$2, If[LessEqual[t, -1.26e-146], t$95$1, If[LessEqual[t, 3.4e-131], N[(x + a), $MachinePrecision], If[LessEqual[t, 2200000000000.0], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if t < -3.5999999999999999e28 or 2.2e12 < t

   1. Initial program 91.9%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 70.0%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   if -3.5999999999999999e28 < t < -1.2599999999999999e-146 or 3.39999999999999995e-131 < t < 2.2e12

   1. Initial program 97.2%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf 47.3%

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

   if -1.2599999999999999e-146 < t < 3.39999999999999995e-131

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 75.0%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in b around 0 53.5%

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

   5. Taylor expanded in t around 0 53.5%

      \[\leadsto x - \color{blue}{-1 \cdot a} \]
   6. Step-by-step derivation

      1. neg-mul-1 53.5%

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

   7. Simplified 53.5%

      \[\leadsto x - \color{blue}{\left(-a\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 59.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -3.6 \cdot 10^{+28}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \mathbf{elif}\;t \leq -1.26 \cdot 10^{-146}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{elif}\;t \leq 3.4 \cdot 10^{-131}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 2200000000000:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 17: 84.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(y + -1\right)\\ \mathbf{if}\;z \leq -1.3 \cdot 10^{+59}:\\ \;\;\;\;x - \left(t\_1 + t \cdot a\right)\\ \mathbf{elif}\;z \leq 4.7 \cdot 10^{+64}:\\ \;\;\;\;\left(x + b \cdot \left(\left(y + t\right) - 2\right)\right) + a \cdot \left(1 - t\right)\\ \mathbf{else}:\\ \;\;\;\;x - \left(a \cdot \left(t + -1\right) + t\_1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (+ y -1.0))))
   (if (<= z -1.3e+59)
     (- x (+ t_1 (* t a)))
     (if (<= z 4.7e+64)
       (+ (+ x (* b (- (+ y t) 2.0))) (* a (- 1.0 t)))
       (- x (+ (* a (+ t -1.0)) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (y + -1.0);
	double tmp;
	if (z <= -1.3e+59) {
		tmp = x - (t_1 + (t * a));
	} else if (z <= 4.7e+64) {
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t));
	} else {
		tmp = x - ((a * (t + -1.0)) + t_1);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = z * (y + (-1.0d0))
    if (z <= (-1.3d+59)) then
        tmp = x - (t_1 + (t * a))
    else if (z <= 4.7d+64) then
        tmp = (x + (b * ((y + t) - 2.0d0))) + (a * (1.0d0 - t))
    else
        tmp = x - ((a * (t + (-1.0d0))) + t_1)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (y + -1.0);
	double tmp;
	if (z <= -1.3e+59) {
		tmp = x - (t_1 + (t * a));
	} else if (z <= 4.7e+64) {
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t));
	} else {
		tmp = x - ((a * (t + -1.0)) + t_1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (y + -1.0)
	tmp = 0
	if z <= -1.3e+59:
		tmp = x - (t_1 + (t * a))
	elif z <= 4.7e+64:
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t))
	else:
		tmp = x - ((a * (t + -1.0)) + t_1)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(y + -1.0))
	tmp = 0.0
	if (z <= -1.3e+59)
		tmp = Float64(x - Float64(t_1 + Float64(t * a)));
	elseif (z <= 4.7e+64)
		tmp = Float64(Float64(x + Float64(b * Float64(Float64(y + t) - 2.0))) + Float64(a * Float64(1.0 - t)));
	else
		tmp = Float64(x - Float64(Float64(a * Float64(t + -1.0)) + t_1));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z * (y + -1.0);
	tmp = 0.0;
	if (z <= -1.3e+59)
		tmp = x - (t_1 + (t * a));
	elseif (z <= 4.7e+64)
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t));
	else
		tmp = x - ((a * (t + -1.0)) + t_1);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z * N[(y + -1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.3e+59], N[(x - N[(t$95$1 + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.7e+64], N[(N[(x + N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(a * N[(t + -1.0), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.3e59

