VandenBroeck and Keller, Equation (24)

Percentage Accurate: 99.7% → 99.7%

Time: 9.3s

Alternatives: 11

Speedup: 1.0×

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: 99.7% accurate, 0.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.7%

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

   1. distribute-lft-neg-in 99.7%

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

   2. +-commutative 99.7%

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

   3. *-commutative 99.7%

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

   4. remove-double-neg 99.7%

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

   5. distribute-frac-neg2 99.7%

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

   6. tan-neg 99.7%

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

   7. cancel-sign-sub-inv 99.7%

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

   8. *-commutative 99.7%

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

   9. associate-*r/ 99.7%

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

   10. *-rgt-identity 99.7%

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

   11. tan-neg 99.7%

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

   12. distribute-neg-frac2 99.7%

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

   13. distribute-neg-frac 99.7%

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

   14. remove-double-neg 99.7%

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

3. Simplified 99.7%

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

   1. tan-quot 99.7%

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

   2. associate-/r/ 99.7%

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

6. Applied egg-rr 99.7%

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

7. Final simplification 99.7%

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

Alternative 2: 98.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -43000:\\ \;\;\;\;1 - \frac{x}{\tan B}\\ \mathbf{elif}\;x \leq 27000000000:\\ \;\;\;\;\frac{1}{\sin B} - \frac{x}{B}\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot \cos B}{-\sin B}\\ \end{array} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (if (<= x -43000.0)
   (- 1.0 (/ x (tan B)))
   (if (<= x 27000000000.0)
     (- (/ 1.0 (sin B)) (/ x B))
     (/ (* x (cos B)) (- (sin B))))))

C

double code(double B, double x) {
	double tmp;
	if (x <= -43000.0) {
		tmp = 1.0 - (x / tan(B));
	} else if (x <= 27000000000.0) {
		tmp = (1.0 / sin(B)) - (x / B);
	} else {
		tmp = (x * cos(B)) / -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 <= (-43000.0d0)) then
        tmp = 1.0d0 - (x / tan(b))
    else if (x <= 27000000000.0d0) then
        tmp = (1.0d0 / sin(b)) - (x / b)
    else
        tmp = (x * cos(b)) / -sin(b)
    end if
    code = tmp
end function

Java

public static double code(double B, double x) {
	double tmp;
	if (x <= -43000.0) {
		tmp = 1.0 - (x / Math.tan(B));
	} else if (x <= 27000000000.0) {
		tmp = (1.0 / Math.sin(B)) - (x / B);
	} else {
		tmp = (x * Math.cos(B)) / -Math.sin(B);
	}
	return tmp;
}

Python

def code(B, x):
	tmp = 0
	if x <= -43000.0:
		tmp = 1.0 - (x / math.tan(B))
	elif x <= 27000000000.0:
		tmp = (1.0 / math.sin(B)) - (x / B)
	else:
		tmp = (x * math.cos(B)) / -math.sin(B)
	return tmp

Julia

function code(B, x)
	tmp = 0.0
	if (x <= -43000.0)
		tmp = Float64(1.0 - Float64(x / tan(B)));
	elseif (x <= 27000000000.0)
		tmp = Float64(Float64(1.0 / sin(B)) - Float64(x / B));
	else
		tmp = Float64(Float64(x * cos(B)) / Float64(-sin(B)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(B, x)
	tmp = 0.0;
	if (x <= -43000.0)
		tmp = 1.0 - (x / tan(B));
	elseif (x <= 27000000000.0)
		tmp = (1.0 / sin(B)) - (x / B);
	else
		tmp = (x * cos(B)) / -sin(B);
	end
	tmp_2 = tmp;
end

Wolfram

code[B_, x_] := If[LessEqual[x, -43000.0], N[(1.0 - N[(x / N[Tan[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 27000000000.0], N[(N[(1.0 / N[Sin[B], $MachinePrecision]), $MachinePrecision] - N[(x / B), $MachinePrecision]), $MachinePrecision], N[(N[(x * N[Cos[B], $MachinePrecision]), $MachinePrecision] / (-N[Sin[B], $MachinePrecision])), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -43000

