Kahan p13 Example 3

Percentage Accurate: 99.9% → 100.0%

Time: 11.8s

Alternatives: 12

Speedup: 1.4×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. sub-neg 100.0%

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

   2. distribute-neg-frac 100.0%

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

   3. distribute-neg-frac 100.0%

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

   4. metadata-eval 100.0%

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

4. Applied egg-rr 100.0%

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

6. Simplified 100.0%

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

   1. sub-neg 100.0%

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

   2. distribute-neg-frac 100.0%

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

   3. distribute-neg-frac 100.0%

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

   4. metadata-eval 100.0%

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

8. Applied egg-rr 100.0%

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

10. Simplified 100.0%

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

11. Final simplification 100.0%

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

Alternative 2: 99.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.15 \lor \neg \left(t \leq 0.8\right):\\ \;\;\;\;1 + \left(\frac{\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 0.2222222222222222}{t} - 0.16666666666666666\right)\\ \mathbf{else}:\\ \;\;\;\;1 + \frac{-1}{2 - t \cdot \left(\frac{-4}{-1 - t} - 4\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (or (<= t -1.15) (not (<= t 0.8)))
   (+
    1.0
    (-
     (/
      (-
       (/ (+ 0.037037037037037035 (/ 0.04938271604938271 t)) t)
       0.2222222222222222)
      t)
     0.16666666666666666))
   (+ 1.0 (/ -1.0 (- 2.0 (* t (- (/ -4.0 (- -1.0 t)) 4.0)))))))

C

double code(double t) {
	double tmp;
	if ((t <= -1.15) || !(t <= 0.8)) {
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666);
	} else {
		tmp = 1.0 + (-1.0 / (2.0 - (t * ((-4.0 / (-1.0 - t)) - 4.0))));
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-1.15d0)) .or. (.not. (t <= 0.8d0))) then
        tmp = 1.0d0 + (((((0.037037037037037035d0 + (0.04938271604938271d0 / t)) / t) - 0.2222222222222222d0) / t) - 0.16666666666666666d0)
    else
        tmp = 1.0d0 + ((-1.0d0) / (2.0d0 - (t * (((-4.0d0) / ((-1.0d0) - t)) - 4.0d0))))
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if ((t <= -1.15) || !(t <= 0.8)) {
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666);
	} else {
		tmp = 1.0 + (-1.0 / (2.0 - (t * ((-4.0 / (-1.0 - t)) - 4.0))));
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if (t <= -1.15) or not (t <= 0.8):
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666)
	else:
		tmp = 1.0 + (-1.0 / (2.0 - (t * ((-4.0 / (-1.0 - t)) - 4.0))))
	return tmp

Julia

function code(t)
	tmp = 0.0
	if ((t <= -1.15) || !(t <= 0.8))
		tmp = Float64(1.0 + Float64(Float64(Float64(Float64(Float64(0.037037037037037035 + Float64(0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666));
	else
		tmp = Float64(1.0 + Float64(-1.0 / Float64(2.0 - Float64(t * Float64(Float64(-4.0 / Float64(-1.0 - t)) - 4.0)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if ((t <= -1.15) || ~((t <= 0.8)))
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666);
	else
		tmp = 1.0 + (-1.0 / (2.0 - (t * ((-4.0 / (-1.0 - t)) - 4.0))));
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[Or[LessEqual[t, -1.15], N[Not[LessEqual[t, 0.8]], $MachinePrecision]], N[(1.0 + N[(N[(N[(N[(N[(0.037037037037037035 + N[(0.04938271604938271 / t), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision] - 0.2222222222222222), $MachinePrecision] / t), $MachinePrecision] - 0.16666666666666666), $MachinePrecision]), $MachinePrecision], N[(1.0 + N[(-1.0 / N[(2.0 - N[(t * N[(N[(-4.0 / N[(-1.0 - t), $MachinePrecision]), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -1.1499999999999999 or 0.80000000000000004 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.8%

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

      1. mul-1-neg 98.8%

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

      2. unsub-neg 98.8%

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

      3. mul-1-neg 98.8%

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

      4. unsub-neg 98.8%

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

      5. sub-neg 98.8%

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

      6. associate-*r/ 98.8%

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

      7. metadata-eval 98.8%

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

      8. distribute-neg-frac 98.8%

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

      9. metadata-eval 98.8%

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

   5. Simplified 98.8%

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

      1. associate-+r- 98.8%

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

      2. metadata-eval 98.8%

         \[\leadsto 1 - \frac{1}{\color{blue}{6} - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}} \]

   7. Applied egg-rr 98.8%

      \[\leadsto 1 - \frac{1}{\color{blue}{6 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}}} \]

   8. Taylor expanded in t around inf 99.0%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + \left(-1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}} + 0.2222222222222222 \cdot \frac{1}{t}\right)\right)} \]
   9. Step-by-step derivation

      1. +-commutative 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\left(0.2222222222222222 \cdot \frac{1}{t} + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)}\right) \]

      2. mul-1-neg 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(0.2222222222222222 \cdot \frac{1}{t} + \color{blue}{\left(-\frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)}\right)\right) \]

      3. unsub-neg 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\left(0.2222222222222222 \cdot \frac{1}{t} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)}\right) \]

      4. associate-*r/ 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\color{blue}{\frac{0.2222222222222222 \cdot 1}{t}} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)\right) \]

      5. metadata-eval 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\frac{\color{blue}{0.2222222222222222}}{t} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)\right) \]

      6. unpow2 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\frac{0.2222222222222222}{t} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{\color{blue}{t \cdot t}}\right)\right) \]

