Kahan p13 Example 1

Percentage Accurate: 99.9% → 99.9%

Time: 3.6s

Alternatives: 11

Speedup: N/A×

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

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(t)
use fmin_fmax_functions
    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: 99.9% 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

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(t)
use fmin_fmax_functions
    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: 99.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t + t}{t - -1}\\ \frac{\mathsf{fma}\left(t\_1, t\_1, 1\right)}{\mathsf{fma}\left(t\_1, t\_1, 2\right)} \end{array} \end{array} \]

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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. Step-by-step derivation

3. Applied rewrites 99.9%

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

   1. lift-+.f64 N/A

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

   2. add-flip N/A

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

   3. metadata-eval N/A

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

   4. lower--.f64 99.9

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

5. Applied rewrites 99.9%

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

   1. lift-+.f64 N/A

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

   2. add-flip N/A

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

   3. metadata-eval N/A

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

   4. lower--.f64 99.9

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

7. Applied rewrites 99.9%

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

   1. lift-+.f64 N/A

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

   2. add-flip N/A

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

   3. metadata-eval N/A

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

   4. lower--.f64 99.9

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

9. Applied rewrites 99.9%

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

    1. lift-+.f64 N/A

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

    2. add-flip N/A

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

    3. metadata-eval N/A

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

    4. lower--.f64 99.9

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

11. Applied rewrites 99.9%

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

Alternative 2: 99.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t + t}{\left(-1 - t\right) \cdot \left(-1 - t\right)}\\ \mathbf{if}\;\frac{2 \cdot t}{1 + t} \leq 1.9999999999999618:\\ \;\;\;\;\frac{\mathsf{fma}\left(t + t, t\_1, 1\right)}{\mathsf{fma}\left(t + t, t\_1, 2\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\frac{0.04938271604938271}{t} - -0.037037037037037035}{-t} - -0.2222222222222222}{-t} - -0.8333333333333334\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ (+ t t) (* (- -1.0 t) (- -1.0 t)))))
   (if (<= (/ (* 2.0 t) (+ 1.0 t)) 1.9999999999999618)
     (/ (fma (+ t t) t_1 1.0) (fma (+ t t) t_1 2.0))
     (-
      (/
       (-
        (/ (- (/ 0.04938271604938271 t) -0.037037037037037035) (- t))
        -0.2222222222222222)
       (- t))
      -0.8333333333333334))))

C

double code(double t) {
	double t_1 = (t + t) / ((-1.0 - t) * (-1.0 - t));
	double tmp;
	if (((2.0 * t) / (1.0 + t)) <= 1.9999999999999618) {
		tmp = fma((t + t), t_1, 1.0) / fma((t + t), t_1, 2.0);
	} else {
		tmp = (((((0.04938271604938271 / t) - -0.037037037037037035) / -t) - -0.2222222222222222) / -t) - -0.8333333333333334;
	}
	return tmp;
}

Julia

function code(t)
	t_1 = Float64(Float64(t + t) / Float64(Float64(-1.0 - t) * Float64(-1.0 - t)))
	tmp = 0.0
	if (Float64(Float64(2.0 * t) / Float64(1.0 + t)) <= 1.9999999999999618)
		tmp = Float64(fma(Float64(t + t), t_1, 1.0) / fma(Float64(t + t), t_1, 2.0));
	else
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(0.04938271604938271 / t) - -0.037037037037037035) / Float64(-t)) - -0.2222222222222222) / Float64(-t)) - -0.8333333333333334);
	end
	return tmp
end

Wolfram

code[t_] := Block[{t$95$1 = N[(N[(t + t), $MachinePrecision] / N[(N[(-1.0 - t), $MachinePrecision] * N[(-1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(2.0 * t), $MachinePrecision] / N[(1.0 + t), $MachinePrecision]), $MachinePrecision], 1.9999999999999618], N[(N[(N[(t + t), $MachinePrecision] * t$95$1 + 1.0), $MachinePrecision] / N[(N[(t + t), $MachinePrecision] * t$95$1 + 2.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(N[(N[(0.04938271604938271 / t), $MachinePrecision] - -0.037037037037037035), $MachinePrecision] / (-t)), $MachinePrecision] - -0.2222222222222222), $MachinePrecision] / (-t)), $MachinePrecision] - -0.8333333333333334), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) < 1.9999999999999618

   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. Step-by-step derivation

   3. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   5. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   7. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   9. Applied rewrites 99.9%

