Linear.Projection:inversePerspective from linear-1.19.1.3, C

Average Error: 15.6 → 0.0

Time: 3.8s

Precision: binary64

Cost: 576

Math

\[\frac{x + y}{\left(x \cdot 2\right) \cdot y} \]

↓

\[0.5 \cdot \left(\frac{1}{y} + \frac{1}{x}\right) \]

FPCore

(FPCore (x y) :precision binary64 (/ (+ x y) (* (* x 2.0) y)))

↓

(FPCore (x y) :precision binary64 (* 0.5 (+ (/ 1.0 y) (/ 1.0 x))))

C

double code(double x, double y) {
	return (x + y) / ((x * 2.0) * y);
}

↓

double code(double x, double y) {
	return 0.5 * ((1.0 / y) + (1.0 / x));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x + y) / ((x * 2.0d0) * y)
end function

↓

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 0.5d0 * ((1.0d0 / y) + (1.0d0 / x))
end function

Java

public static double code(double x, double y) {
	return (x + y) / ((x * 2.0) * y);
}

↓

public static double code(double x, double y) {
	return 0.5 * ((1.0 / y) + (1.0 / x));
}

Python

def code(x, y):
	return (x + y) / ((x * 2.0) * y)

↓

def code(x, y):
	return 0.5 * ((1.0 / y) + (1.0 / x))

Julia

function code(x, y)
	return Float64(Float64(x + y) / Float64(Float64(x * 2.0) * y))
end

↓

function code(x, y)
	return Float64(0.5 * Float64(Float64(1.0 / y) + Float64(1.0 / x)))
end

MATLAB

function tmp = code(x, y)
	tmp = (x + y) / ((x * 2.0) * y);
end

↓

function tmp = code(x, y)
	tmp = 0.5 * ((1.0 / y) + (1.0 / x));
end

Wolfram

code[x_, y_] := N[(N[(x + y), $MachinePrecision] / N[(N[(x * 2.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_] := N[(0.5 * N[(N[(1.0 / y), $MachinePrecision] + N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Target

Original	15.6
Target	0.0
Herbie	0.0

\[\frac{0.5}{x} + \frac{0.5}{y} \]

Derivation

1. Initial program 15.6

   \[\frac{x + y}{\left(x \cdot 2\right) \cdot y} \]

2. Taylor expanded in x around 0 0.0

   \[\leadsto \color{blue}{0.5 \cdot \frac{1}{y} + 0.5 \cdot \frac{1}{x}} \]

3. Simplified 0.0

   \[\leadsto \color{blue}{0.5 \cdot \left(\frac{1}{y} + \frac{1}{x}\right)} \]
   Proof

   [Start] 0.0	\[ 0.5 \cdot \frac{1}{y} + 0.5 \cdot \frac{1}{x} \]
   rational.json-simplify-1 [=>] 0.0	\[ \color{blue}{0.5 \cdot \frac{1}{x} + 0.5 \cdot \frac{1}{y}} \]
   rational.json-simplify-2 [=>] 0.0	\[ 0.5 \cdot \frac{1}{x} + \color{blue}{\frac{1}{y} \cdot 0.5} \]
   rational.json-simplify-47 [=>] 0.0	\[ \color{blue}{0.5 \cdot \left(\frac{1}{y} + \frac{1}{x}\right)} \]

4. Final simplification 0.0

   \[\leadsto 0.5 \cdot \left(\frac{1}{y} + \frac{1}{x}\right) \]

Alternatives

Alternative 1
Error	26.2
Cost	324

\[\begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{-184}:\\ \;\;\;\;\frac{0.5}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x}\\ \end{array} \]

Alternative 2
Error	30.9
Cost	192

\[\frac{0.5}{x} \]

Reproduce

herbie shell --seed 2023077 
(FPCore (x y)
  :name "Linear.Projection:inversePerspective from linear-1.19.1.3, C"
  :precision binary64

  :herbie-target
  (+ (/ 0.5 x) (/ 0.5 y))

  (/ (+ x y) (* (* x 2.0) y)))