   1. Initial program 90.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 76.6%

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

   4. Taylor expanded in t around inf 76.6%

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

      1. *-commutative 76.6%

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

   6. Simplified 76.6%

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

   if -1.3e59 < z < 4.70000000000000029e64

   1. Initial program 96.6%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 93.9%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   if 4.70000000000000029e64 < z

   1. Initial program 96.6%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 82.6%

      \[\leadsto \color{blue}{x - \left(a \cdot \left(t - 1\right) + z \cdot \left(y - 1\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 87.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.3 \cdot 10^{+59}:\\ \;\;\;\;x - \left(z \cdot \left(y + -1\right) + t \cdot a\right)\\ \mathbf{elif}\;z \leq 4.7 \cdot 10^{+64}:\\ \;\;\;\;\left(x + b \cdot \left(\left(y + t\right) - 2\right)\right) + a \cdot \left(1 - t\right)\\ \mathbf{else}:\\ \;\;\;\;x - \left(a \cdot \left(t + -1\right) + z \cdot \left(y + -1\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 18: 85.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;z \leq -7.2 \cdot 10^{+57}:\\ \;\;\;\;t\_1 + z \cdot \left(1 - y\right)\\ \mathbf{elif}\;z \leq 3.8 \cdot 10^{+64}:\\ \;\;\;\;t\_1 + a \cdot \left(1 - t\right)\\ \mathbf{else}:\\ \;\;\;\;x - \left(a \cdot \left(t + -1\right) + z \cdot \left(y + -1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* b (- (+ y t) 2.0)))))
   (if (<= z -7.2e+57)
     (+ t_1 (* z (- 1.0 y)))
     (if (<= z 3.8e+64)
       (+ t_1 (* a (- 1.0 t)))
       (- x (+ (* a (+ t -1.0)) (* z (+ y -1.0))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (b * ((y + t) - 2.0));
	double tmp;
	if (z <= -7.2e+57) {
		tmp = t_1 + (z * (1.0 - y));
	} else if (z <= 3.8e+64) {
		tmp = t_1 + (a * (1.0 - t));
	} else {
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (b * ((y + t) - 2.0d0))
    if (z <= (-7.2d+57)) then
        tmp = t_1 + (z * (1.0d0 - y))
    else if (z <= 3.8d+64) then
        tmp = t_1 + (a * (1.0d0 - t))
    else
        tmp = x - ((a * (t + (-1.0d0))) + (z * (y + (-1.0d0))))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (b * ((y + t) - 2.0));
	double tmp;
	if (z <= -7.2e+57) {
		tmp = t_1 + (z * (1.0 - y));
	} else if (z <= 3.8e+64) {
		tmp = t_1 + (a * (1.0 - t));
	} else {
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (b * ((y + t) - 2.0))
	tmp = 0
	if z <= -7.2e+57:
		tmp = t_1 + (z * (1.0 - y))
	elif z <= 3.8e+64:
		tmp = t_1 + (a * (1.0 - t))
	else:
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(b * Float64(Float64(y + t) - 2.0)))
	tmp = 0.0
	if (z <= -7.2e+57)
		tmp = Float64(t_1 + Float64(z * Float64(1.0 - y)));
	elseif (z <= 3.8e+64)
		tmp = Float64(t_1 + Float64(a * Float64(1.0 - t)));
	else
		tmp = Float64(x - Float64(Float64(a * Float64(t + -1.0)) + Float64(z * Float64(y + -1.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (b * ((y + t) - 2.0));
	tmp = 0.0;
	if (z <= -7.2e+57)
		tmp = t_1 + (z * (1.0 - y));
	elseif (z <= 3.8e+64)
		tmp = t_1 + (a * (1.0 - t));
	else
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -7.2e+57], N[(t$95$1 + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.8e+64], N[(t$95$1 + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(a * N[(t + -1.0), $MachinePrecision]), $MachinePrecision] + N[(z * N[(y + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if z < -7.2000000000000005e57

   1. Initial program 90.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0 77.4%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - z \cdot \left(y - 1\right)} \]

   if -7.2000000000000005e57 < z < 3.8000000000000001e64

   1. Initial program 96.6%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 93.9%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   if 3.8000000000000001e64 < z