      7. associate-/r* 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\frac{0.2222222222222222}{t} - \color{blue}{\frac{\frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}}\right)\right) \]

      8. div-sub 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\frac{0.2222222222222222 - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}}\right) \]

      9. associate-*r/ 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \frac{0.2222222222222222 - \frac{0.037037037037037035 + \color{blue}{\frac{0.04938271604938271 \cdot 1}{t}}}{t}}{t}\right) \]

      10. metadata-eval 99.0%

          \[\leadsto 1 - \left(0.16666666666666666 + \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{\color{blue}{0.04938271604938271}}{t}}{t}}{t}\right) \]

   10. Simplified 99.0%

       \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}}{t}\right)} \]

   if -1.1499999999999999 < t < 0.80000000000000004

   1. Initial program 100.0%

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

      1. sub-neg 100.0%

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

      2. distribute-neg-frac 100.0%

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

      3. distribute-neg-frac 100.0%

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

      4. metadata-eval 100.0%

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

   4. Applied egg-rr 100.0%

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

   6. Simplified 100.0%

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

   7. Taylor expanded in t around 0 99.1%

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

      1. sub-neg 99.1%

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

      2. distribute-lft-in 99.1%

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

      3. distribute-neg-frac 99.1%

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

      4. metadata-eval 99.1%

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

   9. Applied egg-rr 99.1%

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

       1. distribute-lft-in 99.1%

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

       2. *-commutative 99.1%

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

       3. associate-*r* 99.1%

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

       4. distribute-lft-in 99.1%

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

       5. metadata-eval 99.1%

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

       6. associate-*r/ 99.1%

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

       7. metadata-eval 99.1%

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

   11. Simplified 99.1%

       \[\leadsto 1 - \frac{1}{2 + \color{blue}{t \cdot \left(4 + \frac{-4}{t + 1}\right)}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1.15 \lor \neg \left(t \leq 0.8\right):\\ \;\;\;\;1 + \left(\frac{\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 0.2222222222222222}{t} - 0.16666666666666666\right)\\ \mathbf{else}:\\ \;\;\;\;1 + \frac{-1}{2 - t \cdot \left(\frac{-4}{-1 - t} - 4\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.35 \lor \neg \left(t \leq 0.85\right):\\ \;\;\;\;1 + \left(\frac{\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 0.2222222222222222}{t} - 0.16666666666666666\right)\\ \mathbf{else}:\\ \;\;\;\;1 + \frac{-1}{2 + t \cdot \left(t \cdot \left(4 + t \cdot -4\right)\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (or (<= t -0.35) (not (<= t 0.85)))
   (+
    1.0
    (-
     (/
      (-
       (/ (+ 0.037037037037037035 (/ 0.04938271604938271 t)) t)
       0.2222222222222222)
      t)
     0.16666666666666666))
   (+ 1.0 (/ -1.0 (+ 2.0 (* t (* t (+ 4.0 (* t -4.0)))))))))

C

double code(double t) {
	double tmp;
	if ((t <= -0.35) || !(t <= 0.85)) {
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666);
	} else {
		tmp = 1.0 + (-1.0 / (2.0 + (t * (t * (4.0 + (t * -4.0))))));
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-0.35d0)) .or. (.not. (t <= 0.85d0))) then
        tmp = 1.0d0 + (((((0.037037037037037035d0 + (0.04938271604938271d0 / t)) / t) - 0.2222222222222222d0) / t) - 0.16666666666666666d0)
    else
        tmp = 1.0d0 + ((-1.0d0) / (2.0d0 + (t * (t * (4.0d0 + (t * (-4.0d0)))))))
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if ((t <= -0.35) || !(t <= 0.85)) {
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666);
	} else {
		tmp = 1.0 + (-1.0 / (2.0 + (t * (t * (4.0 + (t * -4.0))))));
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if (t <= -0.35) or not (t <= 0.85):
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666)
	else:
		tmp = 1.0 + (-1.0 / (2.0 + (t * (t * (4.0 + (t * -4.0))))))
	return tmp

Julia

function code(t)
	tmp = 0.0
	if ((t <= -0.35) || !(t <= 0.85))
		tmp = Float64(1.0 + Float64(Float64(Float64(Float64(Float64(0.037037037037037035 + Float64(0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666));
	else
		tmp = Float64(1.0 + Float64(-1.0 / Float64(2.0 + Float64(t * Float64(t * Float64(4.0 + Float64(t * -4.0)))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if ((t <= -0.35) || ~((t <= 0.85)))
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666);
	else
		tmp = 1.0 + (-1.0 / (2.0 + (t * (t * (4.0 + (t * -4.0))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[Or[LessEqual[t, -0.35], N[Not[LessEqual[t, 0.85]], $MachinePrecision]], N[(1.0 + N[(N[(N[(N[(N[(0.037037037037037035 + N[(0.04938271604938271 / t), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision] - 0.2222222222222222), $MachinePrecision] / t), $MachinePrecision] - 0.16666666666666666), $MachinePrecision]), $MachinePrecision], N[(1.0 + N[(-1.0 / N[(2.0 + N[(t * N[(t * N[(4.0 + N[(t * -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -0.34999999999999998 or 0.849999999999999978 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.8%