   1. Initial program 99.6%

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

      1. distribute-lft-neg-in 99.6%

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

      2. +-commutative 99.6%

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

      3. *-commutative 99.6%

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

      4. remove-double-neg 99.6%

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

      5. distribute-frac-neg2 99.6%

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

      6. tan-neg 99.6%

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

      7. cancel-sign-sub-inv 99.6%

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

      8. *-commutative 99.6%

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

      9. associate-*r/ 99.7%

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

      10. *-rgt-identity 99.7%

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

      11. tan-neg 99.7%

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

      12. distribute-neg-frac2 99.7%

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

      13. distribute-neg-frac 99.7%

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

      14. remove-double-neg 99.7%

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

   3. Simplified 99.7%

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

      1. add-exp-log 46.3%

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

      2. log-rec 46.3%

         \[\leadsto e^{\color{blue}{-\log \sin B}} - \frac{x}{\tan B} \]

   6. Applied egg-rr 46.3%

      \[\leadsto \color{blue}{e^{-\log \sin B}} - \frac{x}{\tan B} \]
   7. Step-by-step derivation

      1. exp-neg 46.3%

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

      2. add-exp-log 99.7%

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

      3. add-sqr-sqrt 46.3%

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

      4. associate-/r* 46.3%

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

      5. metadata-eval 46.3%

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

      6. sqrt-div 46.3%

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

      7. add-exp-log 46.3%

         \[\leadsto \frac{\sqrt{\frac{1}{\color{blue}{e^{\log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      8. exp-neg 46.3%

         \[\leadsto \frac{\sqrt{\color{blue}{e^{-\log \sin B}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      9. pow1 46.3%

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

      10. pow1 46.3%

          \[\leadsto \frac{\sqrt{\color{blue}{e^{-\log \sin B}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      11. add-sqr-sqrt 46.3%

          \[\leadsto \frac{\sqrt{e^{\color{blue}{\sqrt{-\log \sin B} \cdot \sqrt{-\log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      12. sqrt-unprod 46.3%

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

      13. sqr-neg 46.3%

          \[\leadsto \frac{\sqrt{e^{\sqrt{\color{blue}{\log \sin B \cdot \log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      14. sqrt-unprod 0.0%

          \[\leadsto \frac{\sqrt{e^{\color{blue}{\sqrt{\log \sin B} \cdot \sqrt{\log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      15. add-sqr-sqrt 45.8%

          \[\leadsto \frac{\sqrt{e^{\color{blue}{\log \sin B}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      16. add-exp-log 45.8%

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

   8. Applied egg-rr 45.8%

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

      1. *-inverses 97.9%

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

   10. Simplified 97.9%

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

   if -43000 < x < 2.7e10

   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 98.7%

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

   if 2.7e10 < x

   1. Initial program 99.6%

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

      1. distribute-lft-neg-in 99.6%

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

      2. +-commutative 99.6%

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

      3. *-commutative 99.6%

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

      4. remove-double-neg 99.6%

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

      5. distribute-frac-neg2 99.6%

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

      6. tan-neg 99.6%

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

      7. cancel-sign-sub-inv 99.6%

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

      8. *-commutative 99.6%

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

      9. associate-*r/ 99.6%

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

      10. *-rgt-identity 99.6%

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

      11. tan-neg 99.6%

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

      12. distribute-neg-frac2 99.6%

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

      13. distribute-neg-frac 99.6%

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

      14. remove-double-neg 99.6%

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

   3. Simplified 99.6%

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

      1. tan-quot 99.6%

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

      2. associate-/r/ 99.6%

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

   6. Applied egg-rr 99.6%

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

   7. Taylor expanded in x around inf 99.7%

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

      1. mul-1-neg 99.7%

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

      2. distribute-frac-neg2 99.7%

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

   9. Simplified 99.7%

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

4. Final simplification 98.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -43000:\\ \;\;\;\;1 - \frac{x}{\tan B}\\ \mathbf{elif}\;x \leq 27000000000:\\ \;\;\;\;\frac{1}{\sin B} - \frac{x}{B}\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot \cos B}{-\sin B}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[B_, x_] := N[(N[(1.0 / N[Sin[B], $MachinePrecision]), $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. Step-by-step derivation

