Kahan p13 Example 3

Percentage Accurate: 100.0% → 100.0%

Time: 13.2s

Alternatives: 11

Speedup: 1.2×

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\\ 1 - \frac{1}{2 + t\_1 \cdot t\_1} \end{array} \end{array} \]

FPCore

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

C

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

Fortran

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 - (1.0d0 / (2.0d0 + (t_1 * t_1)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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[(1.0 - N[(1.0 / N[(2.0 + N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\\ 1 - \frac{1}{2 + t\_1 \cdot t\_1} \end{array} \end{array} \]

FPCore

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

C

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

Fortran

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 - (1.0d0 / (2.0d0 + (t_1 * t_1)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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[(1.0 - N[(1.0 / N[(2.0 + N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Alternative 1: 100.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ 1 + \frac{1}{\left(\frac{\frac{4}{-1 - t} - -4}{1 + t} + \left(\frac{4}{1 + t} - 4\right)\right) - 2} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (+
  1.0
  (/
   1.0
   (-
    (+ (/ (- (/ 4.0 (- -1.0 t)) -4.0) (+ 1.0 t)) (- (/ 4.0 (+ 1.0 t)) 4.0))
    2.0))))

C

double code(double t) {
	return 1.0 + (1.0 / (((((4.0 / (-1.0 - t)) - -4.0) / (1.0 + t)) + ((4.0 / (1.0 + t)) - 4.0)) - 2.0));
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    code = 1.0d0 + (1.0d0 / (((((4.0d0 / ((-1.0d0) - t)) - (-4.0d0)) / (1.0d0 + t)) + ((4.0d0 / (1.0d0 + t)) - 4.0d0)) - 2.0d0))
end function

Java

public static double code(double t) {
	return 1.0 + (1.0 / (((((4.0 / (-1.0 - t)) - -4.0) / (1.0 + t)) + ((4.0 / (1.0 + t)) - 4.0)) - 2.0));
}

Python

def code(t):
	return 1.0 + (1.0 / (((((4.0 / (-1.0 - t)) - -4.0) / (1.0 + t)) + ((4.0 / (1.0 + t)) - 4.0)) - 2.0))

Julia

function code(t)
	return Float64(1.0 + Float64(1.0 / Float64(Float64(Float64(Float64(Float64(4.0 / Float64(-1.0 - t)) - -4.0) / Float64(1.0 + t)) + Float64(Float64(4.0 / Float64(1.0 + t)) - 4.0)) - 2.0)))
end

MATLAB

function tmp = code(t)
	tmp = 1.0 + (1.0 / (((((4.0 / (-1.0 - t)) - -4.0) / (1.0 + t)) + ((4.0 / (1.0 + t)) - 4.0)) - 2.0));
end

Wolfram

code[t_] := N[(1.0 + N[(1.0 / N[(N[(N[(N[(N[(4.0 / N[(-1.0 - t), $MachinePrecision]), $MachinePrecision] - -4.0), $MachinePrecision] / N[(1.0 + t), $MachinePrecision]), $MachinePrecision] + N[(N[(4.0 / N[(1.0 + t), $MachinePrecision]), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

   1. sub-neg 100.0%

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

   2. distribute-lft-in 100.0%

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

   3. sub-neg 100.0%

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

   4. distribute-neg-frac 100.0%

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

   5. distribute-neg-frac 100.0%

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

   6. metadata-eval 100.0%

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

   7. sub-neg 100.0%

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

   8. distribute-neg-frac 100.0%

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

   9. distribute-neg-frac 100.0%

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

   10. metadata-eval 100.0%

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

   11. distribute-neg-frac 100.0%

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

4. Applied egg-rr 100.0%

   \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(\left(2 + \frac{\frac{-2}{t}}{1 + \frac{1}{t}}\right) \cdot 2 + \left(2 + \frac{\frac{-2}{t}}{1 + \frac{1}{t}}\right) \cdot \frac{\frac{-2}{t}}{1 + \frac{1}{t}}\right)}} \]
5. Step-by-step derivation

   1. associate-*r/ 100.0%

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

   2. associate-/l/ 99.8%

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

6. Applied egg-rr 99.8%

   \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \frac{\frac{-2}{t}}{1 + \frac{1}{t}}\right) \cdot 2 + \color{blue}{\frac{\left(2 + \frac{-2}{\left(1 + \frac{1}{t}\right) \cdot t}\right) \cdot \frac{-2}{t}}{1 + \frac{1}{t}}}\right)} \]
7. Step-by-step derivation

   1. associate-/l* 99.8%

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

   2. associate-/l/ 99.8%

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

   3. *-commutative 99.8%

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

   4. associate-*l/ 99.8%

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

   5. *-commutative 99.8%

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

8. Simplified 100.0%

   \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \frac{\frac{-2}{t}}{1 + \frac{1}{t}}\right) \cdot 2 + \color{blue}{\frac{-4 + \frac{4}{t + 1}}{t + 1}}\right)} \]
9. Step-by-step derivation

   1. *-un-lft-identity 100.0%

      \[\leadsto 1 - \frac{1}{2 + \left(\color{blue}{\left(1 \cdot \left(2 + \frac{\frac{-2}{t}}{1 + \frac{1}{t}}\right)\right)} \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

   2. associate-/l/ 99.8%

      \[\leadsto 1 - \frac{1}{2 + \left(\left(1 \cdot \left(2 + \color{blue}{\frac{-2}{\left(1 + \frac{1}{t}\right) \cdot t}}\right)\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

10. Applied egg-rr 99.8%

    \[\leadsto 1 - \frac{1}{2 + \left(\color{blue}{\left(1 \cdot \left(2 + \frac{-2}{\left(1 + \frac{1}{t}\right) \cdot t}\right)\right)} \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]
11. Step-by-step derivation

    1. *-lft-identity 99.8%

       \[\leadsto 1 - \frac{1}{2 + \left(\color{blue}{\left(2 + \frac{-2}{\left(1 + \frac{1}{t}\right) \cdot t}\right)} \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    2. metadata-eval 99.8%

       \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \frac{\color{blue}{-2}}{\left(1 + \frac{1}{t}\right) \cdot t}\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    3. distribute-neg-frac 99.8%

       \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \color{blue}{\left(-\frac{2}{\left(1 + \frac{1}{t}\right) \cdot t}\right)}\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    4. *-commutative 99.8%

       \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \left(-\frac{2}{\color{blue}{t \cdot \left(1 + \frac{1}{t}\right)}}\right)\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    5. distribute-rgt-in 99.8%

       \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \left(-\frac{2}{\color{blue}{1 \cdot t + \frac{1}{t} \cdot t}}\right)\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    6. *-lft-identity 99.8%

       \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \left(-\frac{2}{\color{blue}{t} + \frac{1}{t} \cdot t}\right)\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    7. lft-mult-inverse 100.0%

       \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \left(-\frac{2}{t + \color{blue}{1}}\right)\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    8. distribute-neg-frac2 100.0%

       \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \color{blue}{\frac{2}{-\left(t + 1\right)}}\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    9. +-commutative 100.0%

       \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \frac{2}{-\color{blue}{\left(1 + t\right)}}\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    10. distribute-neg-in 100.0%

        \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \frac{2}{\color{blue}{\left(-1\right) + \left(-t\right)}}\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    11. metadata-eval 100.0%

        \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \frac{2}{\color{blue}{-1} + \left(-t\right)}\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    12. unsub-neg 100.0%

        \[\leadsto 1 - \frac{1}{2 + \left(\left(2 + \frac{2}{\color{blue}{-1 - t}}\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

12. Simplified 100.0%

    \[\leadsto 1 - \frac{1}{2 + \left(\color{blue}{\left(2 + \frac{2}{-1 - t}\right)} \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]
13. Step-by-step derivation

    1. *-un-lft-identity 100.0%

       \[\leadsto 1 - \frac{1}{2 + \color{blue}{1 \cdot \left(\left(2 + \frac{2}{-1 - t}\right) \cdot 2 + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)}} \]

    2. *-commutative 100.0%

       \[\leadsto 1 - \frac{1}{2 + 1 \cdot \left(\color{blue}{2 \cdot \left(2 + \frac{2}{-1 - t}\right)} + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)} \]

    3. fma-define 100.0%

       \[\leadsto 1 - \frac{1}{2 + 1 \cdot \color{blue}{\mathsf{fma}\left(2, 2 + \frac{2}{-1 - t}, \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)}} \]

14. Applied egg-rr 100.0%

    \[\leadsto 1 - \frac{1}{2 + \color{blue}{1 \cdot \mathsf{fma}\left(2, 2 + \frac{2}{-1 - t}, \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)}} \]
15. Step-by-step derivation

    1. *-lft-identity 100.0%

       \[\leadsto 1 - \frac{1}{2 + \color{blue}{\mathsf{fma}\left(2, 2 + \frac{2}{-1 - t}, \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)}} \]

    2. fma-undefine 100.0%

       \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(2 \cdot \left(2 + \frac{2}{-1 - t}\right) + \frac{-4 + \frac{4}{t + 1}}{t + 1}\right)}} \]