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

      1. mul-1-neg 98.8%

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

      2. unsub-neg 98.8%

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

      3. mul-1-neg 98.8%

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

      4. unsub-neg 98.8%

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

      5. sub-neg 98.8%

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

      6. associate-*r/ 98.8%

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

      7. metadata-eval 98.8%

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

      8. distribute-neg-frac 98.8%

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

      9. metadata-eval 98.8%

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

   5. Simplified 98.8%

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

      1. associate-+r- 98.8%

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

      2. metadata-eval 98.8%

         \[\leadsto 1 - \frac{1}{\color{blue}{6} - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}} \]

   7. Applied egg-rr 98.8%

      \[\leadsto 1 - \frac{1}{\color{blue}{6 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}}} \]

   8. Taylor expanded in t around inf 99.0%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + \left(-1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}} + 0.2222222222222222 \cdot \frac{1}{t}\right)\right)} \]
   9. Step-by-step derivation

      1. +-commutative 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\left(0.2222222222222222 \cdot \frac{1}{t} + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)}\right) \]

      2. mul-1-neg 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(0.2222222222222222 \cdot \frac{1}{t} + \color{blue}{\left(-\frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)}\right)\right) \]

      3. unsub-neg 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\left(0.2222222222222222 \cdot \frac{1}{t} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)}\right) \]

      4. associate-*r/ 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\color{blue}{\frac{0.2222222222222222 \cdot 1}{t}} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)\right) \]

      5. metadata-eval 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\frac{\color{blue}{0.2222222222222222}}{t} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)\right) \]

      6. unpow2 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\frac{0.2222222222222222}{t} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{\color{blue}{t \cdot t}}\right)\right) \]

      7. associate-/r* 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\frac{0.2222222222222222}{t} - \color{blue}{\frac{\frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}}\right)\right) \]

      8. div-sub 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\frac{0.2222222222222222 - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}}\right) \]

      9. associate-*r/ 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \frac{0.2222222222222222 - \frac{0.037037037037037035 + \color{blue}{\frac{0.04938271604938271 \cdot 1}{t}}}{t}}{t}\right) \]

      10. metadata-eval 99.0%

          \[\leadsto 1 - \left(0.16666666666666666 + \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{\color{blue}{0.04938271604938271}}{t}}{t}}{t}\right) \]

   10. Simplified 99.0%

       \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}}{t}\right)} \]

   if -0.34999999999999998 < t < 0.849999999999999978

   1. Initial program 100.0%

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

      1. sub-neg 100.0%

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

      2. distribute-neg-frac 100.0%

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

      3. distribute-neg-frac 100.0%

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

      4. metadata-eval 100.0%

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

   4. Applied egg-rr 100.0%

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

   6. Simplified 100.0%

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

   7. Taylor expanded in t around 0 99.1%

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

      1. sub-neg 99.1%

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

      2. distribute-lft-in 99.1%

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

      3. distribute-neg-frac 99.1%

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

      4. metadata-eval 99.1%

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

   9. Applied egg-rr 99.1%

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

       1. distribute-lft-in 99.1%

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

       2. *-commutative 99.1%

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

       3. associate-*r* 99.1%

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

       4. distribute-lft-in 99.1%

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

       5. metadata-eval 99.1%

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

       6. associate-*r/ 99.1%

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

       7. metadata-eval 99.1%

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

   11. Simplified 99.1%

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

   12. Taylor expanded in t around 0 99.1%

       \[\leadsto 1 - \frac{1}{2 + t \cdot \color{blue}{\left(t \cdot \left(4 + -4 \cdot t\right)\right)}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.35 \lor \neg \left(t \leq 0.85\right):\\ \;\;\;\;1 + \left(\frac{\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 0.2222222222222222}{t} - 0.16666666666666666\right)\\ \mathbf{else}:\\ \;\;\;\;1 + \frac{-1}{2 + t \cdot \left(t \cdot \left(4 + t \cdot -4\right)\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.34 \lor \neg \left(t \leq 0.66\right):\\ \;\;\;\;1 + \left(\frac{\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 0.2222222222222222}{t} - 0.16666666666666666\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{1}{2 + t \cdot \left(t \cdot 4\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (or (<= t -0.34) (not (<= t 0.66)))
   (+
    1.0
    (-
     (/
      (-
       (/ (+ 0.037037037037037035 (/ 0.04938271604938271 t)) t)
       0.2222222222222222)
      t)
     0.16666666666666666))
   (- 1.0 (/ 1.0 (+ 2.0 (* t (* t 4.0)))))))

C

double code(double t) {
	double tmp;
	if ((t <= -0.34) || !(t <= 0.66)) {
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666);
	} else {
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))));
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-0.34d0)) .or. (.not. (t <= 0.66d0))) then
        tmp = 1.0d0 + (((((0.037037037037037035d0 + (0.04938271604938271d0 / t)) / t) - 0.2222222222222222d0) / t) - 0.16666666666666666d0)
    else
        tmp = 1.0d0 - (1.0d0 / (2.0d0 + (t * (t * 4.0d0))))
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if ((t <= -0.34) || !(t <= 0.66)) {
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666);
	} else {
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))));
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if (t <= -0.34) or not (t <= 0.66):
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666)
	else:
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))))
	return tmp

