VandenBroeck and Keller, Equation (24)

Percentage Accurate: 99.7% → 99.8%

Time: 7.8s

Alternatives: 10

Speedup: 1.1×

Specification

Math

\[\begin{array}{l} \\ \left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (+ (- (* x (/ 1.0 (tan B)))) (/ 1.0 (sin B))))

C

double code(double B, double x) {
	return -(x * (1.0 / tan(B))) + (1.0 / sin(B));
}

Fortran

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

Java

public static double code(double B, double x) {
	return -(x * (1.0 / Math.tan(B))) + (1.0 / Math.sin(B));
}

Python

def code(B, x):
	return -(x * (1.0 / math.tan(B))) + (1.0 / math.sin(B))

Julia

function code(B, x)
	return Float64(Float64(-Float64(x * Float64(1.0 / tan(B)))) + Float64(1.0 / sin(B)))
end

MATLAB

function tmp = code(B, x)
	tmp = -(x * (1.0 / tan(B))) + (1.0 / sin(B));
end

Wolfram

code[B_, x_] := N[((-N[(x * N[(1.0 / N[Tan[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]) + N[(1.0 / N[Sin[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (+ (- (* x (/ 1.0 (tan B)))) (/ 1.0 (sin B))))

C

double code(double B, double x) {
	return -(x * (1.0 / tan(B))) + (1.0 / sin(B));
}

Fortran

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

Java

public static double code(double B, double x) {
	return -(x * (1.0 / Math.tan(B))) + (1.0 / Math.sin(B));
}

Python

def code(B, x):
	return -(x * (1.0 / math.tan(B))) + (1.0 / math.sin(B))

Julia

function code(B, x)
	return Float64(Float64(-Float64(x * Float64(1.0 / tan(B)))) + Float64(1.0 / sin(B)))
end

MATLAB

function tmp = code(B, x)
	tmp = -(x * (1.0 / tan(B))) + (1.0 / sin(B));
end

Wolfram

code[B_, x_] := N[((-N[(x * N[(1.0 / N[Tan[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]) + N[(1.0 / N[Sin[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 0.7× speedup

Math

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

FPCore

(FPCore (B x) :precision binary64 (- (pow (sin B) -1.0) (/ x (tan B))))

C

double code(double B, double x) {
	return pow(sin(B), -1.0) - (x / tan(B));
}

Fortran

real(8) function code(b, x)
    real(8), intent (in) :: b
    real(8), intent (in) :: x
    code = (sin(b) ** (-1.0d0)) - (x / tan(b))
end function

Java

public static double code(double B, double x) {
	return Math.pow(Math.sin(B), -1.0) - (x / Math.tan(B));
}

Python

def code(B, x):
	return math.pow(math.sin(B), -1.0) - (x / math.tan(B))

Julia

function code(B, x)
	return Float64((sin(B) ^ -1.0) - Float64(x / tan(B)))
end

MATLAB

function tmp = code(B, x)
	tmp = (sin(B) ^ -1.0) - (x / tan(B));
end

Wolfram

code[B_, x_] := N[(N[Power[N[Sin[B], $MachinePrecision], -1.0], $MachinePrecision] - N[(x / N[Tan[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
2. Add Preprocessing

3. Taylor expanded in B around 0

   \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \color{blue}{\frac{1 + \frac{1}{6} \cdot {B}^{2}}{B}} \]
4. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \color{blue}{\frac{1 + \frac{1}{6} \cdot {B}^{2}}{B}} \]

   2. +-commutative N/A

      \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\color{blue}{\frac{1}{6} \cdot {B}^{2} + 1}}{B} \]

   3. lower-fma.f64 N/A

      \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\color{blue}{\mathsf{fma}\left(\frac{1}{6}, {B}^{2}, 1\right)}}{B} \]

   4. unpow2 N/A

      \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\mathsf{fma}\left(\frac{1}{6}, \color{blue}{B \cdot B}, 1\right)}{B} \]

   5. lower-*.f64 58.8

      \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\mathsf{fma}\left(0.16666666666666666, \color{blue}{B \cdot B}, 1\right)}{B} \]

5. Applied rewrites 58.8%

   \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \color{blue}{\frac{\mathsf{fma}\left(0.16666666666666666, B \cdot B, 1\right)}{B}} \]
6. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \color{blue}{\left(-x \cdot \frac{1}{\tan B}\right) + \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B}} \]

   2. +-commutative N/A

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(-x \cdot \frac{1}{\tan B}\right)} \]

   3. lift-neg.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \color{blue}{\left(\mathsf{neg}\left(x \cdot \frac{1}{\tan B}\right)\right)} \]

   4. lift-*.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(\color{blue}{x \cdot \frac{1}{\tan B}}\right)\right) \]

   5. lift-/.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(x \cdot \color{blue}{\frac{1}{\tan B}}\right)\right) \]

   6. div-inv N/A

      \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(\color{blue}{\frac{x}{\tan B}}\right)\right) \]

   7. lift-/.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(\color{blue}{\frac{x}{\tan B}}\right)\right) \]

   8. unsub-neg N/A

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} - \frac{x}{\tan B}} \]

   9. lower--.f64 58.9

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(0.16666666666666666, B \cdot B, 1\right)}{B} - \frac{x}{\tan B}} \]