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

       1. lift-+.f64 N/A

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

       2. add-flip N/A

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

       3. metadata-eval N/A

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

       4. lower--.f64 99.9

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

   11. Applied rewrites 99.9%

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

       1. lift-fma.f64 N/A

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

       2. lift--.f64 N/A

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

       3. lift-/.f64 N/A

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

       4. lift-+.f64 N/A

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

       5. lift--.f64 N/A

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

       6. lift-/.f64 N/A

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

       7. lift-+.f64 N/A

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

       8. add-flip N/A

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

       9. pow2 N/A

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

       10. count-2-rev N/A

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

       11. sub-flip N/A

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

       12. metadata-eval N/A

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

       13. +-commutative N/A

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

       14. pow2 N/A

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

       15. metadata-eval N/A

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

       16. lower--.f64 N/A

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

   13. Applied rewrites 74.0%

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

   15. Applied rewrites 79.2%

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

   if 1.9999999999999618 < (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t))

   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. Step-by-step derivation

   3. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   5. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   7. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   9. Applied rewrites 99.9%

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

       1. lift-+.f64 N/A

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

       2. add-flip N/A

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

       3. metadata-eval N/A

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

       4. lower--.f64 99.9

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

   11. Applied rewrites 99.9%

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

   12. Taylor expanded in t around -inf

       \[\leadsto \color{blue}{\frac{5}{6} + -1 \cdot \frac{\frac{2}{9} + -1 \cdot \frac{\frac{1}{27} + \frac{4}{81} \cdot \frac{1}{t}}{t}}{t}} \]
   13. Step-by-step derivation

       1. +-commutative N/A

          \[\leadsto -1 \cdot \frac{\frac{2}{9} + -1 \cdot \frac{\frac{1}{27} + \frac{4}{81} \cdot \frac{1}{t}}{t}}{t} + \color{blue}{\frac{5}{6}} \]

       2. lower-+.f64 N/A

          \[\leadsto -1 \cdot \frac{\frac{2}{9} + -1 \cdot \frac{\frac{1}{27} + \frac{4}{81} \cdot \frac{1}{t}}{t}}{t} + \color{blue}{\frac{5}{6}} \]

   14. Applied rewrites 50.7%

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

   16. Applied rewrites 50.7%

       \[\leadsto \color{blue}{\frac{\frac{\frac{0.04938271604938271}{t} - -0.037037037037037035}{-t} - -0.2222222222222222}{-t} - -0.8333333333333334} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 99.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(\mathsf{fma}\left(12, t, -8\right), t, 4\right) \cdot \left(t \cdot t\right)\\ \mathbf{if}\;\frac{2 \cdot t}{1 + t} \leq 5 \cdot 10^{-6}:\\ \;\;\;\;\frac{1 + t\_1}{2 + t\_1}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\frac{0.04938271604938271}{t} - -0.037037037037037035}{-t} - -0.2222222222222222}{-t} - -0.8333333333333334\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (* (fma (fma 12.0 t -8.0) t 4.0) (* t t))))
   (if (<= (/ (* 2.0 t) (+ 1.0 t)) 5e-6)
     (/ (+ 1.0 t_1) (+ 2.0 t_1))
     (-
      (/
       (-
        (/ (- (/ 0.04938271604938271 t) -0.037037037037037035) (- t))
        -0.2222222222222222)
       (- t))
      -0.8333333333333334))))

C

double code(double t) {
	double t_1 = fma(fma(12.0, t, -8.0), t, 4.0) * (t * t);
	double tmp;
	if (((2.0 * t) / (1.0 + t)) <= 5e-6) {
		tmp = (1.0 + t_1) / (2.0 + t_1);
	} else {
		tmp = (((((0.04938271604938271 / t) - -0.037037037037037035) / -t) - -0.2222222222222222) / -t) - -0.8333333333333334;
	}
	return tmp;
}

Julia

function code(t)
	t_1 = Float64(fma(fma(12.0, t, -8.0), t, 4.0) * Float64(t * t))
	tmp = 0.0
	if (Float64(Float64(2.0 * t) / Float64(1.0 + t)) <= 5e-6)
		tmp = Float64(Float64(1.0 + t_1) / Float64(2.0 + t_1));
	else
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(0.04938271604938271 / t) - -0.037037037037037035) / Float64(-t)) - -0.2222222222222222) / Float64(-t)) - -0.8333333333333334);
	end
	return tmp
end

