Kahan p13 Example 1

Percentage Accurate: 100.0% → 100.0%

Time: 14.6s

Alternatives: 7

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{2 \cdot t}{1 + t}\\ t_2 := t\_1 \cdot t\_1\\ \frac{1 + t\_2}{2 + t\_2} \end{array} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{2 \cdot t}{1 + t}\\ t_2 := t\_1 \cdot t\_1\\ \frac{1 + t\_2}{2 + t\_2} \end{array} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t}{1 + t}\\ t_2 := \frac{1 + t}{t}\\ \frac{1 + \frac{t\_1}{t\_2 \cdot 0.25}}{2 + \left(\left(1 + \frac{4}{\frac{t\_2}{t\_1}}\right) + -1\right)} \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ t (+ 1.0 t))) (t_2 (/ (+ 1.0 t) t)))
   (/
    (+ 1.0 (/ t_1 (* t_2 0.25)))
    (+ 2.0 (+ (+ 1.0 (/ 4.0 (/ t_2 t_1))) -1.0)))))

C

double code(double t) {
	double t_1 = t / (1.0 + t);
	double t_2 = (1.0 + t) / t;
	return (1.0 + (t_1 / (t_2 * 0.25))) / (2.0 + ((1.0 + (4.0 / (t_2 / t_1))) + -1.0));
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    t_1 = t / (1.0d0 + t)
    t_2 = (1.0d0 + t) / t
    code = (1.0d0 + (t_1 / (t_2 * 0.25d0))) / (2.0d0 + ((1.0d0 + (4.0d0 / (t_2 / t_1))) + (-1.0d0)))
end function

Java

public static double code(double t) {
	double t_1 = t / (1.0 + t);
	double t_2 = (1.0 + t) / t;
	return (1.0 + (t_1 / (t_2 * 0.25))) / (2.0 + ((1.0 + (4.0 / (t_2 / t_1))) + -1.0));
}

Python

def code(t):
	t_1 = t / (1.0 + t)
	t_2 = (1.0 + t) / t
	return (1.0 + (t_1 / (t_2 * 0.25))) / (2.0 + ((1.0 + (4.0 / (t_2 / t_1))) + -1.0))

Julia

function code(t)
	t_1 = Float64(t / Float64(1.0 + t))
	t_2 = Float64(Float64(1.0 + t) / t)
	return Float64(Float64(1.0 + Float64(t_1 / Float64(t_2 * 0.25))) / Float64(2.0 + Float64(Float64(1.0 + Float64(4.0 / Float64(t_2 / t_1))) + -1.0)))
end

MATLAB

function tmp = code(t)
	t_1 = t / (1.0 + t);
	t_2 = (1.0 + t) / t;
	tmp = (1.0 + (t_1 / (t_2 * 0.25))) / (2.0 + ((1.0 + (4.0 / (t_2 / t_1))) + -1.0));
end

Wolfram

code[t_] := Block[{t$95$1 = N[(t / N[(1.0 + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(1.0 + t), $MachinePrecision] / t), $MachinePrecision]}, N[(N[(1.0 + N[(t$95$1 / N[(t$95$2 * 0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(2.0 + N[(N[(1.0 + N[(4.0 / N[(t$95$2 / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Initial program 100.0%

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

   1. associate-/l* 99.9%

      \[\leadsto \frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \color{blue}{\frac{2}{\frac{1 + t}{t}}} \cdot \frac{2 \cdot t}{1 + t}} \]

   2. associate-/l* 99.9%

      \[\leadsto \frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2}{\frac{1 + t}{t}} \cdot \color{blue}{\frac{2}{\frac{1 + t}{t}}}} \]

   3. frac-times 99.9%

      \[\leadsto \frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \color{blue}{\frac{2 \cdot 2}{\frac{1 + t}{t} \cdot \frac{1 + t}{t}}}} \]

   4. metadata-eval 99.9%

      \[\leadsto \frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{\color{blue}{4}}{\frac{1 + t}{t} \cdot \frac{1 + t}{t}}} \]

