Kahan p13 Example 2

Average Error: 0.0 → 0.0

Time: 50.0s

Precision: binary64

Cost: 3264

Math

\[\frac{1 + \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right)}{2 + \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right)} \]

↓

\[\begin{array}{l} t_1 := 2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\\ \frac{1 + \left(2 - \frac{\frac{2}{\frac{1 + t}{t}}}{t}\right) \cdot t_1}{2 + t_1 \cdot t_1} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (/
  (+
   1.0
   (*
    (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t))))
    (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t))))))
  (+
   2.0
   (*
    (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t))))
    (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t))))))))

↓

(FPCore (t)
 :precision binary64
 (let* ((t_1 (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t))))))
   (/
    (+ 1.0 (* (- 2.0 (/ (/ 2.0 (/ (+ 1.0 t) t)) t)) t_1))
    (+ 2.0 (* t_1 t_1)))))

C

double code(double t) {
	return (1.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))))) / (2.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t))))));
}

↓

double code(double t) {
	double t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)));
	return (1.0 + ((2.0 - ((2.0 / ((1.0 + t) / t)) / t)) * t_1)) / (2.0 + (t_1 * t_1));
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    code = (1.0d0 + ((2.0d0 - ((2.0d0 / t) / (1.0d0 + (1.0d0 / t)))) * (2.0d0 - ((2.0d0 / t) / (1.0d0 + (1.0d0 / t)))))) / (2.0d0 + ((2.0d0 - ((2.0d0 / t) / (1.0d0 + (1.0d0 / t)))) * (2.0d0 - ((2.0d0 / t) / (1.0d0 + (1.0d0 / t))))))
end function

↓

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    t_1 = 2.0d0 - ((2.0d0 / t) / (1.0d0 + (1.0d0 / t)))
    code = (1.0d0 + ((2.0d0 - ((2.0d0 / ((1.0d0 + t) / t)) / t)) * t_1)) / (2.0d0 + (t_1 * t_1))
end function

Java

public static double code(double t) {
	return (1.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))))) / (2.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t))))));
}

↓

public static double code(double t) {
	double t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)));
	return (1.0 + ((2.0 - ((2.0 / ((1.0 + t) / t)) / t)) * t_1)) / (2.0 + (t_1 * t_1));
}

Python

def code(t):
	return (1.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))))) / (2.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t))))))

↓

def code(t):
	t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))
	return (1.0 + ((2.0 - ((2.0 / ((1.0 + t) / t)) / t)) * t_1)) / (2.0 + (t_1 * t_1))

Julia

function code(t)
	return Float64(Float64(1.0 + Float64(Float64(2.0 - Float64(Float64(2.0 / t) / Float64(1.0 + Float64(1.0 / t)))) * Float64(2.0 - Float64(Float64(2.0 / t) / Float64(1.0 + Float64(1.0 / t)))))) / Float64(2.0 + Float64(Float64(2.0 - Float64(Float64(2.0 / t) / Float64(1.0 + Float64(1.0 / t)))) * Float64(2.0 - Float64(Float64(2.0 / t) / Float64(1.0 + Float64(1.0 / t)))))))
end

↓

function code(t)
	t_1 = Float64(2.0 - Float64(Float64(2.0 / t) / Float64(1.0 + Float64(1.0 / t))))
	return Float64(Float64(1.0 + Float64(Float64(2.0 - Float64(Float64(2.0 / Float64(Float64(1.0 + t) / t)) / t)) * t_1)) / Float64(2.0 + Float64(t_1 * t_1)))
end

MATLAB

function tmp = code(t)
	tmp = (1.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))))) / (2.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t))))));
end

↓

function tmp = code(t)
	t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)));
	tmp = (1.0 + ((2.0 - ((2.0 / ((1.0 + t) / t)) / t)) * t_1)) / (2.0 + (t_1 * t_1));
end

Wolfram

code[t_] := N[(N[(1.0 + N[(N[(2.0 - N[(N[(2.0 / t), $MachinePrecision] / N[(1.0 + N[(1.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(2.0 - N[(N[(2.0 / t), $MachinePrecision] / N[(1.0 + N[(1.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(2.0 + N[(N[(2.0 - N[(N[(2.0 / t), $MachinePrecision] / N[(1.0 + N[(1.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(2.0 - N[(N[(2.0 / t), $MachinePrecision] / N[(1.0 + N[(1.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

↓

code[t_] := Block[{t$95$1 = N[(2.0 - N[(N[(2.0 / t), $MachinePrecision] / N[(1.0 + N[(1.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(N[(1.0 + N[(N[(2.0 - N[(N[(2.0 / N[(N[(1.0 + t), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision] / N[(2.0 + N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 0.0

   \[\frac{1 + \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right)}{2 + \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right)} \]