7. Applied rewrites 58.9%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(B \cdot B, 0.16666666666666666, 1\right)}{B} - \frac{x}{\tan B}} \]

8. Taylor expanded in B around inf

   \[\leadsto \color{blue}{\frac{1}{\sin B}} - \frac{x}{\tan B} \]
9. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{1}{\sin B}} - \frac{x}{\tan B} \]

   2. lower-sin.f64 99.8

      \[\leadsto \frac{1}{\color{blue}{\sin B}} - \frac{x}{\tan B} \]

10. Applied rewrites 99.8%

    \[\leadsto \color{blue}{\frac{1}{\sin B}} - \frac{x}{\tan B} \]

11. Final simplification 99.8%

    \[\leadsto {\sin B}^{-1} - \frac{x}{\tan B} \]
12. Add Preprocessing

Alternative 2: 99.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{1 - \cos B \cdot x}{\sin B} \end{array} \]

FPCore

(FPCore (B x) :precision binary64 (/ (- 1.0 (* (cos B) x)) (sin B)))

C

double code(double B, double x) {
	return (1.0 - (cos(B) * x)) / sin(B);
}

Fortran

real(8) function code(b, x)
    real(8), intent (in) :: b
    real(8), intent (in) :: x
    code = (1.0d0 - (cos(b) * x)) / sin(b)
end function

Java

public static double code(double B, double x) {
	return (1.0 - (Math.cos(B) * x)) / Math.sin(B);
}

Python

def code(B, x):
	return (1.0 - (math.cos(B) * x)) / math.sin(B)

Julia

function code(B, x)
	return Float64(Float64(1.0 - Float64(cos(B) * x)) / sin(B))
end

MATLAB

function tmp = code(B, x)
	tmp = (1.0 - (cos(B) * x)) / sin(B);
end

Wolfram

code[B_, x_] := N[(N[(1.0 - N[(N[Cos[B], $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision] / N[Sin[B], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. un-div-inv N/A

      \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]

   4. lower-/.f64 99.8

      \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]

4. Applied rewrites 99.8%

   \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]
5. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \color{blue}{\left(-\frac{x}{\tan B}\right) + \frac{1}{\sin B}} \]

   2. +-commutative N/A

      \[\leadsto \color{blue}{\frac{1}{\sin B} + \left(-\frac{x}{\tan B}\right)} \]

   3. lift-neg.f64 N/A

      \[\leadsto \frac{1}{\sin B} + \color{blue}{\left(\mathsf{neg}\left(\frac{x}{\tan B}\right)\right)} \]

   4. unsub-neg N/A

      \[\leadsto \color{blue}{\frac{1}{\sin B} - \frac{x}{\tan B}} \]

   5. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{1}{\sin B}} - \frac{x}{\tan B} \]

   6. lift-/.f64 N/A

      \[\leadsto \frac{1}{\sin B} - \color{blue}{\frac{x}{\tan B}} \]

   7. clear-num N/A

      \[\leadsto \frac{1}{\sin B} - \color{blue}{\frac{1}{\frac{\tan B}{x}}} \]

   8. lift-/.f64 N/A

      \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{\frac{\tan B}{x}}} \]

   9. *-lft-identity N/A

      \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{1 \cdot \frac{\tan B}{x}}} \]

   10. lift-*.f64 N/A

       \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{1 \cdot \frac{\tan B}{x}}} \]

   11. frac-sub N/A

       \[\leadsto \color{blue}{\frac{1 \cdot \left(1 \cdot \frac{\tan B}{x}\right) - \sin B \cdot 1}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)}} \]

   12. *-lft-identity N/A

       \[\leadsto \frac{\color{blue}{1 \cdot \frac{\tan B}{x}} - \sin B \cdot 1}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

   13. metadata-eval N/A

       \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \sin B \cdot \color{blue}{\frac{1}{1}}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

   14. div-inv N/A

       \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \color{blue}{\frac{\sin B}{1}}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

   15. /-rgt-identity N/A

       \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \color{blue}{\sin B}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

   16. lift--.f64 N/A

       \[\leadsto \frac{\color{blue}{1 \cdot \frac{\tan B}{x} - \sin B}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

   17. *-commutative N/A

       \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \sin B}{\color{blue}{\left(1 \cdot \frac{\tan B}{x}\right) \cdot \sin B}} \]

   18. associate-/r* N/A

       \[\leadsto \color{blue}{\frac{\frac{1 \cdot \frac{\tan B}{x} - \sin B}{1 \cdot \frac{\tan B}{x}}}{\sin B}} \]

6. Applied rewrites 99.7%

   \[\leadsto \color{blue}{\frac{1 - \sin B \cdot \frac{x}{\tan B}}{\sin B}} \]

7. Taylor expanded in B around inf

   \[\leadsto \frac{1 - \color{blue}{x \cdot \cos B}}{\sin B} \]
8. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{1 - \color{blue}{\cos B \cdot x}}{\sin B} \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{1 - \color{blue}{\cos B \cdot x}}{\sin B} \]

   3. lower-cos.f64 99.7

      \[\leadsto \frac{1 - \color{blue}{\cos B} \cdot x}{\sin B} \]

