Toniolo and Linder, Equation (7)

Percentage Accurate: 32.7% → 82.0%

Time: 6.1s

Alternatives: 8

Speedup: 39.7×

Specification

Math

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

FPCore

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

C

double code(double x, double l, double t) {
	return (sqrt(2.0) * t) / sqrt(((((x + 1.0) / (x - 1.0)) * ((l * l) + (2.0 * (t * t)))) - (l * l)));
}

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

Java

public static double code(double x, double l, double t) {
	return (Math.sqrt(2.0) * t) / Math.sqrt(((((x + 1.0) / (x - 1.0)) * ((l * l) + (2.0 * (t * t)))) - (l * l)));
}

Python

def code(x, l, t):
	return (math.sqrt(2.0) * t) / math.sqrt(((((x + 1.0) / (x - 1.0)) * ((l * l) + (2.0 * (t * t)))) - (l * l)))

Julia

function code(x, l, t)
	return Float64(Float64(sqrt(2.0) * t) / sqrt(Float64(Float64(Float64(Float64(x + 1.0) / Float64(x - 1.0)) * Float64(Float64(l * l) + Float64(2.0 * Float64(t * t)))) - Float64(l * l))))
end

MATLAB

function tmp = code(x, l, t)
	tmp = (sqrt(2.0) * t) / sqrt(((((x + 1.0) / (x - 1.0)) * ((l * l) + (2.0 * (t * t)))) - (l * l)));
end

Wolfram

code[x_, l_, t_] := N[(N[(N[Sqrt[2.0], $MachinePrecision] * t), $MachinePrecision] / N[Sqrt[N[(N[(N[(N[(x + 1.0), $MachinePrecision] / N[(x - 1.0), $MachinePrecision]), $MachinePrecision] * N[(N[(l * l), $MachinePrecision] + N[(2.0 * N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(l * l), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Initial Program: 32.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double l, double t) {
	return (sqrt(2.0) * t) / sqrt(((((x + 1.0) / (x - 1.0)) * ((l * l) + (2.0 * (t * t)))) - (l * l)));
}

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

Java

public static double code(double x, double l, double t) {
	return (Math.sqrt(2.0) * t) / Math.sqrt(((((x + 1.0) / (x - 1.0)) * ((l * l) + (2.0 * (t * t)))) - (l * l)));
}

Python

def code(x, l, t):
	return (math.sqrt(2.0) * t) / math.sqrt(((((x + 1.0) / (x - 1.0)) * ((l * l) + (2.0 * (t * t)))) - (l * l)))

Julia

function code(x, l, t)
	return Float64(Float64(sqrt(2.0) * t) / sqrt(Float64(Float64(Float64(Float64(x + 1.0) / Float64(x - 1.0)) * Float64(Float64(l * l) + Float64(2.0 * Float64(t * t)))) - Float64(l * l))))
end

MATLAB

function tmp = code(x, l, t)
	tmp = (sqrt(2.0) * t) / sqrt(((((x + 1.0) / (x - 1.0)) * ((l * l) + (2.0 * (t * t)))) - (l * l)));
end

Wolfram

code[x_, l_, t_] := N[(N[(N[Sqrt[2.0], $MachinePrecision] * t), $MachinePrecision] / N[Sqrt[N[(N[(N[(N[(x + 1.0), $MachinePrecision] / N[(x - 1.0), $MachinePrecision]), $MachinePrecision] * N[(N[(l * l), $MachinePrecision] + N[(2.0 * N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(l * l), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Alternative 1: 82.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} t\_m = \left|t\right| \\ t\_s = \mathsf{copysign}\left(1, t\right) \\ \begin{array}{l} t_2 := \mathsf{fma}\left(t\_m \cdot t\_m, 2, \ell \cdot \ell\right)\\ t_3 := -t\_2\\ t\_s \cdot \begin{array}{l} \mathbf{if}\;t\_m \leq 1.1 \cdot 10^{-6}:\\ \;\;\;\;\frac{\sqrt{2} \cdot t\_m}{\sqrt{\mathsf{fma}\left(2 \cdot t\_m, t\_m, -\frac{\left(-\frac{\left(\frac{t\_2}{x} + \mathsf{fma}\left(2 \cdot t\_m, t\_m, \ell \cdot \ell - t\_3\right)\right) - \frac{t\_3}{x}}{x}\right) + \left(t\_3 - t\_2\right)}{x}\right)}}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}\\ \end{array} \end{array} \end{array} \]

