Kahan p13 Example 2

Percentage Accurate: 99.9% → 100.0%

Time: 13.4s

Alternatives: 7

Speedup: 1.9×

Specification

Math

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

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t))))) (t_2 (* t_1 t_1)))
   (/ (+ 1.0 t_2) (+ 2.0 t_2))))

C

double code(double t) {
	double t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)));
	double t_2 = t_1 * t_1;
	return (1.0 + t_2) / (2.0 + t_2);
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    t_1 = 2.0d0 - ((2.0d0 / t) / (1.0d0 + (1.0d0 / t)))
    t_2 = t_1 * t_1
    code = (1.0d0 + t_2) / (2.0d0 + t_2)
end function

Java

public static double code(double t) {
	double t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)));
	double t_2 = t_1 * t_1;
	return (1.0 + t_2) / (2.0 + t_2);
}

Python

def code(t):
	t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))
	t_2 = t_1 * t_1
	return (1.0 + t_2) / (2.0 + t_2)

Julia

function code(t)
	t_1 = Float64(2.0 - Float64(Float64(2.0 / t) / Float64(1.0 + Float64(1.0 / t))))
	t_2 = Float64(t_1 * t_1)
	return Float64(Float64(1.0 + t_2) / Float64(2.0 + t_2))
end

MATLAB

function tmp = code(t)
	t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)));
	t_2 = t_1 * t_1;
	tmp = (1.0 + t_2) / (2.0 + t_2);
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]}, Block[{t$95$2 = N[(t$95$1 * t$95$1), $MachinePrecision]}, N[(N[(1.0 + t$95$2), $MachinePrecision] / N[(2.0 + t$95$2), $MachinePrecision]), $MachinePrecision]]]

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t))))) (t_2 (* t_1 t_1)))
   (/ (+ 1.0 t_2) (+ 2.0 t_2))))

C

double code(double t) {
	double t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)));
	double t_2 = t_1 * t_1;
	return (1.0 + t_2) / (2.0 + t_2);
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    t_1 = 2.0d0 - ((2.0d0 / t) / (1.0d0 + (1.0d0 / t)))
    t_2 = t_1 * t_1
    code = (1.0d0 + t_2) / (2.0d0 + t_2)
end function

Java

public static double code(double t) {
	double t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)));
	double t_2 = t_1 * t_1;
	return (1.0 + t_2) / (2.0 + t_2);
}

Python

def code(t):
	t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))
	t_2 = t_1 * t_1
	return (1.0 + t_2) / (2.0 + t_2)

Julia

function code(t)
	t_1 = Float64(2.0 - Float64(Float64(2.0 / t) / Float64(1.0 + Float64(1.0 / t))))
	t_2 = Float64(t_1 * t_1)
	return Float64(Float64(1.0 + t_2) / Float64(2.0 + t_2))
end

MATLAB

function tmp = code(t)
	t_1 = 2.0 - ((2.0 / t) / (1.0 + (1.0 / t)));
	t_2 = t_1 * t_1;
	tmp = (1.0 + t_2) / (2.0 + t_2);
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]}, Block[{t$95$2 = N[(t$95$1 * t$95$1), $MachinePrecision]}, N[(N[(1.0 + t$95$2), $MachinePrecision] / N[(2.0 + t$95$2), $MachinePrecision]), $MachinePrecision]]]

Alternative 1: 100.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{8 + \frac{-4}{t + 1}}{-1 - t}\\ \frac{5 + t_1}{t_1 + 6} \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ (+ 8.0 (/ -4.0 (+ t 1.0))) (- -1.0 t))))
   (/ (+ 5.0 t_1) (+ t_1 6.0))))

C

double code(double t) {
	double t_1 = (8.0 + (-4.0 / (t + 1.0))) / (-1.0 - t);
	return (5.0 + t_1) / (t_1 + 6.0);
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    t_1 = (8.0d0 + ((-4.0d0) / (t + 1.0d0))) / ((-1.0d0) - t)
    code = (5.0d0 + t_1) / (t_1 + 6.0d0)
end function

