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

Average Accuracy: 57.7% → 78.7%

Time: 6.5s

Precision: binary64

Cost: 448

Math

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

↓

\[y \cdot x - y \cdot z \]

FPCore

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

↓

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

C

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

↓

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

Fortran

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

Java

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

↓

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

Python

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

↓

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

Julia

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

↓

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

MATLAB

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

↓

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

Wolfram

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

↓

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

Target

Original	57.7%
Target	78.7%
Herbie	78.7%

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

Derivation

1. Initial program 62.1%

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

2. Taylor expanded in x around 0 81.6%

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

3. Simplified 81.6%

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

   [Start] 81.6	\[ y \cdot x + -1 \cdot \left(y \cdot z\right) \]
   fma-def [=>] 81.6	\[ \color{blue}{\mathsf{fma}\left(y, x, -1 \cdot \left(y \cdot z\right)\right)} \]
   mul-1-neg [=>] 81.6	\[ \mathsf{fma}\left(y, x, \color{blue}{-y \cdot z}\right) \]
   fma-neg [<=] 81.6	\[ \color{blue}{y \cdot x - y \cdot z} \]

4. Final simplification 81.6%

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

Alternatives

Alternative 1
Accuracy	59.6%
Cost	521

\[\begin{array}{l} \mathbf{if}\;z \leq -7.5 \cdot 10^{+34} \lor \neg \left(z \leq 1.35 \cdot 10^{-70}\right):\\ \;\;\;\;y \cdot \left(-z\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternative 2
Accuracy	78.7%
Cost	320

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

Alternative 3
Accuracy	41.8%
Cost	192

\[y \cdot x \]

Alternative 4
Accuracy	3.2%
Cost	64

\[-1 \]

Alternative 5
Accuracy	6.7%
Cost	64

\[0 \]

Reproduce

herbie shell --seed 2023157 
(FPCore (x y z)
  :name "Linear.Quaternion:$c/ from linear-1.19.1.3, B"
  :precision binary64

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

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