FPCore

t\_m = (fabs.f64 t)
t\_s = (copysign.f64 #s(literal 1 binary64) t)
(FPCore (t_s x l t_m)
 :precision binary64
 (let* ((t_2 (fma (* t_m t_m) 2.0 (* l l))) (t_3 (- t_2)))
   (*
    t_s
    (if (<= t_m 1.1e-6)
      (/
       (* (sqrt 2.0) t_m)
       (sqrt
        (fma
         (* 2.0 t_m)
         t_m
         (-
          (/
           (+
            (-
             (/
              (- (+ (/ t_2 x) (fma (* 2.0 t_m) t_m (- (* l l) t_3))) (/ t_3 x))
              x))
            (- t_3 t_2))
           x)))))
      (sqrt (/ 2.0 (* (/ (- x -1.0) (- x 1.0)) 2.0)))))))

C

t\_m = fabs(t);
t\_s = copysign(1.0, t);
double code(double t_s, double x, double l, double t_m) {
	double t_2 = fma((t_m * t_m), 2.0, (l * l));
	double t_3 = -t_2;
	double tmp;
	if (t_m <= 1.1e-6) {
		tmp = (sqrt(2.0) * t_m) / sqrt(fma((2.0 * t_m), t_m, -((-((((t_2 / x) + fma((2.0 * t_m), t_m, ((l * l) - t_3))) - (t_3 / x)) / x) + (t_3 - t_2)) / x)));
	} else {
		tmp = sqrt((2.0 / (((x - -1.0) / (x - 1.0)) * 2.0)));
	}
	return t_s * tmp;
}

Julia

t\_m = abs(t)
t\_s = copysign(1.0, t)
function code(t_s, x, l, t_m)
	t_2 = fma(Float64(t_m * t_m), 2.0, Float64(l * l))
	t_3 = Float64(-t_2)
	tmp = 0.0
	if (t_m <= 1.1e-6)
		tmp = Float64(Float64(sqrt(2.0) * t_m) / sqrt(fma(Float64(2.0 * t_m), t_m, Float64(-Float64(Float64(Float64(-Float64(Float64(Float64(Float64(t_2 / x) + fma(Float64(2.0 * t_m), t_m, Float64(Float64(l * l) - t_3))) - Float64(t_3 / x)) / x)) + Float64(t_3 - t_2)) / x)))));
	else
		tmp = sqrt(Float64(2.0 / Float64(Float64(Float64(x - -1.0) / Float64(x - 1.0)) * 2.0)));
	end
	return Float64(t_s * tmp)
end

Wolfram

t\_m = N[Abs[t], $MachinePrecision]
t\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[t]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[t$95$s_, x_, l_, t$95$m_] := Block[{t$95$2 = N[(N[(t$95$m * t$95$m), $MachinePrecision] * 2.0 + N[(l * l), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = (-t$95$2)}, N[(t$95$s * If[LessEqual[t$95$m, 1.1e-6], N[(N[(N[Sqrt[2.0], $MachinePrecision] * t$95$m), $MachinePrecision] / N[Sqrt[N[(N[(2.0 * t$95$m), $MachinePrecision] * t$95$m + (-N[(N[((-N[(N[(N[(N[(t$95$2 / x), $MachinePrecision] + N[(N[(2.0 * t$95$m), $MachinePrecision] * t$95$m + N[(N[(l * l), $MachinePrecision] - t$95$3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(t$95$3 / x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]) + N[(t$95$3 - t$95$2), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision])), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[Sqrt[N[(2.0 / N[(N[(N[(x - -1.0), $MachinePrecision] / N[(x - 1.0), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if t < 1.1000000000000001e-6

   1. Initial program 32.7%

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

   2. Taylor expanded in x around -inf

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

   4. Applied rewrites 51.7%

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

   if 1.1000000000000001e-6 < t

   1. Initial program 32.7%

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

   2. Taylor expanded in t around inf

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

      1. sqrt-undiv N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      3. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      4. div-add-rev N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. div-add-rev N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      9. +-commutative N/A

         \[\leadsto \sqrt{\frac{2}{\frac{x + 1}{x - 1} \cdot 2}} \]

      10. add-flip N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - \left(\mathsf{neg}\left(1\right)\right)}{x - 1} \cdot 2}} \]

      11. metadata-eval N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      12. lower--.f64 N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      13. lift--.f64 77.3