Julia

function code(t)
	tmp = 0.0
	if ((t <= -0.34) || !(t <= 0.66))
		tmp = Float64(1.0 + Float64(Float64(Float64(Float64(Float64(0.037037037037037035 + Float64(0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666));
	else
		tmp = Float64(1.0 - Float64(1.0 / Float64(2.0 + Float64(t * Float64(t * 4.0)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if ((t <= -0.34) || ~((t <= 0.66)))
		tmp = 1.0 + (((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t) - 0.16666666666666666);
	else
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))));
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[Or[LessEqual[t, -0.34], N[Not[LessEqual[t, 0.66]], $MachinePrecision]], N[(1.0 + N[(N[(N[(N[(N[(0.037037037037037035 + N[(0.04938271604938271 / t), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision] - 0.2222222222222222), $MachinePrecision] / t), $MachinePrecision] - 0.16666666666666666), $MachinePrecision]), $MachinePrecision], N[(1.0 - N[(1.0 / N[(2.0 + N[(t * N[(t * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -0.340000000000000024 or 0.660000000000000031 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.8%

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

      1. mul-1-neg 98.8%

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

      2. unsub-neg 98.8%

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

      3. mul-1-neg 98.8%

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

      4. unsub-neg 98.8%

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

      5. sub-neg 98.8%

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

      6. associate-*r/ 98.8%

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

      7. metadata-eval 98.8%

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

      8. distribute-neg-frac 98.8%

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

      9. metadata-eval 98.8%

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

   5. Simplified 98.8%

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

      1. associate-+r- 98.8%

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

      2. metadata-eval 98.8%

         \[\leadsto 1 - \frac{1}{\color{blue}{6} - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}} \]

   7. Applied egg-rr 98.8%

      \[\leadsto 1 - \frac{1}{\color{blue}{6 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}}} \]

   8. Taylor expanded in t around inf 99.0%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + \left(-1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}} + 0.2222222222222222 \cdot \frac{1}{t}\right)\right)} \]
   9. Step-by-step derivation

      1. +-commutative 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\left(0.2222222222222222 \cdot \frac{1}{t} + -1 \cdot \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)}\right) \]

      2. mul-1-neg 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(0.2222222222222222 \cdot \frac{1}{t} + \color{blue}{\left(-\frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)}\right)\right) \]

      3. unsub-neg 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\left(0.2222222222222222 \cdot \frac{1}{t} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)}\right) \]

      4. associate-*r/ 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\color{blue}{\frac{0.2222222222222222 \cdot 1}{t}} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)\right) \]

      5. metadata-eval 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\frac{\color{blue}{0.2222222222222222}}{t} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{{t}^{2}}\right)\right) \]

      6. unpow2 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\frac{0.2222222222222222}{t} - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{\color{blue}{t \cdot t}}\right)\right) \]

      7. associate-/r* 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \left(\frac{0.2222222222222222}{t} - \color{blue}{\frac{\frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}}\right)\right) \]

      8. div-sub 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \color{blue}{\frac{0.2222222222222222 - \frac{0.037037037037037035 + 0.04938271604938271 \cdot \frac{1}{t}}{t}}{t}}\right) \]

      9. associate-*r/ 99.0%

         \[\leadsto 1 - \left(0.16666666666666666 + \frac{0.2222222222222222 - \frac{0.037037037037037035 + \color{blue}{\frac{0.04938271604938271 \cdot 1}{t}}}{t}}{t}\right) \]

      10. metadata-eval 99.0%

          \[\leadsto 1 - \left(0.16666666666666666 + \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{\color{blue}{0.04938271604938271}}{t}}{t}}{t}\right) \]

   10. Simplified 99.0%

       \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + \frac{0.2222222222222222 - \frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t}}{t}\right)} \]

   if -0.340000000000000024 < t < 0.660000000000000031

   1. Initial program 100.0%

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

      1. sub-neg 100.0%

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

      2. distribute-neg-frac 100.0%

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

      3. distribute-neg-frac 100.0%

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

      4. metadata-eval 100.0%

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

   4. Applied egg-rr 100.0%

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

   6. Simplified 100.0%

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

   7. Taylor expanded in t around 0 99.1%

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

      1. sub-neg 99.1%

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

      2. distribute-lft-in 99.1%

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

      3. distribute-neg-frac 99.1%

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

      4. metadata-eval 99.1%

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

   9. Applied egg-rr 99.1%

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

       1. distribute-lft-in 99.1%

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

       2. *-commutative 99.1%

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

       3. associate-*r* 99.1%

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

       4. distribute-lft-in 99.1%

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

       5. metadata-eval 99.1%

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

       6. associate-*r/ 99.1%

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

       7. metadata-eval 99.1%

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

   11. Simplified 99.1%

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

   12. Taylor expanded in t around 0 99.0%

       \[\leadsto 1 - \frac{1}{2 + t \cdot \color{blue}{\left(4 \cdot t\right)}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.34 \lor \neg \left(t \leq 0.66\right):\\ \;\;\;\;1 + \left(\frac{\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 0.2222222222222222}{t} - 0.16666666666666666\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{1}{2 + t \cdot \left(t \cdot 4\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 99.3% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.34 \lor \neg \left(t \leq 0.66\right):\\ \;\;\;\;0.8333333333333334 + \frac{\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{1}{2 + t \cdot \left(t \cdot 4\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (or (<= t -0.34) (not (<= t 0.66)))
   (+
    0.8333333333333334
    (/
     (-
      (/ (+ 0.037037037037037035 (/ 0.04938271604938271 t)) t)
      0.2222222222222222)
     t))
   (- 1.0 (/ 1.0 (+ 2.0 (* t (* t 4.0)))))))