    3. +-commutative 100.0%

       \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(\frac{-4 + \frac{4}{t + 1}}{t + 1} + 2 \cdot \left(2 + \frac{2}{-1 - t}\right)\right)}} \]

    4. distribute-lft-in 100.0%

       \[\leadsto 1 - \frac{1}{2 + \left(\frac{-4 + \frac{4}{t + 1}}{t + 1} + \color{blue}{\left(2 \cdot 2 + 2 \cdot \frac{2}{-1 - t}\right)}\right)} \]

    5. metadata-eval 100.0%

       \[\leadsto 1 - \frac{1}{2 + \left(\frac{-4 + \frac{4}{t + 1}}{t + 1} + \left(\color{blue}{4} + 2 \cdot \frac{2}{-1 - t}\right)\right)} \]

    6. associate-*r/ 100.0%

       \[\leadsto 1 - \frac{1}{2 + \left(\frac{-4 + \frac{4}{t + 1}}{t + 1} + \left(4 + \color{blue}{\frac{2 \cdot 2}{-1 - t}}\right)\right)} \]

    7. metadata-eval 100.0%

       \[\leadsto 1 - \frac{1}{2 + \left(\frac{-4 + \frac{4}{t + 1}}{t + 1} + \left(4 + \frac{\color{blue}{4}}{-1 - t}\right)\right)} \]

16. Simplified 100.0%

    \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(\frac{-4 + \frac{4}{t + 1}}{t + 1} + \left(4 + \frac{4}{-1 - t}\right)\right)}} \]

17. Final simplification 100.0%

    \[\leadsto 1 + \frac{1}{\left(\frac{\frac{4}{-1 - t} - -4}{1 + t} + \left(\frac{4}{1 + t} - 4\right)\right) - 2} \]
18. Add Preprocessing

Alternative 2: 99.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}\\ \mathbf{if}\;t \leq -0.76:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222 - t\_1}{t}\\ \mathbf{elif}\;t \leq 0.8:\\ \;\;\;\;1 - \frac{1}{2 + t \cdot \left(4 + \frac{-4}{1 + t}\right)}\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 + \frac{\left(t\_1 - 1.2222222222222223\right) - -1}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ (+ 0.037037037037037035 (/ 0.04938271604938271 t)) t)))
   (if (<= t -0.76)
     (- 0.8333333333333334 (/ (- 0.2222222222222222 t_1) t))
     (if (<= t 0.8)
       (- 1.0 (/ 1.0 (+ 2.0 (* t (+ 4.0 (/ -4.0 (+ 1.0 t)))))))
       (+ 0.8333333333333334 (/ (- (- t_1 1.2222222222222223) -1.0) t))))))

C

double code(double t) {
	double t_1 = (0.037037037037037035 + (0.04938271604938271 / t)) / t;
	double tmp;
	if (t <= -0.76) {
		tmp = 0.8333333333333334 - ((0.2222222222222222 - t_1) / t);
	} else if (t <= 0.8) {
		tmp = 1.0 - (1.0 / (2.0 + (t * (4.0 + (-4.0 / (1.0 + t))))));
	} else {
		tmp = 0.8333333333333334 + (((t_1 - 1.2222222222222223) - -1.0) / t);
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (0.037037037037037035d0 + (0.04938271604938271d0 / t)) / t
    if (t <= (-0.76d0)) then
        tmp = 0.8333333333333334d0 - ((0.2222222222222222d0 - t_1) / t)
    else if (t <= 0.8d0) then
        tmp = 1.0d0 - (1.0d0 / (2.0d0 + (t * (4.0d0 + ((-4.0d0) / (1.0d0 + t))))))
    else
        tmp = 0.8333333333333334d0 + (((t_1 - 1.2222222222222223d0) - (-1.0d0)) / t)
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double t_1 = (0.037037037037037035 + (0.04938271604938271 / t)) / t;
	double tmp;
	if (t <= -0.76) {
		tmp = 0.8333333333333334 - ((0.2222222222222222 - t_1) / t);
	} else if (t <= 0.8) {
		tmp = 1.0 - (1.0 / (2.0 + (t * (4.0 + (-4.0 / (1.0 + t))))));
	} else {
		tmp = 0.8333333333333334 + (((t_1 - 1.2222222222222223) - -1.0) / t);
	}
	return tmp;
}

Python

def code(t):
	t_1 = (0.037037037037037035 + (0.04938271604938271 / t)) / t
	tmp = 0
	if t <= -0.76:
		tmp = 0.8333333333333334 - ((0.2222222222222222 - t_1) / t)
	elif t <= 0.8:
		tmp = 1.0 - (1.0 / (2.0 + (t * (4.0 + (-4.0 / (1.0 + t))))))
	else:
		tmp = 0.8333333333333334 + (((t_1 - 1.2222222222222223) - -1.0) / t)
	return tmp

Julia

function code(t)
	t_1 = Float64(Float64(0.037037037037037035 + Float64(0.04938271604938271 / t)) / t)
	tmp = 0.0
	if (t <= -0.76)
		tmp = Float64(0.8333333333333334 - Float64(Float64(0.2222222222222222 - t_1) / t));
	elseif (t <= 0.8)
		tmp = Float64(1.0 - Float64(1.0 / Float64(2.0 + Float64(t * Float64(4.0 + Float64(-4.0 / Float64(1.0 + t)))))));
	else
		tmp = Float64(0.8333333333333334 + Float64(Float64(Float64(t_1 - 1.2222222222222223) - -1.0) / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	t_1 = (0.037037037037037035 + (0.04938271604938271 / t)) / t;
	tmp = 0.0;
	if (t <= -0.76)
		tmp = 0.8333333333333334 - ((0.2222222222222222 - t_1) / t);
	elseif (t <= 0.8)
		tmp = 1.0 - (1.0 / (2.0 + (t * (4.0 + (-4.0 / (1.0 + t))))));
	else
		tmp = 0.8333333333333334 + (((t_1 - 1.2222222222222223) - -1.0) / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := Block[{t$95$1 = N[(N[(0.037037037037037035 + N[(0.04938271604938271 / t), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]}, If[LessEqual[t, -0.76], N[(0.8333333333333334 - N[(N[(0.2222222222222222 - t$95$1), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.8], N[(1.0 - N[(1.0 / N[(2.0 + N[(t * N[(4.0 + N[(-4.0 / N[(1.0 + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(0.8333333333333334 + N[(N[(N[(t$95$1 - 1.2222222222222223), $MachinePrecision] - -1.0), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if t < -0.76000000000000001

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.7%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 + -1 \cdot \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]
   4. Step-by-step derivation

      1. mul-1-neg 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 + \color{blue}{\left(-\frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}\right)} \]

      2. unsub-neg 98.7%

         \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]

      3. mul-1-neg 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 + \color{blue}{\left(-\frac{12 - 16 \cdot \frac{1}{t}}{t}\right)}}{t}\right)} \]

      4. unsub-neg 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{\color{blue}{8 - \frac{12 - 16 \cdot \frac{1}{t}}{t}}}{t}\right)} \]

      5. sub-neg 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{\color{blue}{12 + \left(-16 \cdot \frac{1}{t}\right)}}{t}}{t}\right)} \]

      6. associate-*r/ 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\color{blue}{\frac{16 \cdot 1}{t}}\right)}{t}}{t}\right)} \]

      7. metadata-eval 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\frac{\color{blue}{16}}{t}\right)}{t}}{t}\right)} \]

      8. distribute-neg-frac 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \color{blue}{\frac{-16}{t}}}{t}}{t}\right)} \]

      9. metadata-eval 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \frac{\color{blue}{-16}}{t}}{t}}{t}\right)} \]

   5. Simplified 98.7%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}\right)}} \]

   6. Taylor expanded in t around -inf 98.9%

      \[\leadsto \color{blue}{0.8333333333333334 + -1 \cdot \frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}} \]
   7. Step-by-step derivation

      1. mul-1-neg 98.9%

         \[\leadsto 0.8333333333333334 + \color{blue}{\left(-\frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}\right)} \]

      2. unsub-neg 98.9%

         \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}} \]

      3. mul-1-neg 98.9%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 + \color{blue}{\left(-\frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}\right)}}{t} \]

      4. unsub-neg 98.9%

         \[\leadsto 0.8333333333333334 - \frac{\color{blue}{0.2222222222222222 - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}}{t} \]

      5. associate-*r/ 98.9%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \color{blue}{\frac{0.04938271604938271 \cdot 1}{t}}}{t}}{t} \]

      6. metadata-eval 98.9%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{\color{blue}{0.04938271604938271}}{t}}{t}}{t} \]

   8. Simplified 98.9%

      \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}}{t}} \]

   if -0.76000000000000001 < t < 0.80000000000000004

   1. Initial program 100.0%

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

      1. div-inv 100.0%

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

      2. associate-/l* 100.0%

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

   4. Applied egg-rr 100.0%

      \[\leadsto 1 - \frac{1}{2 + \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 - \color{blue}{2 \cdot \frac{\frac{1}{t}}{1 + \frac{1}{t}}}\right)} \]
   5. Step-by-step derivation