   1. Initial program 96.6%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 82.6%

      \[\leadsto \color{blue}{x - \left(a \cdot \left(t - 1\right) + z \cdot \left(y - 1\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 88.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -7.2 \cdot 10^{+57}:\\ \;\;\;\;\left(x + b \cdot \left(\left(y + t\right) - 2\right)\right) + z \cdot \left(1 - y\right)\\ \mathbf{elif}\;z \leq 3.8 \cdot 10^{+64}:\\ \;\;\;\;\left(x + b \cdot \left(\left(y + t\right) - 2\right)\right) + a \cdot \left(1 - t\right)\\ \mathbf{else}:\\ \;\;\;\;x - \left(a \cdot \left(t + -1\right) + z \cdot \left(y + -1\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 19: 84.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -5.2 \cdot 10^{+129}:\\ \;\;\;\;x - \left(a \cdot \left(t + -1\right) + z \cdot \left(y + -1\right)\right)\\ \mathbf{elif}\;a \leq 2.3 \cdot 10^{+104}:\\ \;\;\;\;z + \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(z + a \cdot \left(1 - t\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= a -5.2e+129)
   (- x (+ (* a (+ t -1.0)) (* z (+ y -1.0))))
   (if (<= a 2.3e+104)
     (+ z (+ x (+ (* b (- t 2.0)) (* y (- b z)))))
     (+ x (+ z (* a (- 1.0 t)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -5.2e+129) {
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	} else if (a <= 2.3e+104) {
		tmp = z + (x + ((b * (t - 2.0)) + (y * (b - z))));
	} else {
		tmp = x + (z + (a * (1.0 - t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (a <= (-5.2d+129)) then
        tmp = x - ((a * (t + (-1.0d0))) + (z * (y + (-1.0d0))))
    else if (a <= 2.3d+104) then
        tmp = z + (x + ((b * (t - 2.0d0)) + (y * (b - z))))
    else
        tmp = x + (z + (a * (1.0d0 - t)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -5.2e+129) {
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	} else if (a <= 2.3e+104) {
		tmp = z + (x + ((b * (t - 2.0)) + (y * (b - z))));
	} else {
		tmp = x + (z + (a * (1.0 - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if a <= -5.2e+129:
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)))
	elif a <= 2.3e+104:
		tmp = z + (x + ((b * (t - 2.0)) + (y * (b - z))))
	else:
		tmp = x + (z + (a * (1.0 - t)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (a <= -5.2e+129)
		tmp = Float64(x - Float64(Float64(a * Float64(t + -1.0)) + Float64(z * Float64(y + -1.0))));
	elseif (a <= 2.3e+104)
		tmp = Float64(z + Float64(x + Float64(Float64(b * Float64(t - 2.0)) + Float64(y * Float64(b - z)))));
	else
		tmp = Float64(x + Float64(z + Float64(a * Float64(1.0 - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (a <= -5.2e+129)
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	elseif (a <= 2.3e+104)
		tmp = z + (x + ((b * (t - 2.0)) + (y * (b - z))));
	else
		tmp = x + (z + (a * (1.0 - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[a, -5.2e+129], N[(x - N[(N[(a * N[(t + -1.0), $MachinePrecision]), $MachinePrecision] + N[(z * N[(y + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.3e+104], N[(z + N[(x + N[(N[(b * N[(t - 2.0), $MachinePrecision]), $MachinePrecision] + N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(z + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if a < -5.20000000000000024e129

   1. Initial program 95.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 82.0%

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

   if -5.20000000000000024e129 < a < 2.29999999999999985e104

   1. Initial program 96.5%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 97.7%

      \[\leadsto \color{blue}{\left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \left(-1 \cdot z + a \cdot \left(t - 1\right)\right)} \]

   4. Taylor expanded in z around inf 89.7%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{-1 \cdot z} \]
   5. Step-by-step derivation

      1. mul-1-neg 89.7%

         \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   6. Simplified 89.7%

      \[\leadsto \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right) - \color{blue}{\left(-z\right)} \]

   if 2.29999999999999985e104 < a

   1. Initial program 90.5%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 87.8%

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

   4. Taylor expanded in y around 0 90.2%

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

      1. sub-neg 90.2%

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

      2. sub-neg 90.2%

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

      3. metadata-eval 90.2%

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

      4. distribute-neg-in 90.2%

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

      5. neg-mul-1 90.2%

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

      6. remove-double-neg 90.2%

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

      7. distribute-rgt-neg-in 90.2%

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

      8. neg-sub0 90.2%

         \[\leadsto x + \left(z + a \cdot \color{blue}{\left(0 - \left(t + -1\right)\right)}\right) \]