   1. distribute-lft-neg-in 99.7%

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

   2. +-commutative 99.7%

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

   3. *-commutative 99.7%

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

   4. remove-double-neg 99.7%

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

   5. distribute-frac-neg2 99.7%

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

   6. tan-neg 99.7%

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

   7. cancel-sign-sub-inv 99.7%

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

   8. *-commutative 99.7%

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

   9. associate-*r/ 99.7%

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

   10. *-rgt-identity 99.7%

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

   11. tan-neg 99.7%

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

   12. distribute-neg-frac2 99.7%

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

   13. distribute-neg-frac 99.7%

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

   14. remove-double-neg 99.7%

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

3. Simplified 99.7%

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

5. Final simplification 99.7%

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

Alternative 4: 98.3% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7200:\\ \;\;\;\;1 - \frac{x}{\tan B}\\ \mathbf{elif}\;x \leq 6200000:\\ \;\;\;\;\frac{1}{\sin B} - \frac{x}{B}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{-1}{\tan B}\\ \end{array} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (if (<= x -7200.0)
   (- 1.0 (/ x (tan B)))
   (if (<= x 6200000.0) (- (/ 1.0 (sin B)) (/ x B)) (* x (/ -1.0 (tan B))))))

C

double code(double B, double x) {
	double tmp;
	if (x <= -7200.0) {
		tmp = 1.0 - (x / tan(B));
	} else if (x <= 6200000.0) {
		tmp = (1.0 / sin(B)) - (x / B);
	} else {
		tmp = x * (-1.0 / tan(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 <= (-7200.0d0)) then
        tmp = 1.0d0 - (x / tan(b))
    else if (x <= 6200000.0d0) then
        tmp = (1.0d0 / sin(b)) - (x / b)
    else
        tmp = x * ((-1.0d0) / tan(b))
    end if
    code = tmp
end function

Java

public static double code(double B, double x) {
	double tmp;
	if (x <= -7200.0) {
		tmp = 1.0 - (x / Math.tan(B));
	} else if (x <= 6200000.0) {
		tmp = (1.0 / Math.sin(B)) - (x / B);
	} else {
		tmp = x * (-1.0 / Math.tan(B));
	}
	return tmp;
}

Python

def code(B, x):
	tmp = 0
	if x <= -7200.0:
		tmp = 1.0 - (x / math.tan(B))
	elif x <= 6200000.0:
		tmp = (1.0 / math.sin(B)) - (x / B)
	else:
		tmp = x * (-1.0 / math.tan(B))
	return tmp

Julia

function code(B, x)
	tmp = 0.0
	if (x <= -7200.0)
		tmp = Float64(1.0 - Float64(x / tan(B)));
	elseif (x <= 6200000.0)
		tmp = Float64(Float64(1.0 / sin(B)) - Float64(x / B));
	else
		tmp = Float64(x * Float64(-1.0 / tan(B)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(B, x)
	tmp = 0.0;
	if (x <= -7200.0)
		tmp = 1.0 - (x / tan(B));
	elseif (x <= 6200000.0)
		tmp = (1.0 / sin(B)) - (x / B);
	else
		tmp = x * (-1.0 / tan(B));
	end
	tmp_2 = tmp;
end

Wolfram

code[B_, x_] := If[LessEqual[x, -7200.0], N[(1.0 - N[(x / N[Tan[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 6200000.0], N[(N[(1.0 / N[Sin[B], $MachinePrecision]), $MachinePrecision] - N[(x / B), $MachinePrecision]), $MachinePrecision], N[(x * N[(-1.0 / N[Tan[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -7200

   1. Initial program 99.6%

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

      1. distribute-lft-neg-in 99.6%

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

      2. +-commutative 99.6%

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

      3. *-commutative 99.6%

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

      4. remove-double-neg 99.6%

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

      5. distribute-frac-neg2 99.6%

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

      6. tan-neg 99.6%

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

      7. cancel-sign-sub-inv 99.6%

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

      8. *-commutative 99.6%

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

      9. associate-*r/ 99.7%

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

      10. *-rgt-identity 99.7%

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

      11. tan-neg 99.7%

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

      12. distribute-neg-frac2 99.7%

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

      13. distribute-neg-frac 99.7%

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

      14. remove-double-neg 99.7%

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

   3. Simplified 99.7%

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

      1. add-exp-log 46.3%

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

      2. log-rec 46.3%

         \[\leadsto e^{\color{blue}{-\log \sin B}} - \frac{x}{\tan B} \]