   5. associate-*l/ 100.0%

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

   6. associate-/l* 99.9%

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

   7. *-commutative 99.9%

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

   8. expm1-log1p-u 100.0%

      \[\leadsto \frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{t \cdot 4}{\left(1 + t\right) \cdot \frac{1 + t}{t}}\right)\right)}} \]

   9. associate-/l/ 100.0%

      \[\leadsto \frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \mathsf{expm1}\left(\mathsf{log1p}\left(\color{blue}{\frac{\frac{t \cdot 4}{\frac{1 + t}{t}}}{1 + t}}\right)\right)} \]

   10. expm1-udef 99.1%

       \[\leadsto \frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \color{blue}{\left(e^{\mathsf{log1p}\left(\frac{\frac{t \cdot 4}{\frac{1 + t}{t}}}{1 + t}\right)} - 1\right)}} \]

4. Applied egg-rr 100.0%

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

   1. unpow2 100.0%

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

   2. +-commutative 100.0%

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

   3. clear-num 100.0%

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

   4. un-div-inv 100.0%

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

   5. +-commutative 100.0%

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

6. Applied egg-rr 100.0%

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

   1. associate-/l* 100.0%

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

   2. associate-/l* 100.0%

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

   3. frac-times 100.0%

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

   4. metadata-eval 100.0%

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

   5. associate-*l/ 100.0%

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

   6. associate-/l* 100.0%

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

   7. *-commutative 100.0%

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

   8. associate-/l/ 100.0%

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

   9. div-inv 100.0%

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

   10. associate-/l* 100.0%

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

   11. associate-*l/ 100.0%

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

   12. div-inv 100.0%

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

   13. +-commutative 100.0%

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

   14. div-inv 100.0%

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

   15. +-commutative 100.0%

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

   16. metadata-eval 100.0%

       \[\leadsto \frac{1 + \frac{\frac{t}{t + 1}}{\frac{t + 1}{t} \cdot \color{blue}{0.25}}}{2 + \left(\left(1 + \frac{4}{\frac{\frac{t + 1}{t}}{\frac{t}{t + 1}}}\right) - 1\right)} \]

8. Applied egg-rr 100.0%

   \[\leadsto \frac{1 + \color{blue}{\frac{\frac{t}{t + 1}}{\frac{t + 1}{t} \cdot 0.25}}}{2 + \left(\left(1 + \frac{4}{\frac{\frac{t + 1}{t}}{\frac{t}{t + 1}}}\right) - 1\right)} \]

9. Final simplification 100.0%

   \[\leadsto \frac{1 + \frac{\frac{t}{1 + t}}{\frac{1 + t}{t} \cdot 0.25}}{2 + \left(\left(1 + \frac{4}{\frac{\frac{1 + t}{t}}{\frac{t}{1 + t}}}\right) + -1\right)} \]
10. Add Preprocessing

Alternative 2: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t \cdot 2}{1 + t}\\ t_2 := t\_1 \cdot t\_1\\ \frac{1 + t\_2}{2 + t\_2} \end{array} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]
2. Add Preprocessing

3. Final simplification 100.0%

   \[\leadsto \frac{1 + \frac{t \cdot 2}{1 + t} \cdot \frac{t \cdot 2}{1 + t}}{2 + \frac{t \cdot 2}{1 + t} \cdot \frac{t \cdot 2}{1 + t}} \]
4. Add Preprocessing

Alternative 3: 99.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.58 \lor \neg \left(t \leq 0.75\right):\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + \frac{t \cdot 2}{1 + t} \cdot \left(t \cdot 2\right)}{2 + \left(t \cdot 2\right) \cdot \left(t \cdot 2\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (or (<= t -0.58) (not (<= t 0.75)))
   (- 0.8333333333333334 (/ 0.2222222222222222 t))
   (/
    (+ 1.0 (* (/ (* t 2.0) (+ 1.0 t)) (* t 2.0)))
    (+ 2.0 (* (* t 2.0) (* t 2.0))))))

