Kahan p13 Example 2

Percentage Accurate: 99.9% → 100.0%

Time: 26.7s

Alternatives: 8

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, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-2}{1 + t}\\ t_2 := t_1 \cdot \left(t_1 - -4\right)\\ \frac{5 + t_2}{t_2 + 6} \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ -2.0 (+ 1.0 t))) (t_2 (* t_1 (- t_1 -4.0))))
   (/ (+ 5.0 t_2) (+ t_2 6.0))))

C

double code(double t) {
	double t_1 = -2.0 / (1.0 + t);
	double t_2 = t_1 * (t_1 - -4.0);
	return (5.0 + t_2) / (t_2 + 6.0);
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    t_1 = (-2.0d0) / (1.0d0 + t)
    t_2 = t_1 * (t_1 - (-4.0d0))
    code = (5.0d0 + t_2) / (t_2 + 6.0d0)
end function

Java

public static double code(double t) {
	double t_1 = -2.0 / (1.0 + t);
	double t_2 = t_1 * (t_1 - -4.0);
	return (5.0 + t_2) / (t_2 + 6.0);
}

Python

def code(t):
	t_1 = -2.0 / (1.0 + t)
	t_2 = t_1 * (t_1 - -4.0)
	return (5.0 + t_2) / (t_2 + 6.0)

Julia

function code(t)
	t_1 = Float64(-2.0 / Float64(1.0 + t))
	t_2 = Float64(t_1 * Float64(t_1 - -4.0))
	return Float64(Float64(5.0 + t_2) / Float64(t_2 + 6.0))
end

MATLAB

function tmp = code(t)
	t_1 = -2.0 / (1.0 + t);
	t_2 = t_1 * (t_1 - -4.0);
	tmp = (5.0 + t_2) / (t_2 + 6.0);
end

Wolfram

code[t_] := Block[{t$95$1 = N[(-2.0 / N[(1.0 + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * N[(t$95$1 - -4.0), $MachinePrecision]), $MachinePrecision]}, N[(N[(5.0 + t$95$2), $MachinePrecision] / N[(t$95$2 + 6.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Initial program 99.6%

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

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. Final simplification 100.0%

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

Alternative 2: 100.0% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-2}{1 + t}\\ \frac{5 + t_1 \cdot \left(t_1 - -4\right)}{6 + \frac{\frac{4}{1 + t} + -8}{1 + t}} \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ -2.0 (+ 1.0 t))))
   (/
    (+ 5.0 (* t_1 (- t_1 -4.0)))
    (+ 6.0 (/ (+ (/ 4.0 (+ 1.0 t)) -8.0) (+ 1.0 t))))))

C

double code(double t) {
	double t_1 = -2.0 / (1.0 + t);
	return (5.0 + (t_1 * (t_1 - -4.0))) / (6.0 + (((4.0 / (1.0 + t)) + -8.0) / (1.0 + t)));
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    t_1 = (-2.0d0) / (1.0d0 + t)
    code = (5.0d0 + (t_1 * (t_1 - (-4.0d0)))) / (6.0d0 + (((4.0d0 / (1.0d0 + t)) + (-8.0d0)) / (1.0d0 + t)))
end function

Java

public static double code(double t) {
	double t_1 = -2.0 / (1.0 + t);
	return (5.0 + (t_1 * (t_1 - -4.0))) / (6.0 + (((4.0 / (1.0 + t)) + -8.0) / (1.0 + t)));
}

Python

def code(t):
	t_1 = -2.0 / (1.0 + t)
	return (5.0 + (t_1 * (t_1 - -4.0))) / (6.0 + (((4.0 / (1.0 + t)) + -8.0) / (1.0 + t)))

Julia

function code(t)
	t_1 = Float64(-2.0 / Float64(1.0 + t))
	return Float64(Float64(5.0 + Float64(t_1 * Float64(t_1 - -4.0))) / Float64(6.0 + Float64(Float64(Float64(4.0 / Float64(1.0 + t)) + -8.0) / Float64(1.0 + t))))
end

