bug333 (missed optimization)

Percentage Accurate: 8.4% → 100.0%

Time: 2.8s

Alternatives: 3

Speedup: 207.0×

Specification

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

Math

\[\begin{array}{l} \\ \sqrt{1 + x} - \sqrt{1 - x} \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

function tmp = code(x)
	tmp = sqrt((1.0 + x)) - 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]

Initial Program: 8.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{1 + x} - \sqrt{1 - x} \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

function tmp = code(x)
	tmp = sqrt((1.0 + x)) - 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]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x + x}{\sqrt{x + 1} + \sqrt{1 - x}} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 8.9%

   \[\sqrt{1 + x} - \sqrt{1 - x} \]
2. Step-by-step derivation

   1. +-commutative 8.9%

      \[\leadsto \sqrt{\color{blue}{x + 1}} - \sqrt{1 - x} \]

   2. metadata-eval 8.9%

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

   3. sub-neg 8.9%

      \[\leadsto \sqrt{\color{blue}{x - -1}} - \sqrt{1 - x} \]

3. Simplified 8.9%

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

   1. flip-- 8.9%

      \[\leadsto \color{blue}{\frac{\sqrt{x - -1} \cdot \sqrt{x - -1} - \sqrt{1 - x} \cdot \sqrt{1 - x}}{\sqrt{x - -1} + \sqrt{1 - x}}} \]

   2. div-inv 8.9%

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

   3. add-sqr-sqrt 8.9%

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

   4. add-sqr-sqrt 9.0%

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

   5. associate--r- 21.4%

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

   6. sub-neg 21.4%

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

   7. metadata-eval 21.4%

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

   8. +-commutative 21.4%

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

   9. add-exp-log 21.4%

      \[\leadsto \left(\left(\color{blue}{e^{\log \left(1 + x\right)}} - 1\right) + x\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]

   10. log1p-udef 21.4%

       \[\leadsto \left(\left(e^{\color{blue}{\mathsf{log1p}\left(x\right)}} - 1\right) + x\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]

   11. expm1-udef 100.0%

       \[\leadsto \left(\color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(x\right)\right)} + x\right) \cdot \frac{1}{\sqrt{x - -1} + \sqrt{1 - x}} \]

   12. expm1-log1p-u 100.0%

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

   13. sub-neg 100.0%

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

   14. metadata-eval 100.0%

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

5. Applied egg-rr 100.0%

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

   1. associate-*r/ 100.0%

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

   2. *-rgt-identity 100.0%

      \[\leadsto \frac{\color{blue}{x + x}}{\sqrt{x + 1} + \sqrt{1 - x}} \]

7. Simplified 100.0%

   \[\leadsto \color{blue}{\frac{x + x}{\sqrt{x + 1} + \sqrt{1 - x}}} \]

8. Final simplification 100.0%

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

Alternative 2: 99.6% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ x + 0.125 \cdot {x}^{3} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ x (* 0.125 (pow x 3.0))))

C

double code(double x) {
	return x + (0.125 * pow(x, 3.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x + (0.125d0 * (x ** 3.0d0))
end function

Java

public static double code(double x) {
	return x + (0.125 * Math.pow(x, 3.0));
}

Python

def code(x):
	return x + (0.125 * math.pow(x, 3.0))

Julia

function code(x)
	return Float64(x + Float64(0.125 * (x ^ 3.0)))
end

MATLAB

function tmp = code(x)
	tmp = x + (0.125 * (x ^ 3.0));
end

Wolfram

code[x_] := N[(x + N[(0.125 * N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 8.9%

   \[\sqrt{1 + x} - \sqrt{1 - x} \]
2. Step-by-step derivation

   1. +-commutative 8.9%

      \[\leadsto \sqrt{\color{blue}{x + 1}} - \sqrt{1 - x} \]

   2. metadata-eval 8.9%

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

   3. sub-neg 8.9%

      \[\leadsto \sqrt{\color{blue}{x - -1}} - \sqrt{1 - x} \]

3. Simplified 8.9%

   \[\leadsto \color{blue}{\sqrt{x - -1} - \sqrt{1 - x}} \]

4. Taylor expanded in x around 0 99.8%

   \[\leadsto \color{blue}{0.125 \cdot {x}^{3} + x} \]

5. Final simplification 99.8%

   \[\leadsto x + 0.125 \cdot {x}^{3} \]

Alternative 3: 99.1% accurate, 207.0× speedup

Math

\[\begin{array}{l} \\ x \end{array} \]

FPCore

(FPCore (x) :precision binary64 x)

C

double code(double x) {
	return x;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x
end function

Java

public static double code(double x) {
	return x;
}

Python

def code(x):
	return x

Julia

function code(x)
	return x
end

MATLAB

function tmp = code(x)
	tmp = x;
end

Wolfram

code[x_] := x

Derivation

1. Initial program 8.9%

   \[\sqrt{1 + x} - \sqrt{1 - x} \]
2. Step-by-step derivation

   1. +-commutative 8.9%

      \[\leadsto \sqrt{\color{blue}{x + 1}} - \sqrt{1 - x} \]

   2. metadata-eval 8.9%

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

   3. sub-neg 8.9%

      \[\leadsto \sqrt{\color{blue}{x - -1}} - \sqrt{1 - x} \]

3. Simplified 8.9%

   \[\leadsto \color{blue}{\sqrt{x - -1} - \sqrt{1 - x}} \]

4. Taylor expanded in x around 0 99.4%

   \[\leadsto \color{blue}{x} \]

5. Final simplification 99.4%

   \[\leadsto x \]

Developer target: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{2 \cdot x}{\sqrt{1 + x} + \sqrt{1 - x}} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/ (* 2.0 x) (+ (sqrt (+ 1.0 x)) (sqrt (- 1.0 x)))))

C

double code(double x) {
	return (2.0 * x) / (sqrt((1.0 + x)) + sqrt((1.0 - x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (2.0d0 * x) / (sqrt((1.0d0 + x)) + sqrt((1.0d0 - x)))
end function

Java

public static double code(double x) {
	return (2.0 * x) / (Math.sqrt((1.0 + x)) + Math.sqrt((1.0 - x)));
}

Python

def code(x):
	return (2.0 * x) / (math.sqrt((1.0 + x)) + math.sqrt((1.0 - x)))

Julia

function code(x)
	return Float64(Float64(2.0 * x) / Float64(sqrt(Float64(1.0 + x)) + sqrt(Float64(1.0 - x))))
end

MATLAB

function tmp = code(x)
	tmp = (2.0 * x) / (sqrt((1.0 + x)) + sqrt((1.0 - x)));
end

Wolfram

code[x_] := N[(N[(2.0 * x), $MachinePrecision] / N[(N[Sqrt[N[(1.0 + x), $MachinePrecision]], $MachinePrecision] + N[Sqrt[N[(1.0 - x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023203 
(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))))