invcot (example 3.9)

Average Error: 60.0 → 0.1

Time: 19.0s

Precision: binary64

Cost: 704

\[-0.026 < x \land x < 0.026\]

Math

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

↓

\[\frac{x}{3} + \left(x \cdot x\right) \cdot \left(0.022222222222222223 \cdot x\right) \]

FPCore

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

↓

(FPCore (x)
 :precision binary64
 (+ (/ x 3.0) (* (* x x) (* 0.022222222222222223 x))))

C

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

↓

double code(double x) {
	return (x / 3.0) + ((x * x) * (0.022222222222222223 * x));
}

Fortran

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

↓

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x / 3.0d0) + ((x * x) * (0.022222222222222223d0 * x))
end function

Java

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

↓

public static double code(double x) {
	return (x / 3.0) + ((x * x) * (0.022222222222222223 * x));
}

Python

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

↓

def code(x):
	return (x / 3.0) + ((x * x) * (0.022222222222222223 * x))

Julia

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

↓

function code(x)
	return Float64(Float64(x / 3.0) + Float64(Float64(x * x) * Float64(0.022222222222222223 * x)))
end

MATLAB

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

↓

function tmp = code(x)
	tmp = (x / 3.0) + ((x * x) * (0.022222222222222223 * x));
end

Wolfram

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

↓

code[x_] := N[(N[(x / 3.0), $MachinePrecision] + N[(N[(x * x), $MachinePrecision] * N[(0.022222222222222223 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Target

Original	60.0
Target	0.1
Herbie	0.1

\[\begin{array}{l} \mathbf{if}\;\left|x\right| < 0.026:\\ \;\;\;\;\frac{x}{3} \cdot \left(1 + \frac{x \cdot x}{15}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x} - \frac{1}{\tan x}\\ \end{array} \]

Derivation

1. Initial program 60.0

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

2. Taylor expanded in x around 0 0.4

   \[\leadsto \color{blue}{0.3333333333333333 \cdot x + 0.022222222222222223 \cdot {x}^{3}} \]

3. Applied egg-rr 0.1

   \[\leadsto \color{blue}{\frac{x}{3}} + 0.022222222222222223 \cdot {x}^{3} \]

4. Applied egg-rr 0.1

   \[\leadsto \frac{x}{3} + \color{blue}{\left(x \cdot x\right) \cdot \left(0.022222222222222223 \cdot x\right)} \]

Alternatives

Alternative 1
Error	0.4
Cost	576

\[\left(0.3333333333333333 + 0.022222222222222223 \cdot \left(x \cdot x\right)\right) \cdot x \]

Alternative 2
Error	0.6
Cost	192

\[0.3333333333333333 \cdot x \]

Alternative 3
Error	0.3
Cost	192

\[\frac{x}{3} \]

Reproduce

herbie shell --seed 2023010 
(FPCore (x)
  :name "invcot (example 3.9)"
  :precision binary64
  :pre (and (< -0.026 x) (< x 0.026))

  :herbie-target
  (if (< (fabs x) 0.026) (* (/ x 3.0) (+ 1.0 (/ (* x x) 15.0))) (- (/ 1.0 x) (/ 1.0 (tan x))))

  (- (/ 1.0 x) (/ 1.0 (tan x))))