   6. Applied egg-rr 46.3%

      \[\leadsto \color{blue}{e^{-\log \sin B}} - \frac{x}{\tan B} \]
   7. Step-by-step derivation

      1. exp-neg 46.3%

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

      2. add-exp-log 99.7%

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

      3. add-sqr-sqrt 46.3%

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

      4. associate-/r* 46.3%

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

      5. metadata-eval 46.3%

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

      6. sqrt-div 46.3%

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

      7. add-exp-log 46.3%

         \[\leadsto \frac{\sqrt{\frac{1}{\color{blue}{e^{\log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      8. exp-neg 46.3%

         \[\leadsto \frac{\sqrt{\color{blue}{e^{-\log \sin B}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      9. pow1 46.3%

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

      10. pow1 46.3%

          \[\leadsto \frac{\sqrt{\color{blue}{e^{-\log \sin B}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      11. add-sqr-sqrt 46.3%

          \[\leadsto \frac{\sqrt{e^{\color{blue}{\sqrt{-\log \sin B} \cdot \sqrt{-\log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      12. sqrt-unprod 46.3%

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

      13. sqr-neg 46.3%

          \[\leadsto \frac{\sqrt{e^{\sqrt{\color{blue}{\log \sin B \cdot \log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      14. sqrt-unprod 0.0%

          \[\leadsto \frac{\sqrt{e^{\color{blue}{\sqrt{\log \sin B} \cdot \sqrt{\log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      15. add-sqr-sqrt 45.8%

          \[\leadsto \frac{\sqrt{e^{\color{blue}{\log \sin B}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      16. add-exp-log 45.8%

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

   8. Applied egg-rr 45.8%

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

      1. *-inverses 97.9%

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

   10. Simplified 97.9%

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

   if -7200 < x < 6.2e6

   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 98.7%

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

   if 6.2e6 < 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 x around inf 99.7%

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

      1. mul-1-neg 99.7%

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

      2. associate-/l* 99.6%

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

      3. distribute-rgt-neg-in 99.6%

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

      4. distribute-neg-frac 99.6%

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

   5. Simplified 99.6%

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

      1. distribute-frac-neg 99.6%

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

      2. clear-num 99.6%

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

      3. tan-quot 99.6%

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

      4. neg-sub0 99.6%

         \[\leadsto x \cdot \color{blue}{\left(0 - \frac{1}{\tan B}\right)} \]

   7. Applied egg-rr 99.6%

      \[\leadsto x \cdot \color{blue}{\left(0 - \frac{1}{\tan B}\right)} \]
   8. Step-by-step derivation

      1. neg-sub0 99.6%

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

      2. distribute-neg-frac 99.6%

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

      3. metadata-eval 99.6%

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

   9. Simplified 99.6%

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

4. Final simplification 98.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -7200:\\ \;\;\;\;1 - \frac{x}{\tan B}\\ \mathbf{elif}\;x \leq 6200000:\\ \;\;\;\;\frac{1}{\sin B} - \frac{x}{B}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{-1}{\tan B}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 97.6% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.3:\\ \;\;\;\;\frac{x}{-\tan B}\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;\frac{1}{\sin B}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{-1}{\tan B}\\ \end{array} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (if (<= x -1.3)
   (/ x (- (tan B)))
   (if (<= x 1.0) (/ 1.0 (sin B)) (* x (/ -1.0 (tan B))))))

C

double code(double B, double x) {
	double tmp;
	if (x <= -1.3) {
		tmp = x / -tan(B);
	} else if (x <= 1.0) {
		tmp = 1.0 / sin(B);
	} else {
		tmp = x * (-1.0 / tan(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 <= (-1.3d0)) then
        tmp = x / -tan(b)
    else if (x <= 1.0d0) then
        tmp = 1.0d0 / sin(b)
    else
        tmp = x * ((-1.0d0) / tan(b))
    end if
    code = tmp
end function

