bug333 (missed optimization)

Percentage Accurate: 8.4% → 100.0%

Time: 5.7s

Precision: binary64

Cost: 13824

\[-1 \leq x \land x \leq 1\]

Math

\[\sqrt{1 + x} - \sqrt{1 - x} \]

↓

\[\frac{x + x}{{\left(\left(x + 1\right) \cdot \left(x + 1\right)\right)}^{0.25} + \sqrt{1 - x}} \]

FPCore

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

↓

(FPCore (x)
 :precision binary64
 (/ (+ x x) (+ (pow (* (+ x 1.0) (+ x 1.0)) 0.25) (sqrt (- 1.0 x)))))

C

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

↓

double code(double x) {
	return (x + x) / (pow(((x + 1.0) * (x + 1.0)), 0.25) + sqrt((1.0 - x)));
}

Fortran

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

↓

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x + x) / ((((x + 1.0d0) * (x + 1.0d0)) ** 0.25d0) + sqrt((1.0d0 - x)))
end function

Java

public static double code(double x) {
	return Math.sqrt((1.0 + x)) - Math.sqrt((1.0 - x));
}

↓

public static double code(double x) {
	return (x + x) / (Math.pow(((x + 1.0) * (x + 1.0)), 0.25) + Math.sqrt((1.0 - x)));
}

Python

def code(x):
	return math.sqrt((1.0 + x)) - math.sqrt((1.0 - x))

↓

def code(x):
	return (x + x) / (math.pow(((x + 1.0) * (x + 1.0)), 0.25) + math.sqrt((1.0 - x)))

Julia

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

↓

function code(x)
	return Float64(Float64(x + x) / Float64((Float64(Float64(x + 1.0) * Float64(x + 1.0)) ^ 0.25) + sqrt(Float64(1.0 - x))))
end

MATLAB

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

↓

function tmp = code(x)
	tmp = (x + x) / ((((x + 1.0) * (x + 1.0)) ^ 0.25) + sqrt((1.0 - x)));
end

Wolfram

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

↓

code[x_] := N[(N[(x + x), $MachinePrecision] / N[(N[Power[N[(N[(x + 1.0), $MachinePrecision] * N[(x + 1.0), $MachinePrecision]), $MachinePrecision], 0.25], $MachinePrecision] + N[Sqrt[N[(1.0 - x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Target

Original	8.4%
Target	100.0%
Herbie	100.0%

\[\frac{2 \cdot x}{\sqrt{1 + x} + \sqrt{1 - x}} \]

Derivation

1. Initial program 7.3%

   \[\sqrt{1 + x} - \sqrt{1 - x} \]

2. Simplified 7.3%

   \[\leadsto \color{blue}{\sqrt{x - -1} - \sqrt{1 - x}} \]
   Step-by-step derivation

   [Start] 7.3	\[ \sqrt{1 + x} - \sqrt{1 - x} \]
   +-commutative [=>] 7.3	\[ \sqrt{\color{blue}{x + 1}} - \sqrt{1 - x} \]
   metadata-eval [<=] 7.3	\[ \sqrt{x + \color{blue}{\left(--1\right)}} - \sqrt{1 - x} \]
   sub-neg [<=] 7.3	\[ \sqrt{\color{blue}{x - -1}} - \sqrt{1 - x} \]

3. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\left(x + x\right) \cdot \frac{1}{\sqrt{x + 1} + \sqrt{1 - x}}} \]
   Step-by-step derivation

   [Start] 7.3	\[ \sqrt{x - -1} - \sqrt{1 - x} \]
   flip-- [=>] 7.3	\[ \color{blue}{\frac{\sqrt{x - -1} \cdot \sqrt{x - -1} - \sqrt{1 - x} \cdot \sqrt{1 - x}}{\sqrt{x - -1} + \sqrt{1 - x}}} \]
   div-inv [=>] 7.3	\[ \color{blue}{\left(\sqrt{x - -1} \cdot \sqrt{x - -1} - \sqrt{1 - x} \cdot \sqrt{1 - x}\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}}} \]
   add-sqr-sqrt [<=] 7.3	\[ \left(\color{blue}{\left(x - -1\right)} - \sqrt{1 - x} \cdot \sqrt{1 - x}\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]
   add-sqr-sqrt [<=] 7.4	\[ \left(\left(x - -1\right) - \color{blue}{\left(1 - x\right)}\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]
   associate--r- [=>] 19.9	\[ \color{blue}{\left(\left(\left(x - -1\right) - 1\right) + x\right)} \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]
   sub-neg [=>] 19.9	\[ \left(\left(\color{blue}{\left(x + \left(--1\right)\right)} - 1\right) + x\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]
   metadata-eval [=>] 19.9	\[ \left(\left(\left(x + \color{blue}{1}\right) - 1\right) + x\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]
   +-commutative [=>] 19.9	\[ \left(\left(\color{blue}{\left(1 + x\right)} - 1\right) + x\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]
   add-exp-log [=>] 19.9	\[ \left(\left(\color{blue}{e^{\log \left(1 + x\right)}} - 1\right) + x\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]
   log1p-udef [<=] 19.9	\[ \left(\left(e^{\color{blue}{\mathsf{log1p}\left(x\right)}} - 1\right) + x\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]
   expm1-udef [<=] 100.0	\[ \left(\color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(x\right)\right)} + x\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]
   expm1-log1p-u [<=] 100.0	\[ \left(\color{blue}{x} + x\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]
   sub-neg [=>] 100.0	\[ \left(x + x\right) \cdot \frac{1}{\sqrt{\color{blue}{x + \left(--1\right)}} + \sqrt{1 - x}} \]
   metadata-eval [=>] 100.0	\[ \left(x + x\right) \cdot \frac{1}{\sqrt{x + \color{blue}{1}} + \sqrt{1 - x}} \]