Java

public static double code(double t) {
	double t_1 = (8.0 + (-4.0 / (t + 1.0))) / (-1.0 - t);
	return (5.0 + t_1) / (t_1 + 6.0);
}

Python

def code(t):
	t_1 = (8.0 + (-4.0 / (t + 1.0))) / (-1.0 - t)
	return (5.0 + t_1) / (t_1 + 6.0)

Julia

function code(t)
	t_1 = Float64(Float64(8.0 + Float64(-4.0 / Float64(t + 1.0))) / Float64(-1.0 - t))
	return Float64(Float64(5.0 + t_1) / Float64(t_1 + 6.0))
end

MATLAB

function tmp = code(t)
	t_1 = (8.0 + (-4.0 / (t + 1.0))) / (-1.0 - t);
	tmp = (5.0 + t_1) / (t_1 + 6.0);
end

Wolfram

code[t_] := Block[{t$95$1 = N[(N[(8.0 + N[(-4.0 / N[(t + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(-1.0 - t), $MachinePrecision]), $MachinePrecision]}, N[(N[(5.0 + t$95$1), $MachinePrecision] / N[(t$95$1 + 6.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\frac{5 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. associate-*l/ 100.0%

      \[\leadsto \frac{5 + \color{blue}{\frac{2 \cdot \left(\frac{2}{1 + t} - 4\right)}{1 + t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   2. frac-2neg 100.0%

      \[\leadsto \frac{5 + \color{blue}{\frac{-2 \cdot \left(\frac{2}{1 + t} - 4\right)}{-\left(1 + t\right)}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   3. sub-neg 100.0%

      \[\leadsto \frac{5 + \frac{-2 \cdot \color{blue}{\left(\frac{2}{1 + t} + \left(-4\right)\right)}}{-\left(1 + t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   4. distribute-lft-in 100.0%

      \[\leadsto \frac{5 + \frac{-\color{blue}{\left(2 \cdot \frac{2}{1 + t} + 2 \cdot \left(-4\right)\right)}}{-\left(1 + t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   5. +-commutative 100.0%

      \[\leadsto \frac{5 + \frac{-\left(2 \cdot \frac{2}{\color{blue}{t + 1}} + 2 \cdot \left(-4\right)\right)}{-\left(1 + t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   6. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-\left(2 \cdot \frac{2}{t + 1} + 2 \cdot \color{blue}{-4}\right)}{-\left(1 + t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   7. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-\left(2 \cdot \frac{2}{t + 1} + \color{blue}{-8}\right)}{-\left(1 + t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   8. distribute-neg-in 100.0%

      \[\leadsto \frac{5 + \frac{-\left(2 \cdot \frac{2}{t + 1} + -8\right)}{\color{blue}{\left(-1\right) + \left(-t\right)}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   9. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-\left(2 \cdot \frac{2}{t + 1} + -8\right)}{\color{blue}{-1} + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

6. Applied egg-rr 100.0%

   \[\leadsto \frac{5 + \color{blue}{\frac{-\left(2 \cdot \frac{2}{t + 1} + -8\right)}{-1 + \left(-t\right)}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]
7. Step-by-step derivation

   1. +-commutative 100.0%

      \[\leadsto \frac{5 + \frac{-\color{blue}{\left(-8 + 2 \cdot \frac{2}{t + 1}\right)}}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   2. distribute-neg-in 100.0%

      \[\leadsto \frac{5 + \frac{\color{blue}{\left(--8\right) + \left(-2 \cdot \frac{2}{t + 1}\right)}}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   3. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{\color{blue}{8} + \left(-2 \cdot \frac{2}{t + 1}\right)}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   4. associate-*r/ 100.0%

      \[\leadsto \frac{5 + \frac{8 + \left(-\color{blue}{\frac{2 \cdot 2}{t + 1}}\right)}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   5. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{8 + \left(-\frac{\color{blue}{4}}{t + 1}\right)}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   6. distribute-neg-frac 100.0%

      \[\leadsto \frac{5 + \frac{8 + \color{blue}{\frac{-4}{t + 1}}}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   7. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{8 + \frac{\color{blue}{-4}}{t + 1}}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   8. unsub-neg 100.0%

      \[\leadsto \frac{5 + \frac{8 + \frac{-4}{t + 1}}{\color{blue}{-1 - t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