C

double code(double t) {
	double tmp;
	if ((t <= -0.58) || !(t <= 0.75)) {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	} else {
		tmp = (1.0 + (((t * 2.0) / (1.0 + t)) * (t * 2.0))) / (2.0 + ((t * 2.0) * (t * 2.0)));
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-0.58d0)) .or. (.not. (t <= 0.75d0))) then
        tmp = 0.8333333333333334d0 - (0.2222222222222222d0 / t)
    else
        tmp = (1.0d0 + (((t * 2.0d0) / (1.0d0 + t)) * (t * 2.0d0))) / (2.0d0 + ((t * 2.0d0) * (t * 2.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if ((t <= -0.58) || !(t <= 0.75)) {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	} else {
		tmp = (1.0 + (((t * 2.0) / (1.0 + t)) * (t * 2.0))) / (2.0 + ((t * 2.0) * (t * 2.0)));
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if (t <= -0.58) or not (t <= 0.75):
		tmp = 0.8333333333333334 - (0.2222222222222222 / t)
	else:
		tmp = (1.0 + (((t * 2.0) / (1.0 + t)) * (t * 2.0))) / (2.0 + ((t * 2.0) * (t * 2.0)))
	return tmp

Julia

function code(t)
	tmp = 0.0
	if ((t <= -0.58) || !(t <= 0.75))
		tmp = Float64(0.8333333333333334 - Float64(0.2222222222222222 / t));
	else
		tmp = Float64(Float64(1.0 + Float64(Float64(Float64(t * 2.0) / Float64(1.0 + t)) * Float64(t * 2.0))) / Float64(2.0 + Float64(Float64(t * 2.0) * Float64(t * 2.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if ((t <= -0.58) || ~((t <= 0.75)))
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	else
		tmp = (1.0 + (((t * 2.0) / (1.0 + t)) * (t * 2.0))) / (2.0 + ((t * 2.0) * (t * 2.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[Or[LessEqual[t, -0.58], N[Not[LessEqual[t, 0.75]], $MachinePrecision]], N[(0.8333333333333334 - N[(0.2222222222222222 / t), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 + N[(N[(N[(t * 2.0), $MachinePrecision] / N[(1.0 + t), $MachinePrecision]), $MachinePrecision] * N[(t * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(2.0 + N[(N[(t * 2.0), $MachinePrecision] * N[(t * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -0.57999999999999996 or 0.75 < t

   1. Initial program 100.0%

      \[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 98.9%

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

      1. associate-*r/ 98.9%

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

      2. metadata-eval 98.9%

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

   5. Simplified 98.9%

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

   if -0.57999999999999996 < t < 0.75

   1. Initial program 99.9%

      \[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 98.6%

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

      1. *-commutative 98.6%

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

   5. Simplified 98.6%

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

   6. Taylor expanded in t around 0 98.6%

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

      1. *-commutative 98.6%

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

   8. Simplified 98.6%

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

   9. Taylor expanded in t around 0 98.7%

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

       1. *-commutative 98.6%

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

   11. Simplified 98.7%

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

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -0.58 \lor \neg \left(t \leq 0.75\right):\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + \frac{t \cdot 2}{1 + t} \cdot \left(t \cdot 2\right)}{2 + \left(t \cdot 2\right) \cdot \left(t \cdot 2\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.9% accurate, 1.1× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]
2. Step-by-step derivation

   1. associate-*r/ 99.9%

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

   2. associate-*r* 99.9%

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

   3. *-commutative 99.9%

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

   4. associate-/l* 99.9%

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

   5. associate-*l/ 99.9%

      \[\leadsto \frac{1 + \frac{t \cdot \color{blue}{\frac{2 \cdot 2}{\frac{1 + t}{t}}}}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]

   6. associate-*r/ 99.9%

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

   7. metadata-eval 99.9%

      \[\leadsto \frac{1 + \frac{\frac{t \cdot \color{blue}{4}}{\frac{1 + t}{t}}}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]