      9. +-commutative 90.2%

         \[\leadsto x + \left(z + a \cdot \left(0 - \color{blue}{\left(-1 + t\right)}\right)\right) \]

      10. associate--r+ 90.2%

          \[\leadsto x + \left(z + a \cdot \color{blue}{\left(\left(0 - -1\right) - t\right)}\right) \]

      11. metadata-eval 90.2%

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

   6. Simplified 90.2%

      \[\leadsto \color{blue}{x + \left(z + a \cdot \left(1 - t\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 88.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -5.2 \cdot 10^{+129}:\\ \;\;\;\;x - \left(a \cdot \left(t + -1\right) + z \cdot \left(y + -1\right)\right)\\ \mathbf{elif}\;a \leq 2.3 \cdot 10^{+104}:\\ \;\;\;\;z + \left(x + \left(b \cdot \left(t - 2\right) + y \cdot \left(b - z\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(z + a \cdot \left(1 - t\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 20: 34.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(-a\right)\\ \mathbf{if}\;t \leq -3.1 \cdot 10^{+32}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq -2.25 \cdot 10^{-48}:\\ \;\;\;\;z\\ \mathbf{elif}\;t \leq -2.5 \cdot 10^{-142}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;t \leq 0.055:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* t (- a))))
   (if (<= t -3.1e+32)
     t_1
     (if (<= t -2.25e-48)
       z
       (if (<= t -2.5e-142) (* y b) (if (<= t 0.055) (+ x a) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * -a;
	double tmp;
	if (t <= -3.1e+32) {
		tmp = t_1;
	} else if (t <= -2.25e-48) {
		tmp = z;
	} else if (t <= -2.5e-142) {
		tmp = y * b;
	} else if (t <= 0.055) {
		tmp = x + a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t * -a
    if (t <= (-3.1d+32)) then
        tmp = t_1
    else if (t <= (-2.25d-48)) then
        tmp = z
    else if (t <= (-2.5d-142)) then
        tmp = y * b
    else if (t <= 0.055d0) then
        tmp = x + a
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * -a;
	double tmp;
	if (t <= -3.1e+32) {
		tmp = t_1;
	} else if (t <= -2.25e-48) {
		tmp = z;
	} else if (t <= -2.5e-142) {
		tmp = y * b;
	} else if (t <= 0.055) {
		tmp = x + a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t * -a
	tmp = 0
	if t <= -3.1e+32:
		tmp = t_1
	elif t <= -2.25e-48:
		tmp = z
	elif t <= -2.5e-142:
		tmp = y * b
	elif t <= 0.055:
		tmp = x + a
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t * Float64(-a))
	tmp = 0.0
	if (t <= -3.1e+32)
		tmp = t_1;
	elseif (t <= -2.25e-48)
		tmp = z;
	elseif (t <= -2.5e-142)
		tmp = Float64(y * b);
	elseif (t <= 0.055)
		tmp = Float64(x + a);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t * -a;
	tmp = 0.0;
	if (t <= -3.1e+32)
		tmp = t_1;
	elseif (t <= -2.25e-48)
		tmp = z;
	elseif (t <= -2.5e-142)
		tmp = y * b;
	elseif (t <= 0.055)
		tmp = x + a;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t * (-a)), $MachinePrecision]}, If[LessEqual[t, -3.1e+32], t$95$1, If[LessEqual[t, -2.25e-48], z, If[LessEqual[t, -2.5e-142], N[(y * b), $MachinePrecision], If[LessEqual[t, 0.055], N[(x + a), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if t < -3.09999999999999993e32 or 0.0550000000000000003 < t

   1. Initial program 92.1%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 68.6%

      \[\leadsto \color{blue}{t \cdot \left(b - a\right)} \]