C

double code(double t) {
	double tmp;
	if ((t <= -0.34) || !(t <= 0.66)) {
		tmp = 0.8333333333333334 + ((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t);
	} else {
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))));
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-0.34d0)) .or. (.not. (t <= 0.66d0))) then
        tmp = 0.8333333333333334d0 + ((((0.037037037037037035d0 + (0.04938271604938271d0 / t)) / t) - 0.2222222222222222d0) / t)
    else
        tmp = 1.0d0 - (1.0d0 / (2.0d0 + (t * (t * 4.0d0))))
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if ((t <= -0.34) || !(t <= 0.66)) {
		tmp = 0.8333333333333334 + ((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t);
	} else {
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))));
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if (t <= -0.34) or not (t <= 0.66):
		tmp = 0.8333333333333334 + ((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t)
	else:
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))))
	return tmp

Julia

function code(t)
	tmp = 0.0
	if ((t <= -0.34) || !(t <= 0.66))
		tmp = Float64(0.8333333333333334 + Float64(Float64(Float64(Float64(0.037037037037037035 + Float64(0.04938271604938271 / t)) / t) - 0.2222222222222222) / t));
	else
		tmp = Float64(1.0 - Float64(1.0 / Float64(2.0 + Float64(t * Float64(t * 4.0)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if ((t <= -0.34) || ~((t <= 0.66)))
		tmp = 0.8333333333333334 + ((((0.037037037037037035 + (0.04938271604938271 / t)) / t) - 0.2222222222222222) / t);
	else
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))));
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[Or[LessEqual[t, -0.34], N[Not[LessEqual[t, 0.66]], $MachinePrecision]], N[(0.8333333333333334 + N[(N[(N[(N[(0.037037037037037035 + N[(0.04938271604938271 / t), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision] - 0.2222222222222222), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], N[(1.0 - N[(1.0 / N[(2.0 + N[(t * N[(t * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -0.340000000000000024 or 0.660000000000000031 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.8%

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

      1. mul-1-neg 98.8%

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

      2. unsub-neg 98.8%

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

      3. mul-1-neg 98.8%

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

      4. unsub-neg 98.8%

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

      5. sub-neg 98.8%

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

      6. associate-*r/ 98.8%

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

      7. metadata-eval 98.8%

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

      8. distribute-neg-frac 98.8%

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

      9. metadata-eval 98.8%

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

   5. Simplified 98.8%

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

      1. associate-+r- 98.8%

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

      2. metadata-eval 98.8%

         \[\leadsto 1 - \frac{1}{\color{blue}{6} - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}} \]

   7. Applied egg-rr 98.8%

      \[\leadsto 1 - \frac{1}{\color{blue}{6 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}}} \]

   8. Taylor expanded in t around -inf 99.0%

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

      1. mul-1-neg 99.0%

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

      2. unsub-neg 99.0%

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

      3. mul-1-neg 99.0%

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

      4. unsub-neg 99.0%

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

      5. associate-*r/ 99.0%

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

      6. metadata-eval 99.0%

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

   10. Simplified 99.0%

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

   if -0.340000000000000024 < t < 0.660000000000000031

   1. Initial program 100.0%

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

      1. sub-neg 100.0%

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

      2. distribute-neg-frac 100.0%

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

      3. distribute-neg-frac 100.0%

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

      4. metadata-eval 100.0%

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

   4. Applied egg-rr 100.0%

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

   6. Simplified 100.0%

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

   7. Taylor expanded in t around 0 99.1%

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

      1. sub-neg 99.1%

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

      2. distribute-lft-in 99.1%

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

      3. distribute-neg-frac 99.1%

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

      4. metadata-eval 99.1%

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

   9. Applied egg-rr 99.1%

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

       1. distribute-lft-in 99.1%

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

       2. *-commutative 99.1%

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

       3. associate-*r* 99.1%

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

       4. distribute-lft-in 99.1%

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

       5. metadata-eval 99.1%

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

       6. associate-*r/ 99.1%

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

       7. metadata-eval 99.1%

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

   11. Simplified 99.1%

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

   12. Taylor expanded in t around 0 99.0%

       \[\leadsto 1 - \frac{1}{2 + t \cdot \color{blue}{\left(4 \cdot t\right)}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.34 \lor \neg \left(t \leq 0.66\right):\\ \;\;\;\;0.8333333333333334 + \frac{\frac{0.037037037037037035 + \frac{0.04938271604938271}{t}}{t} - 0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{1}{2 + t \cdot \left(t \cdot 4\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 99.2% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\frac{0.037037037037037035}{t} + -0.2222222222222222}{t}\\ \mathbf{if}\;t \leq -0.65:\\ \;\;\;\;0.8333333333333334 + t\_1\\ \mathbf{elif}\;t \leq 0.43:\\ \;\;\;\;1 - \frac{1}{2 + t \cdot \left(t \cdot 4\right)}\\ \mathbf{else}:\\ \;\;\;\;1 + \left(t\_1 - 0.16666666666666666\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ (+ (/ 0.037037037037037035 t) -0.2222222222222222) t)))
   (if (<= t -0.65)
     (+ 0.8333333333333334 t_1)
     (if (<= t 0.43)
       (- 1.0 (/ 1.0 (+ 2.0 (* t (* t 4.0)))))
       (+ 1.0 (- t_1 0.16666666666666666))))))