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

   4. Applied rewrites 77.3%

      \[\leadsto \color{blue}{\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 81.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} t\_m = \left|t\right| \\ t\_s = \mathsf{copysign}\left(1, t\right) \\ \begin{array}{l} t_2 := \mathsf{fma}\left(t\_m \cdot t\_m, 2, \ell \cdot \ell\right)\\ t_3 := -t\_2\\ t\_s \cdot \begin{array}{l} \mathbf{if}\;t\_m \leq 1.1 \cdot 10^{-6}:\\ \;\;\;\;\frac{\sqrt{2} \cdot t\_m}{\sqrt{\mathsf{fma}\left(2 \cdot t\_m, t\_m, -\frac{\left(\frac{t\_3}{x} + \left(t\_3 - t\_2\right)\right) - \frac{t\_2}{x}}{x}\right)}}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}\\ \end{array} \end{array} \end{array} \]

FPCore

t\_m = (fabs.f64 t)
t\_s = (copysign.f64 #s(literal 1 binary64) t)
(FPCore (t_s x l t_m)
 :precision binary64
 (let* ((t_2 (fma (* t_m t_m) 2.0 (* l l))) (t_3 (- t_2)))
   (*
    t_s
    (if (<= t_m 1.1e-6)
      (/
       (* (sqrt 2.0) t_m)
       (sqrt
        (fma
         (* 2.0 t_m)
         t_m
         (- (/ (- (+ (/ t_3 x) (- t_3 t_2)) (/ t_2 x)) x)))))
      (sqrt (/ 2.0 (* (/ (- x -1.0) (- x 1.0)) 2.0)))))))

C

t\_m = fabs(t);
t\_s = copysign(1.0, t);
double code(double t_s, double x, double l, double t_m) {
	double t_2 = fma((t_m * t_m), 2.0, (l * l));
	double t_3 = -t_2;
	double tmp;
	if (t_m <= 1.1e-6) {
		tmp = (sqrt(2.0) * t_m) / sqrt(fma((2.0 * t_m), t_m, -((((t_3 / x) + (t_3 - t_2)) - (t_2 / x)) / x)));
	} else {
		tmp = sqrt((2.0 / (((x - -1.0) / (x - 1.0)) * 2.0)));
	}
	return t_s * tmp;
}

Julia

t\_m = abs(t)
t\_s = copysign(1.0, t)
function code(t_s, x, l, t_m)
	t_2 = fma(Float64(t_m * t_m), 2.0, Float64(l * l))
	t_3 = Float64(-t_2)
	tmp = 0.0
	if (t_m <= 1.1e-6)
		tmp = Float64(Float64(sqrt(2.0) * t_m) / sqrt(fma(Float64(2.0 * t_m), t_m, Float64(-Float64(Float64(Float64(Float64(t_3 / x) + Float64(t_3 - t_2)) - Float64(t_2 / x)) / x)))));
	else
		tmp = sqrt(Float64(2.0 / Float64(Float64(Float64(x - -1.0) / Float64(x - 1.0)) * 2.0)));
	end
	return Float64(t_s * tmp)
end

Wolfram

t\_m = N[Abs[t], $MachinePrecision]
t\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[t]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[t$95$s_, x_, l_, t$95$m_] := Block[{t$95$2 = N[(N[(t$95$m * t$95$m), $MachinePrecision] * 2.0 + N[(l * l), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = (-t$95$2)}, N[(t$95$s * If[LessEqual[t$95$m, 1.1e-6], N[(N[(N[Sqrt[2.0], $MachinePrecision] * t$95$m), $MachinePrecision] / N[Sqrt[N[(N[(2.0 * t$95$m), $MachinePrecision] * t$95$m + (-N[(N[(N[(N[(t$95$3 / x), $MachinePrecision] + N[(t$95$3 - t$95$2), $MachinePrecision]), $MachinePrecision] - N[(t$95$2 / x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision])), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[Sqrt[N[(2.0 / N[(N[(N[(x - -1.0), $MachinePrecision] / N[(x - 1.0), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if t < 1.1000000000000001e-6

   1. Initial program 32.7%

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

   2. Taylor expanded in x around -inf

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

      1. +-commutative N/A

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

      2. pow2 N/A

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

      3. associate-*r* N/A

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

      4. lower-fma.f64 N/A

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

   4. Applied rewrites 51.6%

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

   if 1.1000000000000001e-6 < t

   1. Initial program 32.7%

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

   2. Taylor expanded in t around inf

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

      1. sqrt-undiv N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      3. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      4. div-add-rev N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. div-add-rev N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      9. +-commutative N/A

         \[\leadsto \sqrt{\frac{2}{\frac{x + 1}{x - 1} \cdot 2}} \]