      1. associate-/r* 100.0%

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

      2. associate-*r/ 100.0%

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

      3. metadata-eval 100.0%

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

      4. distribute-lft-in 100.0%

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

      5. *-rgt-identity 100.0%

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

      6. rgt-mult-inverse 100.0%

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

   6. Simplified 100.0%

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

   7. Taylor expanded in t around 0 99.1%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(2 \cdot t\right)} \cdot \left(2 - \frac{2}{t + 1}\right)} \]
   8. Step-by-step derivation

      1. pow1 99.1%

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

   9. Applied egg-rr 99.1%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{{\left(\left(2 \cdot t\right) \cdot \left(2 - \frac{2}{t + 1}\right)\right)}^{1}}} \]
   10. Step-by-step derivation

       1. unpow1 99.1%

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

       2. *-commutative 99.1%

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

       3. associate-*l* 99.1%

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

       4. sub-neg 99.1%

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

       5. distribute-neg-frac 99.1%

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

       6. metadata-eval 99.1%

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

       7. distribute-lft-in 99.1%

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

       8. metadata-eval 99.1%

          \[\leadsto 1 - \frac{1}{2 + t \cdot \left(\color{blue}{4} + 2 \cdot \frac{-2}{t + 1}\right)} \]

       9. associate-*r/ 99.1%

          \[\leadsto 1 - \frac{1}{2 + t \cdot \left(4 + \color{blue}{\frac{2 \cdot -2}{t + 1}}\right)} \]

       10. metadata-eval 99.1%

           \[\leadsto 1 - \frac{1}{2 + t \cdot \left(4 + \frac{\color{blue}{-4}}{t + 1}\right)} \]

   11. Simplified 99.1%

       \[\leadsto 1 - \frac{1}{2 + \color{blue}{t \cdot \left(4 + \frac{-4}{t + 1}\right)}} \]

   if 0.80000000000000004 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 99.0%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 + -1 \cdot \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]
   4. Step-by-step derivation

      1. mul-1-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 + \color{blue}{\left(-\frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}\right)} \]

      2. unsub-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]

      3. mul-1-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 + \color{blue}{\left(-\frac{12 - 16 \cdot \frac{1}{t}}{t}\right)}}{t}\right)} \]

      4. unsub-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{\color{blue}{8 - \frac{12 - 16 \cdot \frac{1}{t}}{t}}}{t}\right)} \]

      5. sub-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{\color{blue}{12 + \left(-16 \cdot \frac{1}{t}\right)}}{t}}{t}\right)} \]

      6. associate-*r/ 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\color{blue}{\frac{16 \cdot 1}{t}}\right)}{t}}{t}\right)} \]

      7. metadata-eval 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\frac{\color{blue}{16}}{t}\right)}{t}}{t}\right)} \]

      8. distribute-neg-frac 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \color{blue}{\frac{-16}{t}}}{t}}{t}\right)} \]

      9. metadata-eval 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \frac{\color{blue}{-16}}{t}}{t}}{t}\right)} \]

   5. Simplified 99.0%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}\right)}} \]

   6. Taylor expanded in t around -inf 99.1%

      \[\leadsto \color{blue}{0.8333333333333334 + -1 \cdot \frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}} \]
   7. Step-by-step derivation

      1. mul-1-neg 99.1%

         \[\leadsto 0.8333333333333334 + \color{blue}{\left(-\frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}\right)} \]

      2. unsub-neg 99.1%

         \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}} \]

      3. mul-1-neg 99.1%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 + \color{blue}{\left(-\frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}\right)}}{t} \]

      4. unsub-neg 99.1%

         \[\leadsto 0.8333333333333334 - \frac{\color{blue}{0.2222222222222222 - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}}{t} \]

      5. associate-*r/ 99.1%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \color{blue}{\frac{0.04938271604938271 \cdot 1}{t}}}{t}}{t} \]

      6. metadata-eval 99.1%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{\color{blue}{0.04938271604938271}}{t}}{t}}{t} \]

   8. Simplified 99.1%

      \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}}{t}} \]
   9. Step-by-step derivation

      1. expm1-log1p-u 99.1%

         \[\leadsto 0.8333333333333334 - \frac{\color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}\right)\right)}}{t} \]

      2. +-commutative 99.1%

         \[\leadsto 0.8333333333333334 - \frac{\mathsf{expm1}\left(\mathsf{log1p}\left(0.2222222222222222 - \frac{\color{blue}{\frac{0.04938271604938271}{t} + 0.037037037037037035}}{t}\right)\right)}{t} \]

   10. Applied egg-rr 99.1%

       \[\leadsto 0.8333333333333334 - \frac{\color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(0.2222222222222222 - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)\right)}}{t} \]
   11. Step-by-step derivation

       1. expm1-undefine 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\color{blue}{e^{\mathsf{log1p}\left(0.2222222222222222 - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)} - 1}}{t} \]

       2. sub-neg 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\color{blue}{e^{\mathsf{log1p}\left(0.2222222222222222 - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)} + \left(-1\right)}}{t} \]

       3. log1p-undefine 99.1%

          \[\leadsto 0.8333333333333334 - \frac{e^{\color{blue}{\log \left(1 + \left(0.2222222222222222 - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)\right)}} + \left(-1\right)}{t} \]

       4. rem-exp-log 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\color{blue}{\left(1 + \left(0.2222222222222222 - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)\right)} + \left(-1\right)}{t} \]

       5. associate-+r- 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\color{blue}{\left(\left(1 + 0.2222222222222222\right) - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)} + \left(-1\right)}{t} \]

       6. metadata-eval 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\left(\color{blue}{1.2222222222222223} - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right) + \left(-1\right)}{t} \]

       7. +-commutative 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\left(1.2222222222222223 - \frac{\color{blue}{0.037037037037037035 + \frac{0.04938271604938271}{t}}}{t}\right) + \left(-1\right)}{t} \]

       8. metadata-eval 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\left(1.2222222222222223 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}\right) + \color{blue}{-1}}{t} \]

   12. Simplified 99.1%

       \[\leadsto 0.8333333333333334 - \frac{\color{blue}{\left(1.2222222222222223 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}\right) + -1}}{t} \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.76:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}}{t}\\ \mathbf{elif}\;t \leq 0.8:\\ \;\;\;\;1 - \frac{1}{2 + t \cdot \left(4 + \frac{-4}{1 + t}\right)}\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 + \frac{\left(\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 1.2222222222222223\right) - -1}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}\\ \mathbf{if}\;t \leq -0.335:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222 - t\_1}{t}\\ \mathbf{elif}\;t \leq 0.68:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 + \frac{\left(t\_1 - 1.2222222222222223\right) - -1}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ (+ 0.037037037037037035 (/ 0.04938271604938271 t)) t)))
   (if (<= t -0.335)
     (- 0.8333333333333334 (/ (- 0.2222222222222222 t_1) t))
     (if (<= t 0.68)
       0.5
       (+ 0.8333333333333334 (/ (- (- t_1 1.2222222222222223) -1.0) t))))))

C

double code(double t) {
	double t_1 = (0.037037037037037035 + (0.04938271604938271 / t)) / t;
	double tmp;
	if (t <= -0.335) {
		tmp = 0.8333333333333334 - ((0.2222222222222222 - t_1) / t);
	} else if (t <= 0.68) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334 + (((t_1 - 1.2222222222222223) - -1.0) / t);
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (0.037037037037037035d0 + (0.04938271604938271d0 / t)) / t
    if (t <= (-0.335d0)) then
        tmp = 0.8333333333333334d0 - ((0.2222222222222222d0 - t_1) / t)
    else if (t <= 0.68d0) then
        tmp = 0.5d0
    else
        tmp = 0.8333333333333334d0 + (((t_1 - 1.2222222222222223d0) - (-1.0d0)) / t)
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double t_1 = (0.037037037037037035 + (0.04938271604938271 / t)) / t;
	double tmp;
	if (t <= -0.335) {
		tmp = 0.8333333333333334 - ((0.2222222222222222 - t_1) / t);
	} else if (t <= 0.68) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334 + (((t_1 - 1.2222222222222223) - -1.0) / t);
	}
	return tmp;
}

Python

def code(t):
	t_1 = (0.037037037037037035 + (0.04938271604938271 / t)) / t
	tmp = 0
	if t <= -0.335:
		tmp = 0.8333333333333334 - ((0.2222222222222222 - t_1) / t)
	elif t <= 0.68:
		tmp = 0.5
	else:
		tmp = 0.8333333333333334 + (((t_1 - 1.2222222222222223) - -1.0) / t)
	return tmp