9. Applied rewrites 99.7%

   \[\leadsto \frac{1 - \color{blue}{\cos B \cdot x}}{\sin B} \]
10. Add Preprocessing

Alternative 3: 98.7% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3200 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{1}{B} - \frac{x}{\tan B}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - x}{\sin B}\\ \end{array} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (if (or (<= x -3200.0) (not (<= x 1.0)))
   (- (/ 1.0 B) (/ x (tan B)))
   (/ (- 1.0 x) (sin B))))

C

double code(double B, double x) {
	double tmp;
	if ((x <= -3200.0) || !(x <= 1.0)) {
		tmp = (1.0 / B) - (x / tan(B));
	} else {
		tmp = (1.0 - x) / sin(B);
	}
	return tmp;
}

Fortran

real(8) function code(b, x)
    real(8), intent (in) :: b
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-3200.0d0)) .or. (.not. (x <= 1.0d0))) then
        tmp = (1.0d0 / b) - (x / tan(b))
    else
        tmp = (1.0d0 - x) / sin(b)
    end if
    code = tmp
end function

Java

public static double code(double B, double x) {
	double tmp;
	if ((x <= -3200.0) || !(x <= 1.0)) {
		tmp = (1.0 / B) - (x / Math.tan(B));
	} else {
		tmp = (1.0 - x) / Math.sin(B);
	}
	return tmp;
}

Python

def code(B, x):
	tmp = 0
	if (x <= -3200.0) or not (x <= 1.0):
		tmp = (1.0 / B) - (x / math.tan(B))
	else:
		tmp = (1.0 - x) / math.sin(B)
	return tmp

Julia

function code(B, x)
	tmp = 0.0
	if ((x <= -3200.0) || !(x <= 1.0))
		tmp = Float64(Float64(1.0 / B) - Float64(x / tan(B)));
	else
		tmp = Float64(Float64(1.0 - x) / sin(B));
	end
	return tmp
end

MATLAB

function tmp_2 = code(B, x)
	tmp = 0.0;
	if ((x <= -3200.0) || ~((x <= 1.0)))
		tmp = (1.0 / B) - (x / tan(B));
	else
		tmp = (1.0 - x) / sin(B);
	end
	tmp_2 = tmp;
end

Wolfram

code[B_, x_] := If[Or[LessEqual[x, -3200.0], N[Not[LessEqual[x, 1.0]], $MachinePrecision]], N[(N[(1.0 / B), $MachinePrecision] - N[(x / N[Tan[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 - x), $MachinePrecision] / N[Sin[B], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -3200 or 1 < x

   1. Initial program 99.6%

      \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
   2. Add Preprocessing

   3. Taylor expanded in B around 0

      \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \color{blue}{\frac{1 + \frac{1}{6} \cdot {B}^{2}}{B}} \]
   4. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \color{blue}{\frac{1 + \frac{1}{6} \cdot {B}^{2}}{B}} \]

      2. +-commutative N/A

         \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\color{blue}{\frac{1}{6} \cdot {B}^{2} + 1}}{B} \]

      3. lower-fma.f64 N/A

         \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\color{blue}{\mathsf{fma}\left(\frac{1}{6}, {B}^{2}, 1\right)}}{B} \]

      4. unpow2 N/A

         \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\mathsf{fma}\left(\frac{1}{6}, \color{blue}{B \cdot B}, 1\right)}{B} \]

      5. lower-*.f64 66.6

         \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\mathsf{fma}\left(0.16666666666666666, \color{blue}{B \cdot B}, 1\right)}{B} \]

   5. Applied rewrites 66.6%

      \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \color{blue}{\frac{\mathsf{fma}\left(0.16666666666666666, B \cdot B, 1\right)}{B}} \]
   6. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(-x \cdot \frac{1}{\tan B}\right) + \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B}} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(-x \cdot \frac{1}{\tan B}\right)} \]

      3. lift-neg.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \color{blue}{\left(\mathsf{neg}\left(x \cdot \frac{1}{\tan B}\right)\right)} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(\color{blue}{x \cdot \frac{1}{\tan B}}\right)\right) \]

      5. lift-/.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(x \cdot \color{blue}{\frac{1}{\tan B}}\right)\right) \]

      6. div-inv N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(\color{blue}{\frac{x}{\tan B}}\right)\right) \]

      7. lift-/.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(\color{blue}{\frac{x}{\tan B}}\right)\right) \]

      8. unsub-neg N/A

         \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} - \frac{x}{\tan B}} \]

      9. lower--.f64 66.7

         \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(0.16666666666666666, B \cdot B, 1\right)}{B} - \frac{x}{\tan B}} \]

   7. Applied rewrites 66.7%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(B \cdot B, 0.16666666666666666, 1\right)}{B} - \frac{x}{\tan B}} \]

   8. Taylor expanded in B around 0

      \[\leadsto \frac{1}{B} - \frac{x}{\tan B} \]
   9. Step-by-step derivation

   10. Applied rewrites 98.0%

       \[\leadsto \frac{1}{B} - \frac{x}{\tan B} \]

   if -3200 < x < 1

   1. Initial program 99.8%

      \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. un-div-inv N/A

         \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]

      4. lower-/.f64 99.8

         \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]