   4. Taylor expanded in b around 0 40.3%

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

      1. associate-*r* 40.3%

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

      2. mul-1-neg 40.3%

         \[\leadsto \color{blue}{\left(-a\right)} \cdot t \]

   6. Simplified 40.3%

      \[\leadsto \color{blue}{\left(-a\right) \cdot t} \]

   if -3.09999999999999993e32 < t < -2.24999999999999994e-48

   1. Initial program 91.3%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 52.4%

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

   4. Taylor expanded in y around 0 31.6%

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

   if -2.24999999999999994e-48 < t < -2.5000000000000001e-142

   1. Initial program 99.9%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 75.9%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in y around inf 48.6%

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

   if -2.5000000000000001e-142 < t < 0.0550000000000000003

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 72.6%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in b around 0 47.1%

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

   5. Taylor expanded in t around 0 47.1%

      \[\leadsto x - \color{blue}{-1 \cdot a} \]
   6. Step-by-step derivation

      1. neg-mul-1 47.1%

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

   7. Simplified 47.1%

      \[\leadsto x - \color{blue}{\left(-a\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 42.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -3.1 \cdot 10^{+32}:\\ \;\;\;\;t \cdot \left(-a\right)\\ \mathbf{elif}\;t \leq -2.25 \cdot 10^{-48}:\\ \;\;\;\;z\\ \mathbf{elif}\;t \leq -2.5 \cdot 10^{-142}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;t \leq 0.055:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(-a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 21: 20.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.1 \cdot 10^{+14}:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq 1.35 \cdot 10^{-102}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 6.2 \cdot 10^{-25}:\\ \;\;\;\;a\\ \mathbf{elif}\;z \leq 5.5 \cdot 10^{+81}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -5.1e+14)
   z
   (if (<= z 1.35e-102) x (if (<= z 6.2e-25) a (if (<= z 5.5e+81) x z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -5.1e+14) {
		tmp = z;
	} else if (z <= 1.35e-102) {
		tmp = x;
	} else if (z <= 6.2e-25) {
		tmp = a;
	} else if (z <= 5.5e+81) {
		tmp = x;
	} else {
		tmp = z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (z <= (-5.1d+14)) then
        tmp = z
    else if (z <= 1.35d-102) then
        tmp = x
    else if (z <= 6.2d-25) then
        tmp = a
    else if (z <= 5.5d+81) then
        tmp = x
    else
        tmp = z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -5.1e+14) {
		tmp = z;
	} else if (z <= 1.35e-102) {
		tmp = x;
	} else if (z <= 6.2e-25) {
		tmp = a;
	} else if (z <= 5.5e+81) {
		tmp = x;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -5.1e+14:
		tmp = z
	elif z <= 1.35e-102:
		tmp = x
	elif z <= 6.2e-25:
		tmp = a
	elif z <= 5.5e+81:
		tmp = x
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -5.1e+14)
		tmp = z;
	elseif (z <= 1.35e-102)
		tmp = x;
	elseif (z <= 6.2e-25)
		tmp = a;
	elseif (z <= 5.5e+81)
		tmp = x;
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -5.1e+14)
		tmp = z;
	elseif (z <= 1.35e-102)
		tmp = x;
	elseif (z <= 6.2e-25)
		tmp = a;
	elseif (z <= 5.5e+81)
		tmp = x;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -5.1e+14], z, If[LessEqual[z, 1.35e-102], x, If[LessEqual[z, 6.2e-25], a, If[LessEqual[z, 5.5e+81], x, z]]]]

Derivation

1. Split input into 3 regimes

2. if z < -5.1e14 or 5.5000000000000003e81 < z

   1. Initial program 93.6%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 57.7%

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

   4. Taylor expanded in y around 0 29.8%

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

   if -5.1e14 < z < 1.35e-102 or 6.19999999999999989e-25 < z < 5.5000000000000003e81

   1. Initial program 96.1%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 23.7%

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

   if 1.35e-102 < z < 6.19999999999999989e-25

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 57.8%

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

   4. Taylor expanded in t around 0 34.8%

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

4. Final simplification 27.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5.1 \cdot 10^{+14}:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq 1.35 \cdot 10^{-102}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 6.2 \cdot 10^{-25}:\\ \;\;\;\;a\\ \mathbf{elif}\;z \leq 5.5 \cdot 10^{+81}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \]
5. Add Preprocessing