C

double code(double t) {
	double t_1 = ((0.037037037037037035 / t) + -0.2222222222222222) / t;
	double tmp;
	if (t <= -0.65) {
		tmp = 0.8333333333333334 + t_1;
	} else if (t <= 0.43) {
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))));
	} else {
		tmp = 1.0 + (t_1 - 0.16666666666666666);
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((0.037037037037037035d0 / t) + (-0.2222222222222222d0)) / t
    if (t <= (-0.65d0)) then
        tmp = 0.8333333333333334d0 + t_1
    else if (t <= 0.43d0) then
        tmp = 1.0d0 - (1.0d0 / (2.0d0 + (t * (t * 4.0d0))))
    else
        tmp = 1.0d0 + (t_1 - 0.16666666666666666d0)
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double t_1 = ((0.037037037037037035 / t) + -0.2222222222222222) / t;
	double tmp;
	if (t <= -0.65) {
		tmp = 0.8333333333333334 + t_1;
	} else if (t <= 0.43) {
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))));
	} else {
		tmp = 1.0 + (t_1 - 0.16666666666666666);
	}
	return tmp;
}

Python

def code(t):
	t_1 = ((0.037037037037037035 / t) + -0.2222222222222222) / t
	tmp = 0
	if t <= -0.65:
		tmp = 0.8333333333333334 + t_1
	elif t <= 0.43:
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))))
	else:
		tmp = 1.0 + (t_1 - 0.16666666666666666)
	return tmp

Julia

function code(t)
	t_1 = Float64(Float64(Float64(0.037037037037037035 / t) + -0.2222222222222222) / t)
	tmp = 0.0
	if (t <= -0.65)
		tmp = Float64(0.8333333333333334 + t_1);
	elseif (t <= 0.43)
		tmp = Float64(1.0 - Float64(1.0 / Float64(2.0 + Float64(t * Float64(t * 4.0)))));
	else
		tmp = Float64(1.0 + Float64(t_1 - 0.16666666666666666));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	t_1 = ((0.037037037037037035 / t) + -0.2222222222222222) / t;
	tmp = 0.0;
	if (t <= -0.65)
		tmp = 0.8333333333333334 + t_1;
	elseif (t <= 0.43)
		tmp = 1.0 - (1.0 / (2.0 + (t * (t * 4.0))));
	else
		tmp = 1.0 + (t_1 - 0.16666666666666666);
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := Block[{t$95$1 = N[(N[(N[(0.037037037037037035 / t), $MachinePrecision] + -0.2222222222222222), $MachinePrecision] / t), $MachinePrecision]}, If[LessEqual[t, -0.65], N[(0.8333333333333334 + t$95$1), $MachinePrecision], If[LessEqual[t, 0.43], N[(1.0 - N[(1.0 / N[(2.0 + N[(t * N[(t * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(1.0 + N[(t$95$1 - 0.16666666666666666), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if t < -0.650000000000000022

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 99.4%

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

      1. mul-1-neg 99.4%

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

      2. unsub-neg 99.4%

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

      3. mul-1-neg 99.4%

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

      4. unsub-neg 99.4%

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

      5. sub-neg 99.4%

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

      6. associate-*r/ 99.4%

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

      7. metadata-eval 99.4%

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

      8. distribute-neg-frac 99.4%

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

      9. metadata-eval 99.4%

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

   5. Simplified 99.4%

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

      1. associate-+r- 99.4%

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

      2. metadata-eval 99.4%

         \[\leadsto 1 - \frac{1}{\color{blue}{6} - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}} \]

   7. Applied egg-rr 99.4%

      \[\leadsto 1 - \frac{1}{\color{blue}{6 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}}} \]

   8. Taylor expanded in t around inf 99.3%

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

      1. associate--l+ 99.3%

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

      2. +-commutative 99.3%

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

   10. Simplified 99.3%

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

   if -0.650000000000000022 < t < 0.429999999999999993

   1. Initial program 100.0%

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

      1. sub-neg 100.0%

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

      2. distribute-neg-frac 100.0%

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

      3. distribute-neg-frac 100.0%

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

      4. metadata-eval 100.0%

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

   4. Applied egg-rr 100.0%

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

   6. Simplified 100.0%

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

   7. Taylor expanded in t around 0 99.1%

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

      1. sub-neg 99.1%

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

      2. distribute-lft-in 99.1%

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

      3. distribute-neg-frac 99.1%

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

      4. metadata-eval 99.1%

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

   9. Applied egg-rr 99.1%

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

       1. distribute-lft-in 99.1%

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

       2. *-commutative 99.1%

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

       3. associate-*r* 99.1%

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

       4. distribute-lft-in 99.1%

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

       5. metadata-eval 99.1%

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

       6. associate-*r/ 99.1%

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

       7. metadata-eval 99.1%

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

   11. Simplified 99.1%

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

   12. Taylor expanded in t around 0 99.0%

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

   if 0.429999999999999993 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.4%

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

      1. mul-1-neg 98.4%

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

      2. unsub-neg 98.4%

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

      3. sub-neg 98.4%

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

      4. associate-*r/ 98.4%

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

      5. metadata-eval 98.4%

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

      6. metadata-eval 98.4%

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

   5. Simplified 98.4%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 - \frac{\frac{0.037037037037037035}{t} + -0.2222222222222222}{t}\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.65:\\ \;\;\;\;0.8333333333333334 + \frac{\frac{0.037037037037037035}{t} + -0.2222222222222222}{t}\\ \mathbf{elif}\;t \leq 0.43:\\ \;\;\;\;1 - \frac{1}{2 + t \cdot \left(t \cdot 4\right)}\\ \mathbf{else}:\\ \;\;\;\;1 + \left(\frac{\frac{0.037037037037037035}{t} + -0.2222222222222222}{t} - 0.16666666666666666\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 99.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\frac{0.037037037037037035}{t} + -0.2222222222222222}{t}\\ \mathbf{if}\;t \leq -0.5:\\ \;\;\;\;0.8333333333333334 + t\_1\\ \mathbf{elif}\;t \leq 0.23:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;1 + \left(t\_1 - 0.16666666666666666\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ (+ (/ 0.037037037037037035 t) -0.2222222222222222) t)))
   (if (<= t -0.5)
     (+ 0.8333333333333334 t_1)
     (if (<= t 0.23) 0.5 (+ 1.0 (- t_1 0.16666666666666666))))))