Wolfram

code[t_] := Block[{t$95$1 = N[(N[(N[(12.0 * t + -8.0), $MachinePrecision] * t + 4.0), $MachinePrecision] * N[(t * t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(2.0 * t), $MachinePrecision] / N[(1.0 + t), $MachinePrecision]), $MachinePrecision], 5e-6], N[(N[(1.0 + t$95$1), $MachinePrecision] / N[(2.0 + t$95$1), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(N[(N[(0.04938271604938271 / t), $MachinePrecision] - -0.037037037037037035), $MachinePrecision] / (-t)), $MachinePrecision] - -0.2222222222222222), $MachinePrecision] / (-t)), $MachinePrecision] - -0.8333333333333334), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) < 5.00000000000000041e-6

   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. Taylor expanded in t around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. sub-flip N/A

         \[\leadsto \frac{1 + \mathsf{fma}\left(12 \cdot t + \left(\mathsf{neg}\left(8\right)\right), t, 4\right) \cdot {t}^{2}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]

      7. metadata-eval N/A

         \[\leadsto \frac{1 + \mathsf{fma}\left(12 \cdot t + -8, t, 4\right) \cdot {t}^{2}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]

      8. lower-fma.f64 N/A

         \[\leadsto \frac{1 + \mathsf{fma}\left(\mathsf{fma}\left(12, t, -8\right), t, 4\right) \cdot {t}^{2}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]

      9. unpow2 N/A

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

      10. lower-*.f64 51.8

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

   4. Applied rewrites 51.8%

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

   5. Taylor expanded in t around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

         \[\leadsto \frac{1 + \mathsf{fma}\left(\mathsf{fma}\left(12, t, -8\right), t, 4\right) \cdot \left(t \cdot t\right)}{2 + \left(\left(12 \cdot t - 8\right) \cdot t + 4\right) \cdot {t}^{2}} \]

      5. lower-fma.f64 N/A

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

      6. sub-flip N/A

         \[\leadsto \frac{1 + \mathsf{fma}\left(\mathsf{fma}\left(12, t, -8\right), t, 4\right) \cdot \left(t \cdot t\right)}{2 + \mathsf{fma}\left(12 \cdot t + \left(\mathsf{neg}\left(8\right)\right), t, 4\right) \cdot {t}^{2}} \]

      7. metadata-eval N/A

         \[\leadsto \frac{1 + \mathsf{fma}\left(\mathsf{fma}\left(12, t, -8\right), t, 4\right) \cdot \left(t \cdot t\right)}{2 + \mathsf{fma}\left(12 \cdot t + -8, t, 4\right) \cdot {t}^{2}} \]

      8. lower-fma.f64 N/A

         \[\leadsto \frac{1 + \mathsf{fma}\left(\mathsf{fma}\left(12, t, -8\right), t, 4\right) \cdot \left(t \cdot t\right)}{2 + \mathsf{fma}\left(\mathsf{fma}\left(12, t, -8\right), t, 4\right) \cdot {t}^{2}} \]

      9. unpow2 N/A

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

      10. lower-*.f64 52.5

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

   7. Applied rewrites 52.5%

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

   if 5.00000000000000041e-6 < (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t))

   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. Step-by-step derivation

   3. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   5. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   7. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   9. Applied rewrites 99.9%

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

       1. lift-+.f64 N/A

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

       2. add-flip N/A

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

       3. metadata-eval N/A

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

       4. lower--.f64 99.9

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

   11. Applied rewrites 99.9%

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

   12. Taylor expanded in t around -inf

       \[\leadsto \color{blue}{\frac{5}{6} + -1 \cdot \frac{\frac{2}{9} + -1 \cdot \frac{\frac{1}{27} + \frac{4}{81} \cdot \frac{1}{t}}{t}}{t}} \]
   13. Step-by-step derivation

       1. +-commutative N/A

          \[\leadsto -1 \cdot \frac{\frac{2}{9} + -1 \cdot \frac{\frac{1}{27} + \frac{4}{81} \cdot \frac{1}{t}}{t}}{t} + \color{blue}{\frac{5}{6}} \]

       2. lower-+.f64 N/A

          \[\leadsto -1 \cdot \frac{\frac{2}{9} + -1 \cdot \frac{\frac{1}{27} + \frac{4}{81} \cdot \frac{1}{t}}{t}}{t} + \color{blue}{\frac{5}{6}} \]