Java

public static double code(double B, double x) {
	double tmp;
	if (x <= -1.3) {
		tmp = x / -Math.tan(B);
	} else if (x <= 1.0) {
		tmp = 1.0 / Math.sin(B);
	} else {
		tmp = x * (-1.0 / Math.tan(B));
	}
	return tmp;
}

Python

def code(B, x):
	tmp = 0
	if x <= -1.3:
		tmp = x / -math.tan(B)
	elif x <= 1.0:
		tmp = 1.0 / math.sin(B)
	else:
		tmp = x * (-1.0 / math.tan(B))
	return tmp

Julia

function code(B, x)
	tmp = 0.0
	if (x <= -1.3)
		tmp = Float64(x / Float64(-tan(B)));
	elseif (x <= 1.0)
		tmp = Float64(1.0 / sin(B));
	else
		tmp = Float64(x * Float64(-1.0 / tan(B)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(B, x)
	tmp = 0.0;
	if (x <= -1.3)
		tmp = x / -tan(B);
	elseif (x <= 1.0)
		tmp = 1.0 / sin(B);
	else
		tmp = x * (-1.0 / tan(B));
	end
	tmp_2 = tmp;
end

Wolfram

code[B_, x_] := If[LessEqual[x, -1.3], N[(x / (-N[Tan[B], $MachinePrecision])), $MachinePrecision], If[LessEqual[x, 1.0], N[(1.0 / N[Sin[B], $MachinePrecision]), $MachinePrecision], N[(x * N[(-1.0 / N[Tan[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.30000000000000004

   1. Initial program 99.5%

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

   3. Taylor expanded in x around inf 95.6%

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

      1. mul-1-neg 95.6%

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

      2. associate-/l* 95.5%

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

      3. distribute-rgt-neg-in 95.5%

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

      4. distribute-neg-frac 95.5%

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

   5. Simplified 95.5%

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

      1. distribute-frac-neg 95.5%

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

      2. clear-num 95.3%

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

      3. tan-quot 95.4%

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

      4. neg-sub0 95.4%

         \[\leadsto x \cdot \color{blue}{\left(0 - \frac{1}{\tan B}\right)} \]

   7. Applied egg-rr 95.4%

      \[\leadsto x \cdot \color{blue}{\left(0 - \frac{1}{\tan B}\right)} \]
   8. Step-by-step derivation

      1. neg-sub0 95.4%

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

      2. distribute-neg-frac 95.4%

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

      3. metadata-eval 95.4%

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

   9. Simplified 95.4%

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

       1. *-commutative 95.4%

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

       2. associate-*l/ 95.5%

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

       3. neg-mul-1 95.5%

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

   11. Applied egg-rr 95.5%

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

   if -1.30000000000000004 < x < 1

   1. Initial program 99.7%

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

   3. Taylor expanded in x around 0 98.1%

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

   if 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 x around inf 98.6%

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

      1. mul-1-neg 98.6%

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

      2. associate-/l* 98.5%

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

      3. distribute-rgt-neg-in 98.5%

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

      4. distribute-neg-frac 98.5%

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

   5. Simplified 98.5%

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

      1. distribute-frac-neg 98.5%

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

      2. clear-num 98.5%

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

      3. tan-quot 98.5%

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

      4. neg-sub0 98.5%

         \[\leadsto x \cdot \color{blue}{\left(0 - \frac{1}{\tan B}\right)} \]

   7. Applied egg-rr 98.5%

      \[\leadsto x \cdot \color{blue}{\left(0 - \frac{1}{\tan B}\right)} \]
   8. Step-by-step derivation

      1. neg-sub0 98.5%

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

      2. distribute-neg-frac 98.5%

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

      3. metadata-eval 98.5%

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

   9. Simplified 98.5%

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

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.3:\\ \;\;\;\;\frac{x}{-\tan B}\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;\frac{1}{\sin B}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{-1}{\tan B}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 97.6% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.15:\\ \;\;\;\;1 - \frac{x}{\tan B}\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;\frac{1}{\sin B}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{-1}{\tan B}\\ \end{array} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (if (<= x -1.15)
   (- 1.0 (/ x (tan B)))
   (if (<= x 1.0) (/ 1.0 (sin B)) (* x (/ -1.0 (tan B))))))