Julia

function code(t)
	t_1 = Float64(Float64(0.037037037037037035 + Float64(0.04938271604938271 / t)) / t)
	tmp = 0.0
	if (t <= -0.335)
		tmp = Float64(0.8333333333333334 - Float64(Float64(0.2222222222222222 - t_1) / t));
	elseif (t <= 0.68)
		tmp = 0.5;
	else
		tmp = Float64(0.8333333333333334 + Float64(Float64(Float64(t_1 - 1.2222222222222223) - -1.0) / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	t_1 = (0.037037037037037035 + (0.04938271604938271 / t)) / t;
	tmp = 0.0;
	if (t <= -0.335)
		tmp = 0.8333333333333334 - ((0.2222222222222222 - t_1) / t);
	elseif (t <= 0.68)
		tmp = 0.5;
	else
		tmp = 0.8333333333333334 + (((t_1 - 1.2222222222222223) - -1.0) / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := Block[{t$95$1 = N[(N[(0.037037037037037035 + N[(0.04938271604938271 / t), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]}, If[LessEqual[t, -0.335], N[(0.8333333333333334 - N[(N[(0.2222222222222222 - t$95$1), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.68], 0.5, N[(0.8333333333333334 + N[(N[(N[(t$95$1 - 1.2222222222222223), $MachinePrecision] - -1.0), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if t < -0.33500000000000002

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.7%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 + -1 \cdot \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]
   4. Step-by-step derivation

      1. mul-1-neg 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 + \color{blue}{\left(-\frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}\right)} \]

      2. unsub-neg 98.7%

         \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]

      3. mul-1-neg 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 + \color{blue}{\left(-\frac{12 - 16 \cdot \frac{1}{t}}{t}\right)}}{t}\right)} \]

      4. unsub-neg 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{\color{blue}{8 - \frac{12 - 16 \cdot \frac{1}{t}}{t}}}{t}\right)} \]

      5. sub-neg 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{\color{blue}{12 + \left(-16 \cdot \frac{1}{t}\right)}}{t}}{t}\right)} \]

      6. associate-*r/ 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\color{blue}{\frac{16 \cdot 1}{t}}\right)}{t}}{t}\right)} \]

      7. metadata-eval 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\frac{\color{blue}{16}}{t}\right)}{t}}{t}\right)} \]

      8. distribute-neg-frac 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \color{blue}{\frac{-16}{t}}}{t}}{t}\right)} \]

      9. metadata-eval 98.7%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \frac{\color{blue}{-16}}{t}}{t}}{t}\right)} \]

   5. Simplified 98.7%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}\right)}} \]

   6. Taylor expanded in t around -inf 98.9%

      \[\leadsto \color{blue}{0.8333333333333334 + -1 \cdot \frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}} \]
   7. Step-by-step derivation

      1. mul-1-neg 98.9%

         \[\leadsto 0.8333333333333334 + \color{blue}{\left(-\frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}\right)} \]

      2. unsub-neg 98.9%

         \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}} \]

      3. mul-1-neg 98.9%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 + \color{blue}{\left(-\frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}\right)}}{t} \]

      4. unsub-neg 98.9%

         \[\leadsto 0.8333333333333334 - \frac{\color{blue}{0.2222222222222222 - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}}{t} \]

      5. associate-*r/ 98.9%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \color{blue}{\frac{0.04938271604938271 \cdot 1}{t}}}{t}}{t} \]

      6. metadata-eval 98.9%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{\color{blue}{0.04938271604938271}}{t}}{t}}{t} \]

   8. Simplified 98.9%

      \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}}{t}} \]

   if -0.33500000000000002 < t < 0.680000000000000049

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

      \[\leadsto 1 - \color{blue}{0.5} \]

   if 0.680000000000000049 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 99.0%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 + -1 \cdot \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]
   4. Step-by-step derivation

      1. mul-1-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 + \color{blue}{\left(-\frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}\right)} \]

      2. unsub-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]

      3. mul-1-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 + \color{blue}{\left(-\frac{12 - 16 \cdot \frac{1}{t}}{t}\right)}}{t}\right)} \]

      4. unsub-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{\color{blue}{8 - \frac{12 - 16 \cdot \frac{1}{t}}{t}}}{t}\right)} \]

      5. sub-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{\color{blue}{12 + \left(-16 \cdot \frac{1}{t}\right)}}{t}}{t}\right)} \]

      6. associate-*r/ 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\color{blue}{\frac{16 \cdot 1}{t}}\right)}{t}}{t}\right)} \]

      7. metadata-eval 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\frac{\color{blue}{16}}{t}\right)}{t}}{t}\right)} \]

      8. distribute-neg-frac 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \color{blue}{\frac{-16}{t}}}{t}}{t}\right)} \]

      9. metadata-eval 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \frac{\color{blue}{-16}}{t}}{t}}{t}\right)} \]

   5. Simplified 99.0%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}\right)}} \]

   6. Taylor expanded in t around -inf 99.1%

      \[\leadsto \color{blue}{0.8333333333333334 + -1 \cdot \frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}} \]
   7. Step-by-step derivation

      1. mul-1-neg 99.1%

         \[\leadsto 0.8333333333333334 + \color{blue}{\left(-\frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}\right)} \]

      2. unsub-neg 99.1%

         \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}} \]

      3. mul-1-neg 99.1%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 + \color{blue}{\left(-\frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}\right)}}{t} \]

      4. unsub-neg 99.1%

         \[\leadsto 0.8333333333333334 - \frac{\color{blue}{0.2222222222222222 - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}}{t} \]

      5. associate-*r/ 99.1%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \color{blue}{\frac{0.04938271604938271 \cdot 1}{t}}}{t}}{t} \]

      6. metadata-eval 99.1%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{\color{blue}{0.04938271604938271}}{t}}{t}}{t} \]

   8. Simplified 99.1%

      \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}}{t}} \]
   9. Step-by-step derivation

      1. expm1-log1p-u 99.1%

         \[\leadsto 0.8333333333333334 - \frac{\color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}\right)\right)}}{t} \]

      2. +-commutative 99.1%

         \[\leadsto 0.8333333333333334 - \frac{\mathsf{expm1}\left(\mathsf{log1p}\left(0.2222222222222222 - \frac{\color{blue}{\frac{0.04938271604938271}{t} + 0.037037037037037035}}{t}\right)\right)}{t} \]

   10. Applied egg-rr 99.1%

       \[\leadsto 0.8333333333333334 - \frac{\color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(0.2222222222222222 - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)\right)}}{t} \]
   11. Step-by-step derivation

       1. expm1-undefine 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\color{blue}{e^{\mathsf{log1p}\left(0.2222222222222222 - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)} - 1}}{t} \]

       2. sub-neg 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\color{blue}{e^{\mathsf{log1p}\left(0.2222222222222222 - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)} + \left(-1\right)}}{t} \]

       3. log1p-undefine 99.1%

          \[\leadsto 0.8333333333333334 - \frac{e^{\color{blue}{\log \left(1 + \left(0.2222222222222222 - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)\right)}} + \left(-1\right)}{t} \]

       4. rem-exp-log 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\color{blue}{\left(1 + \left(0.2222222222222222 - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)\right)} + \left(-1\right)}{t} \]

       5. associate-+r- 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\color{blue}{\left(\left(1 + 0.2222222222222222\right) - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right)} + \left(-1\right)}{t} \]

       6. metadata-eval 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\left(\color{blue}{1.2222222222222223} - \frac{\frac{0.04938271604938271}{t} + 0.037037037037037035}{t}\right) + \left(-1\right)}{t} \]

       7. +-commutative 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\left(1.2222222222222223 - \frac{\color{blue}{0.037037037037037035 + \frac{0.04938271604938271}{t}}}{t}\right) + \left(-1\right)}{t} \]

       8. metadata-eval 99.1%

          \[\leadsto 0.8333333333333334 - \frac{\left(1.2222222222222223 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}\right) + \color{blue}{-1}}{t} \]

   12. Simplified 99.1%

       \[\leadsto 0.8333333333333334 - \frac{\color{blue}{\left(1.2222222222222223 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}\right) + -1}}{t} \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.335:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}}{t}\\ \mathbf{elif}\;t \leq 0.68:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 + \frac{\left(\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 1.2222222222222223\right) - -1}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.2% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.335 \lor \neg \left(t \leq 0.68\right):\\ \;\;\;\;0.8333333333333334 + \frac{\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (or (<= t -0.335) (not (<= t 0.68)))
   (+
    0.8333333333333334
    (/
     (-
      (/ (+ 0.037037037037037035 (/ 0.04938271604938271 t)) t)
      0.2222222222222222)
     t))
   0.5))

C

double code(double t) {
	double tmp;
	if ((t <= -0.335) || !(t <= 0.68)) {
		tmp = 0.8333333333333334 + ((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t);
	} else {
		tmp = 0.5;
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-0.335d0)) .or. (.not. (t <= 0.68d0))) then
        tmp = 0.8333333333333334d0 + ((((0.037037037037037035d0 + (0.04938271604938271d0 / t)) / t) - 0.2222222222222222d0) / t)
    else
        tmp = 0.5d0
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if ((t <= -0.335) || !(t <= 0.68)) {
		tmp = 0.8333333333333334 + ((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t);
	} else {
		tmp = 0.5;
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if (t <= -0.335) or not (t <= 0.68):
		tmp = 0.8333333333333334 + ((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t)
	else:
		tmp = 0.5
	return tmp

