Average Error: 0.2 → 0.2
Time: 6.4s
Precision: binary64
\[\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B} \]
↓
\[\mathsf{fma}\left(-1, \frac{1}{-\sin B}, \frac{-x}{\tan B}\right) \]
(FPCore (B x)
 :precision binary64
 (+ (- (* x (/ 1.0 (tan B)))) (/ 1.0 (sin B))))
↓
(FPCore (B x)
 :precision binary64
 (fma -1.0 (/ 1.0 (- (sin B))) (/ (- x) (tan B))))
double code(double B, double x) {
	return -(x * (1.0 / tan(B))) + (1.0 / sin(B));
}
↓
double code(double B, double x) {
	return fma(-1.0, (1.0 / -sin(B)), (-x / tan(B)));
}

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

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

\left(-x \cdot \frac{1}{\tan B}\right) + \frac{1}{\sin B}
↓
\mathsf{fma}\left(-1, \frac{1}{-\sin B}, \frac{-x}{\tan B}\right)

Error

Bits error versus B

Bits error versus x

Derivation

  1. Initial program 0.2

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

  2. Simplified0.2

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

  3. Applied egg-rr0.2

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

  4. Final simplification0.2

    \[\leadsto \mathsf{fma}\left(-1, \frac{1}{-\sin B}, \frac{-x}{\tan B}\right) \]

Reproduce

herbie shell --seed 2022145 
(FPCore (B x)
  :name "VandenBroeck and Keller, Equation (24)"
  :precision binary64
  (+ (- (* x (/ 1.0 (tan B)))) (/ 1.0 (sin B))))