8. Simplified 100.0%

   \[\leadsto \frac{5 + \color{blue}{\frac{8 + \frac{-4}{t + 1}}{-1 - t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]
9. Step-by-step derivation

   1. associate-*l/ 100.0%

      \[\leadsto \frac{5 + \color{blue}{\frac{2 \cdot \left(\frac{2}{1 + t} - 4\right)}{1 + t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   2. frac-2neg 100.0%

      \[\leadsto \frac{5 + \color{blue}{\frac{-2 \cdot \left(\frac{2}{1 + t} - 4\right)}{-\left(1 + t\right)}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   3. sub-neg 100.0%

      \[\leadsto \frac{5 + \frac{-2 \cdot \color{blue}{\left(\frac{2}{1 + t} + \left(-4\right)\right)}}{-\left(1 + t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   4. distribute-lft-in 100.0%

      \[\leadsto \frac{5 + \frac{-\color{blue}{\left(2 \cdot \frac{2}{1 + t} + 2 \cdot \left(-4\right)\right)}}{-\left(1 + t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   5. +-commutative 100.0%

      \[\leadsto \frac{5 + \frac{-\left(2 \cdot \frac{2}{\color{blue}{t + 1}} + 2 \cdot \left(-4\right)\right)}{-\left(1 + t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   6. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-\left(2 \cdot \frac{2}{t + 1} + 2 \cdot \color{blue}{-4}\right)}{-\left(1 + t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   7. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-\left(2 \cdot \frac{2}{t + 1} + \color{blue}{-8}\right)}{-\left(1 + t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   8. distribute-neg-in 100.0%

      \[\leadsto \frac{5 + \frac{-\left(2 \cdot \frac{2}{t + 1} + -8\right)}{\color{blue}{\left(-1\right) + \left(-t\right)}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   9. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-\left(2 \cdot \frac{2}{t + 1} + -8\right)}{\color{blue}{-1} + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

10. Applied egg-rr 100.0%

    \[\leadsto \frac{5 + \frac{8 + \frac{-4}{t + 1}}{-1 - t}}{6 + \color{blue}{\frac{-\left(2 \cdot \frac{2}{t + 1} + -8\right)}{-1 + \left(-t\right)}}} \]
11. Step-by-step derivation

    1. +-commutative 100.0%

       \[\leadsto \frac{5 + \frac{-\color{blue}{\left(-8 + 2 \cdot \frac{2}{t + 1}\right)}}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

    2. distribute-neg-in 100.0%

       \[\leadsto \frac{5 + \frac{\color{blue}{\left(--8\right) + \left(-2 \cdot \frac{2}{t + 1}\right)}}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

    3. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{\color{blue}{8} + \left(-2 \cdot \frac{2}{t + 1}\right)}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

    4. associate-*r/ 100.0%

       \[\leadsto \frac{5 + \frac{8 + \left(-\color{blue}{\frac{2 \cdot 2}{t + 1}}\right)}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

    5. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{8 + \left(-\frac{\color{blue}{4}}{t + 1}\right)}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

    6. distribute-neg-frac 100.0%

       \[\leadsto \frac{5 + \frac{8 + \color{blue}{\frac{-4}{t + 1}}}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

    7. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{8 + \frac{\color{blue}{-4}}{t + 1}}{-1 + \left(-t\right)}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