   14. Applied rewrites 50.7%

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

   16. Applied rewrites 50.7%

       \[\leadsto \color{blue}{\frac{\frac{\frac{0.04938271604938271}{t} - -0.037037037037037035}{-t} - -0.2222222222222222}{-t} - -0.8333333333333334} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 99.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{2 \cdot t}{1 + t}\\ t_2 := t\_1 \cdot t\_1\\ \mathbf{if}\;\frac{1 + t\_2}{2 + t\_2} \leq 0.6:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(t - 2, t, 1\right), t \cdot t, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\frac{0.04938271604938271}{t} - -0.037037037037037035}{-t} - -0.2222222222222222}{-t} - -0.8333333333333334\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ (* 2.0 t) (+ 1.0 t))) (t_2 (* t_1 t_1)))
   (if (<= (/ (+ 1.0 t_2) (+ 2.0 t_2)) 0.6)
     (fma (fma (- t 2.0) t 1.0) (* t t) 0.5)
     (-
      (/
       (-
        (/ (- (/ 0.04938271604938271 t) -0.037037037037037035) (- t))
        -0.2222222222222222)
       (- t))
      -0.8333333333333334))))

C

double code(double t) {
	double t_1 = (2.0 * t) / (1.0 + t);
	double t_2 = t_1 * t_1;
	double tmp;
	if (((1.0 + t_2) / (2.0 + t_2)) <= 0.6) {
		tmp = fma(fma((t - 2.0), t, 1.0), (t * t), 0.5);
	} else {
		tmp = (((((0.04938271604938271 / t) - -0.037037037037037035) / -t) - -0.2222222222222222) / -t) - -0.8333333333333334;
	}
	return tmp;
}

Julia

function code(t)
	t_1 = Float64(Float64(2.0 * t) / Float64(1.0 + t))
	t_2 = Float64(t_1 * t_1)
	tmp = 0.0
	if (Float64(Float64(1.0 + t_2) / Float64(2.0 + t_2)) <= 0.6)
		tmp = fma(fma(Float64(t - 2.0), t, 1.0), Float64(t * t), 0.5);
	else
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(0.04938271604938271 / t) - -0.037037037037037035) / Float64(-t)) - -0.2222222222222222) / Float64(-t)) - -0.8333333333333334);
	end
	return tmp
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]}, If[LessEqual[N[(N[(1.0 + t$95$2), $MachinePrecision] / N[(2.0 + t$95$2), $MachinePrecision]), $MachinePrecision], 0.6], N[(N[(N[(t - 2.0), $MachinePrecision] * t + 1.0), $MachinePrecision] * N[(t * t), $MachinePrecision] + 0.5), $MachinePrecision], N[(N[(N[(N[(N[(N[(0.04938271604938271 / t), $MachinePrecision] - -0.037037037037037035), $MachinePrecision] / (-t)), $MachinePrecision] - -0.2222222222222222), $MachinePrecision] / (-t)), $MachinePrecision] - -0.8333333333333334), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 #s(literal 1 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)))) (+.f64 #s(literal 2 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t))))) < 0.599999999999999978

   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. Taylor expanded in t around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. *-commutative N/A

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

      6. lower-fma.f64 N/A

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

      7. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(t - 2, t, 1\right), {t}^{2}, \frac{1}{2}\right) \]

      8. unpow2 N/A

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

      9. lower-*.f64 51.8

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(t - 2, t, 1\right), t \cdot \color{blue}{t}, 0.5\right) \]

   4. Applied rewrites 51.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(t - 2, t, 1\right), t \cdot t, 0.5\right)} \]

   if 0.599999999999999978 < (/.f64 (+.f64 #s(literal 1 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)))) (+.f64 #s(literal 2 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)))))

   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. Step-by-step derivation

   3. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   5. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   7. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 99.9

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

   9. Applied rewrites 99.9%

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

       1. lift-+.f64 N/A

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

       2. add-flip N/A

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

       3. metadata-eval N/A

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

       4. lower--.f64 99.9

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

   11. Applied rewrites 99.9%

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

   12. Taylor expanded in t around -inf

       \[\leadsto \color{blue}{\frac{5}{6} + -1 \cdot \frac{\frac{2}{9} + -1 \cdot \frac{\frac{1}{27} + \frac{4}{81} \cdot \frac{1}{t}}{t}}{t}} \]
   13. Step-by-step derivation

       1. +-commutative N/A

          \[\leadsto -1 \cdot \frac{\frac{2}{9} + -1 \cdot \frac{\frac{1}{27} + \frac{4}{81} \cdot \frac{1}{t}}{t}}{t} + \color{blue}{\frac{5}{6}} \]

       2. lower-+.f64 N/A

          \[\leadsto -1 \cdot \frac{\frac{2}{9} + -1 \cdot \frac{\frac{1}{27} + \frac{4}{81} \cdot \frac{1}{t}}{t}}{t} + \color{blue}{\frac{5}{6}} \]