Alternative 22: 71.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -3.15 \cdot 10^{+88} \lor \neg \left(b \leq 2.3 \cdot 10^{+27}\right):\\ \;\;\;\;x + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(z + a \cdot \left(1 - t\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= b -3.15e+88) (not (<= b 2.3e+27)))
   (+ x (* b (- (+ y t) 2.0)))
   (+ x (+ z (* a (- 1.0 t))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -3.15e+88) || !(b <= 2.3e+27)) {
		tmp = x + (b * ((y + t) - 2.0));
	} else {
		tmp = x + (z + (a * (1.0 - t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((b <= (-3.15d+88)) .or. (.not. (b <= 2.3d+27))) then
        tmp = x + (b * ((y + t) - 2.0d0))
    else
        tmp = x + (z + (a * (1.0d0 - t)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -3.15e+88) || !(b <= 2.3e+27)) {
		tmp = x + (b * ((y + t) - 2.0));
	} else {
		tmp = x + (z + (a * (1.0 - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (b <= -3.15e+88) or not (b <= 2.3e+27):
		tmp = x + (b * ((y + t) - 2.0))
	else:
		tmp = x + (z + (a * (1.0 - t)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((b <= -3.15e+88) || !(b <= 2.3e+27))
		tmp = Float64(x + Float64(b * Float64(Float64(y + t) - 2.0)));
	else
		tmp = Float64(x + Float64(z + Float64(a * Float64(1.0 - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((b <= -3.15e+88) || ~((b <= 2.3e+27)))
		tmp = x + (b * ((y + t) - 2.0));
	else
		tmp = x + (z + (a * (1.0 - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[b, -3.15e+88], N[Not[LessEqual[b, 2.3e+27]], $MachinePrecision]], N[(x + N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(z + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if b < -3.15e88 or 2.3000000000000001e27 < b

   1. Initial program 90.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 82.3%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in a around 0 73.2%

      \[\leadsto \color{blue}{x + b \cdot \left(\left(t + y\right) - 2\right)} \]

   if -3.15e88 < b < 2.3000000000000001e27

   1. Initial program 98.7%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in b around 0 86.8%

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

   4. Taylor expanded in y around 0 69.4%

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

      1. sub-neg 69.4%

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

      2. sub-neg 69.4%

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

      3. metadata-eval 69.4%

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

      4. distribute-neg-in 69.4%

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

      5. neg-mul-1 69.4%

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

      6. remove-double-neg 69.4%

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

      7. distribute-rgt-neg-in 69.4%

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

      8. neg-sub0 69.4%

         \[\leadsto x + \left(z + a \cdot \color{blue}{\left(0 - \left(t + -1\right)\right)}\right) \]

      9. +-commutative 69.4%

         \[\leadsto x + \left(z + a \cdot \left(0 - \color{blue}{\left(-1 + t\right)}\right)\right) \]

      10. associate--r+ 69.4%

          \[\leadsto x + \left(z + a \cdot \color{blue}{\left(\left(0 - -1\right) - t\right)}\right) \]