C

double code(double t) {
	double t_1 = ((0.037037037037037035 / t) + -0.2222222222222222) / t;
	double tmp;
	if (t <= -0.5) {
		tmp = 0.8333333333333334 + t_1;
	} else if (t <= 0.23) {
		tmp = 0.5;
	} else {
		tmp = 1.0 + (t_1 - 0.16666666666666666);
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((0.037037037037037035d0 / t) + (-0.2222222222222222d0)) / t
    if (t <= (-0.5d0)) then
        tmp = 0.8333333333333334d0 + t_1
    else if (t <= 0.23d0) then
        tmp = 0.5d0
    else
        tmp = 1.0d0 + (t_1 - 0.16666666666666666d0)
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double t_1 = ((0.037037037037037035 / t) + -0.2222222222222222) / t;
	double tmp;
	if (t <= -0.5) {
		tmp = 0.8333333333333334 + t_1;
	} else if (t <= 0.23) {
		tmp = 0.5;
	} else {
		tmp = 1.0 + (t_1 - 0.16666666666666666);
	}
	return tmp;
}

Python

def code(t):
	t_1 = ((0.037037037037037035 / t) + -0.2222222222222222) / t
	tmp = 0
	if t <= -0.5:
		tmp = 0.8333333333333334 + t_1
	elif t <= 0.23:
		tmp = 0.5
	else:
		tmp = 1.0 + (t_1 - 0.16666666666666666)
	return tmp

Julia

function code(t)
	t_1 = Float64(Float64(Float64(0.037037037037037035 / t) + -0.2222222222222222) / t)
	tmp = 0.0
	if (t <= -0.5)
		tmp = Float64(0.8333333333333334 + t_1);
	elseif (t <= 0.23)
		tmp = 0.5;
	else
		tmp = Float64(1.0 + Float64(t_1 - 0.16666666666666666));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	t_1 = ((0.037037037037037035 / t) + -0.2222222222222222) / t;
	tmp = 0.0;
	if (t <= -0.5)
		tmp = 0.8333333333333334 + t_1;
	elseif (t <= 0.23)
		tmp = 0.5;
	else
		tmp = 1.0 + (t_1 - 0.16666666666666666);
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := Block[{t$95$1 = N[(N[(N[(0.037037037037037035 / t), $MachinePrecision] + -0.2222222222222222), $MachinePrecision] / t), $MachinePrecision]}, If[LessEqual[t, -0.5], N[(0.8333333333333334 + t$95$1), $MachinePrecision], If[LessEqual[t, 0.23], 0.5, N[(1.0 + N[(t$95$1 - 0.16666666666666666), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if t < -0.5

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 99.4%

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

      1. mul-1-neg 99.4%

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

      2. unsub-neg 99.4%

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

      3. mul-1-neg 99.4%

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

      4. unsub-neg 99.4%

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

      5. sub-neg 99.4%

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

      6. associate-*r/ 99.4%

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

      7. metadata-eval 99.4%

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

      8. distribute-neg-frac 99.4%

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

      9. metadata-eval 99.4%

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

   5. Simplified 99.4%

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

      1. associate-+r- 99.4%

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

      2. metadata-eval 99.4%

         \[\leadsto 1 - \frac{1}{\color{blue}{6} - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}} \]

   7. Applied egg-rr 99.4%

      \[\leadsto 1 - \frac{1}{\color{blue}{6 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}}} \]

   8. Taylor expanded in t around inf 99.3%

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

      1. associate--l+ 99.3%

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

      2. +-commutative 99.3%

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

   10. Simplified 99.3%

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

   if -0.5 < t < 0.23000000000000001

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

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

   if 0.23000000000000001 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.4%

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

      1. mul-1-neg 98.4%

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

      2. unsub-neg 98.4%

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

      3. sub-neg 98.4%

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

      4. associate-*r/ 98.4%

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

      5. metadata-eval 98.4%

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

      6. metadata-eval 98.4%

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

   5. Simplified 98.4%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 - \frac{\frac{0.037037037037037035}{t} + -0.2222222222222222}{t}\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.5%

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

Alternative 8: 99.0% accurate, 1.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Split input into 2 regimes

2. if t < -0.5 or 0.23000000000000001 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around -inf 98.8%

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

      1. mul-1-neg 98.8%

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

      2. unsub-neg 98.8%

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

      3. mul-1-neg 98.8%

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

      4. unsub-neg 98.8%

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

      5. sub-neg 98.8%

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

      6. associate-*r/ 98.8%

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

      7. metadata-eval 98.8%

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

      8. distribute-neg-frac 98.8%

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

      9. metadata-eval 98.8%

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

   5. Simplified 98.8%

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

      1. associate-+r- 98.8%

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

      2. metadata-eval 98.8%

         \[\leadsto 1 - \frac{1}{\color{blue}{6} - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}} \]

   7. Applied egg-rr 98.8%

      \[\leadsto 1 - \frac{1}{\color{blue}{6 - \frac{8 - \frac{12 + \frac{-16}{t}}{t}}{t}}} \]

