Main:bigenough2 from A

Percentage Accurate: 100.0% → 100.0%

Time: 2.1s

Alternatives: 7

Speedup: 1.0×

• 22.0% 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, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y, x + z, x\right) \end{array} \]

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. +-commutative 100.0%

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

   2. fma-def 100.0%

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

   3. +-commutative 100.0%

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

3. Simplified 100.0%

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

4. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(y, x + z, x\right) \]

Alternative 2: 60.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.5 \cdot 10^{+208}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -1.75 \cdot 10^{+149}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq -4.4 \cdot 10^{+74}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -2.8 \cdot 10^{+18}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq -8 \cdot 10^{-72}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 2.45 \cdot 10^{-80}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4 \cdot 10^{+18} \lor \neg \left(y \leq 9.2 \cdot 10^{+141}\right) \land y \leq 8.2 \cdot 10^{+195}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -6.5e+208)
   (* y z)
   (if (<= y -1.75e+149)
     (* y x)
     (if (<= y -4.4e+74)
       (* y z)
       (if (<= y -2.8e+18)
         (* y x)
         (if (<= y -8e-72)
           (* y z)
           (if (<= y 2.45e-80)
             x
             (if (or (<= y 4e+18) (and (not (<= y 9.2e+141)) (<= y 8.2e+195)))
               (* y z)
               (* y x)))))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -6.5e+208) {
		tmp = y * z;
	} else if (y <= -1.75e+149) {
		tmp = y * x;
	} else if (y <= -4.4e+74) {
		tmp = y * z;
	} else if (y <= -2.8e+18) {
		tmp = y * x;
	} else if (y <= -8e-72) {
		tmp = y * z;
	} else if (y <= 2.45e-80) {
		tmp = x;
	} else if ((y <= 4e+18) || (!(y <= 9.2e+141) && (y <= 8.2e+195))) {
		tmp = y * z;
	} 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 (y <= (-6.5d+208)) then
        tmp = y * z
    else if (y <= (-1.75d+149)) then
        tmp = y * x
    else if (y <= (-4.4d+74)) then
        tmp = y * z
    else if (y <= (-2.8d+18)) then
        tmp = y * x
    else if (y <= (-8d-72)) then
        tmp = y * z
    else if (y <= 2.45d-80) then
        tmp = x
    else if ((y <= 4d+18) .or. (.not. (y <= 9.2d+141)) .and. (y <= 8.2d+195)) then
        tmp = y * z
    else
        tmp = y * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -6.5e+208) {
		tmp = y * z;
	} else if (y <= -1.75e+149) {
		tmp = y * x;
	} else if (y <= -4.4e+74) {
		tmp = y * z;
	} else if (y <= -2.8e+18) {
		tmp = y * x;
	} else if (y <= -8e-72) {
		tmp = y * z;
	} else if (y <= 2.45e-80) {
		tmp = x;
	} else if ((y <= 4e+18) || (!(y <= 9.2e+141) && (y <= 8.2e+195))) {
		tmp = y * z;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -6.5e+208:
		tmp = y * z
	elif y <= -1.75e+149:
		tmp = y * x
	elif y <= -4.4e+74:
		tmp = y * z
	elif y <= -2.8e+18:
		tmp = y * x
	elif y <= -8e-72:
		tmp = y * z
	elif y <= 2.45e-80:
		tmp = x
	elif (y <= 4e+18) or (not (y <= 9.2e+141) and (y <= 8.2e+195)):
		tmp = y * z
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -6.5e+208)
		tmp = Float64(y * z);
	elseif (y <= -1.75e+149)
		tmp = Float64(y * x);
	elseif (y <= -4.4e+74)
		tmp = Float64(y * z);
	elseif (y <= -2.8e+18)
		tmp = Float64(y * x);
	elseif (y <= -8e-72)
		tmp = Float64(y * z);
	elseif (y <= 2.45e-80)
		tmp = x;
	elseif ((y <= 4e+18) || (!(y <= 9.2e+141) && (y <= 8.2e+195)))
		tmp = Float64(y * z);
	else
		tmp = Float64(y * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -6.5e+208)
		tmp = y * z;
	elseif (y <= -1.75e+149)
		tmp = y * x;
	elseif (y <= -4.4e+74)
		tmp = y * z;
	elseif (y <= -2.8e+18)
		tmp = y * x;
	elseif (y <= -8e-72)
		tmp = y * z;
	elseif (y <= 2.45e-80)
		tmp = x;
	elseif ((y <= 4e+18) || (~((y <= 9.2e+141)) && (y <= 8.2e+195)))
		tmp = y * z;
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -6.5e+208], N[(y * z), $MachinePrecision], If[LessEqual[y, -1.75e+149], N[(y * x), $MachinePrecision], If[LessEqual[y, -4.4e+74], N[(y * z), $MachinePrecision], If[LessEqual[y, -2.8e+18], N[(y * x), $MachinePrecision], If[LessEqual[y, -8e-72], N[(y * z), $MachinePrecision], If[LessEqual[y, 2.45e-80], x, If[Or[LessEqual[y, 4e+18], And[N[Not[LessEqual[y, 9.2e+141]], $MachinePrecision], LessEqual[y, 8.2e+195]]], N[(y * z), $MachinePrecision], N[(y * x), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -6.5000000000000001e208 or -1.75000000000000006e149 < y < -4.4000000000000002e74 or -2.8e18 < y < -7.9999999999999997e-72 or 2.44999999999999995e-80 < y < 4e18 or 9.2000000000000006e141 < y < 8.2000000000000001e195

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0 73.1%

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

   if -6.5000000000000001e208 < y < -1.75000000000000006e149 or -4.4000000000000002e74 < y < -2.8e18 or 4e18 < y < 9.2000000000000006e141 or 8.2000000000000001e195 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in z around 0 71.4%