   14. Applied rewrites 50.7%

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

   16. Applied rewrites 50.7%

       \[\leadsto \color{blue}{\frac{\frac{\frac{0.04938271604938271}{t} - -0.037037037037037035}{-t} - -0.2222222222222222}{-t} - -0.8333333333333334} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 99.1% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{2 \cdot t}{1 + t} \leq 5 \cdot 10^{-6}:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(t - 2, t, 1\right), t \cdot t, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{0.037037037037037035}{t} - 0.2222222222222222}{t} - -0.8333333333333334\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (<= (/ (* 2.0 t) (+ 1.0 t)) 5e-6)
   (fma (fma (- t 2.0) t 1.0) (* t t) 0.5)
   (-
    (/ (- (/ 0.037037037037037035 t) 0.2222222222222222) t)
    -0.8333333333333334)))

C

double code(double t) {
	double tmp;
	if (((2.0 * t) / (1.0 + t)) <= 5e-6) {
		tmp = fma(fma((t - 2.0), t, 1.0), (t * t), 0.5);
	} else {
		tmp = (((0.037037037037037035 / t) - 0.2222222222222222) / t) - -0.8333333333333334;
	}
	return tmp;
}

Julia

function code(t)
	tmp = 0.0
	if (Float64(Float64(2.0 * t) / Float64(1.0 + t)) <= 5e-6)
		tmp = fma(fma(Float64(t - 2.0), t, 1.0), Float64(t * t), 0.5);
	else
		tmp = Float64(Float64(Float64(Float64(0.037037037037037035 / t) - 0.2222222222222222) / t) - -0.8333333333333334);
	end
	return tmp
end

Wolfram

code[t_] := If[LessEqual[N[(N[(2.0 * t), $MachinePrecision] / N[(1.0 + t), $MachinePrecision]), $MachinePrecision], 5e-6], N[(N[(N[(t - 2.0), $MachinePrecision] * t + 1.0), $MachinePrecision] * N[(t * t), $MachinePrecision] + 0.5), $MachinePrecision], N[(N[(N[(N[(0.037037037037037035 / t), $MachinePrecision] - 0.2222222222222222), $MachinePrecision] / t), $MachinePrecision] - -0.8333333333333334), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) < 5.00000000000000041e-6

   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. Taylor expanded in t around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. *-commutative N/A

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

      6. lower-fma.f64 N/A

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

      7. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(t - 2, t, 1\right), {t}^{2}, \frac{1}{2}\right) \]

      8. unpow2 N/A

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

      9. lower-*.f64 51.8

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(t - 2, t, 1\right), t \cdot \color{blue}{t}, 0.5\right) \]

   4. Applied rewrites 51.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(t - 2, t, 1\right), t \cdot t, 0.5\right)} \]

   if 5.00000000000000041e-6 < (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t))

   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. Taylor expanded in t around -inf

      \[\leadsto \color{blue}{\frac{5}{6} + -1 \cdot \frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto -1 \cdot \frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t} + \color{blue}{\frac{5}{6}} \]

      2. lower-+.f64 N/A

         \[\leadsto -1 \cdot \frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t} + \color{blue}{\frac{5}{6}} \]

      3. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right)\right) + \frac{5}{6} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right) + \frac{5}{6} \]

      5. lower-/.f64 N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right) + \frac{5}{6} \]

      6. lower--.f64 N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right) + \frac{5}{6} \]

      7. mult-flip-rev N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{\frac{1}{27}}{t}}{t}\right) + \frac{5}{6} \]

      8. lower-/.f64 51.6

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

   4. Applied rewrites 51.6%

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

   6. Applied rewrites 51.6%

      \[\leadsto \color{blue}{\frac{\frac{0.037037037037037035}{t} - 0.2222222222222222}{t} - -0.8333333333333334} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 99.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{2 \cdot t}{1 + t} \leq 5 \cdot 10^{-6}:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(-2, t, 1\right), t \cdot t, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{0.037037037037037035}{t} - 0.2222222222222222}{t} - -0.8333333333333334\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (<= (/ (* 2.0 t) (+ 1.0 t)) 5e-6)
   (fma (fma -2.0 t 1.0) (* t t) 0.5)
   (-
    (/ (- (/ 0.037037037037037035 t) 0.2222222222222222) t)
    -0.8333333333333334)))

C

double code(double t) {
	double tmp;
	if (((2.0 * t) / (1.0 + t)) <= 5e-6) {
		tmp = fma(fma(-2.0, t, 1.0), (t * t), 0.5);
	} else {
		tmp = (((0.037037037037037035 / t) - 0.2222222222222222) / t) - -0.8333333333333334;
	}
	return tmp;
}