   4. Applied rewrites 99.8%

      \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(-\frac{x}{\tan B}\right) + \frac{1}{\sin B}} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{\sin B} + \left(-\frac{x}{\tan B}\right)} \]

      3. lift-neg.f64 N/A

         \[\leadsto \frac{1}{\sin B} + \color{blue}{\left(\mathsf{neg}\left(\frac{x}{\tan B}\right)\right)} \]

      4. unsub-neg N/A

         \[\leadsto \color{blue}{\frac{1}{\sin B} - \frac{x}{\tan B}} \]

      5. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\sin B}} - \frac{x}{\tan B} \]

      6. lift-/.f64 N/A

         \[\leadsto \frac{1}{\sin B} - \color{blue}{\frac{x}{\tan B}} \]

      7. clear-num N/A

         \[\leadsto \frac{1}{\sin B} - \color{blue}{\frac{1}{\frac{\tan B}{x}}} \]

      8. lift-/.f64 N/A

         \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{\frac{\tan B}{x}}} \]

      9. *-lft-identity N/A

         \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{1 \cdot \frac{\tan B}{x}}} \]

      10. lift-*.f64 N/A

          \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{1 \cdot \frac{\tan B}{x}}} \]

      11. frac-sub N/A

          \[\leadsto \color{blue}{\frac{1 \cdot \left(1 \cdot \frac{\tan B}{x}\right) - \sin B \cdot 1}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)}} \]

      12. *-lft-identity N/A

          \[\leadsto \frac{\color{blue}{1 \cdot \frac{\tan B}{x}} - \sin B \cdot 1}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

      13. metadata-eval N/A

          \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \sin B \cdot \color{blue}{\frac{1}{1}}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

      14. div-inv N/A

          \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \color{blue}{\frac{\sin B}{1}}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

      15. /-rgt-identity N/A

          \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \color{blue}{\sin B}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

      16. lift--.f64 N/A

          \[\leadsto \frac{\color{blue}{1 \cdot \frac{\tan B}{x} - \sin B}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

      17. *-commutative N/A

          \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \sin B}{\color{blue}{\left(1 \cdot \frac{\tan B}{x}\right) \cdot \sin B}} \]

      18. associate-/r* N/A

          \[\leadsto \color{blue}{\frac{\frac{1 \cdot \frac{\tan B}{x} - \sin B}{1 \cdot \frac{\tan B}{x}}}{\sin B}} \]

   6. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\frac{1 - \sin B \cdot \frac{x}{\tan B}}{\sin B}} \]

   7. Taylor expanded in B around 0

      \[\leadsto \frac{\color{blue}{1 - x}}{\sin B} \]
   8. Step-by-step derivation

      1. lower--.f64 99.1

         \[\leadsto \frac{\color{blue}{1 - x}}{\sin B} \]

   9. Applied rewrites 99.1%

      \[\leadsto \frac{\color{blue}{1 - x}}{\sin B} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3200 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{1}{B} - \frac{x}{\tan B}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - x}{\sin B}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 86.1% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.36 \cdot 10^{+14} \lor \neg \left(x \leq 2.7 \cdot 10^{+104}\right):\\ \;\;\;\;0.16666666666666666 \cdot B - \frac{x}{\tan B}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - x}{\sin B}\\ \end{array} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (if (or (<= x -1.36e+14) (not (<= x 2.7e+104)))
   (- (* 0.16666666666666666 B) (/ x (tan B)))
   (/ (- 1.0 x) (sin B))))

C

double code(double B, double x) {
	double tmp;
	if ((x <= -1.36e+14) || !(x <= 2.7e+104)) {
		tmp = (0.16666666666666666 * B) - (x / tan(B));
	} else {
		tmp = (1.0 - x) / sin(B);
	}
	return tmp;
}

Fortran

real(8) function code(b, x)
    real(8), intent (in) :: b
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-1.36d+14)) .or. (.not. (x <= 2.7d+104))) then
        tmp = (0.16666666666666666d0 * b) - (x / tan(b))
    else
        tmp = (1.0d0 - x) / sin(b)
    end if
    code = tmp
end function

Java

public static double code(double B, double x) {
	double tmp;
	if ((x <= -1.36e+14) || !(x <= 2.7e+104)) {
		tmp = (0.16666666666666666 * B) - (x / Math.tan(B));
	} else {
		tmp = (1.0 - x) / Math.sin(B);
	}
	return tmp;
}

Python

def code(B, x):
	tmp = 0
	if (x <= -1.36e+14) or not (x <= 2.7e+104):
		tmp = (0.16666666666666666 * B) - (x / math.tan(B))
	else:
		tmp = (1.0 - x) / math.sin(B)
	return tmp

Julia

function code(B, x)
	tmp = 0.0
	if ((x <= -1.36e+14) || !(x <= 2.7e+104))
		tmp = Float64(Float64(0.16666666666666666 * B) - Float64(x / tan(B)));
	else
		tmp = Float64(Float64(1.0 - x) / sin(B));
	end
	return tmp
end

MATLAB

function tmp_2 = code(B, x)
	tmp = 0.0;
	if ((x <= -1.36e+14) || ~((x <= 2.7e+104)))
		tmp = (0.16666666666666666 * B) - (x / tan(B));
	else
		tmp = (1.0 - x) / sin(B);
	end
	tmp_2 = tmp;
end