MATLAB

function tmp = code(t)
	t_1 = -2.0 / (1.0 + t);
	tmp = (5.0 + (t_1 * (t_1 - -4.0))) / (6.0 + (((4.0 / (1.0 + t)) + -8.0) / (1.0 + t)));
end

Wolfram

code[t_] := Block[{t$95$1 = N[(-2.0 / N[(1.0 + t), $MachinePrecision]), $MachinePrecision]}, N[(N[(5.0 + N[(t$95$1 * N[(t$95$1 - -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(6.0 + N[(N[(N[(4.0 / N[(1.0 + t), $MachinePrecision]), $MachinePrecision] + -8.0), $MachinePrecision] / N[(1.0 + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 99.6%

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

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. add-log-exp 100.0%

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

   2. *-un-lft-identity 100.0%

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

   3. log-prod 100.0%

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

   4. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(\color{blue}{0} + \log \left(e^{\frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}\right)\right)} \]

   5. associate-*l/ 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \log \left(e^{\color{blue}{\frac{-2 \cdot \left(\frac{-2}{1 + t} - -4\right)}{1 + t}}}\right)\right)} \]

   6. add-log-exp 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \color{blue}{\frac{-2 \cdot \left(\frac{-2}{1 + t} - -4\right)}{1 + t}}\right)} \]

   7. sub-neg 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \color{blue}{\left(\frac{-2}{1 + t} + \left(--4\right)\right)}}{1 + t}\right)} \]

   8. distribute-lft-in 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\color{blue}{-2 \cdot \frac{-2}{1 + t} + -2 \cdot \left(--4\right)}}{1 + t}\right)} \]

   9. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + -2 \cdot \color{blue}{4}}{1 + t}\right)} \]

   10. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + \color{blue}{-8}}{1 + t}\right)} \]

   11. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + \color{blue}{{-2}^{3}}}{1 + t}\right)} \]

   12. fma-def 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\color{blue}{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, {-2}^{3}\right)}}{1 + t}\right)} \]

   13. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, \color{blue}{-8}\right)}{1 + t}\right)} \]

6. Applied egg-rr 100.0%

   \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \color{blue}{\left(0 + \frac{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, -8\right)}{1 + t}\right)}} \]
7. Step-by-step derivation

   1. +-lft-identity 100.0%

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

   2. fma-udef 100.0%

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

   3. associate-*r/ 100.0%

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

   4. metadata-eval 100.0%

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

   5. +-commutative 100.0%

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

   6. +-commutative 100.0%

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

8. Simplified 100.0%

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

9. Final simplification 100.0%

   \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \frac{\frac{4}{1 + t} + -8}{1 + t}} \]
10. Add Preprocessing

Alternative 3: 99.0% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-8 + \frac{4}{t}}{1 + t}\\ \mathbf{if}\;t \leq -0.42:\\ \;\;\;\;\frac{5 + t_1}{6 + t_1}\\ \mathbf{elif}\;t \leq 2:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{6 + \frac{-8}{t}}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ (+ -8.0 (/ 4.0 t)) (+ 1.0 t))))
   (if (<= t -0.42)
     (/ (+ 5.0 t_1) (+ 6.0 t_1))
     (if (<= t 2.0) 0.5 (/ (- 5.0 (/ 8.0 t)) (+ 6.0 (/ -8.0 t)))))))

C

double code(double t) {
	double t_1 = (-8.0 + (4.0 / t)) / (1.0 + t);
	double tmp;
	if (t <= -0.42) {
		tmp = (5.0 + t_1) / (6.0 + t_1);
	} else if (t <= 2.0) {
		tmp = 0.5;
	} else {
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((-8.0d0) + (4.0d0 / t)) / (1.0d0 + t)
    if (t <= (-0.42d0)) then
        tmp = (5.0d0 + t_1) / (6.0d0 + t_1)
    else if (t <= 2.0d0) then
        tmp = 0.5d0
    else
        tmp = (5.0d0 - (8.0d0 / t)) / (6.0d0 + ((-8.0d0) / t))
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double t_1 = (-8.0 + (4.0 / t)) / (1.0 + t);
	double tmp;
	if (t <= -0.42) {
		tmp = (5.0 + t_1) / (6.0 + t_1);
	} else if (t <= 2.0) {
		tmp = 0.5;
	} else {
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	}
	return tmp;
}