      10. add-flip N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - \left(\mathsf{neg}\left(1\right)\right)}{x - 1} \cdot 2}} \]

      11. metadata-eval N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      12. lower--.f64 N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      13. lift--.f64 77.3

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

   4. Applied rewrites 77.3%

      \[\leadsto \color{blue}{\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 81.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} t\_m = \left|t\right| \\ t\_s = \mathsf{copysign}\left(1, t\right) \\ t\_s \cdot \begin{array}{l} \mathbf{if}\;t\_m \leq 1.1 \cdot 10^{-6}:\\ \;\;\;\;\frac{\sqrt{2} \cdot t\_m}{\sqrt{\mathsf{fma}\left(\frac{t\_m \cdot t\_m}{x}, 2, \mathsf{fma}\left(t\_m \cdot t\_m, 2, \frac{\ell \cdot \ell}{x}\right)\right) - \frac{-\mathsf{fma}\left(t\_m \cdot t\_m, 2, \ell \cdot \ell\right)}{x}}}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}\\ \end{array} \end{array} \]

FPCore

t\_m = (fabs.f64 t)
t\_s = (copysign.f64 #s(literal 1 binary64) t)
(FPCore (t_s x l t_m)
 :precision binary64
 (*
  t_s
  (if (<= t_m 1.1e-6)
    (/
     (* (sqrt 2.0) t_m)
     (sqrt
      (-
       (fma (/ (* t_m t_m) x) 2.0 (fma (* t_m t_m) 2.0 (/ (* l l) x)))
       (/ (- (fma (* t_m t_m) 2.0 (* l l))) x))))
    (sqrt (/ 2.0 (* (/ (- x -1.0) (- x 1.0)) 2.0))))))

C

t\_m = fabs(t);
t\_s = copysign(1.0, t);
double code(double t_s, double x, double l, double t_m) {
	double tmp;
	if (t_m <= 1.1e-6) {
		tmp = (sqrt(2.0) * t_m) / sqrt((fma(((t_m * t_m) / x), 2.0, fma((t_m * t_m), 2.0, ((l * l) / x))) - (-fma((t_m * t_m), 2.0, (l * l)) / x)));
	} else {
		tmp = sqrt((2.0 / (((x - -1.0) / (x - 1.0)) * 2.0)));
	}
	return t_s * tmp;
}

Julia

t\_m = abs(t)
t\_s = copysign(1.0, t)
function code(t_s, x, l, t_m)
	tmp = 0.0
	if (t_m <= 1.1e-6)
		tmp = Float64(Float64(sqrt(2.0) * t_m) / sqrt(Float64(fma(Float64(Float64(t_m * t_m) / x), 2.0, fma(Float64(t_m * t_m), 2.0, Float64(Float64(l * l) / x))) - Float64(Float64(-fma(Float64(t_m * t_m), 2.0, Float64(l * l))) / x))));
	else
		tmp = sqrt(Float64(2.0 / Float64(Float64(Float64(x - -1.0) / Float64(x - 1.0)) * 2.0)));
	end
	return Float64(t_s * tmp)
end

Wolfram

t\_m = N[Abs[t], $MachinePrecision]
t\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[t]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[t$95$s_, x_, l_, t$95$m_] := N[(t$95$s * If[LessEqual[t$95$m, 1.1e-6], N[(N[(N[Sqrt[2.0], $MachinePrecision] * t$95$m), $MachinePrecision] / N[Sqrt[N[(N[(N[(N[(t$95$m * t$95$m), $MachinePrecision] / x), $MachinePrecision] * 2.0 + N[(N[(t$95$m * t$95$m), $MachinePrecision] * 2.0 + N[(N[(l * l), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[((-N[(N[(t$95$m * t$95$m), $MachinePrecision] * 2.0 + N[(l * l), $MachinePrecision]), $MachinePrecision]) / x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[Sqrt[N[(2.0 / N[(N[(N[(x - -1.0), $MachinePrecision] / N[(x - 1.0), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if t < 1.1000000000000001e-6