      11. metadata-eval 69.4%

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

   6. Simplified 69.4%

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

4. Final simplification 70.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \leq -3.15 \cdot 10^{+88} \lor \neg \left(b \leq 2.3 \cdot 10^{+27}\right):\\ \;\;\;\;x + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(z + a \cdot \left(1 - t\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 23: 25.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.3 \cdot 10^{+144}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;y \leq -1.2 \cdot 10^{-153}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4200:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;y \cdot b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y -2.3e+144)
   (* y b)
   (if (<= y -1.2e-153) x (if (<= y 4200.0) z (* y b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -2.3e+144) {
		tmp = y * b;
	} else if (y <= -1.2e-153) {
		tmp = x;
	} else if (y <= 4200.0) {
		tmp = z;
	} else {
		tmp = y * b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (y <= (-2.3d+144)) then
        tmp = y * b
    else if (y <= (-1.2d-153)) then
        tmp = x
    else if (y <= 4200.0d0) then
        tmp = z
    else
        tmp = y * b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -2.3e+144) {
		tmp = y * b;
	} else if (y <= -1.2e-153) {
		tmp = x;
	} else if (y <= 4200.0) {
		tmp = z;
	} else {
		tmp = y * b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if y <= -2.3e+144:
		tmp = y * b
	elif y <= -1.2e-153:
		tmp = x
	elif y <= 4200.0:
		tmp = z
	else:
		tmp = y * b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= -2.3e+144)
		tmp = Float64(y * b);
	elseif (y <= -1.2e-153)
		tmp = x;
	elseif (y <= 4200.0)
		tmp = z;
	else
		tmp = Float64(y * b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (y <= -2.3e+144)
		tmp = y * b;
	elseif (y <= -1.2e-153)
		tmp = x;
	elseif (y <= 4200.0)
		tmp = z;
	else
		tmp = y * b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, -2.3e+144], N[(y * b), $MachinePrecision], If[LessEqual[y, -1.2e-153], x, If[LessEqual[y, 4200.0], z, N[(y * b), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -2.3000000000000001e144 or 4200 < y

   1. Initial program 91.1%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 71.3%

      \[\leadsto \color{blue}{\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) - a \cdot \left(t - 1\right)} \]

   4. Taylor expanded in y around inf 36.5%

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

   if -2.3000000000000001e144 < y < -1.2000000000000001e-153

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 25.7%

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

   if -1.2000000000000001e-153 < y < 4200

   1. Initial program 95.5%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 29.3%

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

   4. Taylor expanded in y around 0 28.7%

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

4. Final simplification 30.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.3 \cdot 10^{+144}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;y \leq -1.2 \cdot 10^{-153}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4200:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;y \cdot b\\ \end{array} \]
5. Add Preprocessing

Alternative 24: 22.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -7.5 \cdot 10^{+106}:\\ \;\;\;\;a\\ \mathbf{elif}\;a \leq 3.1 \cdot 10^{+66}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= a -7.5e+106) a (if (<= a 3.1e+66) x a)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -7.5e+106) {
		tmp = a;
	} else if (a <= 3.1e+66) {
		tmp = x;
	} else {
		tmp = a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (a <= (-7.5d+106)) then
        tmp = a
    else if (a <= 3.1d+66) then
        tmp = x
    else
        tmp = a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -7.5e+106) {
		tmp = a;
	} else if (a <= 3.1e+66) {
		tmp = x;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if a <= -7.5e+106:
		tmp = a
	elif a <= 3.1e+66:
		tmp = x
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (a <= -7.5e+106)
		tmp = a;
	elseif (a <= 3.1e+66)
		tmp = x;
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (a <= -7.5e+106)
		tmp = a;
	elseif (a <= 3.1e+66)
		tmp = x;
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[a, -7.5e+106], a, If[LessEqual[a, 3.1e+66], x, a]]

Derivation

1. Split input into 2 regimes

2. if a < -7.50000000000000058e106 or 3.10000000000000019e66 < a

   1. Initial program 93.4%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 64.7%

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

   4. Taylor expanded in t around 0 22.1%

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

   if -7.50000000000000058e106 < a < 3.10000000000000019e66

   1. Initial program 96.4%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 20.8%

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

4. Final simplification 21.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -7.5 \cdot 10^{+106}:\\ \;\;\;\;a\\ \mathbf{elif}\;a \leq 3.1 \cdot 10^{+66}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \]
5. Add Preprocessing

Alternative 25: 11.4% accurate, 21.0× speedup

Math

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

FPCore

(FPCore (x y z t a b) :precision binary64 a)

C

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

Fortran

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

Java

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

Python

def code(x, y, z, t, a, b):
	return a

Julia

function code(x, y, z, t, a, b)
	return a
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_, b_] := a

Derivation

1. Initial program 95.3%

   \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
2. Add Preprocessing

3. Taylor expanded in a around inf 29.7%

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

4. Taylor expanded in t around 0 10.6%

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

5. Final simplification 10.6%

   \[\leadsto a \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024130 
(FPCore (x y z t a b)
  :name "Statistics.Distribution.Beta:$centropy from math-functions-0.1.5.2"
  :precision binary64
  (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* (- (+ y t) 2.0) b)))