    8. unsub-neg 100.0%

       \[\leadsto \frac{5 + \frac{8 + \frac{-4}{t + 1}}{\color{blue}{-1 - t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

12. Simplified 100.0%

    \[\leadsto \frac{5 + \frac{8 + \frac{-4}{t + 1}}{-1 - t}}{6 + \color{blue}{\frac{8 + \frac{-4}{t + 1}}{-1 - t}}} \]

13. Final simplification 100.0%

    \[\leadsto \frac{5 + \frac{8 + \frac{-4}{t + 1}}{-1 - t}}{\frac{8 + \frac{-4}{t + 1}}{-1 - t} + 6} \]
14. Add Preprocessing

Alternative 2: 99.2% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.48:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{6 + \frac{-8}{t}}\\ \mathbf{elif}\;t \leq 0.75:\\ \;\;\;\;\frac{1 + \left(2 - \frac{2}{t + 1}\right) \cdot \left(t \cdot 2\right)}{2 + \left(t \cdot 2\right) \cdot \left(t \cdot 2\right)}\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (<= t -0.48)
   (/ (- 5.0 (/ 8.0 t)) (+ 6.0 (/ -8.0 t)))
   (if (<= t 0.75)
     (/
      (+ 1.0 (* (- 2.0 (/ 2.0 (+ t 1.0))) (* t 2.0)))
      (+ 2.0 (* (* t 2.0) (* t 2.0))))
     (- 0.8333333333333334 (/ 0.2222222222222222 t)))))

C

double code(double t) {
	double tmp;
	if (t <= -0.48) {
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	} else if (t <= 0.75) {
		tmp = (1.0 + ((2.0 - (2.0 / (t + 1.0))) * (t * 2.0))) / (2.0 + ((t * 2.0) * (t * 2.0)));
	} else {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if (t <= (-0.48d0)) then
        tmp = (5.0d0 - (8.0d0 / t)) / (6.0d0 + ((-8.0d0) / t))
    else if (t <= 0.75d0) then
        tmp = (1.0d0 + ((2.0d0 - (2.0d0 / (t + 1.0d0))) * (t * 2.0d0))) / (2.0d0 + ((t * 2.0d0) * (t * 2.0d0)))
    else
        tmp = 0.8333333333333334d0 - (0.2222222222222222d0 / t)
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if (t <= -0.48) {
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	} else if (t <= 0.75) {
		tmp = (1.0 + ((2.0 - (2.0 / (t + 1.0))) * (t * 2.0))) / (2.0 + ((t * 2.0) * (t * 2.0)));
	} else {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if t <= -0.48:
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t))
	elif t <= 0.75:
		tmp = (1.0 + ((2.0 - (2.0 / (t + 1.0))) * (t * 2.0))) / (2.0 + ((t * 2.0) * (t * 2.0)))
	else:
		tmp = 0.8333333333333334 - (0.2222222222222222 / t)
	return tmp

Julia

function code(t)
	tmp = 0.0
	if (t <= -0.48)
		tmp = Float64(Float64(5.0 - Float64(8.0 / t)) / Float64(6.0 + Float64(-8.0 / t)));
	elseif (t <= 0.75)
		tmp = Float64(Float64(1.0 + Float64(Float64(2.0 - Float64(2.0 / Float64(t + 1.0))) * Float64(t * 2.0))) / Float64(2.0 + Float64(Float64(t * 2.0) * Float64(t * 2.0))));
	else
		tmp = Float64(0.8333333333333334 - Float64(0.2222222222222222 / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if (t <= -0.48)
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	elseif (t <= 0.75)
		tmp = (1.0 + ((2.0 - (2.0 / (t + 1.0))) * (t * 2.0))) / (2.0 + ((t * 2.0) * (t * 2.0)));
	else
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[LessEqual[t, -0.48], N[(N[(5.0 - N[(8.0 / t), $MachinePrecision]), $MachinePrecision] / N[(6.0 + N[(-8.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.75], N[(N[(1.0 + N[(N[(2.0 - N[(2.0 / N[(t + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(t * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(2.0 + N[(N[(t * 2.0), $MachinePrecision] * N[(t * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(0.8333333333333334 - N[(0.2222222222222222 / t), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -0.47999999999999998