   1. Initial program 32.7%

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

   2. Taylor expanded in x around inf

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

      1. lower--.f64 N/A

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

   4. Applied rewrites 51.4%

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

   if 1.1000000000000001e-6 < t

   1. Initial program 32.7%

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

   2. Taylor expanded in t around inf

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

      1. sqrt-undiv N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      3. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      4. div-add-rev N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. div-add-rev N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      9. +-commutative N/A

         \[\leadsto \sqrt{\frac{2}{\frac{x + 1}{x - 1} \cdot 2}} \]

      10. add-flip N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - \left(\mathsf{neg}\left(1\right)\right)}{x - 1} \cdot 2}} \]

      11. metadata-eval N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      12. lower--.f64 N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      13. lift--.f64 77.3

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

   4. Applied rewrites 77.3%

      \[\leadsto \color{blue}{\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 78.3% accurate, 1.8× speedup

Math

\[\begin{array}{l} t\_m = \left|t\right| \\ t\_s = \mathsf{copysign}\left(1, t\right) \\ t\_s \cdot \begin{array}{l} \mathbf{if}\;t\_m \leq 5.1 \cdot 10^{-247}:\\ \;\;\;\;t\_m \cdot \sqrt{\frac{x}{\ell \cdot \ell}}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}\\ \end{array} \end{array} \]

FPCore

t\_m = (fabs.f64 t)
t\_s = (copysign.f64 #s(literal 1 binary64) t)
(FPCore (t_s x l t_m)
 :precision binary64
 (*
  t_s
  (if (<= t_m 5.1e-247)
    (* t_m (sqrt (/ x (* l l))))
    (sqrt (/ 2.0 (* (/ (- x -1.0) (- x 1.0)) 2.0))))))

C

t\_m = fabs(t);
t\_s = copysign(1.0, t);
double code(double t_s, double x, double l, double t_m) {
	double tmp;
	if (t_m <= 5.1e-247) {
		tmp = t_m * sqrt((x / (l * l)));
	} else {
		tmp = sqrt((2.0 / (((x - -1.0) / (x - 1.0)) * 2.0)));
	}
	return t_s * tmp;
}

Fortran

t\_m =     private
t\_s =     private
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_s, x, l, t_m)
use fmin_fmax_functions
    real(8), intent (in) :: t_s
    real(8), intent (in) :: x
    real(8), intent (in) :: l
    real(8), intent (in) :: t_m
    real(8) :: tmp
    if (t_m <= 5.1d-247) then
        tmp = t_m * sqrt((x / (l * l)))
    else
        tmp = sqrt((2.0d0 / (((x - (-1.0d0)) / (x - 1.0d0)) * 2.0d0)))
    end if
    code = t_s * tmp
end function

Java

t\_m = Math.abs(t);
t\_s = Math.copySign(1.0, t);
public static double code(double t_s, double x, double l, double t_m) {
	double tmp;
	if (t_m <= 5.1e-247) {
		tmp = t_m * Math.sqrt((x / (l * l)));
	} else {
		tmp = Math.sqrt((2.0 / (((x - -1.0) / (x - 1.0)) * 2.0)));
	}
	return t_s * tmp;
}

Python

t\_m = math.fabs(t)
t\_s = math.copysign(1.0, t)
def code(t_s, x, l, t_m):
	tmp = 0
	if t_m <= 5.1e-247:
		tmp = t_m * math.sqrt((x / (l * l)))
	else:
		tmp = math.sqrt((2.0 / (((x - -1.0) / (x - 1.0)) * 2.0)))
	return t_s * tmp

Julia

t\_m = abs(t)
t\_s = copysign(1.0, t)
function code(t_s, x, l, t_m)
	tmp = 0.0
	if (t_m <= 5.1e-247)
		tmp = Float64(t_m * sqrt(Float64(x / Float64(l * l))));
	else
		tmp = sqrt(Float64(2.0 / Float64(Float64(Float64(x - -1.0) / Float64(x - 1.0)) * 2.0)));
	end
	return Float64(t_s * tmp)
end

MATLAB

t\_m = abs(t);
t\_s = sign(t) * abs(1.0);
function tmp_2 = code(t_s, x, l, t_m)
	tmp = 0.0;
	if (t_m <= 5.1e-247)
		tmp = t_m * sqrt((x / (l * l)));
	else
		tmp = sqrt((2.0 / (((x - -1.0) / (x - 1.0)) * 2.0)));
	end
	tmp_2 = t_s * tmp;
end