Wolfram

code[B_, x_] := If[Or[LessEqual[x, -1.36e+14], N[Not[LessEqual[x, 2.7e+104]], $MachinePrecision]], N[(N[(0.16666666666666666 * B), $MachinePrecision] - N[(x / N[Tan[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 - x), $MachinePrecision] / N[Sin[B], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.36e14 or 2.69999999999999985e104 < x

   1. Initial program 99.6%

      \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
   2. Add Preprocessing

   3. Taylor expanded in B around 0

      \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \color{blue}{\frac{1 + \frac{1}{6} \cdot {B}^{2}}{B}} \]
   4. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \color{blue}{\frac{1 + \frac{1}{6} \cdot {B}^{2}}{B}} \]

      2. +-commutative N/A

         \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\color{blue}{\frac{1}{6} \cdot {B}^{2} + 1}}{B} \]

      3. lower-fma.f64 N/A

         \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\color{blue}{\mathsf{fma}\left(\frac{1}{6}, {B}^{2}, 1\right)}}{B} \]

      4. unpow2 N/A

         \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\mathsf{fma}\left(\frac{1}{6}, \color{blue}{B \cdot B}, 1\right)}{B} \]

      5. lower-*.f64 69.1

         \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \frac{\mathsf{fma}\left(0.16666666666666666, \color{blue}{B \cdot B}, 1\right)}{B} \]

   5. Applied rewrites 69.1%

      \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + \color{blue}{\frac{\mathsf{fma}\left(0.16666666666666666, B \cdot B, 1\right)}{B}} \]
   6. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(-x \cdot \frac{1}{\tan B}\right) + \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B}} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(-x \cdot \frac{1}{\tan B}\right)} \]

      3. lift-neg.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \color{blue}{\left(\mathsf{neg}\left(x \cdot \frac{1}{\tan B}\right)\right)} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(\color{blue}{x \cdot \frac{1}{\tan B}}\right)\right) \]

      5. lift-/.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(x \cdot \color{blue}{\frac{1}{\tan B}}\right)\right) \]

      6. div-inv N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(\color{blue}{\frac{x}{\tan B}}\right)\right) \]

      7. lift-/.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} + \left(\mathsf{neg}\left(\color{blue}{\frac{x}{\tan B}}\right)\right) \]

      8. unsub-neg N/A

         \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{1}{6}, B \cdot B, 1\right)}{B} - \frac{x}{\tan B}} \]

      9. lower--.f64 69.2

         \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(0.16666666666666666, B \cdot B, 1\right)}{B} - \frac{x}{\tan B}} \]

   7. Applied rewrites 69.2%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(B \cdot B, 0.16666666666666666, 1\right)}{B} - \frac{x}{\tan B}} \]

   8. Taylor expanded in B around inf

      \[\leadsto \frac{1}{6} \cdot \color{blue}{B} - \frac{x}{\tan B} \]
   9. Step-by-step derivation

   10. Applied rewrites 77.3%

       \[\leadsto 0.16666666666666666 \cdot \color{blue}{B} - \frac{x}{\tan B} \]

   if -1.36e14 < x < 2.69999999999999985e104

   1. Initial program 99.8%

      \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. un-div-inv N/A

         \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]

      4. lower-/.f64 99.8

         \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]

   4. Applied rewrites 99.8%

      \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(-\frac{x}{\tan B}\right) + \frac{1}{\sin B}} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{\sin B} + \left(-\frac{x}{\tan B}\right)} \]

      3. lift-neg.f64 N/A

         \[\leadsto \frac{1}{\sin B} + \color{blue}{\left(\mathsf{neg}\left(\frac{x}{\tan B}\right)\right)} \]

      4. unsub-neg N/A

         \[\leadsto \color{blue}{\frac{1}{\sin B} - \frac{x}{\tan B}} \]

      5. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\sin B}} - \frac{x}{\tan B} \]

      6. lift-/.f64 N/A

         \[\leadsto \frac{1}{\sin B} - \color{blue}{\frac{x}{\tan B}} \]

      7. clear-num N/A

         \[\leadsto \frac{1}{\sin B} - \color{blue}{\frac{1}{\frac{\tan B}{x}}} \]

      8. lift-/.f64 N/A

         \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{\frac{\tan B}{x}}} \]

      9. *-lft-identity N/A

         \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{1 \cdot \frac{\tan B}{x}}} \]

      10. lift-*.f64 N/A

          \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{1 \cdot \frac{\tan B}{x}}} \]

      11. frac-sub N/A

          \[\leadsto \color{blue}{\frac{1 \cdot \left(1 \cdot \frac{\tan B}{x}\right) - \sin B \cdot 1}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)}} \]

      12. *-lft-identity N/A

          \[\leadsto \frac{\color{blue}{1 \cdot \frac{\tan B}{x}} - \sin B \cdot 1}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

      13. metadata-eval N/A

          \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \sin B \cdot \color{blue}{\frac{1}{1}}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

      14. div-inv N/A

          \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \color{blue}{\frac{\sin B}{1}}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

      15. /-rgt-identity N/A

          \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \color{blue}{\sin B}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

      16. lift--.f64 N/A

          \[\leadsto \frac{\color{blue}{1 \cdot \frac{\tan B}{x} - \sin B}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