   1. Initial program 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\frac{5 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)}} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 100.0%

      \[\leadsto \frac{\color{blue}{5 - 8 \cdot \frac{1}{t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]
   6. Step-by-step derivation

      1. associate-*r/ 100.0%

         \[\leadsto \frac{5 - \color{blue}{\frac{8 \cdot 1}{t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

      2. metadata-eval 100.0%

         \[\leadsto \frac{5 - \frac{\color{blue}{8}}{t}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   7. Simplified 100.0%

      \[\leadsto \frac{\color{blue}{5 - \frac{8}{t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   8. Taylor expanded in t around inf 100.0%

      \[\leadsto \frac{5 - \frac{8}{t}}{6 + \color{blue}{\frac{-8}{t}}} \]

   if -0.47999999999999998 < t < 0.75

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 99.7%

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

   4. Taylor expanded in t around 0 99.7%

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

   5. Taylor expanded in t around 0 99.9%

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

      1. expm1-log1p-u 99.9%

         \[\leadsto \frac{1 + \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 \cdot t\right)\right)\right)}}{2 + \left(2 \cdot t\right) \cdot \left(2 \cdot t\right)} \]

      2. expm1-udef 99.9%

         \[\leadsto \frac{1 + \color{blue}{\left(e^{\mathsf{log1p}\left(\left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 \cdot t\right)\right)} - 1\right)}}{2 + \left(2 \cdot t\right) \cdot \left(2 \cdot t\right)} \]

      3. div-inv 99.9%

         \[\leadsto \frac{1 + \left(e^{\mathsf{log1p}\left(\left(2 - \color{blue}{\frac{2}{t} \cdot \frac{1}{1 + \frac{1}{t}}}\right) \cdot \left(2 \cdot t\right)\right)} - 1\right)}{2 + \left(2 \cdot t\right) \cdot \left(2 \cdot t\right)} \]

      4. frac-times 99.9%

         \[\leadsto \frac{1 + \left(e^{\mathsf{log1p}\left(\left(2 - \color{blue}{\frac{2 \cdot 1}{t \cdot \left(1 + \frac{1}{t}\right)}}\right) \cdot \left(2 \cdot t\right)\right)} - 1\right)}{2 + \left(2 \cdot t\right) \cdot \left(2 \cdot t\right)} \]

      5. metadata-eval 99.9%

         \[\leadsto \frac{1 + \left(e^{\mathsf{log1p}\left(\left(2 - \frac{\color{blue}{2}}{t \cdot \left(1 + \frac{1}{t}\right)}\right) \cdot \left(2 \cdot t\right)\right)} - 1\right)}{2 + \left(2 \cdot t\right) \cdot \left(2 \cdot t\right)} \]

      6. *-commutative 99.9%

         \[\leadsto \frac{1 + \left(e^{\mathsf{log1p}\left(\left(2 - \frac{2}{t \cdot \left(1 + \frac{1}{t}\right)}\right) \cdot \color{blue}{\left(t \cdot 2\right)}\right)} - 1\right)}{2 + \left(2 \cdot t\right) \cdot \left(2 \cdot t\right)} \]

   7. Applied egg-rr 99.9%

      \[\leadsto \frac{1 + \color{blue}{\left(e^{\mathsf{log1p}\left(\left(2 - \frac{2}{t \cdot \left(1 + \frac{1}{t}\right)}\right) \cdot \left(t \cdot 2\right)\right)} - 1\right)}}{2 + \left(2 \cdot t\right) \cdot \left(2 \cdot t\right)} \]
   8. Step-by-step derivation

