SynthBasics:oscSampleBasedAux from YampaSynth-0.2

Percentage Accurate: 100.0% → 100.0%

Time: 4.9s

Alternatives: 6

Speedup: 1.0×

• 20.6% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

3. Final simplification 100.0%

   \[\leadsto x + y \cdot \left(z - x\right) \]
4. Add Preprocessing

Alternative 2: 75.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.85 \cdot 10^{+264}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -1.08 \cdot 10^{+164} \lor \neg \left(y \leq -2.25 \cdot 10^{+98}\right) \land y \leq -1.06 \cdot 10^{+17}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.85e+264)
   (* y z)
   (if (or (<= y -1.08e+164) (and (not (<= y -2.25e+98)) (<= y -1.06e+17)))
     (* y (- x))
     (+ x (* y z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.85e+264) {
		tmp = y * z;
	} else if ((y <= -1.08e+164) || (!(y <= -2.25e+98) && (y <= -1.06e+17))) {
		tmp = y * -x;
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-1.85d+264)) then
        tmp = y * z
    else if ((y <= (-1.08d+164)) .or. (.not. (y <= (-2.25d+98))) .and. (y <= (-1.06d+17))) then
        tmp = y * -x
    else
        tmp = x + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.85e+264) {
		tmp = y * z;
	} else if ((y <= -1.08e+164) || (!(y <= -2.25e+98) && (y <= -1.06e+17))) {
		tmp = y * -x;
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.85e+264:
		tmp = y * z
	elif (y <= -1.08e+164) or (not (y <= -2.25e+98) and (y <= -1.06e+17)):
		tmp = y * -x
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.85e+264)
		tmp = Float64(y * z);
	elseif ((y <= -1.08e+164) || (!(y <= -2.25e+98) && (y <= -1.06e+17)))
		tmp = Float64(y * Float64(-x));
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.85e+264)
		tmp = y * z;
	elseif ((y <= -1.08e+164) || (~((y <= -2.25e+98)) && (y <= -1.06e+17)))
		tmp = y * -x;
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.85e+264], N[(y * z), $MachinePrecision], If[Or[LessEqual[y, -1.08e+164], And[N[Not[LessEqual[y, -2.25e+98]], $MachinePrecision], LessEqual[y, -1.06e+17]]], N[(y * (-x)), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.85e264

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 88.9%

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

   4. Taylor expanded in x around 0 88.9%

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

   if -1.85e264 < y < -1.08e164 or -2.2500000000000001e98 < y < -1.06e17

   1. Initial program 100.0%

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

   3. Taylor expanded in z around 0 73.7%

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

      1. mul-1-neg 73.7%

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

      2. distribute-lft-neg-out 73.7%

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

      3. *-commutative 73.7%

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

   5. Simplified 73.7%

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

   6. Taylor expanded in y around inf 73.7%

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

      1. associate-*r* 73.7%

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

      2. mul-1-neg 73.7%

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

   8. Simplified 73.7%

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

   if -1.08e164 < y < -2.2500000000000001e98 or -1.06e17 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 86.3%

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

4. Final simplification 84.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.85 \cdot 10^{+264}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -1.08 \cdot 10^{+164} \lor \neg \left(y \leq -2.25 \cdot 10^{+98}\right) \land y \leq -1.06 \cdot 10^{+17}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 54.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.15 \cdot 10^{-11}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 2 \cdot 10^{+35}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;x \leq 3.1 \cdot 10^{+181}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -2.15e-11)
   x
   (if (<= x 2e+35) (* y z) (if (<= x 3.1e+181) x (* y (- x))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -2.15e-11) {
		tmp = x;
	} else if (x <= 2e+35) {
		tmp = y * z;
	} else if (x <= 3.1e+181) {
		tmp = x;
	} else {
		tmp = y * -x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-2.15d-11)) then
        tmp = x
    else if (x <= 2d+35) then
        tmp = y * z
    else if (x <= 3.1d+181) then
        tmp = x
    else
        tmp = y * -x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -2.15e-11) {
		tmp = x;
	} else if (x <= 2e+35) {
		tmp = y * z;
	} else if (x <= 3.1e+181) {
		tmp = x;
	} else {
		tmp = y * -x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -2.15e-11:
		tmp = x
	elif x <= 2e+35:
		tmp = y * z
	elif x <= 3.1e+181:
		tmp = x
	else:
		tmp = y * -x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -2.15e-11)
		tmp = x;
	elseif (x <= 2e+35)
		tmp = Float64(y * z);
	elseif (x <= 3.1e+181)
		tmp = x;
	else
		tmp = Float64(y * Float64(-x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -2.15e-11)
		tmp = x;
	elseif (x <= 2e+35)
		tmp = y * z;
	elseif (x <= 3.1e+181)
		tmp = x;
	else
		tmp = y * -x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -2.15e-11], x, If[LessEqual[x, 2e+35], N[(y * z), $MachinePrecision], If[LessEqual[x, 3.1e+181], x, N[(y * (-x)), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -2.15000000000000001e-11 or 1.9999999999999999e35 < x < 3.09999999999999989e181