      17. *-commutative N/A

          \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \sin B}{\color{blue}{\left(1 \cdot \frac{\tan B}{x}\right) \cdot \sin B}} \]

      18. associate-/r* N/A

          \[\leadsto \color{blue}{\frac{\frac{1 \cdot \frac{\tan B}{x} - \sin B}{1 \cdot \frac{\tan B}{x}}}{\sin B}} \]

   6. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\frac{1 - \sin B \cdot \frac{x}{\tan B}}{\sin B}} \]

   7. Taylor expanded in B around 0

      \[\leadsto \frac{\color{blue}{1 - x}}{\sin B} \]
   8. Step-by-step derivation

      1. lower--.f64 92.6

         \[\leadsto \frac{\color{blue}{1 - x}}{\sin B} \]

   9. Applied rewrites 92.6%

      \[\leadsto \frac{\color{blue}{1 - x}}{\sin B} \]
3. Recombined 2 regimes into one program.

4. Final simplification 85.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.36 \cdot 10^{+14} \lor \neg \left(x \leq 2.7 \cdot 10^{+104}\right):\\ \;\;\;\;0.16666666666666666 \cdot B - \frac{x}{\tan B}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - x}{\sin B}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 76.2% accurate, 2.0× speedup

Math

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

FPCore

(FPCore (B x) :precision binary64 (/ (- 1.0 x) (sin B)))

C

double code(double B, double x) {
	return (1.0 - x) / sin(B);
}

Fortran

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

Java

public static double code(double B, double x) {
	return (1.0 - x) / Math.sin(B);
}

Python

def code(B, x):
	return (1.0 - x) / math.sin(B)

Julia

function code(B, x)
	return Float64(Float64(1.0 - x) / sin(B))
end

MATLAB

function tmp = code(B, x)
	tmp = (1.0 - x) / sin(B);
end

Wolfram

code[B_, x_] := N[(N[(1.0 - x), $MachinePrecision] / N[Sin[B], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. un-div-inv N/A

      \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]

   4. lower-/.f64 99.8

      \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]

4. Applied rewrites 99.8%

   \[\leadsto \left(-\color{blue}{\frac{x}{\tan B}}\right) + \frac{1}{\sin B} \]
5. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \color{blue}{\left(-\frac{x}{\tan B}\right) + \frac{1}{\sin B}} \]

   2. +-commutative N/A

      \[\leadsto \color{blue}{\frac{1}{\sin B} + \left(-\frac{x}{\tan B}\right)} \]

   3. lift-neg.f64 N/A

      \[\leadsto \frac{1}{\sin B} + \color{blue}{\left(\mathsf{neg}\left(\frac{x}{\tan B}\right)\right)} \]

   4. unsub-neg N/A

      \[\leadsto \color{blue}{\frac{1}{\sin B} - \frac{x}{\tan B}} \]

   5. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{1}{\sin B}} - \frac{x}{\tan B} \]

   6. lift-/.f64 N/A

      \[\leadsto \frac{1}{\sin B} - \color{blue}{\frac{x}{\tan B}} \]

   7. clear-num N/A

      \[\leadsto \frac{1}{\sin B} - \color{blue}{\frac{1}{\frac{\tan B}{x}}} \]

   8. lift-/.f64 N/A

      \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{\frac{\tan B}{x}}} \]

   9. *-lft-identity N/A

      \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{1 \cdot \frac{\tan B}{x}}} \]

   10. lift-*.f64 N/A

       \[\leadsto \frac{1}{\sin B} - \frac{1}{\color{blue}{1 \cdot \frac{\tan B}{x}}} \]

   11. frac-sub N/A

       \[\leadsto \color{blue}{\frac{1 \cdot \left(1 \cdot \frac{\tan B}{x}\right) - \sin B \cdot 1}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)}} \]

   12. *-lft-identity N/A

       \[\leadsto \frac{\color{blue}{1 \cdot \frac{\tan B}{x}} - \sin B \cdot 1}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

   13. metadata-eval N/A

       \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \sin B \cdot \color{blue}{\frac{1}{1}}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

   14. div-inv N/A

       \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \color{blue}{\frac{\sin B}{1}}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

   15. /-rgt-identity N/A

       \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \color{blue}{\sin B}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

   16. lift--.f64 N/A

       \[\leadsto \frac{\color{blue}{1 \cdot \frac{\tan B}{x} - \sin B}}{\sin B \cdot \left(1 \cdot \frac{\tan B}{x}\right)} \]

   17. *-commutative N/A

       \[\leadsto \frac{1 \cdot \frac{\tan B}{x} - \sin B}{\color{blue}{\left(1 \cdot \frac{\tan B}{x}\right) \cdot \sin B}} \]

   18. associate-/r* N/A

       \[\leadsto \color{blue}{\frac{\frac{1 \cdot \frac{\tan B}{x} - \sin B}{1 \cdot \frac{\tan B}{x}}}{\sin B}} \]

6. Applied rewrites 99.7%

   \[\leadsto \color{blue}{\frac{1 - \sin B \cdot \frac{x}{\tan B}}{\sin B}} \]

7. Taylor expanded in B around 0

   \[\leadsto \frac{\color{blue}{1 - x}}{\sin B} \]
8. Step-by-step derivation

   1. lower--.f64 73.3

      \[\leadsto \frac{\color{blue}{1 - x}}{\sin B} \]