Julia

function code(t)
	tmp = 0.0
	if ((t <= -0.335) || !(t <= 0.68))
		tmp = Float64(0.8333333333333334 + Float64(Float64(Float64(Float64(0.037037037037037035 + Float64(0.04938271604938271 / t)) / t) - 0.2222222222222222) / t));
	else
		tmp = 0.5;
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if ((t <= -0.335) || ~((t <= 0.68)))
		tmp = 0.8333333333333334 + ((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t);
	else
		tmp = 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[Or[LessEqual[t, -0.335], N[Not[LessEqual[t, 0.68]], $MachinePrecision]], N[(0.8333333333333334 + N[(N[(N[(N[(0.037037037037037035 + N[(0.04938271604938271 / t), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision] - 0.2222222222222222), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], 0.5]

Derivation

1. Split input into 2 regimes

2. if t < -0.33500000000000002 or 0.680000000000000049 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.9%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 + -1 \cdot \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]
   4. Step-by-step derivation

      1. mul-1-neg 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 + \color{blue}{\left(-\frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}\right)} \]

      2. unsub-neg 98.9%

         \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]

      3. mul-1-neg 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 + \color{blue}{\left(-\frac{12 - 16 \cdot \frac{1}{t}}{t}\right)}}{t}\right)} \]

      4. unsub-neg 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{\color{blue}{8 - \frac{12 - 16 \cdot \frac{1}{t}}{t}}}{t}\right)} \]

      5. sub-neg 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{\color{blue}{12 + \left(-16 \cdot \frac{1}{t}\right)}}{t}}{t}\right)} \]

      6. associate-*r/ 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\color{blue}{\frac{16 \cdot 1}{t}}\right)}{t}}{t}\right)} \]

      7. metadata-eval 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\frac{\color{blue}{16}}{t}\right)}{t}}{t}\right)} \]

      8. distribute-neg-frac 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \color{blue}{\frac{-16}{t}}}{t}}{t}\right)} \]

      9. metadata-eval 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \frac{\color{blue}{-16}}{t}}{t}}{t}\right)} \]

   5. Simplified 98.9%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}\right)}} \]

   6. Taylor expanded in t around -inf 99.0%

      \[\leadsto \color{blue}{0.8333333333333334 + -1 \cdot \frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}} \]
   7. Step-by-step derivation

      1. mul-1-neg 99.0%

         \[\leadsto 0.8333333333333334 + \color{blue}{\left(-\frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}\right)} \]

      2. unsub-neg 99.0%

         \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}} \]

      3. mul-1-neg 99.0%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 + \color{blue}{\left(-\frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}\right)}}{t} \]

      4. unsub-neg 99.0%

         \[\leadsto 0.8333333333333334 - \frac{\color{blue}{0.2222222222222222 - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}}{t} \]

      5. associate-*r/ 99.0%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \color{blue}{\frac{0.04938271604938271 \cdot 1}{t}}}{t}}{t} \]

      6. metadata-eval 99.0%

         \[\leadsto 0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{\color{blue}{0.04938271604938271}}{t}}{t}}{t} \]

   8. Simplified 99.0%

      \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}}{t}} \]

   if -0.33500000000000002 < t < 0.680000000000000049

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

      \[\leadsto 1 - \color{blue}{0.5} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.335 \lor \neg \left(t \leq 0.68\right):\\ \;\;\;\;0.8333333333333334 + \frac{\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 100.0% accurate, 1.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. div-inv 100.0%

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

   2. associate-/l* 100.0%

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

4. Applied egg-rr 100.0%

   \[\leadsto 1 - \frac{1}{2 + \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 - \color{blue}{2 \cdot \frac{\frac{1}{t}}{1 + \frac{1}{t}}}\right)} \]
5. Step-by-step derivation

   1. associate-/r* 100.0%

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

   2. associate-*r/ 100.0%

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

   3. metadata-eval 100.0%

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

   4. distribute-lft-in 100.0%

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

   5. *-rgt-identity 100.0%

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

   6. rgt-mult-inverse 100.0%

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

6. Simplified 100.0%

   \[\leadsto 1 - \frac{1}{2 + \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 - \color{blue}{\frac{2}{t + 1}}\right)} \]
7. Step-by-step derivation

   1. sub-neg 100.0%

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

   2. distribute-neg-frac 100.0%

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

   3. distribute-neg-frac 100.0%

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

   4. metadata-eval 100.0%

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

8. Applied egg-rr 100.0%

   \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(2 + \frac{\frac{-2}{t}}{1 + \frac{1}{t}}\right)} \cdot \left(2 - \frac{2}{t + 1}\right)} \]
9. Step-by-step derivation

   1. associate-/r* 100.0%

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

   2. distribute-lft-in 100.0%

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

   3. *-rgt-identity 100.0%

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

   4. rgt-mult-inverse 100.0%

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

10. Simplified 100.0%

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

11. Final simplification 100.0%

    \[\leadsto 1 + \frac{1}{\left(2 + \frac{-2}{1 + t}\right) \cdot \left(\frac{2}{1 + t} - 2\right) - 2} \]
12. Add Preprocessing

Alternative 6: 99.1% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.52 \lor \neg \left(t \leq 0.23\right):\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222 + \frac{-0.037037037037037035}{t}}{t}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (or (<= t -0.52) (not (<= t 0.23)))
   (-
    0.8333333333333334
    (/ (+ 0.2222222222222222 (/ -0.037037037037037035 t)) t))
   0.5))

C

double code(double t) {
	double tmp;
	if ((t <= -0.52) || !(t <= 0.23)) {
		tmp = 0.8333333333333334 - ((0.2222222222222222 + (-0.037037037037037035 / t)) / t);
	} else {
		tmp = 0.5;
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-0.52d0)) .or. (.not. (t <= 0.23d0))) then
        tmp = 0.8333333333333334d0 - ((0.2222222222222222d0 + ((-0.037037037037037035d0) / t)) / t)
    else
        tmp = 0.5d0
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if ((t <= -0.52) || !(t <= 0.23)) {
		tmp = 0.8333333333333334 - ((0.2222222222222222 + (-0.037037037037037035 / t)) / t);
	} else {
		tmp = 0.5;
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if (t <= -0.52) or not (t <= 0.23):
		tmp = 0.8333333333333334 - ((0.2222222222222222 + (-0.037037037037037035 / t)) / t)
	else:
		tmp = 0.5
	return tmp

Julia

function code(t)
	tmp = 0.0
	if ((t <= -0.52) || !(t <= 0.23))
		tmp = Float64(0.8333333333333334 - Float64(Float64(0.2222222222222222 + Float64(-0.037037037037037035 / t)) / t));
	else
		tmp = 0.5;
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if ((t <= -0.52) || ~((t <= 0.23)))
		tmp = 0.8333333333333334 - ((0.2222222222222222 + (-0.037037037037037035 / t)) / t);
	else
		tmp = 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[Or[LessEqual[t, -0.52], N[Not[LessEqual[t, 0.23]], $MachinePrecision]], N[(0.8333333333333334 - N[(N[(0.2222222222222222 + N[(-0.037037037037037035 / t), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], 0.5]

Derivation

1. Split input into 2 regimes

2. if t < -0.52000000000000002 or 0.23000000000000001 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.9%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 + -1 \cdot \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]
   4. Step-by-step derivation

      1. mul-1-neg 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 + \color{blue}{\left(-\frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}\right)} \]

      2. unsub-neg 98.9%

         \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]

      3. mul-1-neg 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 + \color{blue}{\left(-\frac{12 - 16 \cdot \frac{1}{t}}{t}\right)}}{t}\right)} \]

      4. unsub-neg 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{\color{blue}{8 - \frac{12 - 16 \cdot \frac{1}{t}}{t}}}{t}\right)} \]

      5. sub-neg 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{\color{blue}{12 + \left(-16 \cdot \frac{1}{t}\right)}}{t}}{t}\right)} \]

      6. associate-*r/ 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\color{blue}{\frac{16 \cdot 1}{t}}\right)}{t}}{t}\right)} \]

      7. metadata-eval 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\frac{\color{blue}{16}}{t}\right)}{t}}{t}\right)} \]

      8. distribute-neg-frac 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \color{blue}{\frac{-16}{t}}}{t}}{t}\right)} \]

      9. metadata-eval 98.9%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \frac{\color{blue}{-16}}{t}}{t}}{t}\right)} \]

   5. Simplified 98.9%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}\right)}} \]

   6. Taylor expanded in t around inf 98.7%

      \[\leadsto \color{blue}{\left(0.8333333333333334 + \frac{0.037037037037037035}{{t}^{2}}\right) - 0.2222222222222222 \cdot \frac{1}{t}} \]
   7. Step-by-step derivation

      1. associate--l+ 98.7%

         \[\leadsto \color{blue}{0.8333333333333334 + \left(\frac{0.037037037037037035}{{t}^{2}} - 0.2222222222222222 \cdot \frac{1}{t}\right)} \]

