Ian Simplification

Percentage Accurate: 6.8% → 8.3%

Time: 19.7s

Alternatives: 5

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 6.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 8.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{-2 \cdot \left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{0.5 - 0.5 \cdot x}\right)\right)}\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (log
  (*
   (+ 1.0 (expm1 (* PI 0.5)))
   (exp (* -2.0 (- (* PI 0.5) (acos (sqrt (- 0.5 (* 0.5 x))))))))))

C

double code(double x) {
	return log(((1.0 + expm1((((double) M_PI) * 0.5))) * exp((-2.0 * ((((double) M_PI) * 0.5) - acos(sqrt((0.5 - (0.5 * x)))))))));
}

Java

public static double code(double x) {
	return Math.log(((1.0 + Math.expm1((Math.PI * 0.5))) * Math.exp((-2.0 * ((Math.PI * 0.5) - Math.acos(Math.sqrt((0.5 - (0.5 * x)))))))));
}

Python

def code(x):
	return math.log(((1.0 + math.expm1((math.pi * 0.5))) * math.exp((-2.0 * ((math.pi * 0.5) - math.acos(math.sqrt((0.5 - (0.5 * x)))))))))

Julia

function code(x)
	return log(Float64(Float64(1.0 + expm1(Float64(pi * 0.5))) * exp(Float64(-2.0 * Float64(Float64(pi * 0.5) - acos(sqrt(Float64(0.5 - Float64(0.5 * x)))))))))
end

Wolfram

code[x_] := N[Log[N[(N[(1.0 + N[(Exp[N[(Pi * 0.5), $MachinePrecision]] - 1), $MachinePrecision]), $MachinePrecision] * N[Exp[N[(-2.0 * N[(N[(Pi * 0.5), $MachinePrecision] - N[ArcCos[N[Sqrt[N[(0.5 - N[(0.5 * x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 7.3%

   \[\frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \]
2. Step-by-step derivation

   1. sub-neg 7.3%

      \[\leadsto \color{blue}{\frac{\pi}{2} + \left(-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)\right)} \]

   2. log1p-expm1-u 7.3%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(\mathsf{expm1}\left(\frac{\pi}{2}\right)\right)} + \left(-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)\right) \]

   3. log1p-udef 7.3%

      \[\leadsto \color{blue}{\log \left(1 + \mathsf{expm1}\left(\frac{\pi}{2}\right)\right)} + \left(-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)\right) \]

   4. add-log-exp 7.3%

      \[\leadsto \log \left(1 + \mathsf{expm1}\left(\frac{\pi}{2}\right)\right) + \color{blue}{\log \left(e^{-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)}\right)} \]

   5. sum-log 7.3%

      \[\leadsto \color{blue}{\log \left(\left(1 + \mathsf{expm1}\left(\frac{\pi}{2}\right)\right) \cdot e^{-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)}\right)} \]

   6. div-inv 7.3%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\color{blue}{\pi \cdot \frac{1}{2}}\right)\right) \cdot e^{-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)}\right) \]

   7. metadata-eval 7.3%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot \color{blue}{0.5}\right)\right) \cdot e^{-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)}\right) \]

   8. *-commutative 7.3%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{-\color{blue}{\sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \cdot 2}}\right) \]

   9. distribute-rgt-neg-in 7.3%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\color{blue}{\sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \cdot \left(-2\right)}}\right) \]

3. Applied egg-rr 7.3%

   \[\leadsto \color{blue}{\log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\sin^{-1} \left(\sqrt{0.5 - x \cdot 0.5}\right) \cdot -2}\right)} \]
4. Step-by-step derivation

   1. asin-acos 8.5%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\color{blue}{\left(\frac{\pi}{2} - \cos^{-1} \left(\sqrt{0.5 - x \cdot 0.5}\right)\right)} \cdot -2}\right) \]

   2. div-inv 8.5%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\left(\color{blue}{\pi \cdot \frac{1}{2}} - \cos^{-1} \left(\sqrt{0.5 - x \cdot 0.5}\right)\right) \cdot -2}\right) \]

   3. metadata-eval 8.5%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\left(\pi \cdot \color{blue}{0.5} - \cos^{-1} \left(\sqrt{0.5 - x \cdot 0.5}\right)\right) \cdot -2}\right) \]

   4. *-commutative 8.5%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{0.5 - \color{blue}{0.5 \cdot x}}\right)\right) \cdot -2}\right) \]