C

double code(double B, double x) {
	double tmp;
	if (x <= -1.15) {
		tmp = 1.0 - (x / tan(B));
	} else if (x <= 1.0) {
		tmp = 1.0 / sin(B);
	} else {
		tmp = x * (-1.0 / tan(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 <= (-1.15d0)) then
        tmp = 1.0d0 - (x / tan(b))
    else if (x <= 1.0d0) then
        tmp = 1.0d0 / sin(b)
    else
        tmp = x * ((-1.0d0) / tan(b))
    end if
    code = tmp
end function

Java

public static double code(double B, double x) {
	double tmp;
	if (x <= -1.15) {
		tmp = 1.0 - (x / Math.tan(B));
	} else if (x <= 1.0) {
		tmp = 1.0 / Math.sin(B);
	} else {
		tmp = x * (-1.0 / Math.tan(B));
	}
	return tmp;
}

Python

def code(B, x):
	tmp = 0
	if x <= -1.15:
		tmp = 1.0 - (x / math.tan(B))
	elif x <= 1.0:
		tmp = 1.0 / math.sin(B)
	else:
		tmp = x * (-1.0 / math.tan(B))
	return tmp

Julia

function code(B, x)
	tmp = 0.0
	if (x <= -1.15)
		tmp = Float64(1.0 - Float64(x / tan(B)));
	elseif (x <= 1.0)
		tmp = Float64(1.0 / sin(B));
	else
		tmp = Float64(x * Float64(-1.0 / tan(B)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(B, x)
	tmp = 0.0;
	if (x <= -1.15)
		tmp = 1.0 - (x / tan(B));
	elseif (x <= 1.0)
		tmp = 1.0 / sin(B);
	else
		tmp = x * (-1.0 / tan(B));
	end
	tmp_2 = tmp;
end

Wolfram

code[B_, x_] := If[LessEqual[x, -1.15], N[(1.0 - N[(x / N[Tan[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.0], N[(1.0 / N[Sin[B], $MachinePrecision]), $MachinePrecision], N[(x * N[(-1.0 / N[Tan[B], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.1499999999999999

   1. Initial program 99.5%

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

      1. distribute-lft-neg-in 99.5%

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

      2. +-commutative 99.5%

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

      3. *-commutative 99.5%

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

      4. remove-double-neg 99.5%

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

      5. distribute-frac-neg2 99.5%

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

      6. tan-neg 99.5%

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

      7. cancel-sign-sub-inv 99.5%

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

      8. *-commutative 99.5%

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

      9. associate-*r/ 99.7%

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

      10. *-rgt-identity 99.7%

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

      11. tan-neg 99.7%

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

      12. distribute-neg-frac2 99.7%

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

      13. distribute-neg-frac 99.7%

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

      14. remove-double-neg 99.7%

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

   3. Simplified 99.7%

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

      1. add-exp-log 48.1%

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

      2. log-rec 48.1%

         \[\leadsto e^{\color{blue}{-\log \sin B}} - \frac{x}{\tan B} \]

   6. Applied egg-rr 48.1%

      \[\leadsto \color{blue}{e^{-\log \sin B}} - \frac{x}{\tan B} \]
   7. Step-by-step derivation

      1. exp-neg 48.1%

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

      2. add-exp-log 99.7%

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

      3. add-sqr-sqrt 48.1%

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

      4. associate-/r* 48.1%

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

      5. metadata-eval 48.1%

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

      6. sqrt-div 48.1%

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

      7. add-exp-log 48.1%

         \[\leadsto \frac{\sqrt{\frac{1}{\color{blue}{e^{\log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      8. exp-neg 48.1%

         \[\leadsto \frac{\sqrt{\color{blue}{e^{-\log \sin B}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      9. pow1 48.1%

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

      10. pow1 48.1%

          \[\leadsto \frac{\sqrt{\color{blue}{e^{-\log \sin B}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      11. add-sqr-sqrt 48.1%

          \[\leadsto \frac{\sqrt{e^{\color{blue}{\sqrt{-\log \sin B} \cdot \sqrt{-\log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      12. sqrt-unprod 48.1%