9. Applied rewrites 73.3%

   \[\leadsto \frac{\color{blue}{1 - x}}{\sin B} \]
10. Add Preprocessing

Alternative 6: 50.3% accurate, 7.3× speedup

Math

\[\begin{array}{l} \\ \frac{\mathsf{fma}\left(\mathsf{fma}\left(0.3333333333333333, x, 0.16666666666666666\right), B \cdot B, 1\right) - x}{B} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (/ (- (fma (fma 0.3333333333333333 x 0.16666666666666666) (* B B) 1.0) x) B))

C

double code(double B, double x) {
	return (fma(fma(0.3333333333333333, x, 0.16666666666666666), (B * B), 1.0) - x) / B;
}

Julia

function code(B, x)
	return Float64(Float64(fma(fma(0.3333333333333333, x, 0.16666666666666666), Float64(B * B), 1.0) - x) / B)
end

Wolfram

code[B_, x_] := N[(N[(N[(N[(0.3333333333333333 * x + 0.16666666666666666), $MachinePrecision] * N[(B * B), $MachinePrecision] + 1.0), $MachinePrecision] - x), $MachinePrecision] / B), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
2. Add Preprocessing

3. Taylor expanded in B around 0

   \[\leadsto \color{blue}{\frac{\left(1 + {B}^{2} \cdot \left(\frac{1}{6} + \frac{1}{3} \cdot x\right)\right) - x}{B}} \]
4. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\left(1 + {B}^{2} \cdot \left(\frac{1}{6} + \frac{1}{3} \cdot x\right)\right) - x}{B}} \]

   2. lower--.f64 N/A

      \[\leadsto \frac{\color{blue}{\left(1 + {B}^{2} \cdot \left(\frac{1}{6} + \frac{1}{3} \cdot x\right)\right) - x}}{B} \]

   3. +-commutative N/A

      \[\leadsto \frac{\color{blue}{\left({B}^{2} \cdot \left(\frac{1}{6} + \frac{1}{3} \cdot x\right) + 1\right)} - x}{B} \]

   4. *-commutative N/A

      \[\leadsto \frac{\left(\color{blue}{\left(\frac{1}{6} + \frac{1}{3} \cdot x\right) \cdot {B}^{2}} + 1\right) - x}{B} \]

   5. lower-fma.f64 N/A

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\frac{1}{6} + \frac{1}{3} \cdot x, {B}^{2}, 1\right)} - x}{B} \]

   6. +-commutative N/A

      \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\frac{1}{3} \cdot x + \frac{1}{6}}, {B}^{2}, 1\right) - x}{B} \]

   7. lower-fma.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{1}{3}, x, \frac{1}{6}\right)}, {B}^{2}, 1\right) - x}{B} \]

   8. unpow2 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{3}, x, \frac{1}{6}\right), \color{blue}{B \cdot B}, 1\right) - x}{B} \]

   9. lower-*.f64 48.3

      \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(0.3333333333333333, x, 0.16666666666666666\right), \color{blue}{B \cdot B}, 1\right) - x}{B} \]

5. Applied rewrites 48.3%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(0.3333333333333333, x, 0.16666666666666666\right), B \cdot B, 1\right) - x}{B}} \]

6. Final simplification 48.3%

   \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(0.3333333333333333, x, 0.16666666666666666\right), B \cdot B, 1\right) - x}{B} \]
7. Add Preprocessing

Alternative 7: 50.3% accurate, 8.0× speedup

Math

\[\begin{array}{l} \\ \frac{\mathsf{fma}\left(x, \mathsf{fma}\left(0.3333333333333333 \cdot B, B, -1\right), 1\right)}{B} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (/ (fma x (fma (* 0.3333333333333333 B) B -1.0) 1.0) B))

C

double code(double B, double x) {
	return fma(x, fma((0.3333333333333333 * B), B, -1.0), 1.0) / B;
}

Julia

function code(B, x)
	return Float64(fma(x, fma(Float64(0.3333333333333333 * B), B, -1.0), 1.0) / B)
end

Wolfram

code[B_, x_] := N[(N[(x * N[(N[(0.3333333333333333 * B), $MachinePrecision] * B + -1.0), $MachinePrecision] + 1.0), $MachinePrecision] / B), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. rem-exp-log N/A

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

   2. lower-exp.f64 N/A

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

   3. lift-/.f64 N/A

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

   4. log-rec N/A

      \[\leadsto \left(-x \cdot \frac{1}{\tan B}\right) + e^{\color{blue}{\mathsf{neg}\left(\log \sin B\right)}} \]

   5. lower-neg.f64 N/A

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

   6. lower-log.f64 52.4

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

4. Applied rewrites 52.4%

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

5. Taylor expanded in B around 0

   \[\leadsto \color{blue}{\frac{B \cdot \left(e^{\mathsf{neg}\left(\log B\right)} + \frac{1}{3} \cdot \left(B \cdot x\right)\right) - x}{B}} \]
6. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{B \cdot \left(e^{\mathsf{neg}\left(\log B\right)} + \frac{1}{3} \cdot \left(B \cdot x\right)\right) - x}{B}} \]