      1. expm1-def 99.9%

         \[\leadsto \frac{1 + \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\left(2 - \frac{2}{t \cdot \left(1 + \frac{1}{t}\right)}\right) \cdot \left(t \cdot 2\right)\right)\right)}}{2 + \left(2 \cdot t\right) \cdot \left(2 \cdot t\right)} \]

      2. expm1-log1p 99.9%

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

      3. distribute-lft-in 99.9%

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

      4. rgt-mult-inverse 99.9%

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

      5. *-rgt-identity 99.9%

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

   9. Simplified 99.9%

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

   if 0.75 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around inf 100.0%

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

      1. associate-*r/ 100.0%

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

      2. metadata-eval 100.0%

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

   5. Simplified 100.0%

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

4. Final simplification 100.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.48:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{6 + \frac{-8}{t}}\\ \mathbf{elif}\;t \leq 0.75:\\ \;\;\;\;\frac{1 + \left(2 - \frac{2}{t + 1}\right) \cdot \left(t \cdot 2\right)}{2 + \left(t \cdot 2\right) \cdot \left(t \cdot 2\right)}\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.1% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(t \cdot 2\right) \cdot \left(t \cdot 2\right)\\ \mathbf{if}\;t \leq -0.47:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{6 + \frac{-8}{t}}\\ \mathbf{elif}\;t \leq 0.7:\\ \;\;\;\;\frac{1 + t_1}{2 + t_1}\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (* (* t 2.0) (* t 2.0))))
   (if (<= t -0.47)
     (/ (- 5.0 (/ 8.0 t)) (+ 6.0 (/ -8.0 t)))
     (if (<= t 0.7)
       (/ (+ 1.0 t_1) (+ 2.0 t_1))
       (- 0.8333333333333334 (/ 0.2222222222222222 t))))))

C

double code(double t) {
	double t_1 = (t * 2.0) * (t * 2.0);
	double tmp;
	if (t <= -0.47) {
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	} else if (t <= 0.7) {
		tmp = (1.0 + t_1) / (2.0 + t_1);
	} else {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (t * 2.0d0) * (t * 2.0d0)
    if (t <= (-0.47d0)) then
        tmp = (5.0d0 - (8.0d0 / t)) / (6.0d0 + ((-8.0d0) / t))
    else if (t <= 0.7d0) then
        tmp = (1.0d0 + t_1) / (2.0d0 + t_1)
    else
        tmp = 0.8333333333333334d0 - (0.2222222222222222d0 / t)
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double t_1 = (t * 2.0) * (t * 2.0);
	double tmp;
	if (t <= -0.47) {
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	} else if (t <= 0.7) {
		tmp = (1.0 + t_1) / (2.0 + t_1);
	} else {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	}
	return tmp;
}

Python

def code(t):
	t_1 = (t * 2.0) * (t * 2.0)
	tmp = 0
	if t <= -0.47:
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t))
	elif t <= 0.7:
		tmp = (1.0 + t_1) / (2.0 + t_1)
	else:
		tmp = 0.8333333333333334 - (0.2222222222222222 / t)
	return tmp

Julia

function code(t)
	t_1 = Float64(Float64(t * 2.0) * Float64(t * 2.0))
	tmp = 0.0
	if (t <= -0.47)
		tmp = Float64(Float64(5.0 - Float64(8.0 / t)) / Float64(6.0 + Float64(-8.0 / t)));
	elseif (t <= 0.7)
		tmp = Float64(Float64(1.0 + t_1) / Float64(2.0 + t_1));
	else
		tmp = Float64(0.8333333333333334 - Float64(0.2222222222222222 / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	t_1 = (t * 2.0) * (t * 2.0);
	tmp = 0.0;
	if (t <= -0.47)
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	elseif (t <= 0.7)
		tmp = (1.0 + t_1) / (2.0 + t_1);
	else
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := Block[{t$95$1 = N[(N[(t * 2.0), $MachinePrecision] * N[(t * 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -0.47], N[(N[(5.0 - N[(8.0 / t), $MachinePrecision]), $MachinePrecision] / N[(6.0 + N[(-8.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.7], N[(N[(1.0 + t$95$1), $MachinePrecision] / N[(2.0 + t$95$1), $MachinePrecision]), $MachinePrecision], N[(0.8333333333333334 - N[(0.2222222222222222 / t), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if t < -0.46999999999999997