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

      13. sqr-neg 48.1%

          \[\leadsto \frac{\sqrt{e^{\sqrt{\color{blue}{\log \sin B \cdot \log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      14. sqrt-unprod 0.0%

          \[\leadsto \frac{\sqrt{e^{\color{blue}{\sqrt{\log \sin B} \cdot \sqrt{\log \sin B}}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      15. add-sqr-sqrt 45.3%

          \[\leadsto \frac{\sqrt{e^{\color{blue}{\log \sin B}}}}{\sqrt{\sin B}} - \frac{x}{\tan B} \]

      16. add-exp-log 45.3%

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

   8. Applied egg-rr 45.3%

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

      1. *-inverses 95.5%

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

   10. Simplified 95.5%

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

   if -1.1499999999999999 < x < 1

   1. Initial program 99.7%

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

   3. Taylor expanded in x around 0 98.1%

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

   if 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 x around inf 98.6%

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

      1. mul-1-neg 98.6%

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

      2. associate-/l* 98.5%

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

      3. distribute-rgt-neg-in 98.5%

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

      4. distribute-neg-frac 98.5%

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

   5. Simplified 98.5%

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

      1. distribute-frac-neg 98.5%

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

      2. clear-num 98.5%

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

      3. tan-quot 98.5%

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

      4. neg-sub0 98.5%

         \[\leadsto x \cdot \color{blue}{\left(0 - \frac{1}{\tan B}\right)} \]

   7. Applied egg-rr 98.5%

      \[\leadsto x \cdot \color{blue}{\left(0 - \frac{1}{\tan B}\right)} \]
   8. Step-by-step derivation

      1. neg-sub0 98.5%

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

      2. distribute-neg-frac 98.5%

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

      3. metadata-eval 98.5%

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

   9. Simplified 98.5%

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

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.15:\\ \;\;\;\;1 - \frac{x}{\tan B}\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;\frac{1}{\sin B}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{-1}{\tan B}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 97.6% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.55 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{x}{-\tan B}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\sin B}\\ \end{array} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (if (or (<= x -1.55) (not (<= x 1.0))) (/ x (- (tan B))) (/ 1.0 (sin B))))

C

double code(double B, double x) {
	double tmp;
	if ((x <= -1.55) || !(x <= 1.0)) {
		tmp = x / -tan(B);
	} else {
		tmp = 1.0 / 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 <= (-1.55d0)) .or. (.not. (x <= 1.0d0))) then
        tmp = x / -tan(b)
    else
        tmp = 1.0d0 / sin(b)
    end if
    code = tmp
end function

Java

public static double code(double B, double x) {
	double tmp;
	if ((x <= -1.55) || !(x <= 1.0)) {
		tmp = x / -Math.tan(B);
	} else {
		tmp = 1.0 / Math.sin(B);
	}
	return tmp;
}

Python

def code(B, x):
	tmp = 0
	if (x <= -1.55) or not (x <= 1.0):
		tmp = x / -math.tan(B)
	else:
		tmp = 1.0 / math.sin(B)
	return tmp

Julia

function code(B, x)
	tmp = 0.0
	if ((x <= -1.55) || !(x <= 1.0))
		tmp = Float64(x / Float64(-tan(B)));
	else
		tmp = Float64(1.0 / sin(B));
	end
	return tmp
end

MATLAB

function tmp_2 = code(B, x)
	tmp = 0.0;
	if ((x <= -1.55) || ~((x <= 1.0)))
		tmp = x / -tan(B);
	else
		tmp = 1.0 / sin(B);
	end
	tmp_2 = tmp;
end

Wolfram

code[B_, x_] := If[Or[LessEqual[x, -1.55], N[Not[LessEqual[x, 1.0]], $MachinePrecision]], N[(x / (-N[Tan[B], $MachinePrecision])), $MachinePrecision], N[(1.0 / N[Sin[B], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.55000000000000004 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 x around inf 97.0%

