Linear.Quaternion:$c/ from linear-1.19.1.3, C

Percentage Accurate: 63.1% → 100.0%

Time: 5.1s

Precision: binary64

Cost: 320

Math

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

↓

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

FPCore

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

↓

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

C

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

↓

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

↓

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

Java

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

↓

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

Python

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

↓

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

Julia

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

↓

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

MATLAB

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

↓

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

Wolfram

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

↓

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

Target

Original	63.1%
Target	100.0%
Herbie	100.0%

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

Derivation

1. Initial program 64.4%

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

2. Simplified 100.0%

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

   [Start] 64.4%	\[ \left(\left(x \cdot y + y \cdot y\right) - y \cdot z\right) - y \cdot y \]
   +-commutative [=>] 64.4%	\[ \left(\color{blue}{\left(y \cdot y + x \cdot y\right)} - y \cdot z\right) - y \cdot y \]
   associate--l+ [=>] 64.4%	\[ \color{blue}{\left(y \cdot y + \left(x \cdot y - y \cdot z\right)\right)} - y \cdot y \]
   +-commutative [=>] 64.4%	\[ \color{blue}{\left(\left(x \cdot y - y \cdot z\right) + y \cdot y\right)} - y \cdot y \]
   associate--l+ [=>] 72.3%	\[ \color{blue}{\left(x \cdot y - y \cdot z\right) + \left(y \cdot y - y \cdot y\right)} \]
   +-inverses [=>] 98.4%	\[ \left(x \cdot y - y \cdot z\right) + \color{blue}{0} \]
   +-rgt-identity [=>] 98.4%	\[ \color{blue}{x \cdot y - y \cdot z} \]
   *-commutative [=>] 98.4%	\[ \color{blue}{y \cdot x} - y \cdot z \]
   distribute-lft-out-- [=>] 100.0%	\[ \color{blue}{y \cdot \left(x - z\right)} \]

3. Final simplification 100.0%

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

Alternatives

Alternative 1
Accuracy	100.0%
Cost	320

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

Alternative 2
Accuracy	76.7%
Cost	520

\[\begin{array}{l} \mathbf{if}\;x \leq -2.15 \cdot 10^{-50}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;x \leq 6.5 \cdot 10^{+46}:\\ \;\;\;\;y \cdot \left(-z\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternative 3
Accuracy	52.8%
Cost	192

\[y \cdot x \]

Reproduce

herbie shell --seed 2023272 
(FPCore (x y z)
  :name "Linear.Quaternion:$c/ from linear-1.19.1.3, C"
  :precision binary64

  :herbie-target
  (* (- x z) y)

  (- (- (+ (* x y) (* y y)) (* y z)) (* y y)))