5. Applied egg-rr 8.5%

   \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\color{blue}{\left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{0.5 - 0.5 \cdot x}\right)\right)} \cdot -2}\right) \]

6. Final simplification 8.5%

   \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{-2 \cdot \left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{0.5 - 0.5 \cdot x}\right)\right)}\right) \]

Alternative 2: 8.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \log \left(e^{\pi \cdot 0.5 + -2 \cdot \left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{0.5 + x \cdot -0.5}\right)\right)}\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (log
  (exp
   (+ (* PI 0.5) (* -2.0 (- (* PI 0.5) (acos (sqrt (+ 0.5 (* x -0.5))))))))))

C

double code(double x) {
	return log(exp(((((double) M_PI) * 0.5) + (-2.0 * ((((double) M_PI) * 0.5) - acos(sqrt((0.5 + (x * -0.5)))))))));
}

Java

public static double code(double x) {
	return Math.log(Math.exp(((Math.PI * 0.5) + (-2.0 * ((Math.PI * 0.5) - Math.acos(Math.sqrt((0.5 + (x * -0.5)))))))));
}

Python

def code(x):
	return math.log(math.exp(((math.pi * 0.5) + (-2.0 * ((math.pi * 0.5) - math.acos(math.sqrt((0.5 + (x * -0.5)))))))))

Julia

function code(x)
	return log(exp(Float64(Float64(pi * 0.5) + Float64(-2.0 * Float64(Float64(pi * 0.5) - acos(sqrt(Float64(0.5 + Float64(x * -0.5)))))))))
end

MATLAB

function tmp = code(x)
	tmp = log(exp(((pi * 0.5) + (-2.0 * ((pi * 0.5) - acos(sqrt((0.5 + (x * -0.5)))))))));
end

Wolfram

code[x_] := N[Log[N[Exp[N[(N[(Pi * 0.5), $MachinePrecision] + N[(-2.0 * N[(N[(Pi * 0.5), $MachinePrecision] - N[ArcCos[N[Sqrt[N[(0.5 + N[(x * -0.5), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 7.3%

   \[\frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \]
2. Step-by-step derivation

   1. sub-neg 7.3%

      \[\leadsto \color{blue}{\frac{\pi}{2} + \left(-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)\right)} \]

   2. log1p-expm1-u 7.3%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(\mathsf{expm1}\left(\frac{\pi}{2}\right)\right)} + \left(-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)\right) \]

   3. log1p-udef 7.3%

      \[\leadsto \color{blue}{\log \left(1 + \mathsf{expm1}\left(\frac{\pi}{2}\right)\right)} + \left(-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)\right) \]

   4. add-log-exp 7.3%

      \[\leadsto \log \left(1 + \mathsf{expm1}\left(\frac{\pi}{2}\right)\right) + \color{blue}{\log \left(e^{-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)}\right)} \]

   5. sum-log 7.3%

      \[\leadsto \color{blue}{\log \left(\left(1 + \mathsf{expm1}\left(\frac{\pi}{2}\right)\right) \cdot e^{-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)}\right)} \]

   6. div-inv 7.3%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\color{blue}{\pi \cdot \frac{1}{2}}\right)\right) \cdot e^{-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)}\right) \]

   7. metadata-eval 7.3%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot \color{blue}{0.5}\right)\right) \cdot e^{-2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)}\right) \]

   8. *-commutative 7.3%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{-\color{blue}{\sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \cdot 2}}\right) \]

   9. distribute-rgt-neg-in 7.3%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\color{blue}{\sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \cdot \left(-2\right)}}\right) \]

3. Applied egg-rr 7.3%

   \[\leadsto \color{blue}{\log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\sin^{-1} \left(\sqrt{0.5 - x \cdot 0.5}\right) \cdot -2}\right)} \]
4. Step-by-step derivation

   1. asin-acos 8.5%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\color{blue}{\left(\frac{\pi}{2} - \cos^{-1} \left(\sqrt{0.5 - x \cdot 0.5}\right)\right)} \cdot -2}\right) \]

   2. div-inv 8.5%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\left(\color{blue}{\pi \cdot \frac{1}{2}} - \cos^{-1} \left(\sqrt{0.5 - x \cdot 0.5}\right)\right) \cdot -2}\right) \]

   3. metadata-eval 8.5%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\left(\pi \cdot \color{blue}{0.5} - \cos^{-1} \left(\sqrt{0.5 - x \cdot 0.5}\right)\right) \cdot -2}\right) \]