Python

def code(t):
	t_1 = (-8.0 + (4.0 / t)) / (1.0 + t)
	tmp = 0
	if t <= -0.42:
		tmp = (5.0 + t_1) / (6.0 + t_1)
	elif t <= 2.0:
		tmp = 0.5
	else:
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t))
	return tmp

Julia

function code(t)
	t_1 = Float64(Float64(-8.0 + Float64(4.0 / t)) / Float64(1.0 + t))
	tmp = 0.0
	if (t <= -0.42)
		tmp = Float64(Float64(5.0 + t_1) / Float64(6.0 + t_1));
	elseif (t <= 2.0)
		tmp = 0.5;
	else
		tmp = Float64(Float64(5.0 - Float64(8.0 / t)) / Float64(6.0 + Float64(-8.0 / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	t_1 = (-8.0 + (4.0 / t)) / (1.0 + t);
	tmp = 0.0;
	if (t <= -0.42)
		tmp = (5.0 + t_1) / (6.0 + t_1);
	elseif (t <= 2.0)
		tmp = 0.5;
	else
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := Block[{t$95$1 = N[(N[(-8.0 + N[(4.0 / t), $MachinePrecision]), $MachinePrecision] / N[(1.0 + t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -0.42], N[(N[(5.0 + t$95$1), $MachinePrecision] / N[(6.0 + t$95$1), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.0], 0.5, N[(N[(5.0 - N[(8.0 / t), $MachinePrecision]), $MachinePrecision] / N[(6.0 + N[(-8.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if t < -0.419999999999999984

   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. add-log-exp 100.0%

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

      2. *-un-lft-identity 100.0%

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

      3. log-prod 100.0%

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

      4. metadata-eval 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(\color{blue}{0} + \log \left(e^{\frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}\right)\right)} \]

      5. associate-*l/ 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \log \left(e^{\color{blue}{\frac{-2 \cdot \left(\frac{-2}{1 + t} - -4\right)}{1 + t}}}\right)\right)} \]

      6. add-log-exp 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \color{blue}{\frac{-2 \cdot \left(\frac{-2}{1 + t} - -4\right)}{1 + t}}\right)} \]

      7. sub-neg 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \color{blue}{\left(\frac{-2}{1 + t} + \left(--4\right)\right)}}{1 + t}\right)} \]

      8. distribute-lft-in 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\color{blue}{-2 \cdot \frac{-2}{1 + t} + -2 \cdot \left(--4\right)}}{1 + t}\right)} \]

      9. metadata-eval 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + -2 \cdot \color{blue}{4}}{1 + t}\right)} \]

      10. metadata-eval 100.0%

          \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + \color{blue}{-8}}{1 + t}\right)} \]

      11. metadata-eval 100.0%

          \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + \color{blue}{{-2}^{3}}}{1 + t}\right)} \]

      12. fma-def 100.0%

          \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\color{blue}{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, {-2}^{3}\right)}}{1 + t}\right)} \]

      13. metadata-eval 100.0%

          \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, \color{blue}{-8}\right)}{1 + t}\right)} \]

   6. Applied egg-rr 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \color{blue}{\left(0 + \frac{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, -8\right)}{1 + t}\right)}} \]
   7. Step-by-step derivation

      1. +-lft-identity 100.0%

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

      2. fma-udef 100.0%

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

      3. associate-*r/ 100.0%

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

      4. metadata-eval 100.0%

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

      5. +-commutative 100.0%

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

      6. +-commutative 100.0%

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

   8. Simplified 100.0%

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

      1. add-log-exp 100.0%

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

      2. *-un-lft-identity 100.0%

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

      3. log-prod 100.0%

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

      4. metadata-eval 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(\color{blue}{0} + \log \left(e^{\frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}\right)\right)} \]