7. Applied rewrites 48.0%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(0.3333333333333333 \cdot B, B, -1\right), 1\right)}{B}} \]
8. Add Preprocessing

Alternative 8: 49.4% accurate, 8.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{-x}{B}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{B}\\ \end{array} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (if (or (<= x -1.0) (not (<= x 1.0))) (/ (- x) B) (/ 1.0 B)))

C

double code(double B, double x) {
	double tmp;
	if ((x <= -1.0) || !(x <= 1.0)) {
		tmp = -x / B;
	} else {
		tmp = 1.0 / B;
	}
	return tmp;
}

Fortran

real(8) function code(b, x)
    real(8), intent (in) :: b
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-1.0d0)) .or. (.not. (x <= 1.0d0))) then
        tmp = -x / b
    else
        tmp = 1.0d0 / b
    end if
    code = tmp
end function

Java

public static double code(double B, double x) {
	double tmp;
	if ((x <= -1.0) || !(x <= 1.0)) {
		tmp = -x / B;
	} else {
		tmp = 1.0 / B;
	}
	return tmp;
}

Python

def code(B, x):
	tmp = 0
	if (x <= -1.0) or not (x <= 1.0):
		tmp = -x / B
	else:
		tmp = 1.0 / B
	return tmp

Julia

function code(B, x)
	tmp = 0.0
	if ((x <= -1.0) || !(x <= 1.0))
		tmp = Float64(Float64(-x) / B);
	else
		tmp = Float64(1.0 / B);
	end
	return tmp
end

MATLAB

function tmp_2 = code(B, x)
	tmp = 0.0;
	if ((x <= -1.0) || ~((x <= 1.0)))
		tmp = -x / B;
	else
		tmp = 1.0 / B;
	end
	tmp_2 = tmp;
end

Wolfram

code[B_, x_] := If[Or[LessEqual[x, -1.0], N[Not[LessEqual[x, 1.0]], $MachinePrecision]], N[((-x) / B), $MachinePrecision], N[(1.0 / B), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1 or 1 < x

   1. Initial program 99.6%

      \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
   2. Add Preprocessing

   3. Taylor expanded in B around 0

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 46.5

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

   5. Applied rewrites 46.5%

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

   6. Taylor expanded in x around inf

      \[\leadsto \frac{-1 \cdot x}{B} \]
   7. Step-by-step derivation

   8. Applied rewrites 45.3%

      \[\leadsto \frac{-x}{B} \]

   if -1 < x < 1

   1. Initial program 99.8%

      \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
   2. Add Preprocessing

   3. Taylor expanded in B around 0

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 49.7

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

   5. Applied rewrites 49.7%

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

   6. Taylor expanded in x around 0

      \[\leadsto \frac{1}{B} \]
   7. Step-by-step derivation

   8. Applied rewrites 49.5%

      \[\leadsto \frac{1}{B} \]
3. Recombined 2 regimes into one program.

4. Final simplification 47.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{-x}{B}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{B}\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 50.3% accurate, 15.5× speedup

Math

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

FPCore

(FPCore (B x) :precision binary64 (/ (- 1.0 x) B))

C

double code(double B, double x) {
	return (1.0 - x) / B;
}

Fortran

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

Java

public static double code(double B, double x) {
	return (1.0 - x) / B;
}

Python

def code(B, x):
	return (1.0 - x) / B

Julia

function code(B, x)
	return Float64(Float64(1.0 - x) / B)
end

MATLAB

function tmp = code(B, x)
	tmp = (1.0 - x) / B;
end

Wolfram

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

Derivation

1. Initial program 99.7%

   \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
2. Add Preprocessing

3. Taylor expanded in B around 0

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

   1. lower-/.f64 N/A

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

   2. lower--.f64 48.0

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

5. Applied rewrites 48.0%

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

6. Final simplification 48.0%

   \[\leadsto \frac{1 - x}{B} \]
7. Add Preprocessing

Alternative 10: 26.7% accurate, 19.4× speedup

Math

\[\begin{array}{l} \\ \frac{1}{B} \end{array} \]

FPCore

(FPCore (B x) :precision binary64 (/ 1.0 B))

C

double code(double B, double x) {
	return 1.0 / B;
}

Fortran

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

Java

public static double code(double B, double x) {
	return 1.0 / B;
}

Python

def code(B, x):
	return 1.0 / B

Julia

function code(B, x)
	return Float64(1.0 / B)
end

MATLAB

function tmp = code(B, x)
	tmp = 1.0 / B;
end

Wolfram

code[B_, x_] := N[(1.0 / B), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
2. Add Preprocessing

3. Taylor expanded in B around 0

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

   1. lower-/.f64 N/A

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

   2. lower--.f64 48.0

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

5. Applied rewrites 48.0%

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

6. Taylor expanded in x around 0

   \[\leadsto \frac{1}{B} \]
7. Step-by-step derivation

8. Applied rewrites 24.7%

   \[\leadsto \frac{1}{B} \]

9. Final simplification 24.7%

   \[\leadsto \frac{1}{B} \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2024322 
(FPCore (B x)
  :name "VandenBroeck and Keller, Equation (24)"
  :precision binary64
  (+ (- (* x (/ 1.0 (tan B)))) (/ 1.0 (sin B))))