2. Applied egg-rr 0.0

   \[\leadsto \frac{1 + \left(2 - \color{blue}{\frac{\frac{2}{\frac{1 + t}{t}}}{t}}\right) \cdot \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right)}{2 + \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right)} \]

Alternatives

Alternative 1
Error	0.0
Cost	3264

\[\begin{array}{l} t_1 := 2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\\ t_2 := t_1 \cdot t_1\\ \frac{1 + t_2}{2 + t_2} \end{array} \]

Alternative 2
Error	0.5
Cost	2504

\[\begin{array}{l} t_1 := 2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\\ \mathbf{if}\;t \leq -0.48:\\ \;\;\;\;\frac{\frac{8}{t} - 5}{\frac{8}{t} - 6}\\ \mathbf{elif}\;t \leq 1:\\ \;\;\;\;\frac{1 + t_1 \cdot \left(2 \cdot t\right)}{2 + \left(t \cdot t\right) \cdot 4}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + t_1 \cdot \left(2 - \frac{2}{t}\right)}{\left(6 + \frac{-4}{t}\right) + \frac{-4 + \frac{4}{t}}{1 + t}}\\ \end{array} \]

Alternative 3
Error	0.6
Cost	1864

\[\begin{array}{l} t_1 := 6 + \frac{-8}{t}\\ \mathbf{if}\;t \leq -0.48:\\ \;\;\;\;\frac{\frac{8}{t} - 5}{\frac{8}{t} - 6}\\ \mathbf{elif}\;t \leq 1.8:\\ \;\;\;\;\frac{1 + \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 \cdot t\right)}{2 + \left(t \cdot t\right) \cdot 4}\\ \mathbf{else}:\\ \;\;\;\;\frac{5}{t_1} - \frac{\frac{8}{t}}{t_1}\\ \end{array} \]

Alternative 4
Error	0.6
Cost	1352

\[\begin{array}{l} t_1 := \left(t \cdot t\right) \cdot 4\\ t_2 := 6 + \frac{-8}{t}\\ \mathbf{if}\;t \leq -0.47:\\ \;\;\;\;\frac{\frac{8}{t} - 5}{\frac{8}{t} - 6}\\ \mathbf{elif}\;t \leq 2.1:\\ \;\;\;\;\frac{1 + t_1}{2 + t_1}\\ \mathbf{else}:\\ \;\;\;\;\frac{5}{t_2} - \frac{\frac{8}{t}}{t_2}\\ \end{array} \]

Alternative 5
Error	0.6
Cost	1224

\[\begin{array}{l} t_1 := \left(t \cdot t\right) \cdot 4\\ \mathbf{if}\;t \leq -0.47:\\ \;\;\;\;\frac{\frac{8}{t} - 5}{\frac{8}{t} - 6}\\ \mathbf{elif}\;t \leq 2.1:\\ \;\;\;\;\frac{1 + t_1}{2 + t_1}\\ \mathbf{else}:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{2 + \left(4 - \frac{8}{t}\right)}\\ \end{array} \]

Alternative 6
Error	0.6
Cost	1096

\[\begin{array}{l} \mathbf{if}\;t \leq -0.56:\\ \;\;\;\;\frac{\frac{8}{t} - 5}{\frac{8}{t} - 6}\\ \mathbf{elif}\;t \leq 1.66:\\ \;\;\;\;0.5 + t \cdot t\\ \mathbf{else}:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{2 + \left(4 - \frac{8}{t}\right)}\\ \end{array} \]

Alternative 7
Error	0.6
Cost	968

\[\begin{array}{l} t_1 := \frac{5}{6 + \frac{-8}{t}} - \frac{1.3333333333333333}{t}\\ \mathbf{if}\;t \leq -1.06:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.66:\\ \;\;\;\;0.5 + t \cdot t\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 8
Error	0.6
Cost	968

\[\begin{array}{l} t_1 := \frac{\frac{8}{t} - 5}{\frac{8}{t} - 6}\\ \mathbf{if}\;t \leq -0.56:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.66:\\ \;\;\;\;0.5 + t \cdot t\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 9
Error	0.9
Cost	584

\[\begin{array}{l} \mathbf{if}\;t \leq -0.9:\\ \;\;\;\;0.8333333333333334\\ \mathbf{elif}\;t \leq 0.58:\\ \;\;\;\;0.5 + t \cdot t\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334\\ \end{array} \]

Alternative 10
Error	1.0
Cost	328

\[\begin{array}{l} \mathbf{if}\;t \leq -0.33:\\ \;\;\;\;0.8333333333333334\\ \mathbf{elif}\;t \leq 1:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334\\ \end{array} \]

Alternative 11
Error	25.9
Cost	64

\[0.5 \]

Reproduce

herbie shell --seed 2023010 
(FPCore (t)
  :name "Kahan p13 Example 2"
  :precision binary64
  (/ (+ 1.0 (* (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t)))) (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t)))))) (+ 2.0 (* (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t)))) (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t))))))))