Wolfram

t\_m = N[Abs[t], $MachinePrecision]
t\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[t]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[t$95$s_, x_, l_, t$95$m_] := N[(t$95$s * If[LessEqual[t$95$m, 5.1e-247], N[(t$95$m * N[Sqrt[N[(x / N[(l * l), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[Sqrt[N[(2.0 / N[(N[(N[(x - -1.0), $MachinePrecision] / N[(x - 1.0), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if t < 5.1000000000000002e-247

   1. Initial program 32.7%

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

   2. Taylor expanded in t around 0

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

      1. associate-/l* N/A

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

      2. lower-*.f64 N/A

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

      3. sqrt-undiv N/A

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

      4. lower-sqrt.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower--.f64 N/A

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

   4. Applied rewrites 3.0%

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

   5. Taylor expanded in x around inf

      \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]

      2. lower-/.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]

      3. lower--.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]

      4. pow2 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot {\ell}^{2}}} \]

      5. lift-*.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot {\ell}^{2}}} \]

      6. lower-*.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot {\ell}^{2}}} \]

      7. pow2 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot \left(\ell \cdot \ell\right)}} \]

      8. lift-*.f64 15.5

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot \left(\ell \cdot \ell\right)}} \]

   7. Applied rewrites 15.5%

      \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot \left(\ell \cdot \ell\right)}} \]

   8. Taylor expanded in l around 0

      \[\leadsto t \cdot \sqrt{\frac{x}{{\ell}^{2}}} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto t \cdot \sqrt{\frac{x}{{\ell}^{2}}} \]

      2. pow2 N/A

         \[\leadsto t \cdot \sqrt{\frac{x}{\ell \cdot \ell}} \]

      3. lift-*.f64 15.5

         \[\leadsto t \cdot \sqrt{\frac{x}{\ell \cdot \ell}} \]

   10. Applied rewrites 15.5%

       \[\leadsto t \cdot \sqrt{\frac{x}{\ell \cdot \ell}} \]

   if 5.1000000000000002e-247 < t

   1. Initial program 32.7%

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

   2. Taylor expanded in t around inf

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

      1. sqrt-undiv N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      3. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      4. div-add-rev N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. div-add-rev N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      9. +-commutative N/A

         \[\leadsto \sqrt{\frac{2}{\frac{x + 1}{x - 1} \cdot 2}} \]

      10. add-flip N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - \left(\mathsf{neg}\left(1\right)\right)}{x - 1} \cdot 2}} \]

      11. metadata-eval N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      12. lower--.f64 N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      13. lift--.f64 77.3

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

   4. Applied rewrites 77.3%

      \[\leadsto \color{blue}{\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 77.9% accurate, 2.0× speedup

Math

\[\begin{array}{l} t\_m = \left|t\right| \\ t\_s = \mathsf{copysign}\left(1, t\right) \\ t\_s \cdot \begin{array}{l} \mathbf{if}\;t\_m \leq 5.1 \cdot 10^{-247}:\\ \;\;\;\;t\_m \cdot \sqrt{\frac{x}{\ell \cdot \ell}}\\ \mathbf{else}:\\ \;\;\;\;\left(1 + \frac{0.5}{x \cdot x}\right) - \frac{1}{x}\\ \end{array} \end{array} \]

FPCore

t\_m = (fabs.f64 t)
t\_s = (copysign.f64 #s(literal 1 binary64) t)
(FPCore (t_s x l t_m)
 :precision binary64
 (*
  t_s
  (if (<= t_m 5.1e-247)
    (* t_m (sqrt (/ x (* l l))))
    (- (+ 1.0 (/ 0.5 (* x x))) (/ 1.0 x)))))

C

t\_m = fabs(t);
t\_s = copysign(1.0, t);
double code(double t_s, double x, double l, double t_m) {
	double tmp;
	if (t_m <= 5.1e-247) {
		tmp = t_m * sqrt((x / (l * l)));
	} else {
		tmp = (1.0 + (0.5 / (x * x))) - (1.0 / x);
	}
	return t_s * tmp;
}

Fortran

t\_m =     private
t\_s =     private
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_s, x, l, t_m)
use fmin_fmax_functions
    real(8), intent (in) :: t_s
    real(8), intent (in) :: x
    real(8), intent (in) :: l
    real(8), intent (in) :: t_m
    real(8) :: tmp
    if (t_m <= 5.1d-247) then
        tmp = t_m * sqrt((x / (l * l)))
    else
        tmp = (1.0d0 + (0.5d0 / (x * x))) - (1.0d0 / x)
    end if
    code = t_s * tmp
end function