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

   3. Taylor expanded in y around inf 71.4%

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

   if -7.9999999999999997e-72 < y < 2.44999999999999995e-80

   1. Initial program 100.0%

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

   2. Taylor expanded in y around 0 75.8%

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

4. Final simplification 73.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6.5 \cdot 10^{+208}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -1.75 \cdot 10^{+149}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq -4.4 \cdot 10^{+74}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -2.8 \cdot 10^{+18}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq -8 \cdot 10^{-72}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 2.45 \cdot 10^{-80}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4 \cdot 10^{+18} \lor \neg \left(y \leq 9.2 \cdot 10^{+141}\right) \land y \leq 8.2 \cdot 10^{+195}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternative 3: 83.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.2 \cdot 10^{-72} \lor \neg \left(y \leq 2.6 \cdot 10^{-80}\right):\\ \;\;\;\;y \cdot \left(x + z\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -9.2e-72) (not (<= y 2.6e-80))) (* y (+ x z)) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -9.2e-72) || !(y <= 2.6e-80)) {
		tmp = y * (x + 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 ((y <= (-9.2d-72)) .or. (.not. (y <= 2.6d-80))) then
        tmp = y * (x + z)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -9.2e-72) || !(y <= 2.6e-80)) {
		tmp = y * (x + z);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -9.2e-72) or not (y <= 2.6e-80):
		tmp = y * (x + z)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -9.2e-72) || !(y <= 2.6e-80))
		tmp = Float64(y * Float64(x + z));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -9.2e-72) || ~((y <= 2.6e-80)))
		tmp = y * (x + z);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -9.2e-72], N[Not[LessEqual[y, 2.6e-80]], $MachinePrecision]], N[(y * N[(x + z), $MachinePrecision]), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if y < -9.19999999999999978e-72 or 2.6000000000000001e-80 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf 93.2%

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

   if -9.19999999999999978e-72 < y < 2.6000000000000001e-80

   1. Initial program 100.0%

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

   2. Taylor expanded in y around 0 75.8%

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

4. Final simplification 86.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9.2 \cdot 10^{-72} \lor \neg \left(y \leq 2.6 \cdot 10^{-80}\right):\\ \;\;\;\;y \cdot \left(x + z\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 4: 83.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.8 \cdot 10^{-72} \lor \neg \left(y \leq 1.9 \cdot 10^{-80}\right):\\ \;\;\;\;y \cdot \left(x + z\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -5.8e-72) (not (<= y 1.9e-80))) (* y (+ x z)) (+ x (* y x))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -5.8e-72) || !(y <= 1.9e-80)) {
		tmp = y * (x + z);
	} else {
		tmp = x + (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 ((y <= (-5.8d-72)) .or. (.not. (y <= 1.9d-80))) then
        tmp = y * (x + z)
    else
        tmp = x + (y * x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -5.8e-72) || !(y <= 1.9e-80)) {
		tmp = y * (x + z);
	} else {
		tmp = x + (y * x);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -5.8e-72) or not (y <= 1.9e-80):
		tmp = y * (x + z)
	else:
		tmp = x + (y * x)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -5.8e-72) || !(y <= 1.9e-80))
		tmp = Float64(y * Float64(x + z));
	else
		tmp = Float64(x + Float64(y * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -5.8e-72) || ~((y <= 1.9e-80)))
		tmp = y * (x + z);
	else
		tmp = x + (y * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -5.8e-72], N[Not[LessEqual[y, 1.9e-80]], $MachinePrecision]], N[(y * N[(x + z), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -5.79999999999999995e-72 or 1.89999999999999983e-80 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf 93.2%

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

   if -5.79999999999999995e-72 < y < 1.89999999999999983e-80

   1. Initial program 100.0%

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

   2. Taylor expanded in z around 0 75.8%

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

4. Final simplification 86.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5.8 \cdot 10^{-72} \lor \neg \left(y \leq 1.9 \cdot 10^{-80}\right):\\ \;\;\;\;y \cdot \left(x + z\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot x\\ \end{array} \]

Alternative 5: 60.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6400000000:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 0.0275:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -6400000000.0) (* y x) (if (<= y 0.0275) x (* y x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -6400000000.0) {
		tmp = y * x;
	} else if (y <= 0.0275) {
		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 (y <= (-6400000000.0d0)) then
        tmp = y * x
    else if (y <= 0.0275d0) 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 (y <= -6400000000.0) {
		tmp = y * x;
	} else if (y <= 0.0275) {
		tmp = x;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -6400000000.0:
		tmp = y * x
	elif y <= 0.0275:
		tmp = x
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -6400000000.0)
		tmp = Float64(y * x);
	elseif (y <= 0.0275)
		tmp = x;
	else
		tmp = Float64(y * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -6400000000.0)
		tmp = y * x;
	elseif (y <= 0.0275)
		tmp = x;
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -6400000000.0], N[(y * x), $MachinePrecision], If[LessEqual[y, 0.0275], x, N[(y * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -6.4e9 or 0.0275000000000000001 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in z around 0 54.8%

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

   3. Taylor expanded in y around inf 53.7%

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

   if -6.4e9 < y < 0.0275000000000000001

   1. Initial program 100.0%

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

   2. Taylor expanded in y around 0 66.0%

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

4. Final simplification 59.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6400000000:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 0.0275:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternative 6: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

2. Final simplification 100.0%

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

Alternative 7: 36.0% accurate, 7.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 100.0%

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

2. Taylor expanded in y around 0 34.0%

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

3. Final simplification 34.0%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023202 
(FPCore (x y z)
  :name "Main:bigenough2 from A"
  :precision binary64
  (+ x (* y (+ z x))))