Julia

function code(t)
	tmp = 0.0
	if (Float64(Float64(2.0 * t) / Float64(1.0 + t)) <= 5e-6)
		tmp = fma(fma(-2.0, t, 1.0), Float64(t * t), 0.5);
	else
		tmp = Float64(Float64(Float64(Float64(0.037037037037037035 / t) - 0.2222222222222222) / t) - -0.8333333333333334);
	end
	return tmp
end

Wolfram

code[t_] := If[LessEqual[N[(N[(2.0 * t), $MachinePrecision] / N[(1.0 + t), $MachinePrecision]), $MachinePrecision], 5e-6], N[(N[(-2.0 * t + 1.0), $MachinePrecision] * N[(t * t), $MachinePrecision] + 0.5), $MachinePrecision], N[(N[(N[(N[(0.037037037037037035 / t), $MachinePrecision] - 0.2222222222222222), $MachinePrecision] / t), $MachinePrecision] - -0.8333333333333334), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) < 5.00000000000000041e-6

   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. Taylor expanded in t around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 51.1

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(-2, t, 1\right), t \cdot \color{blue}{t}, 0.5\right) \]

   4. Applied rewrites 51.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(-2, t, 1\right), t \cdot t, 0.5\right)} \]

   if 5.00000000000000041e-6 < (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t))

   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. Taylor expanded in t around -inf

      \[\leadsto \color{blue}{\frac{5}{6} + -1 \cdot \frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto -1 \cdot \frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t} + \color{blue}{\frac{5}{6}} \]

      2. lower-+.f64 N/A

         \[\leadsto -1 \cdot \frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t} + \color{blue}{\frac{5}{6}} \]

      3. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right)\right) + \frac{5}{6} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right) + \frac{5}{6} \]

      5. lower-/.f64 N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right) + \frac{5}{6} \]

      6. lower--.f64 N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right) + \frac{5}{6} \]

      7. mult-flip-rev N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{\frac{1}{27}}{t}}{t}\right) + \frac{5}{6} \]

      8. lower-/.f64 51.6

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

   4. Applied rewrites 51.6%

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

   6. Applied rewrites 51.6%

      \[\leadsto \color{blue}{\frac{\frac{0.037037037037037035}{t} - 0.2222222222222222}{t} - -0.8333333333333334} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 99.0% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{2 \cdot t}{1 + t} \leq 5 \cdot 10^{-6}:\\ \;\;\;\;\mathsf{fma}\left(t, t, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{0.037037037037037035}{t} - 0.2222222222222222}{t} - -0.8333333333333334\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (<= (/ (* 2.0 t) (+ 1.0 t)) 5e-6)
   (fma t t 0.5)
   (-
    (/ (- (/ 0.037037037037037035 t) 0.2222222222222222) t)
    -0.8333333333333334)))

C

double code(double t) {
	double tmp;
	if (((2.0 * t) / (1.0 + t)) <= 5e-6) {
		tmp = fma(t, t, 0.5);
	} else {
		tmp = (((0.037037037037037035 / t) - 0.2222222222222222) / t) - -0.8333333333333334;
	}
	return tmp;
}

Julia

function code(t)
	tmp = 0.0
	if (Float64(Float64(2.0 * t) / Float64(1.0 + t)) <= 5e-6)
		tmp = fma(t, t, 0.5);
	else
		tmp = Float64(Float64(Float64(Float64(0.037037037037037035 / t) - 0.2222222222222222) / t) - -0.8333333333333334);
	end
	return tmp
end

Wolfram

code[t_] := If[LessEqual[N[(N[(2.0 * t), $MachinePrecision] / N[(1.0 + t), $MachinePrecision]), $MachinePrecision], 5e-6], N[(t * t + 0.5), $MachinePrecision], N[(N[(N[(N[(0.037037037037037035 / t), $MachinePrecision] - 0.2222222222222222), $MachinePrecision] / t), $MachinePrecision] - -0.8333333333333334), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) < 5.00000000000000041e-6

   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. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{1}{2} + {t}^{2}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

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

      2. unpow2 N/A

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

      3. lower-fma.f64 51.9

         \[\leadsto \mathsf{fma}\left(t, \color{blue}{t}, 0.5\right) \]

   4. Applied rewrites 51.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, t, 0.5\right)} \]

   if 5.00000000000000041e-6 < (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t))

   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. Taylor expanded in t around -inf

      \[\leadsto \color{blue}{\frac{5}{6} + -1 \cdot \frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto -1 \cdot \frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t} + \color{blue}{\frac{5}{6}} \]

      2. lower-+.f64 N/A

         \[\leadsto -1 \cdot \frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t} + \color{blue}{\frac{5}{6}} \]