      2. associate-*r/ 98.7%

         \[\leadsto 0.8333333333333334 + \left(\frac{0.037037037037037035}{{t}^{2}} - \color{blue}{\frac{0.2222222222222222 \cdot 1}{t}}\right) \]

      3. metadata-eval 98.7%

         \[\leadsto 0.8333333333333334 + \left(\frac{0.037037037037037035}{{t}^{2}} - \frac{\color{blue}{0.2222222222222222}}{t}\right) \]

      4. sub-neg 98.7%

         \[\leadsto 0.8333333333333334 + \color{blue}{\left(\frac{0.037037037037037035}{{t}^{2}} + \left(-\frac{0.2222222222222222}{t}\right)\right)} \]

      5. sub-neg 98.7%

         \[\leadsto 0.8333333333333334 + \color{blue}{\left(\frac{0.037037037037037035}{{t}^{2}} - \frac{0.2222222222222222}{t}\right)} \]

      6. unpow2 98.7%

         \[\leadsto 0.8333333333333334 + \left(\frac{0.037037037037037035}{\color{blue}{t \cdot t}} - \frac{0.2222222222222222}{t}\right) \]

      7. associate-/r* 98.7%

         \[\leadsto 0.8333333333333334 + \left(\color{blue}{\frac{\frac{0.037037037037037035}{t}}{t}} - \frac{0.2222222222222222}{t}\right) \]

      8. metadata-eval 98.7%

         \[\leadsto 0.8333333333333334 + \left(\frac{\frac{\color{blue}{0.037037037037037035 \cdot 1}}{t}}{t} - \frac{0.2222222222222222}{t}\right) \]

      9. associate-*r/ 98.7%

         \[\leadsto 0.8333333333333334 + \left(\frac{\color{blue}{0.037037037037037035 \cdot \frac{1}{t}}}{t} - \frac{0.2222222222222222}{t}\right) \]

      10. div-sub 98.7%

          \[\leadsto 0.8333333333333334 + \color{blue}{\frac{0.037037037037037035 \cdot \frac{1}{t} - 0.2222222222222222}{t}} \]

      11. remove-double-neg 98.7%

          \[\leadsto 0.8333333333333334 + \frac{\color{blue}{-\left(-\left(0.037037037037037035 \cdot \frac{1}{t} - 0.2222222222222222\right)\right)}}{t} \]

      12. mul-1-neg 98.7%

          \[\leadsto 0.8333333333333334 + \frac{-\color{blue}{-1 \cdot \left(0.037037037037037035 \cdot \frac{1}{t} - 0.2222222222222222\right)}}{t} \]

      13. sub-neg 98.7%

          \[\leadsto 0.8333333333333334 + \frac{--1 \cdot \color{blue}{\left(0.037037037037037035 \cdot \frac{1}{t} + \left(-0.2222222222222222\right)\right)}}{t} \]

      14. metadata-eval 98.7%

          \[\leadsto 0.8333333333333334 + \frac{--1 \cdot \left(0.037037037037037035 \cdot \frac{1}{t} + \color{blue}{-0.2222222222222222}\right)}{t} \]

      15. distribute-lft-in 98.7%

          \[\leadsto 0.8333333333333334 + \frac{-\color{blue}{\left(-1 \cdot \left(0.037037037037037035 \cdot \frac{1}{t}\right) + -1 \cdot -0.2222222222222222\right)}}{t} \]

      16. neg-mul-1 98.7%

          \[\leadsto 0.8333333333333334 + \frac{-\left(\color{blue}{\left(-0.037037037037037035 \cdot \frac{1}{t}\right)} + -1 \cdot -0.2222222222222222\right)}{t} \]

      17. metadata-eval 98.7%

          \[\leadsto 0.8333333333333334 + \frac{-\left(\left(-0.037037037037037035 \cdot \frac{1}{t}\right) + \color{blue}{0.2222222222222222}\right)}{t} \]

      18. +-commutative 98.7%

          \[\leadsto 0.8333333333333334 + \frac{-\color{blue}{\left(0.2222222222222222 + \left(-0.037037037037037035 \cdot \frac{1}{t}\right)\right)}}{t} \]

      19. sub-neg 98.7%

          \[\leadsto 0.8333333333333334 + \frac{-\color{blue}{\left(0.2222222222222222 - 0.037037037037037035 \cdot \frac{1}{t}\right)}}{t} \]

   8. Simplified 98.7%

      \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 + \frac{-0.037037037037037035}{t}}{t}} \]

   if -0.52000000000000002 < t < 0.23000000000000001

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

      \[\leadsto 1 - \color{blue}{0.5} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.52 \lor \neg \left(t \leq 0.23\right):\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222 + \frac{-0.037037037037037035}{t}}{t}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 99.1% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.52:\\ \;\;\;\;1 + \left(\frac{\frac{0.037037037037037035}{t} + -0.2222222222222222}{t} - 0.16666666666666666\right)\\ \mathbf{elif}\;t \leq 0.23:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222 + \frac{-0.037037037037037035}{t}}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (<= t -0.52)
   (+
    1.0
    (-
     (/ (+ (/ 0.037037037037037035 t) -0.2222222222222222) t)
     0.16666666666666666))
   (if (<= t 0.23)
     0.5
     (-
      0.8333333333333334
      (/ (+ 0.2222222222222222 (/ -0.037037037037037035 t)) t)))))

C

double code(double t) {
	double tmp;
	if (t <= -0.52) {
		tmp = 1.0 + ((((0.037037037037037035 / t) + -0.2222222222222222) / t) - 0.16666666666666666);
	} else if (t <= 0.23) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334 - ((0.2222222222222222 + (-0.037037037037037035 / t)) / t);
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if (t <= (-0.52d0)) then
        tmp = 1.0d0 + ((((0.037037037037037035d0 / t) + (-0.2222222222222222d0)) / t) - 0.16666666666666666d0)
    else if (t <= 0.23d0) then
        tmp = 0.5d0
    else
        tmp = 0.8333333333333334d0 - ((0.2222222222222222d0 + ((-0.037037037037037035d0) / t)) / t)
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if (t <= -0.52) {
		tmp = 1.0 + ((((0.037037037037037035 / t) + -0.2222222222222222) / t) - 0.16666666666666666);
	} else if (t <= 0.23) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334 - ((0.2222222222222222 + (-0.037037037037037035 / t)) / t);
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if t <= -0.52:
		tmp = 1.0 + ((((0.037037037037037035 / t) + -0.2222222222222222) / t) - 0.16666666666666666)
	elif t <= 0.23:
		tmp = 0.5
	else:
		tmp = 0.8333333333333334 - ((0.2222222222222222 + (-0.037037037037037035 / t)) / t)
	return tmp

Julia

function code(t)
	tmp = 0.0
	if (t <= -0.52)
		tmp = Float64(1.0 + Float64(Float64(Float64(Float64(0.037037037037037035 / t) + -0.2222222222222222) / t) - 0.16666666666666666));
	elseif (t <= 0.23)
		tmp = 0.5;
	else
		tmp = Float64(0.8333333333333334 - Float64(Float64(0.2222222222222222 + Float64(-0.037037037037037035 / t)) / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if (t <= -0.52)
		tmp = 1.0 + ((((0.037037037037037035 / t) + -0.2222222222222222) / t) - 0.16666666666666666);
	elseif (t <= 0.23)
		tmp = 0.5;
	else
		tmp = 0.8333333333333334 - ((0.2222222222222222 + (-0.037037037037037035 / t)) / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[LessEqual[t, -0.52], N[(1.0 + N[(N[(N[(N[(0.037037037037037035 / t), $MachinePrecision] + -0.2222222222222222), $MachinePrecision] / t), $MachinePrecision] - 0.16666666666666666), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.23], 0.5, N[(0.8333333333333334 - N[(N[(0.2222222222222222 + N[(-0.037037037037037035 / t), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -0.52000000000000002

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.4%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + -1 \cdot \frac{0.037037037037037035 \cdot \frac{1}{t} - 0.2222222222222222}{t}\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg 98.4%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\left(-\frac{0.037037037037037035 \cdot \frac{1}{t} - 0.2222222222222222}{t}\right)}\right) \]

      2. unsub-neg 98.4%

         \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 - \frac{0.037037037037037035 \cdot \frac{1}{t} - 0.2222222222222222}{t}\right)} \]

      3. sub-neg 98.4%

         \[\leadsto 1 - \left(0.16666666666666666 - \frac{\color{blue}{0.037037037037037035 \cdot \frac{1}{t} + \left(-0.2222222222222222\right)}}{t}\right) \]

      4. associate-*r/ 98.4%

         \[\leadsto 1 - \left(0.16666666666666666 - \frac{\color{blue}{\frac{0.037037037037037035 \cdot 1}{t}} + \left(-0.2222222222222222\right)}{t}\right) \]

      5. metadata-eval 98.4%

         \[\leadsto 1 - \left(0.16666666666666666 - \frac{\frac{\color{blue}{0.037037037037037035}}{t} + \left(-0.2222222222222222\right)}{t}\right) \]