   4. *-commutative 8.5%

      \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{0.5 - \color{blue}{0.5 \cdot x}}\right)\right) \cdot -2}\right) \]

5. Applied egg-rr 8.5%

   \[\leadsto \log \left(\left(1 + \mathsf{expm1}\left(\pi \cdot 0.5\right)\right) \cdot e^{\color{blue}{\left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{0.5 - 0.5 \cdot x}\right)\right)} \cdot -2}\right) \]

6. Taylor expanded in x around 0 8.5%

   \[\leadsto \color{blue}{\log \left(e^{-2 \cdot \left(0.5 \cdot \pi - \cos^{-1} \left(\sqrt{0.5 - 0.5 \cdot x}\right)\right)} \cdot e^{0.5 \cdot \pi}\right)} \]
7. Step-by-step derivation

   1. cancel-sign-sub-inv 8.5%

      \[\leadsto \log \left(e^{-2 \cdot \left(0.5 \cdot \pi - \cos^{-1} \left(\sqrt{\color{blue}{0.5 + \left(-0.5\right) \cdot x}}\right)\right)} \cdot e^{0.5 \cdot \pi}\right) \]

   2. metadata-eval 8.5%

      \[\leadsto \log \left(e^{-2 \cdot \left(0.5 \cdot \pi - \cos^{-1} \left(\sqrt{0.5 + \color{blue}{-0.5} \cdot x}\right)\right)} \cdot e^{0.5 \cdot \pi}\right) \]

   3. rem-exp-log 8.5%

      \[\leadsto \log \color{blue}{\left(e^{\log \left(e^{-2 \cdot \left(0.5 \cdot \pi - \cos^{-1} \left(\sqrt{0.5 + -0.5 \cdot x}\right)\right)} \cdot e^{0.5 \cdot \pi}\right)}\right)} \]

   4. prod-exp 8.5%

      \[\leadsto \log \left(e^{\log \color{blue}{\left(e^{-2 \cdot \left(0.5 \cdot \pi - \cos^{-1} \left(\sqrt{0.5 + -0.5 \cdot x}\right)\right) + 0.5 \cdot \pi}\right)}}\right) \]

   5. rem-log-exp 8.5%

      \[\leadsto \log \left(e^{\color{blue}{-2 \cdot \left(0.5 \cdot \pi - \cos^{-1} \left(\sqrt{0.5 + -0.5 \cdot x}\right)\right) + 0.5 \cdot \pi}}\right) \]

8. Simplified 8.5%

   \[\leadsto \color{blue}{\log \left(e^{-2 \cdot \left(0.5 \cdot \pi - \cos^{-1} \left(\sqrt{0.5 + x \cdot -0.5}\right)\right) + 0.5 \cdot \pi}\right)} \]

9. Final simplification 8.5%

   \[\leadsto \log \left(e^{\pi \cdot 0.5 + -2 \cdot \left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{0.5 + x \cdot -0.5}\right)\right)}\right) \]

Alternative 3: 8.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \frac{\pi}{2} + 2 \cdot \left(\cos^{-1} \left(\sqrt{0.5 - 0.5 \cdot x}\right) - \pi \cdot 0.5\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (+ (/ PI 2.0) (* 2.0 (- (acos (sqrt (- 0.5 (* 0.5 x)))) (* PI 0.5)))))

C

double code(double x) {
	return (((double) M_PI) / 2.0) + (2.0 * (acos(sqrt((0.5 - (0.5 * x)))) - (((double) M_PI) * 0.5)));
}

Java

public static double code(double x) {
	return (Math.PI / 2.0) + (2.0 * (Math.acos(Math.sqrt((0.5 - (0.5 * x)))) - (Math.PI * 0.5)));
}

Python

def code(x):
	return (math.pi / 2.0) + (2.0 * (math.acos(math.sqrt((0.5 - (0.5 * x)))) - (math.pi * 0.5)))

Julia

function code(x)
	return Float64(Float64(pi / 2.0) + Float64(2.0 * Float64(acos(sqrt(Float64(0.5 - Float64(0.5 * x)))) - Float64(pi * 0.5))))
end

MATLAB

function tmp = code(x)
	tmp = (pi / 2.0) + (2.0 * (acos(sqrt((0.5 - (0.5 * x)))) - (pi * 0.5)));
end

Wolfram

code[x_] := N[(N[(Pi / 2.0), $MachinePrecision] + N[(2.0 * N[(N[ArcCos[N[Sqrt[N[(0.5 - N[(0.5 * x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] - N[(Pi * 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 7.3%

   \[\frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \]
2. Step-by-step derivation