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

      1. mul-1-neg 97.0%

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

      2. associate-/l* 97.0%

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

      3. distribute-rgt-neg-in 97.0%

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

      4. distribute-neg-frac 97.0%

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

   5. Simplified 97.0%

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

      1. distribute-frac-neg 97.0%

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

      2. clear-num 96.9%

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

      3. tan-quot 96.9%

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

      4. neg-sub0 96.9%

         \[\leadsto x \cdot \color{blue}{\left(0 - \frac{1}{\tan B}\right)} \]

   7. Applied egg-rr 96.9%

      \[\leadsto x \cdot \color{blue}{\left(0 - \frac{1}{\tan B}\right)} \]
   8. Step-by-step derivation

      1. neg-sub0 96.9%

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

      2. distribute-neg-frac 96.9%

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

      3. metadata-eval 96.9%

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

   9. Simplified 96.9%

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

       1. *-commutative 96.9%

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

       2. associate-*l/ 97.0%

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

       3. neg-mul-1 97.0%

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

   11. Applied egg-rr 97.0%

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

   if -1.55000000000000004 < x < 1

   1. Initial program 99.7%

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

   3. Taylor expanded in x around 0 98.1%

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

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.55 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{x}{-\tan B}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\sin B}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 63.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;B \leq 0.0044:\\ \;\;\;\;\frac{1 - x}{B}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\sin B}\\ \end{array} \end{array} \]

FPCore

(FPCore (B x)
 :precision binary64
 (if (<= B 0.0044) (/ (- 1.0 x) B) (/ 1.0 (sin B))))

C

double code(double B, double x) {
	double tmp;
	if (B <= 0.0044) {
		tmp = (1.0 - x) / B;
	} else {
		tmp = 1.0 / 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 (b <= 0.0044d0) then
        tmp = (1.0d0 - x) / b
    else
        tmp = 1.0d0 / sin(b)
    end if
    code = tmp
end function

Java

public static double code(double B, double x) {
	double tmp;
	if (B <= 0.0044) {
		tmp = (1.0 - x) / B;
	} else {
		tmp = 1.0 / Math.sin(B);
	}
	return tmp;
}

Python

def code(B, x):
	tmp = 0
	if B <= 0.0044:
		tmp = (1.0 - x) / B
	else:
		tmp = 1.0 / math.sin(B)
	return tmp

Julia

function code(B, x)
	tmp = 0.0
	if (B <= 0.0044)
		tmp = Float64(Float64(1.0 - x) / B);
	else
		tmp = Float64(1.0 / sin(B));
	end
	return tmp
end

MATLAB

function tmp_2 = code(B, x)
	tmp = 0.0;
	if (B <= 0.0044)
		tmp = (1.0 - x) / B;
	else
		tmp = 1.0 / sin(B);
	end
	tmp_2 = tmp;
end

Wolfram

code[B_, x_] := If[LessEqual[B, 0.0044], N[(N[(1.0 - x), $MachinePrecision] / B), $MachinePrecision], N[(1.0 / N[Sin[B], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if B < 0.00440000000000000027

   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 64.6%

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

   if 0.00440000000000000027 < B

   1. Initial program 99.5%

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

   3. Taylor expanded in x around 0 59.3%

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

4. Final simplification 63.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;B \leq 0.0044:\\ \;\;\;\;\frac{1 - x}{B}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\sin B}\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 50.2% accurate, 14.9× 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(x / Float64(-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 51.4%

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

   4. Taylor expanded in x around inf 49.6%

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

      1. neg-mul-1 49.6%

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

      2. distribute-neg-frac2 49.6%

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

   6. Simplified 49.6%

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

   if -1 < x < 1

   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 46.9%

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

   4. Taylor expanded in x around 0 46.3%

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

4. Final simplification 47.7%

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

Alternative 10: 51.2% accurate, 42.0× 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 48.8%

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

4. Final simplification 48.8%

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

Alternative 11: 26.7% accurate, 70.0× 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 48.8%

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

4. Taylor expanded in x around 0 27.2%

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

5. Final simplification 27.2%

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

Reproduce

herbie shell --seed 2024057 
(FPCore (B x)
  :name "VandenBroeck and Keller, Equation (24)"
  :precision binary64
  (+ (- (* x (/ 1.0 (tan B)))) (/ 1.0 (sin B))))