      6. metadata-eval 98.4%

         \[\leadsto 1 - \left(0.16666666666666666 - \frac{\frac{0.037037037037037035}{t} + \color{blue}{-0.2222222222222222}}{t}\right) \]

   5. Simplified 98.4%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 - \frac{\frac{0.037037037037037035}{t} + -0.2222222222222222}{t}\right)} \]

   if -0.52000000000000002 < t < 0.23000000000000001

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

      \[\leadsto 1 - \color{blue}{0.5} \]

   if 0.23000000000000001 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 99.0%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 + -1 \cdot \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]
   4. Step-by-step derivation

      1. mul-1-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 + \color{blue}{\left(-\frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}\right)} \]

      2. unsub-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 + -1 \cdot \frac{12 - 16 \cdot \frac{1}{t}}{t}}{t}\right)}} \]

      3. mul-1-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 + \color{blue}{\left(-\frac{12 - 16 \cdot \frac{1}{t}}{t}\right)}}{t}\right)} \]

      4. unsub-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{\color{blue}{8 - \frac{12 - 16 \cdot \frac{1}{t}}{t}}}{t}\right)} \]

      5. sub-neg 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{\color{blue}{12 + \left(-16 \cdot \frac{1}{t}\right)}}{t}}{t}\right)} \]

      6. associate-*r/ 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\color{blue}{\frac{16 \cdot 1}{t}}\right)}{t}}{t}\right)} \]

      7. metadata-eval 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \left(-\frac{\color{blue}{16}}{t}\right)}{t}}{t}\right)} \]

      8. distribute-neg-frac 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \color{blue}{\frac{-16}{t}}}{t}}{t}\right)} \]

      9. metadata-eval 99.0%

         \[\leadsto 1 - \frac{1}{2 + \left(4 - \frac{8 - \frac{12 + \frac{\color{blue}{-16}}{t}}{t}}{t}\right)} \]

   5. Simplified 99.0%

      \[\leadsto 1 - \frac{1}{2 + \color{blue}{\left(4 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}\right)}} \]

   6. Taylor expanded in t around inf 99.0%

      \[\leadsto \color{blue}{\left(0.8333333333333334 + \frac{0.037037037037037035}{{t}^{2}}\right) - 0.2222222222222222 \cdot \frac{1}{t}} \]
   7. Step-by-step derivation

      1. associate--l+ 99.0%

         \[\leadsto \color{blue}{0.8333333333333334 + \left(\frac{0.037037037037037035}{{t}^{2}} - 0.2222222222222222 \cdot \frac{1}{t}\right)} \]

      2. associate-*r/ 99.0%

         \[\leadsto 0.8333333333333334 + \left(\frac{0.037037037037037035}{{t}^{2}} - \color{blue}{\frac{0.2222222222222222 \cdot 1}{t}}\right) \]

      3. metadata-eval 99.0%

         \[\leadsto 0.8333333333333334 + \left(\frac{0.037037037037037035}{{t}^{2}} - \frac{\color{blue}{0.2222222222222222}}{t}\right) \]

      4. sub-neg 99.0%

         \[\leadsto 0.8333333333333334 + \color{blue}{\left(\frac{0.037037037037037035}{{t}^{2}} + \left(-\frac{0.2222222222222222}{t}\right)\right)} \]

      5. sub-neg 99.0%

         \[\leadsto 0.8333333333333334 + \color{blue}{\left(\frac{0.037037037037037035}{{t}^{2}} - \frac{0.2222222222222222}{t}\right)} \]

      6. unpow2 99.0%

         \[\leadsto 0.8333333333333334 + \left(\frac{0.037037037037037035}{\color{blue}{t \cdot t}} - \frac{0.2222222222222222}{t}\right) \]

      7. associate-/r* 99.0%

         \[\leadsto 0.8333333333333334 + \left(\color{blue}{\frac{\frac{0.037037037037037035}{t}}{t}} - \frac{0.2222222222222222}{t}\right) \]

      8. metadata-eval 99.0%

         \[\leadsto 0.8333333333333334 + \left(\frac{\frac{\color{blue}{0.037037037037037035 \cdot 1}}{t}}{t} - \frac{0.2222222222222222}{t}\right) \]

      9. associate-*r/ 99.0%

         \[\leadsto 0.8333333333333334 + \left(\frac{\color{blue}{0.037037037037037035 \cdot \frac{1}{t}}}{t} - \frac{0.2222222222222222}{t}\right) \]

      10. div-sub 99.0%

          \[\leadsto 0.8333333333333334 + \color{blue}{\frac{0.037037037037037035 \cdot \frac{1}{t} - 0.2222222222222222}{t}} \]

      11. remove-double-neg 99.0%

          \[\leadsto 0.8333333333333334 + \frac{\color{blue}{-\left(-\left(0.037037037037037035 \cdot \frac{1}{t} - 0.2222222222222222\right)\right)}}{t} \]

      12. mul-1-neg 99.0%

          \[\leadsto 0.8333333333333334 + \frac{-\color{blue}{-1 \cdot \left(0.037037037037037035 \cdot \frac{1}{t} - 0.2222222222222222\right)}}{t} \]

      13. sub-neg 99.0%

          \[\leadsto 0.8333333333333334 + \frac{--1 \cdot \color{blue}{\left(0.037037037037037035 \cdot \frac{1}{t} + \left(-0.2222222222222222\right)\right)}}{t} \]

      14. metadata-eval 99.0%

          \[\leadsto 0.8333333333333334 + \frac{--1 \cdot \left(0.037037037037037035 \cdot \frac{1}{t} + \color{blue}{-0.2222222222222222}\right)}{t} \]

      15. distribute-lft-in 99.0%

          \[\leadsto 0.8333333333333334 + \frac{-\color{blue}{\left(-1 \cdot \left(0.037037037037037035 \cdot \frac{1}{t}\right) + -1 \cdot -0.2222222222222222\right)}}{t} \]

      16. neg-mul-1 99.0%

          \[\leadsto 0.8333333333333334 + \frac{-\left(\color{blue}{\left(-0.037037037037037035 \cdot \frac{1}{t}\right)} + -1 \cdot -0.2222222222222222\right)}{t} \]

      17. metadata-eval 99.0%

          \[\leadsto 0.8333333333333334 + \frac{-\left(\left(-0.037037037037037035 \cdot \frac{1}{t}\right) + \color{blue}{0.2222222222222222}\right)}{t} \]

      18. +-commutative 99.0%

          \[\leadsto 0.8333333333333334 + \frac{-\color{blue}{\left(0.2222222222222222 + \left(-0.037037037037037035 \cdot \frac{1}{t}\right)\right)}}{t} \]

      19. sub-neg 99.0%

          \[\leadsto 0.8333333333333334 + \frac{-\color{blue}{\left(0.2222222222222222 - 0.037037037037037035 \cdot \frac{1}{t}\right)}}{t} \]

   8. Simplified 99.0%

      \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222 + \frac{-0.037037037037037035}{t}}{t}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.52:\\ \;\;\;\;1 + \left(\frac{\frac{0.037037037037037035}{t} + -0.2222222222222222}{t} - 0.16666666666666666\right)\\ \mathbf{elif}\;t \leq 0.23:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222 + \frac{-0.037037037037037035}{t}}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 99.0% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.49 \lor \neg \left(t \leq 0.68\right):\\ \;\;\;\;1 - \left(0.16666666666666666 + \frac{0.2222222222222222}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (or (<= t -0.49) (not (<= t 0.68)))
   (- 1.0 (+ 0.16666666666666666 (/ 0.2222222222222222 t)))
   0.5))

C

double code(double t) {
	double tmp;
	if ((t <= -0.49) || !(t <= 0.68)) {
		tmp = 1.0 - (0.16666666666666666 + (0.2222222222222222 / t));
	} else {
		tmp = 0.5;
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-0.49d0)) .or. (.not. (t <= 0.68d0))) then
        tmp = 1.0d0 - (0.16666666666666666d0 + (0.2222222222222222d0 / t))
    else
        tmp = 0.5d0
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if ((t <= -0.49) || !(t <= 0.68)) {
		tmp = 1.0 - (0.16666666666666666 + (0.2222222222222222 / t));
	} else {
		tmp = 0.5;
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if (t <= -0.49) or not (t <= 0.68):
		tmp = 1.0 - (0.16666666666666666 + (0.2222222222222222 / t))
	else:
		tmp = 0.5
	return tmp

Julia

function code(t)
	tmp = 0.0
	if ((t <= -0.49) || !(t <= 0.68))
		tmp = Float64(1.0 - Float64(0.16666666666666666 + Float64(0.2222222222222222 / t)));
	else
		tmp = 0.5;
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if ((t <= -0.49) || ~((t <= 0.68)))
		tmp = 1.0 - (0.16666666666666666 + (0.2222222222222222 / t));
	else
		tmp = 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[Or[LessEqual[t, -0.49], N[Not[LessEqual[t, 0.68]], $MachinePrecision]], N[(1.0 - N[(0.16666666666666666 + N[(0.2222222222222222 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.5]