4. Simplified 100.0%

   \[\leadsto \color{blue}{\frac{x + x}{\sqrt{x + 1} + \sqrt{1 - x}}} \]
   Step-by-step derivation

   [Start] 100.0	\[ \left(x + x\right) \cdot \frac{1}{\sqrt{x + 1} + \sqrt{1 - x}} \]
   associate-*r/ [=>] 100.0	\[ \color{blue}{\frac{\left(x + x\right) \cdot 1}{\sqrt{x + 1} + \sqrt{1 - x}}} \]
   *-rgt-identity [=>] 100.0	\[ \frac{\color{blue}{x + x}}{\sqrt{x + 1} + \sqrt{1 - x}} \]

5. Applied egg-rr 100.0%

   \[\leadsto \frac{x + x}{\color{blue}{{\left({\left(x + 1\right)}^{2}\right)}^{0.25}} + \sqrt{1 - x}} \]
   Step-by-step derivation

   [Start] 100.0	\[ \frac{x + x}{\sqrt{x + 1} + \sqrt{1 - x}} \]
   pow1/2 [=>] 100.0	\[ \frac{x + x}{\color{blue}{{\left(x + 1\right)}^{0.5}} + \sqrt{1 - x}} \]
   metadata-eval [<=] 100.0	\[ \frac{x + x}{{\left(x + 1\right)}^{\color{blue}{\left(0.25 + 0.25\right)}} + \sqrt{1 - x}} \]
   metadata-eval [<=] 100.0	\[ \frac{x + x}{{\left(x + 1\right)}^{\left(\color{blue}{0.5 \cdot 0.5} + 0.25\right)} + \sqrt{1 - x}} \]
   metadata-eval [<=] 100.0	\[ \frac{x + x}{{\left(x + 1\right)}^{\left(0.5 \cdot 0.5 + \color{blue}{0.5 \cdot 0.5}\right)} + \sqrt{1 - x}} \]
   pow-prod-up [<=] 100.0	\[ \frac{x + x}{\color{blue}{{\left(x + 1\right)}^{\left(0.5 \cdot 0.5\right)} \cdot {\left(x + 1\right)}^{\left(0.5 \cdot 0.5\right)}} + \sqrt{1 - x}} \]
   pow-prod-down [=>] 100.0	\[ \frac{x + x}{\color{blue}{{\left(\left(x + 1\right) \cdot \left(x + 1\right)\right)}^{\left(0.5 \cdot 0.5\right)}} + \sqrt{1 - x}} \]
   pow2 [=>] 100.0	\[ \frac{x + x}{{\color{blue}{\left({\left(x + 1\right)}^{2}\right)}}^{\left(0.5 \cdot 0.5\right)} + \sqrt{1 - x}} \]
   metadata-eval [=>] 100.0	\[ \frac{x + x}{{\left({\left(x + 1\right)}^{2}\right)}^{\color{blue}{0.25}} + \sqrt{1 - x}} \]

6. Applied egg-rr 100.0%

   \[\leadsto \frac{x + x}{{\color{blue}{\left(\left(x + 1\right) \cdot \left(x + 1\right)\right)}}^{0.25} + \sqrt{1 - x}} \]
   Step-by-step derivation

   [Start] 100.0	\[ \frac{x + x}{{\left({\left(x + 1\right)}^{2}\right)}^{0.25} + \sqrt{1 - x}} \]
   unpow2 [=>] 100.0	\[ \frac{x + x}{{\color{blue}{\left(\left(x + 1\right) \cdot \left(x + 1\right)\right)}}^{0.25} + \sqrt{1 - x}} \]

7. Final simplification 100.0%

   \[\leadsto \frac{x + x}{{\left(\left(x + 1\right) \cdot \left(x + 1\right)\right)}^{0.25} + \sqrt{1 - x}} \]

Alternatives

Alternative 1
Accuracy	100.0%
Cost	13504

\[\frac{x + x}{\sqrt{1 - x} + \sqrt{x + 1}} \]

Alternative 2
Accuracy	99.6%
Cost	704

\[\frac{x + x}{2 + \left(x \cdot x\right) \cdot -0.25} \]

Alternative 3
Accuracy	99.0%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023159 
(FPCore (x)
  :name "bug333 (missed optimization)"
  :precision binary64
  :pre (and (<= -1.0 x) (<= x 1.0))

  :herbie-target
  (/ (* 2.0 x) (+ (sqrt (+ 1.0 x)) (sqrt (- 1.0 x))))

  (- (sqrt (+ 1.0 x)) (sqrt (- 1.0 x))))