   8. associate-*r/ 100.0%

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

3. Simplified 100.0%

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

   1. clear-num 99.9%

      \[\leadsto \frac{1 + \frac{\color{blue}{\frac{1}{\frac{\frac{1 + t}{t}}{t \cdot 4}}}}{1 + t}}{2 + \frac{\frac{t \cdot 4}{\frac{1 + t}{t}}}{1 + t}} \]

   2. associate-/r/ 100.0%

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

   3. clear-num 100.0%

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

   4. +-commutative 100.0%

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

6. Applied egg-rr 100.0%

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

7. Final simplification 100.0%

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

Alternative 5: 99.2% accurate, 2.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Split input into 2 regimes

2. if t < -0.48999999999999999 or 0.680000000000000049 < t

   1. Initial program 100.0%

      \[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 98.9%

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

      1. associate-*r/ 98.9%

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

      2. metadata-eval 98.9%

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

   5. Simplified 98.9%

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

   if -0.48999999999999999 < t < 0.680000000000000049

   1. Initial program 99.9%

      \[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 98.0%

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

4. Final simplification 98.5%

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

Alternative 6: 98.6% accurate, 3.2× speedup

Math

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

FPCore

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

C

double code(double t) {
	double tmp;
	if (t <= -0.33) {
		tmp = 0.8333333333333334;
	} else if (t <= 1.0) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334;
	}
	return tmp;
}

Fortran

real(8) function code(t)
    real(8), intent (in) :: t
    real(8) :: tmp
    if (t <= (-0.33d0)) then
        tmp = 0.8333333333333334d0
    else if (t <= 1.0d0) then
        tmp = 0.5d0
    else
        tmp = 0.8333333333333334d0
    end if
    code = tmp
end function

Java

public static double code(double t) {
	double tmp;
	if (t <= -0.33) {
		tmp = 0.8333333333333334;
	} else if (t <= 1.0) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334;
	}
	return tmp;
}

Python

def code(t):
	tmp = 0
	if t <= -0.33:
		tmp = 0.8333333333333334
	elif t <= 1.0:
		tmp = 0.5
	else:
		tmp = 0.8333333333333334
	return tmp

Julia

function code(t)
	tmp = 0.0
	if (t <= -0.33)
		tmp = 0.8333333333333334;
	elseif (t <= 1.0)
		tmp = 0.5;
	else
		tmp = 0.8333333333333334;
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	tmp = 0.0;
	if (t <= -0.33)
		tmp = 0.8333333333333334;
	elseif (t <= 1.0)
		tmp = 0.5;
	else
		tmp = 0.8333333333333334;
	end
	tmp_2 = tmp;
end

Wolfram

code[t_] := If[LessEqual[t, -0.33], 0.8333333333333334, If[LessEqual[t, 1.0], 0.5, 0.8333333333333334]]

Derivation

1. Split input into 2 regimes

2. if t < -0.330000000000000016 or 1 < t

   1. Initial program 100.0%

      \[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 98.2%

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

   if -0.330000000000000016 < t < 1

   1. Initial program 99.9%

      \[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 97.4%

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

4. Final simplification 97.8%

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

Alternative 7: 58.8% accurate, 35.0× speedup

Math

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

FPCore

(FPCore (t) :precision binary64 0.5)

C

double code(double t) {
	return 0.5;
}

Fortran

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

Java

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

Python

def code(t):
	return 0.5

Julia

function code(t)
	return 0.5
end

MATLAB

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

Wolfram

code[t_] := 0.5

Derivation

1. Initial program 100.0%

   \[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]
2. Add Preprocessing

3. Taylor expanded in t around 0 54.3%

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

4. Final simplification 54.3%

   \[\leadsto 0.5 \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024031 
(FPCore (t)
  :name "Kahan p13 Example 1"
  :precision binary64
  (/ (+ 1.0 (* (/ (* 2.0 t) (+ 1.0 t)) (/ (* 2.0 t) (+ 1.0 t)))) (+ 2.0 (* (/ (* 2.0 t) (+ 1.0 t)) (/ (* 2.0 t) (+ 1.0 t))))))