   1. Initial program 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\frac{5 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)}} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 100.0%

      \[\leadsto \frac{\color{blue}{5 - 8 \cdot \frac{1}{t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]
   6. Step-by-step derivation

      1. associate-*r/ 100.0%

         \[\leadsto \frac{5 - \color{blue}{\frac{8 \cdot 1}{t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

      2. metadata-eval 100.0%

         \[\leadsto \frac{5 - \frac{\color{blue}{8}}{t}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   7. Simplified 100.0%

      \[\leadsto \frac{\color{blue}{5 - \frac{8}{t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   8. Taylor expanded in t around inf 100.0%

      \[\leadsto \frac{5 - \frac{8}{t}}{6 + \color{blue}{\frac{-8}{t}}} \]

   if -0.46999999999999997 < t < 0.69999999999999996

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 99.7%

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

   4. Taylor expanded in t around 0 99.7%

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

   5. Taylor expanded in t around 0 99.9%

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

   6. Taylor expanded in t around 0 99.7%

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

   if 0.69999999999999996 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around inf 100.0%

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

      1. associate-*r/ 100.0%

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

      2. metadata-eval 100.0%

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

   5. Simplified 100.0%

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

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.47:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{6 + \frac{-8}{t}}\\ \mathbf{elif}\;t \leq 0.7:\\ \;\;\;\;\frac{1 + \left(t \cdot 2\right) \cdot \left(t \cdot 2\right)}{2 + \left(t \cdot 2\right) \cdot \left(t \cdot 2\right)}\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 98.9% accurate, 3.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.4:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{6 + \frac{-8}{t}}\\ \mathbf{elif}\;t \leq 0.65:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (<= t -0.4)
   (/ (- 5.0 (/ 8.0 t)) (+ 6.0 (/ -8.0 t)))
   (if (<= t 0.65) 0.5 (- 0.8333333333333334 (/ 0.2222222222222222 t)))))

C

double code(double t) {
	double tmp;
	if (t <= -0.4) {
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	} else if (t <= 0.65) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if (t <= (-0.4d0)) then
        tmp = (5.0d0 - (8.0d0 / t)) / (6.0d0 + ((-8.0d0) / t))
    else if (t <= 0.65d0) then
        tmp = 0.5d0
    else
        tmp = 0.8333333333333334d0 - (0.2222222222222222d0 / t)
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if (t <= -0.4) {
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	} else if (t <= 0.65) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if t <= -0.4:
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t))
	elif t <= 0.65:
		tmp = 0.5
	else:
		tmp = 0.8333333333333334 - (0.2222222222222222 / t)
	return tmp

Julia

function code(t)
	tmp = 0.0
	if (t <= -0.4)
		tmp = Float64(Float64(5.0 - Float64(8.0 / t)) / Float64(6.0 + Float64(-8.0 / t)));
	elseif (t <= 0.65)
		tmp = 0.5;
	else
		tmp = Float64(0.8333333333333334 - Float64(0.2222222222222222 / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if (t <= -0.4)
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	elseif (t <= 0.65)
		tmp = 0.5;
	else
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[LessEqual[t, -0.4], N[(N[(5.0 - N[(8.0 / t), $MachinePrecision]), $MachinePrecision] / N[(6.0 + N[(-8.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.65], 0.5, N[(0.8333333333333334 - N[(0.2222222222222222 / t), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -0.40000000000000002

   1. Initial program 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\frac{5 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)}} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 100.0%

      \[\leadsto \frac{\color{blue}{5 - 8 \cdot \frac{1}{t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]
   6. Step-by-step derivation