      5. associate-*l/ 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \log \left(e^{\color{blue}{\frac{-2 \cdot \left(\frac{-2}{1 + t} - -4\right)}{1 + t}}}\right)\right)} \]

      6. add-log-exp 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \color{blue}{\frac{-2 \cdot \left(\frac{-2}{1 + t} - -4\right)}{1 + t}}\right)} \]

      7. sub-neg 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \color{blue}{\left(\frac{-2}{1 + t} + \left(--4\right)\right)}}{1 + t}\right)} \]

      8. distribute-lft-in 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\color{blue}{-2 \cdot \frac{-2}{1 + t} + -2 \cdot \left(--4\right)}}{1 + t}\right)} \]

      9. metadata-eval 100.0%

         \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + -2 \cdot \color{blue}{4}}{1 + t}\right)} \]

      10. metadata-eval 100.0%

          \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + \color{blue}{-8}}{1 + t}\right)} \]

      11. metadata-eval 100.0%

          \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + \color{blue}{{-2}^{3}}}{1 + t}\right)} \]

      12. fma-def 100.0%

          \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\color{blue}{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, {-2}^{3}\right)}}{1 + t}\right)} \]

      13. metadata-eval 100.0%

          \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, \color{blue}{-8}\right)}{1 + t}\right)} \]

   10. Applied egg-rr 100.0%

       \[\leadsto \frac{5 + \color{blue}{\left(0 + \frac{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, -8\right)}{1 + t}\right)}}{6 + \frac{\frac{4}{t + 1} + -8}{t + 1}} \]
   11. Step-by-step derivation

       1. +-lft-identity 100.0%

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

       2. fma-udef 100.0%

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

       3. associate-*r/ 100.0%

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

       4. metadata-eval 100.0%

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

       5. +-commutative 100.0%

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

       6. +-commutative 100.0%

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

   12. Simplified 100.0%

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

   13. Taylor expanded in t around inf 97.0%

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

   14. Taylor expanded in t around inf 97.5%

       \[\leadsto \frac{5 + \frac{\color{blue}{4 \cdot \frac{1}{t} - 8}}{t + 1}}{6 + \frac{\frac{4}{t} + -8}{t + 1}} \]
   15. Step-by-step derivation

       1. sub-neg 97.5%

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

       2. associate-*r/ 97.5%

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

       3. metadata-eval 97.5%

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

       4. metadata-eval 97.5%

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

   16. Simplified 97.5%

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

   if -0.419999999999999984 < t < 2

   1. Initial program 99.2%

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

   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 0 98.8%

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

   6. Taylor expanded in t around 0 98.9%

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

   if 2 < t

   1. Initial program 100.0%

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

   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%

      \[\leadsto \frac{5 - \frac{8}{t}}{6 + \color{blue}{\frac{-8}{t}}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.42:\\ \;\;\;\;\frac{5 + \frac{-8 + \frac{4}{t}}{1 + t}}{6 + \frac{-8 + \frac{4}{t}}{1 + t}}\\ \mathbf{elif}\;t \leq 2:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{6 + \frac{-8}{t}}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 100.0% accurate, 1.9× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.6%

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

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. add-log-exp 100.0%

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

   2. *-un-lft-identity 100.0%

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

   3. log-prod 100.0%

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

   4. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(\color{blue}{0} + \log \left(e^{\frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}\right)\right)} \]

   5. associate-*l/ 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \log \left(e^{\color{blue}{\frac{-2 \cdot \left(\frac{-2}{1 + t} - -4\right)}{1 + t}}}\right)\right)} \]

   6. add-log-exp 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \color{blue}{\frac{-2 \cdot \left(\frac{-2}{1 + t} - -4\right)}{1 + t}}\right)} \]

   7. sub-neg 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \color{blue}{\left(\frac{-2}{1 + t} + \left(--4\right)\right)}}{1 + t}\right)} \]

   8. distribute-lft-in 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\color{blue}{-2 \cdot \frac{-2}{1 + t} + -2 \cdot \left(--4\right)}}{1 + t}\right)} \]

   9. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + -2 \cdot \color{blue}{4}}{1 + t}\right)} \]