      3. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right)\right) + \frac{5}{6} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right) + \frac{5}{6} \]

      5. lower-/.f64 N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right) + \frac{5}{6} \]

      6. lower--.f64 N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{1}{27} \cdot \frac{1}{t}}{t}\right) + \frac{5}{6} \]

      7. mult-flip-rev N/A

         \[\leadsto \left(-\frac{\frac{2}{9} - \frac{\frac{1}{27}}{t}}{t}\right) + \frac{5}{6} \]

      8. lower-/.f64 51.6

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

   4. Applied rewrites 51.6%

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

   6. Applied rewrites 51.6%

      \[\leadsto \color{blue}{\frac{\frac{0.037037037037037035}{t} - 0.2222222222222222}{t} - -0.8333333333333334} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 98.8% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{2 \cdot t}{1 + t} \leq 5 \cdot 10^{-6}:\\ \;\;\;\;\mathsf{fma}\left(t, t, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334 - \frac{0.2222222222222222}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (if (<= (/ (* 2.0 t) (+ 1.0 t)) 5e-6)
   (fma t t 0.5)
   (- 0.8333333333333334 (/ 0.2222222222222222 t))))

C

double code(double t) {
	double tmp;
	if (((2.0 * t) / (1.0 + t)) <= 5e-6) {
		tmp = fma(t, t, 0.5);
	} else {
		tmp = 0.8333333333333334 - (0.2222222222222222 / t);
	}
	return tmp;
}

Julia

function code(t)
	tmp = 0.0
	if (Float64(Float64(2.0 * t) / Float64(1.0 + t)) <= 5e-6)
		tmp = fma(t, t, 0.5);
	else
		tmp = Float64(0.8333333333333334 - Float64(0.2222222222222222 / t));
	end
	return tmp
end

Wolfram

code[t_] := If[LessEqual[N[(N[(2.0 * t), $MachinePrecision] / N[(1.0 + t), $MachinePrecision]), $MachinePrecision], 5e-6], N[(t * t + 0.5), $MachinePrecision], N[(0.8333333333333334 - N[(0.2222222222222222 / t), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) < 5.00000000000000041e-6

   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. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{1}{2} + {t}^{2}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

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

      2. unpow2 N/A

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

      3. lower-fma.f64 51.9

         \[\leadsto \mathsf{fma}\left(t, \color{blue}{t}, 0.5\right) \]

   4. Applied rewrites 51.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, t, 0.5\right)} \]

   if 5.00000000000000041e-6 < (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t))

   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. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{5}{6} - \frac{2}{9} \cdot \frac{1}{t}} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \frac{5}{6} - \color{blue}{\frac{2}{9} \cdot \frac{1}{t}} \]

      2. mult-flip-rev N/A

         \[\leadsto \frac{5}{6} - \frac{\frac{2}{9}}{\color{blue}{t}} \]

      3. lower-/.f64 51.0

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

   4. Applied rewrites 51.0%

      \[\leadsto \color{blue}{0.8333333333333334 - \frac{0.2222222222222222}{t}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 98.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{2 \cdot t}{1 + t}\\ t_2 := t\_1 \cdot t\_1\\ \mathbf{if}\;\frac{1 + t\_2}{2 + t\_2} \leq 0.6:\\ \;\;\;\;\mathsf{fma}\left(t, t, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ (* 2.0 t) (+ 1.0 t))) (t_2 (* t_1 t_1)))
   (if (<= (/ (+ 1.0 t_2) (+ 2.0 t_2)) 0.6) (fma t t 0.5) 0.8333333333333334)))

C

double code(double t) {
	double t_1 = (2.0 * t) / (1.0 + t);
	double t_2 = t_1 * t_1;
	double tmp;
	if (((1.0 + t_2) / (2.0 + t_2)) <= 0.6) {
		tmp = fma(t, t, 0.5);
	} else {
		tmp = 0.8333333333333334;
	}
	return tmp;
}

Julia

function code(t)
	t_1 = Float64(Float64(2.0 * t) / Float64(1.0 + t))
	t_2 = Float64(t_1 * t_1)
	tmp = 0.0
	if (Float64(Float64(1.0 + t_2) / Float64(2.0 + t_2)) <= 0.6)
		tmp = fma(t, t, 0.5);
	else
		tmp = 0.8333333333333334;
	end
	return tmp
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]}, If[LessEqual[N[(N[(1.0 + t$95$2), $MachinePrecision] / N[(2.0 + t$95$2), $MachinePrecision]), $MachinePrecision], 0.6], N[(t * t + 0.5), $MachinePrecision], 0.8333333333333334]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 #s(literal 1 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)))) (+.f64 #s(literal 2 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t))))) < 0.599999999999999978