   1. asin-acos 8.5%

      \[\leadsto \frac{\pi}{2} - 2 \cdot \color{blue}{\left(\frac{\pi}{2} - \cos^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)\right)} \]

   2. div-inv 8.5%

      \[\leadsto \frac{\pi}{2} - 2 \cdot \left(\color{blue}{\pi \cdot \frac{1}{2}} - \cos^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)\right) \]

   3. metadata-eval 8.5%

      \[\leadsto \frac{\pi}{2} - 2 \cdot \left(\pi \cdot \color{blue}{0.5} - \cos^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)\right) \]

   4. div-sub 8.5%

      \[\leadsto \frac{\pi}{2} - 2 \cdot \left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{\color{blue}{\frac{1}{2} - \frac{x}{2}}}\right)\right) \]

   5. metadata-eval 8.5%

      \[\leadsto \frac{\pi}{2} - 2 \cdot \left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{\color{blue}{0.5} - \frac{x}{2}}\right)\right) \]

   6. div-inv 8.5%

      \[\leadsto \frac{\pi}{2} - 2 \cdot \left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{0.5 - \color{blue}{x \cdot \frac{1}{2}}}\right)\right) \]

   7. metadata-eval 8.5%

      \[\leadsto \frac{\pi}{2} - 2 \cdot \left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{0.5 - x \cdot \color{blue}{0.5}}\right)\right) \]

3. Applied egg-rr 8.5%

   \[\leadsto \frac{\pi}{2} - 2 \cdot \color{blue}{\left(\pi \cdot 0.5 - \cos^{-1} \left(\sqrt{0.5 - x \cdot 0.5}\right)\right)} \]

4. Final simplification 8.5%

   \[\leadsto \frac{\pi}{2} + 2 \cdot \left(\cos^{-1} \left(\sqrt{0.5 - 0.5 \cdot x}\right) - \pi \cdot 0.5\right) \]

Alternative 4: 6.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 7.3%

   \[\frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \]

2. Final simplification 7.3%

   \[\leadsto \frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \]

Alternative 5: 4.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{0.5}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- (/ PI 2.0) (* 2.0 (asin (sqrt 0.5)))))

C

double code(double x) {
	return (((double) M_PI) / 2.0) - (2.0 * asin(sqrt(0.5)));
}

Java

public static double code(double x) {
	return (Math.PI / 2.0) - (2.0 * Math.asin(Math.sqrt(0.5)));
}

Python

def code(x):
	return (math.pi / 2.0) - (2.0 * math.asin(math.sqrt(0.5)))

Julia

function code(x)
	return Float64(Float64(pi / 2.0) - Float64(2.0 * asin(sqrt(0.5))))
end

MATLAB

function tmp = code(x)
	tmp = (pi / 2.0) - (2.0 * asin(sqrt(0.5)));
end

Wolfram

code[x_] := N[(N[(Pi / 2.0), $MachinePrecision] - N[(2.0 * N[ArcSin[N[Sqrt[0.5], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 7.3%

   \[\frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right) \]

2. Taylor expanded in x around 0 7.2%

   \[\leadsto \frac{\pi}{2} - 2 \cdot \color{blue}{\sin^{-1} \left(\sqrt{0.5} \cdot \sqrt{1 - x}\right)} \]

3. Taylor expanded in x around 0 4.3%

   \[\leadsto \frac{\pi}{2} - 2 \cdot \sin^{-1} \color{blue}{\left(\sqrt{0.5}\right)} \]

4. Final simplification 4.3%

   \[\leadsto \frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{0.5}\right) \]

Developer target: 100.0% accurate, 3.1× speedup

Math

\[\begin{array}{l} \\ \sin^{-1} x \end{array} \]

FPCore

(FPCore (x) :precision binary64 (asin x))

C

double code(double x) {
	return asin(x);
}

Fortran

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

Java

public static double code(double x) {
	return Math.asin(x);
}

Python

def code(x):
	return math.asin(x)

Julia

function code(x)
	return asin(x)
end

MATLAB

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

Wolfram

code[x_] := N[ArcSin[x], $MachinePrecision]

Reproduce

herbie shell --seed 2023293 
(FPCore (x)
  :name "Ian Simplification"
  :precision binary64

  :herbie-target
  (asin x)

  (- (/ PI 2.0) (* 2.0 (asin (sqrt (/ (- 1.0 x) 2.0))))))