   10. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + \color{blue}{-8}}{1 + t}\right)} \]

   11. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + \color{blue}{{-2}^{3}}}{1 + t}\right)} \]

   12. fma-def 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\color{blue}{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, {-2}^{3}\right)}}{1 + t}\right)} \]

   13. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, \color{blue}{-8}\right)}{1 + t}\right)} \]

6. Applied egg-rr 100.0%

   \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \color{blue}{\left(0 + \frac{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, -8\right)}{1 + t}\right)}} \]
7. Step-by-step derivation

   1. +-lft-identity 100.0%

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

   2. fma-udef 100.0%

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

   3. associate-*r/ 100.0%

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

   4. metadata-eval 100.0%

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

   5. +-commutative 100.0%

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

   6. +-commutative 100.0%

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

8. Simplified 100.0%

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

   1. add-log-exp 100.0%

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

   2. *-un-lft-identity 100.0%

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

   3. log-prod 100.0%

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

   4. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(\color{blue}{0} + \log \left(e^{\frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}\right)\right)} \]

   5. associate-*l/ 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \log \left(e^{\color{blue}{\frac{-2 \cdot \left(\frac{-2}{1 + t} - -4\right)}{1 + t}}}\right)\right)} \]

   6. add-log-exp 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \color{blue}{\frac{-2 \cdot \left(\frac{-2}{1 + t} - -4\right)}{1 + t}}\right)} \]

   7. sub-neg 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \color{blue}{\left(\frac{-2}{1 + t} + \left(--4\right)\right)}}{1 + t}\right)} \]

   8. distribute-lft-in 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\color{blue}{-2 \cdot \frac{-2}{1 + t} + -2 \cdot \left(--4\right)}}{1 + t}\right)} \]

   9. metadata-eval 100.0%

      \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + -2 \cdot \color{blue}{4}}{1 + t}\right)} \]

   10. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + \color{blue}{-8}}{1 + t}\right)} \]

   11. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{-2 \cdot \frac{-2}{1 + t} + \color{blue}{{-2}^{3}}}{1 + t}\right)} \]

   12. fma-def 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\color{blue}{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, {-2}^{3}\right)}}{1 + t}\right)} \]

   13. metadata-eval 100.0%

       \[\leadsto \frac{5 + \frac{-2}{1 + t} \cdot \left(\frac{-2}{1 + t} - -4\right)}{6 + \left(0 + \frac{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, \color{blue}{-8}\right)}{1 + t}\right)} \]

10. Applied egg-rr 100.0%

    \[\leadsto \frac{5 + \color{blue}{\left(0 + \frac{\mathsf{fma}\left(-2, \frac{-2}{1 + t}, -8\right)}{1 + t}\right)}}{6 + \frac{\frac{4}{t + 1} + -8}{t + 1}} \]
11. Step-by-step derivation

    1. +-lft-identity 100.0%

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

    2. fma-udef 100.0%

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

    3. associate-*r/ 100.0%

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

    4. metadata-eval 100.0%

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

    5. +-commutative 100.0%

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

    6. +-commutative 100.0%

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

12. Simplified 100.0%

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

13. Final simplification 100.0%

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

Alternative 5: 99.0% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.49:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \mathbf{elif}\;t \leq 2:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{6 + \frac{-8}{t}}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (<= t -0.49)
   (- 0.8333333333333334 (/ 0.2222222222222222 t))
   (if (<= t 2.0) 0.5 (/ (- 5.0 (/ 8.0 t)) (+ 6.0 (/ -8.0 t))))))

C

double code(double t) {
	double tmp;
	if (t <= -0.49) {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	} else if (t <= 2.0) {
		tmp = 0.5;
	} else {
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if (t <= (-0.49d0)) then
        tmp = 0.8333333333333334d0 - (0.2222222222222222d0 / t)
    else if (t <= 2.0d0) then
        tmp = 0.5d0
    else
        tmp = (5.0d0 - (8.0d0 / t)) / (6.0d0 + ((-8.0d0) / t))
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if (t <= -0.49) {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	} else if (t <= 2.0) {
		tmp = 0.5;
	} else {
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if t <= -0.49:
		tmp = 0.8333333333333334 - (0.2222222222222222 / t)
	elif t <= 2.0:
		tmp = 0.5
	else:
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t))
	return tmp