   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. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{1}{2} + {t}^{2}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

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

      2. unpow2 N/A

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

      3. lower-fma.f64 51.9

         \[\leadsto \mathsf{fma}\left(t, \color{blue}{t}, 0.5\right) \]

   4. Applied rewrites 51.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, t, 0.5\right)} \]

   if 0.599999999999999978 < (/.f64 (+.f64 #s(literal 1 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)))) (+.f64 #s(literal 2 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)))))

   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. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{5}{6}} \]
   3. Step-by-step derivation

   4. Applied rewrites 58.7%

      \[\leadsto \color{blue}{0.8333333333333334} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 98.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{2 \cdot t}{1 + t}\\ t_2 := t\_1 \cdot t\_1\\ \mathbf{if}\;\frac{1 + t\_2}{2 + t\_2} \leq 0.65:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0.8333333333333334\\ \end{array} \end{array} \]

FPCore

(FPCore (t)
 :precision binary64
 (let* ((t_1 (/ (* 2.0 t) (+ 1.0 t))) (t_2 (* t_1 t_1)))
   (if (<= (/ (+ 1.0 t_2) (+ 2.0 t_2)) 0.65) 0.5 0.8333333333333334)))

C

double code(double t) {
	double t_1 = (2.0 * t) / (1.0 + t);
	double t_2 = t_1 * t_1;
	double tmp;
	if (((1.0 + t_2) / (2.0 + t_2)) <= 0.65) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(t)
use fmin_fmax_functions
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (2.0d0 * t) / (1.0d0 + t)
    t_2 = t_1 * t_1
    if (((1.0d0 + t_2) / (2.0d0 + t_2)) <= 0.65d0) then
        tmp = 0.5d0
    else
        tmp = 0.8333333333333334d0
    end if
    code = tmp
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;
	double tmp;
	if (((1.0 + t_2) / (2.0 + t_2)) <= 0.65) {
		tmp = 0.5;
	} else {
		tmp = 0.8333333333333334;
	}
	return tmp;
}

Python

def code(t):
	t_1 = (2.0 * t) / (1.0 + t)
	t_2 = t_1 * t_1
	tmp = 0
	if ((1.0 + t_2) / (2.0 + t_2)) <= 0.65:
		tmp = 0.5
	else:
		tmp = 0.8333333333333334
	return tmp

Julia

function code(t)
	t_1 = Float64(Float64(2.0 * t) / Float64(1.0 + t))
	t_2 = Float64(t_1 * t_1)
	tmp = 0.0
	if (Float64(Float64(1.0 + t_2) / Float64(2.0 + t_2)) <= 0.65)
		tmp = 0.5;
	else
		tmp = 0.8333333333333334;
	end
	return tmp
end

MATLAB

function tmp_2 = code(t)
	t_1 = (2.0 * t) / (1.0 + t);
	t_2 = t_1 * t_1;
	tmp = 0.0;
	if (((1.0 + t_2) / (2.0 + t_2)) <= 0.65)
		tmp = 0.5;
	else
		tmp = 0.8333333333333334;
	end
	tmp_2 = tmp;
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]}, If[LessEqual[N[(N[(1.0 + t$95$2), $MachinePrecision] / N[(2.0 + t$95$2), $MachinePrecision]), $MachinePrecision], 0.65], 0.5, 0.8333333333333334]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 #s(literal 1 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)))) (+.f64 #s(literal 2 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t))))) < 0.650000000000000022

   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. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{1}{2}} \]
   3. Step-by-step derivation

   4. Applied rewrites 59.4%

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

   if 0.650000000000000022 < (/.f64 (+.f64 #s(literal 1 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)))) (+.f64 #s(literal 2 binary64) (*.f64 (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)) (/.f64 (*.f64 #s(literal 2 binary64) t) (+.f64 #s(literal 1 binary64) t)))))

   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. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{5}{6}} \]
   3. Step-by-step derivation

   4. Applied rewrites 58.7%

      \[\leadsto \color{blue}{0.8333333333333334} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 59.4% accurate, 51.9× 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

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

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

Java

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

Python

def code(t):
	return 0.5

Julia

function code(t)
	return 0.5
end

MATLAB

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

Wolfram

code[t_] := 0.5

Derivation

1. Initial program 99.9%

   \[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}} \]

2. Taylor expanded in t around 0

   \[\leadsto \color{blue}{\frac{1}{2}} \]
3. Step-by-step derivation

4. Applied rewrites 59.4%

   \[\leadsto \color{blue}{0.5} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025134 
(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))))))