   8. Taylor expanded in t around inf 98.8%

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

      1. associate--l+ 98.8%

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

      2. +-commutative 98.8%

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

   10. Simplified 98.8%

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

   if -0.5 < t < 0.23000000000000001

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

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

4. Final simplification 98.5%

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

Alternative 9: 98.9% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.49:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \mathbf{elif}\;t \leq 0.66:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;1 - \left(0.16666666666666666 + \frac{0.2222222222222222}{t}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (<= t -0.49)
   (- 0.8333333333333334 (/ 0.2222222222222222 t))
   (if (<= t 0.66)
     0.5
     (- 1.0 (+ 0.16666666666666666 (/ 0.2222222222222222 t))))))

C

double code(double t) {
	double tmp;
	if (t <= -0.49) {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	} else if (t <= 0.66) {
		tmp = 0.5;
	} else {
		tmp = 1.0 - (0.16666666666666666 + (0.2222222222222222 / 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 <= 0.66d0) then
        tmp = 0.5d0
    else
        tmp = 1.0d0 - (0.16666666666666666d0 + (0.2222222222222222d0 / 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 <= 0.66) {
		tmp = 0.5;
	} else {
		tmp = 1.0 - (0.16666666666666666 + (0.2222222222222222 / t));
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if t <= -0.49:
		tmp = 0.8333333333333334 - (0.2222222222222222 / t)
	elif t <= 0.66:
		tmp = 0.5
	else:
		tmp = 1.0 - (0.16666666666666666 + (0.2222222222222222 / t))
	return tmp

Julia

function code(t)
	tmp = 0.0
	if (t <= -0.49)
		tmp = Float64(0.8333333333333334 - Float64(0.2222222222222222 / t));
	elseif (t <= 0.66)
		tmp = 0.5;
	else
		tmp = Float64(1.0 - Float64(0.16666666666666666 + Float64(0.2222222222222222 / 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 <= 0.66)
		tmp = 0.5;
	else
		tmp = 1.0 - (0.16666666666666666 + (0.2222222222222222 / 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, 0.66], 0.5, N[(1.0 - N[(0.16666666666666666 + N[(0.2222222222222222 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -0.48999999999999999

   1. Initial program 100.0%

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

   3. Taylor expanded in t around inf 99.1%

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

      1. associate-*r/ 99.1%

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

      2. metadata-eval 99.1%

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

   5. Simplified 99.1%

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

   6. Taylor expanded in t around inf 99.1%

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

      1. associate-*r/ 99.1%

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

      2. metadata-eval 99.1%

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

   8. Simplified 99.1%

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

   if -0.48999999999999999 < t < 0.660000000000000031

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

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

   if 0.660000000000000031 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around inf 98.2%

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

      1. associate-*r/ 98.2%

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

      2. metadata-eval 98.2%

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

   5. Simplified 98.2%

      \[\leadsto 1 - \color{blue}{\left(0.16666666666666666 + \frac{0.2222222222222222}{t}\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.49:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \mathbf{elif}\;t \leq 0.66:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;1 - \left(0.16666666666666666 + \frac{0.2222222222222222}{t}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 98.9% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.49 \lor \neg \left(t \leq 0.66\right):\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (or (<= t -0.49) (not (<= t 0.66)))
   (- 0.8333333333333334 (/ 0.2222222222222222 t))
   0.5))

C

double code(double t) {
	double tmp;
	if ((t <= -0.49) || !(t <= 0.66)) {
		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 <= 0.66d0))) 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 <= 0.66)) {
		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 <= 0.66):
		tmp = 0.8333333333333334 - (0.2222222222222222 / t)
	else:
		tmp = 0.5
	return tmp

Julia

function code(t)
	tmp = 0.0
	if ((t <= -0.49) || !(t <= 0.66))
		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 <= 0.66)))
		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, 0.66]], $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 0.660000000000000031 < t

   1. Initial program 100.0%

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

   3. Taylor expanded in t around inf 98.7%

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

      1. associate-*r/ 98.7%

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

      2. metadata-eval 98.7%

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

   5. Simplified 98.7%

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

   6. Taylor expanded in t around inf 98.6%

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

      1. associate-*r/ 98.6%

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

      2. metadata-eval 98.6%

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

   8. Simplified 98.6%

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

   if -0.48999999999999999 < t < 0.660000000000000031

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

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

4. Final simplification 98.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.49 \lor \neg \left(t \leq 0.66\right):\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 98.4% accurate, 2.6× 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%

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

   3. Taylor expanded in t around inf 98.7%

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

      1. associate-*r/ 98.7%

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

      2. metadata-eval 98.7%

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

   5. Simplified 98.7%

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

   6. Taylor expanded in t around inf 98.0%

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

   if -0.330000000000000016 < t < 1

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0 98.2%

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

4. Final simplification 98.1%

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

Alternative 12: 58.8% accurate, 29.0× speedup

Math

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

FPCore

(FPCore (t) :precision binary64 0.8333333333333334)

C

double code(double t) {
	return 0.8333333333333334;
}

Fortran

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

Java

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

Python

def code(t):
	return 0.8333333333333334

Julia

function code(t)
	return 0.8333333333333334
end

MATLAB

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

Wolfram

code[t_] := 0.8333333333333334

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in t around inf 53.9%

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

   1. associate-*r/ 53.9%

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

   2. metadata-eval 53.9%

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

5. Simplified 53.9%

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

6. Taylor expanded in t around inf 61.3%

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

Reproduce

herbie shell --seed 2024096 
(FPCore (t)
  :name "Kahan p13 Example 3"
  :precision binary64
  (- 1.0 (/ 1.0 (+ 2.0 (* (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t)))) (- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t)))))))))