      1. associate-*r/ 100.0%

         \[\leadsto \frac{5 - \color{blue}{\frac{8 \cdot 1}{t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

      2. metadata-eval 100.0%

         \[\leadsto \frac{5 - \frac{\color{blue}{8}}{t}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   7. Simplified 100.0%

      \[\leadsto \frac{\color{blue}{5 - \frac{8}{t}}}{6 + \frac{2}{1 + t} \cdot \left(\frac{2}{1 + t} - 4\right)} \]

   8. Taylor expanded in t around inf 100.0%

      \[\leadsto \frac{5 - \frac{8}{t}}{6 + \color{blue}{\frac{-8}{t}}} \]

   if -0.40000000000000002 < t < 0.650000000000000022

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 99.2%

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

   if 0.650000000000000022 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around inf 100.0%

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

      1. associate-*r/ 100.0%

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

      2. metadata-eval 100.0%

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

   5. Simplified 100.0%

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

4. Final simplification 99.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.4:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{6 + \frac{-8}{t}}\\ \mathbf{elif}\;t \leq 0.65:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 98.9% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.49 \lor \neg \left(t \leq 0.65\right):\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (or (<= t -0.49) (not (<= t 0.65)))
   (- 0.8333333333333334 (/ 0.2222222222222222 t))
   0.5))

C

double code(double t) {
	double tmp;
	if ((t <= -0.49) || !(t <= 0.65)) {
		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 <= 0.65d0))) 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 <= 0.65)) {
		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 <= 0.65):
		tmp = 0.8333333333333334 - (0.2222222222222222 / t)
	else:
		tmp = 0.5
	return tmp

Julia

function code(t)
	tmp = 0.0
	if ((t <= -0.49) || !(t <= 0.65))
		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 <= 0.65)))
		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, 0.65]], $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 0.650000000000000022 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around inf 100.0%

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

      1. associate-*r/ 100.0%

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

      2. metadata-eval 100.0%

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

   5. Simplified 100.0%

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

   if -0.48999999999999999 < t < 0.650000000000000022

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 99.2%

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

4. Final simplification 99.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.49 \lor \neg \left(t \leq 0.65\right):\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 98.4% accurate, 4.6× speedup

Math

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

FPCore

(FPCore (t)
 :precision binary64
 (if (<= t -0.33) 0.8333333333333334 (if (<= t 1.0) 0.5 0.8333333333333334)))

C

double code(double t) {
	double tmp;
	if (t <= -0.33) {
		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 <= (-0.33d0)) 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 <= -0.33) {
		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 <= -0.33:
		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 <= -0.33)
		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 <= -0.33)
		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, -0.33], 0.8333333333333334, If[LessEqual[t, 1.0], 0.5, 0.8333333333333334]]

Derivation

1. Split input into 2 regimes

2. if t < -0.330000000000000016 or 1 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around inf 99.7%

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

   if -0.330000000000000016 < t < 1

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 99.2%

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

4. Final simplification 99.5%

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

Alternative 7: 59.8% accurate, 51.0× speedup

Math

\[\begin{array}{l} \\ 0.5 \end{array} \]

FPCore

(FPCore (t) :precision binary64 0.5)

C

double code(double t) {
	return 0.5;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    code = 0.5d0
end function

Java

public static double code(double t) {
	return 0.5;
}

Python

def code(t):
	return 0.5

Julia

function code(t)
	return 0.5
end

MATLAB

function tmp = code(t)
	tmp = 0.5;
end

Wolfram

code[t_] := 0.5

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in t around 0 52.6%

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

4. Final simplification 52.6%

   \[\leadsto 0.5 \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024019 
(FPCore (t)
  :name "Kahan p13 Example 2"
  :precision binary64
  (/ (+ 1.0 (* (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t)))) (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t)))))) (+ 2.0 (* (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t)))) (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t))))))))