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 71.0%

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

   4. Taylor expanded in x around inf 57.4%

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

   if -2.15000000000000001e-11 < x < 1.9999999999999999e35

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 88.6%

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

   4. Taylor expanded in x around 0 71.4%

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

   if 3.09999999999999989e181 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in z around 0 100.0%

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

      1. mul-1-neg 100.0%

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

      2. distribute-lft-neg-out 100.0%

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

      3. *-commutative 100.0%

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

   5. Simplified 100.0%

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

   6. Taylor expanded in y around inf 62.3%

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

      1. associate-*r* 62.3%

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

      2. mul-1-neg 62.3%

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

   8. Simplified 62.3%

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

4. Final simplification 65.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.15 \cdot 10^{-11}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 2 \cdot 10^{+35}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;x \leq 3.1 \cdot 10^{+181}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 86.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.1 \cdot 10^{+44} \lor \neg \left(x \leq 1.1 \cdot 10^{+107}\right):\\ \;\;\;\;x - x \cdot y\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -2.1e+44) (not (<= x 1.1e+107))) (- x (* x y)) (+ x (* y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.1e+44) || !(x <= 1.1e+107)) {
		tmp = x - (x * y);
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-2.1d+44)) .or. (.not. (x <= 1.1d+107))) then
        tmp = x - (x * y)
    else
        tmp = x + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.1e+44) || !(x <= 1.1e+107)) {
		tmp = x - (x * y);
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -2.1e+44) or not (x <= 1.1e+107):
		tmp = x - (x * y)
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -2.1e+44) || !(x <= 1.1e+107))
		tmp = Float64(x - Float64(x * y));
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -2.1e+44) || ~((x <= 1.1e+107)))
		tmp = x - (x * y);
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -2.1e+44], N[Not[LessEqual[x, 1.1e+107]], $MachinePrecision]], N[(x - N[(x * y), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -2.09999999999999987e44 or 1.1e107 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in z around 0 94.7%

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

      1. mul-1-neg 94.7%

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

      2. distribute-lft-neg-out 94.7%

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

      3. *-commutative 94.7%

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

   5. Simplified 94.7%

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

      1. *-commutative 94.7%

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

      2. distribute-lft-neg-out 94.7%

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

      3. unsub-neg 94.7%

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

   7. Applied egg-rr 94.7%

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

   if -2.09999999999999987e44 < x < 1.1e107

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 88.7%

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

4. Final simplification 90.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.1 \cdot 10^{+44} \lor \neg \left(x \leq 1.1 \cdot 10^{+107}\right):\\ \;\;\;\;x - x \cdot y\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 54.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.8 \cdot 10^{-13}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 2 \cdot 10^{+35}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -7.8e-13) x (if (<= x 2e+35) (* y z) x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.8e-13) {
		tmp = x;
	} else if (x <= 2e+35) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-7.8d-13)) then
        tmp = x
    else if (x <= 2d+35) then
        tmp = y * z
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.8e-13) {
		tmp = x;
	} else if (x <= 2e+35) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -7.8e-13:
		tmp = x
	elif x <= 2e+35:
		tmp = y * z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -7.8e-13)
		tmp = x;
	elseif (x <= 2e+35)
		tmp = Float64(y * z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -7.8e-13)
		tmp = x;
	elseif (x <= 2e+35)
		tmp = y * z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -7.8e-13], x, If[LessEqual[x, 2e+35], N[(y * z), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if x < -7.80000000000000009e-13 or 1.9999999999999999e35 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 66.7%

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

   4. Taylor expanded in x around inf 54.2%

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

   if -7.80000000000000009e-13 < x < 1.9999999999999999e35

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 88.6%

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

   4. Taylor expanded in x around 0 71.4%

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

4. Final simplification 63.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -7.8 \cdot 10^{-13}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 2 \cdot 10^{+35}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 36.4% accurate, 7.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in z around inf 78.7%

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

4. Taylor expanded in x around inf 34.9%

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

5. Final simplification 34.9%

   \[\leadsto x \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024067 
(FPCore (x y z)
  :name "SynthBasics:oscSampleBasedAux from YampaSynth-0.2"
  :precision binary64
  (+ x (* y (- z x))))