Expanding a square

Percentage Accurate: 54.5% → 100.0%

Time: 1.9s

Precision: binary64

Cost: 320

• 24.6% of points produce a very large (infinite) output. You may want to add a precondition.

Math

\[\left(x + 1\right) \cdot \left(x + 1\right) - 1 \]

↓

\[x \cdot \left(x - -2\right) \]

FPCore

(FPCore (x) :precision binary64 (- (* (+ x 1.0) (+ x 1.0)) 1.0))

↓

(FPCore (x) :precision binary64 (* x (- x -2.0)))

C

double code(double x) {
	return ((x + 1.0) * (x + 1.0)) - 1.0;
}

↓

double code(double x) {
	return x * (x - -2.0);
}

Fortran

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

↓

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

Java

public static double code(double x) {
	return ((x + 1.0) * (x + 1.0)) - 1.0;
}

↓

public static double code(double x) {
	return x * (x - -2.0);
}

Python

def code(x):
	return ((x + 1.0) * (x + 1.0)) - 1.0

↓

def code(x):
	return x * (x - -2.0)

Julia

function code(x)
	return Float64(Float64(Float64(x + 1.0) * Float64(x + 1.0)) - 1.0)
end

↓

function code(x)
	return Float64(x * Float64(x - -2.0))
end

MATLAB

function tmp = code(x)
	tmp = ((x + 1.0) * (x + 1.0)) - 1.0;
end

↓

function tmp = code(x)
	tmp = x * (x - -2.0);
end

Wolfram

code[x_] := N[(N[(N[(x + 1.0), $MachinePrecision] * N[(x + 1.0), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]

↓

code[x_] := N[(x * N[(x - -2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 48.8%

   \[\left(x + 1\right) \cdot \left(x + 1\right) - 1 \]

2. Simplified 100.0%

   \[\leadsto \color{blue}{x \cdot \left(x - -2\right)} \]
   Step-by-step derivation

   [Start] 48.8	\[ \left(x + 1\right) \cdot \left(x + 1\right) - 1 \]
   difference-of-sqr-1 [=>] 48.8	\[ \color{blue}{\left(\left(x + 1\right) + 1\right) \cdot \left(\left(x + 1\right) - 1\right)} \]
   *-commutative [=>] 48.8	\[ \color{blue}{\left(\left(x + 1\right) - 1\right) \cdot \left(\left(x + 1\right) + 1\right)} \]
   associate--l+ [=>] 100.0	\[ \color{blue}{\left(x + \left(1 - 1\right)\right)} \cdot \left(\left(x + 1\right) + 1\right) \]
   metadata-eval [=>] 100.0	\[ \left(x + \color{blue}{0}\right) \cdot \left(\left(x + 1\right) + 1\right) \]
   +-rgt-identity [=>] 100.0	\[ \color{blue}{x} \cdot \left(\left(x + 1\right) + 1\right) \]
   associate-+l+ [=>] 100.0	\[ x \cdot \color{blue}{\left(x + \left(1 + 1\right)\right)} \]
   metadata-eval [=>] 100.0	\[ x \cdot \left(x + \color{blue}{2}\right) \]
   metadata-eval [<=] 100.0	\[ x \cdot \left(x + \color{blue}{\left(--2\right)}\right) \]
   metadata-eval [<=] 100.0	\[ x \cdot \left(x + \left(-\color{blue}{2 \cdot -1}\right)\right) \]
   metadata-eval [<=] 100.0	\[ x \cdot \left(x + \left(-\color{blue}{\left(1 + 1\right)} \cdot -1\right)\right) \]
   metadata-eval [<=] 100.0	\[ x \cdot \left(x + \left(-\left(1 + 1\right) \cdot \color{blue}{\left(-1\right)}\right)\right) \]
   sub-neg [<=] 100.0	\[ x \cdot \color{blue}{\left(x - \left(1 + 1\right) \cdot \left(-1\right)\right)} \]
   metadata-eval [=>] 100.0	\[ x \cdot \left(x - \color{blue}{2} \cdot \left(-1\right)\right) \]
   metadata-eval [=>] 100.0	\[ x \cdot \left(x - 2 \cdot \color{blue}{-1}\right) \]
   metadata-eval [=>] 100.0	\[ x \cdot \left(x - \color{blue}{-2}\right) \]

3. Final simplification 100.0%

   \[\leadsto x \cdot \left(x - -2\right) \]

Alternatives

Alternative 1
Accuracy	97.8%
Cost	456

\[\begin{array}{l} \mathbf{if}\;x \leq -2:\\ \;\;\;\;x \cdot x\\ \mathbf{elif}\;x \leq 2:\\ \;\;\;\;x + x\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \]

Alternative 2
Accuracy	51.9%
Cost	192

\[x \cdot x \]

Reproduce

herbie shell --seed 2023159 
(FPCore (x)
  :name "Expanding a square"
  :precision binary64
  (- (* (+ x 1.0) (+ x 1.0)) 1.0))