Derivation

1. Split input into 2 regimes

2. if t < -0.48999999999999999 or 0.680000000000000049 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around inf 98.2%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + 0.2222222222222222 \cdot \frac{1}{t}\right)} \]
   4. Step-by-step derivation

      1. associate-*r/ 98.2%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\frac{0.2222222222222222 \cdot 1}{t}}\right) \]

      2. metadata-eval 98.2%

         \[\leadsto 1 - \left(0.16666666666666666 + \frac{\color{blue}{0.2222222222222222}}{t}\right) \]

   5. Simplified 98.2%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + \frac{0.2222222222222222}{t}\right)} \]

   if -0.48999999999999999 < t < 0.680000000000000049

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

      \[\leadsto 1 - \color{blue}{0.5} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.49 \lor \neg \left(t \leq 0.68\right):\\ \;\;\;\;1 - \left(0.16666666666666666 + \frac{0.2222222222222222}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 99.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.49 \lor \neg \left(t \leq 0.68\right):\\ \;\;\;\;0.8333333333333334 + \frac{-0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (or (<= t -0.49) (not (<= t 0.68)))
   (+ 0.8333333333333334 (/ -0.2222222222222222 t))
   0.5))

C

double code(double t) {
	double tmp;
	if ((t <= -0.49) || !(t <= 0.68)) {
		tmp = 0.8333333333333334 + (-0.2222222222222222 / t);
	} else {
		tmp = 0.5;
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-0.49d0)) .or. (.not. (t <= 0.68d0))) then
        tmp = 0.8333333333333334d0 + ((-0.2222222222222222d0) / t)
    else
        tmp = 0.5d0
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if ((t <= -0.49) || !(t <= 0.68)) {
		tmp = 0.8333333333333334 + (-0.2222222222222222 / t);
	} else {
		tmp = 0.5;
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if (t <= -0.49) or not (t <= 0.68):
		tmp = 0.8333333333333334 + (-0.2222222222222222 / t)
	else:
		tmp = 0.5
	return tmp

Julia

function code(t)
	tmp = 0.0
	if ((t <= -0.49) || !(t <= 0.68))
		tmp = Float64(0.8333333333333334 + Float64(-0.2222222222222222 / t));
	else
		tmp = 0.5;
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if ((t <= -0.49) || ~((t <= 0.68)))
		tmp = 0.8333333333333334 + (-0.2222222222222222 / t);
	else
		tmp = 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[Or[LessEqual[t, -0.49], N[Not[LessEqual[t, 0.68]], $MachinePrecision]], N[(0.8333333333333334 + N[(-0.2222222222222222 / t), $MachinePrecision]), $MachinePrecision], 0.5]

Derivation

1. Split input into 2 regimes

2. if t < -0.48999999999999999 or 0.680000000000000049 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around inf 98.2%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + 0.2222222222222222 \cdot \frac{1}{t}\right)} \]
   4. Step-by-step derivation

      1. associate-*r/ 98.2%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\frac{0.2222222222222222 \cdot 1}{t}}\right) \]

      2. metadata-eval 98.2%

         \[\leadsto 1 - \left(0.16666666666666666 + \frac{\color{blue}{0.2222222222222222}}{t}\right) \]

   5. Simplified 98.2%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + \frac{0.2222222222222222}{t}\right)} \]

   6. Taylor expanded in t around 0 97.9%

      \[\leadsto \color{blue}{\frac{0.8333333333333334 \cdot t - 0.2222222222222222}{t}} \]
   7. Step-by-step derivation

      1. div-sub 97.9%

         \[\leadsto \color{blue}{\frac{0.8333333333333334 \cdot t}{t} - \frac{0.2222222222222222}{t}} \]

      2. sub-neg 97.9%

         \[\leadsto \color{blue}{\frac{0.8333333333333334 \cdot t}{t} + \left(-\frac{0.2222222222222222}{t}\right)} \]

      3. associate-/l* 98.2%

         \[\leadsto \color{blue}{0.8333333333333334 \cdot \frac{t}{t}} + \left(-\frac{0.2222222222222222}{t}\right) \]

      4. *-inverses 98.2%

         \[\leadsto 0.8333333333333334 \cdot \color{blue}{1} + \left(-\frac{0.2222222222222222}{t}\right) \]

      5. metadata-eval 98.2%

         \[\leadsto \color{blue}{0.8333333333333334} + \left(-\frac{0.2222222222222222}{t}\right) \]

      6. distribute-neg-frac 98.2%

         \[\leadsto 0.8333333333333334 + \color{blue}{\frac{-0.2222222222222222}{t}} \]

      7. metadata-eval 98.2%

         \[\leadsto 0.8333333333333334 + \frac{\color{blue}{-0.2222222222222222}}{t} \]

   8. Simplified 98.2%

      \[\leadsto \color{blue}{0.8333333333333334 + \frac{-0.2222222222222222}{t}} \]

   if -0.48999999999999999 < t < 0.680000000000000049

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

      \[\leadsto 1 - \color{blue}{0.5} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.49 \lor \neg \left(t \leq 0.68\right):\\ \;\;\;\;0.8333333333333334 + \frac{-0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 98.4% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.335:\\ \;\;\;\;0.8333333333333334\\ \mathbf{elif}\;t \leq 1:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (<= t -0.335) 0.8333333333333334 (if (<= t 1.0) 0.5 0.8333333333333334)))

C

double code(double t) {
	double tmp;
	if (t <= -0.335) {
		tmp = 0.8333333333333334;
	} else if (t <= 1.0) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334;
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if (t <= (-0.335d0)) then
        tmp = 0.8333333333333334d0
    else if (t <= 1.0d0) then
        tmp = 0.5d0
    else
        tmp = 0.8333333333333334d0
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if (t <= -0.335) {
		tmp = 0.8333333333333334;
	} else if (t <= 1.0) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334;
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if t <= -0.335:
		tmp = 0.8333333333333334
	elif t <= 1.0:
		tmp = 0.5
	else:
		tmp = 0.8333333333333334
	return tmp

Julia

function code(t)
	tmp = 0.0
	if (t <= -0.335)
		tmp = 0.8333333333333334;
	elseif (t <= 1.0)
		tmp = 0.5;
	else
		tmp = 0.8333333333333334;
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if (t <= -0.335)
		tmp = 0.8333333333333334;
	elseif (t <= 1.0)
		tmp = 0.5;
	else
		tmp = 0.8333333333333334;
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[LessEqual[t, -0.335], 0.8333333333333334, If[LessEqual[t, 1.0], 0.5, 0.8333333333333334]]

Derivation

1. Split input into 2 regimes

2. if t < -0.33500000000000002 or 1 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around inf 98.2%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + 0.2222222222222222 \cdot \frac{1}{t}\right)} \]
   4. Step-by-step derivation

      1. associate-*r/ 98.2%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\frac{0.2222222222222222 \cdot 1}{t}}\right) \]

      2. metadata-eval 98.2%

         \[\leadsto 1 - \left(0.16666666666666666 + \frac{\color{blue}{0.2222222222222222}}{t}\right) \]

   5. Simplified 98.2%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + \frac{0.2222222222222222}{t}\right)} \]

   6. Taylor expanded in t around inf 97.1%

      \[\leadsto \color{blue}{0.8333333333333334} \]

   if -0.33500000000000002 < t < 1

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

      \[\leadsto 1 - \color{blue}{0.5} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.335:\\ \;\;\;\;0.8333333333333334\\ \mathbf{elif}\;t \leq 1:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 59.5% accurate, 29.0× speedup

Math

\[\begin{array}{l} \\ 0.8333333333333334 \end{array} \]

FPCore

(FPCore (t) :precision binary64 0.8333333333333334)

C

double code(double t) {
	return 0.8333333333333334;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    code = 0.8333333333333334d0
end function

Java

public static double code(double t) {
	return 0.8333333333333334;
}

Python

def code(t):
	return 0.8333333333333334

Julia

function code(t)
	return 0.8333333333333334
end

MATLAB

function tmp = code(t)
	tmp = 0.8333333333333334;
end

Wolfram

code[t_] := 0.8333333333333334

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in t around inf 53.4%

   \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + 0.2222222222222222 \cdot \frac{1}{t}\right)} \]
4. Step-by-step derivation

   1. associate-*r/ 53.4%

      \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\frac{0.2222222222222222 \cdot 1}{t}}\right) \]

   2. metadata-eval 53.4%

      \[\leadsto 1 - \left(0.16666666666666666 + \frac{\color{blue}{0.2222222222222222}}{t}\right) \]

5. Simplified 53.4%

   \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + \frac{0.2222222222222222}{t}\right)} \]

6. Taylor expanded in t around inf 60.5%

   \[\leadsto \color{blue}{0.8333333333333334} \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024108 
(FPCore (t)
  :name "Kahan p13 Example 3"
  :precision binary64
  (- 1.0 (/ 1.0 (+ 2.0 (* (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t)))) (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t)))))))))