Julia

function code(t)
	tmp = 0.0
	if (t <= -0.49)
		tmp = Float64(0.8333333333333334 - Float64(0.2222222222222222 / t));
	elseif (t <= 2.0)
		tmp = 0.5;
	else
		tmp = Float64(Float64(5.0 - Float64(8.0 / t)) / Float64(6.0 + Float64(-8.0 / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if (t <= -0.49)
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	elseif (t <= 2.0)
		tmp = 0.5;
	else
		tmp = (5.0 - (8.0 / t)) / (6.0 + (-8.0 / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[LessEqual[t, -0.49], N[(0.8333333333333334 - N[(0.2222222222222222 / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.0], 0.5, N[(N[(5.0 - N[(8.0 / t), $MachinePrecision]), $MachinePrecision] / N[(6.0 + N[(-8.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -0.48999999999999999

   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 96.8%

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

      1. associate-*r/ 96.8%

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

      2. metadata-eval 96.8%

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

   7. Simplified 96.8%

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

   8. Taylor expanded in t around inf 97.1%

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

      1. associate-*r/ 97.1%

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

      2. metadata-eval 97.1%

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

   10. Simplified 97.1%

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

   if -0.48999999999999999 < t < 2

   1. Initial program 99.2%

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

   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 0 98.8%

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

   6. Taylor expanded in t around 0 98.9%

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

   if 2 < t

   1. Initial program 100.0%

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

   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%

      \[\leadsto \frac{5 - \frac{8}{t}}{6 + \color{blue}{\frac{-8}{t}}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.49:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \mathbf{elif}\;t \leq 2:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{5 - \frac{8}{t}}{6 + \frac{-8}{t}}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 99.0% accurate, 3.4× 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%

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

   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 98.5%

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

      1. associate-*r/ 98.5%

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

      2. metadata-eval 98.5%

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

   7. Simplified 98.5%

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

   8. Taylor expanded in t around inf 98.7%

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

      1. associate-*r/ 98.7%

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

      2. metadata-eval 98.7%

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

   10. Simplified 98.7%

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

   if -0.48999999999999999 < t < 0.680000000000000049

   1. Initial program 99.2%

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

   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 0 98.8%

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

   6. Taylor expanded in t around 0 98.9%

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

4. Final simplification 98.8%

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

Alternative 7: 98.5% accurate, 4.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.34:\\ \;\;\;\;0.8333333333333334\\ \mathbf{elif}\;t \leq 1:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (<= t -0.34) 0.8333333333333334 (if (<= t 1.0) 0.5 0.8333333333333334)))

C

double code(double t) {
	double tmp;
	if (t <= -0.34) {
		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 <= (-0.34d0)) 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 <= -0.34) {
		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 <= -0.34:
		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 <= -0.34)
		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 <= -0.34)
		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, -0.34], 0.8333333333333334, If[LessEqual[t, 1.0], 0.5, 0.8333333333333334]]

Derivation

1. Split input into 2 regimes

2. if t < -0.340000000000000024 or 1 < t

   1. Initial program 100.0%

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

   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 98.5%

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

      1. associate-*r/ 98.5%

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

      2. metadata-eval 98.5%

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

   7. Simplified 98.5%

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

   8. Taylor expanded in t around inf 98.0%

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

   if -0.340000000000000024 < t < 1

   1. Initial program 99.2%

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

   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 0 98.8%

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

   6. Taylor expanded in t around 0 98.9%

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

4. Final simplification 98.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.34:\\ \;\;\;\;0.8333333333333334\\ \mathbf{elif}\;t \leq 1:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 58.3% 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 99.6%

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

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 0 55.9%

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

6. Taylor expanded in t around 0 57.4%

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

7. Final simplification 57.4%

   \[\leadsto 0.5 \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2024024 
(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))))))))