Java

t\_m = Math.abs(t);
t\_s = Math.copySign(1.0, t);
public static double code(double t_s, double x, double l, double t_m) {
	double tmp;
	if (t_m <= 5.1e-247) {
		tmp = t_m * Math.sqrt((x / (l * l)));
	} else {
		tmp = (1.0 + (0.5 / (x * x))) - (1.0 / x);
	}
	return t_s * tmp;
}

Python

t\_m = math.fabs(t)
t\_s = math.copysign(1.0, t)
def code(t_s, x, l, t_m):
	tmp = 0
	if t_m <= 5.1e-247:
		tmp = t_m * math.sqrt((x / (l * l)))
	else:
		tmp = (1.0 + (0.5 / (x * x))) - (1.0 / x)
	return t_s * tmp

Julia

t\_m = abs(t)
t\_s = copysign(1.0, t)
function code(t_s, x, l, t_m)
	tmp = 0.0
	if (t_m <= 5.1e-247)
		tmp = Float64(t_m * sqrt(Float64(x / Float64(l * l))));
	else
		tmp = Float64(Float64(1.0 + Float64(0.5 / Float64(x * x))) - Float64(1.0 / x));
	end
	return Float64(t_s * tmp)
end

MATLAB

t\_m = abs(t);
t\_s = sign(t) * abs(1.0);
function tmp_2 = code(t_s, x, l, t_m)
	tmp = 0.0;
	if (t_m <= 5.1e-247)
		tmp = t_m * sqrt((x / (l * l)));
	else
		tmp = (1.0 + (0.5 / (x * x))) - (1.0 / x);
	end
	tmp_2 = t_s * tmp;
end

Wolfram

t\_m = N[Abs[t], $MachinePrecision]
t\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[t]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[t$95$s_, x_, l_, t$95$m_] := N[(t$95$s * If[LessEqual[t$95$m, 5.1e-247], N[(t$95$m * N[Sqrt[N[(x / N[(l * l), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(N[(1.0 + N[(0.5 / N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(1.0 / x), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if t < 5.1000000000000002e-247

   1. Initial program 32.7%

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

   2. Taylor expanded in t around 0

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

      1. associate-/l* N/A

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

      2. lower-*.f64 N/A

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

      3. sqrt-undiv N/A

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

      4. lower-sqrt.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower--.f64 N/A

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

   4. Applied rewrites 3.0%

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

   5. Taylor expanded in x around inf

      \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]

      2. lower-/.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]

      3. lower--.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]

      4. pow2 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot {\ell}^{2}}} \]

      5. lift-*.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot {\ell}^{2}}} \]

      6. lower-*.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot {\ell}^{2}}} \]

      7. pow2 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot \left(\ell \cdot \ell\right)}} \]

      8. lift-*.f64 15.5

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot \left(\ell \cdot \ell\right)}} \]

   7. Applied rewrites 15.5%

      \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot \left(\ell \cdot \ell\right)}} \]

   8. Taylor expanded in l around 0

      \[\leadsto t \cdot \sqrt{\frac{x}{{\ell}^{2}}} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto t \cdot \sqrt{\frac{x}{{\ell}^{2}}} \]

      2. pow2 N/A

         \[\leadsto t \cdot \sqrt{\frac{x}{\ell \cdot \ell}} \]

      3. lift-*.f64 15.5

         \[\leadsto t \cdot \sqrt{\frac{x}{\ell \cdot \ell}} \]

   10. Applied rewrites 15.5%

       \[\leadsto t \cdot \sqrt{\frac{x}{\ell \cdot \ell}} \]

   if 5.1000000000000002e-247 < t

   1. Initial program 32.7%

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

   2. Taylor expanded in t around inf

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

      1. sqrt-undiv N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      3. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      4. div-add-rev N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. div-add-rev N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      9. +-commutative N/A

         \[\leadsto \sqrt{\frac{2}{\frac{x + 1}{x - 1} \cdot 2}} \]

      10. add-flip N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - \left(\mathsf{neg}\left(1\right)\right)}{x - 1} \cdot 2}} \]

      11. metadata-eval N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      12. lower--.f64 N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      13. lift--.f64 77.3

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

   4. Applied rewrites 77.3%

      \[\leadsto \color{blue}{\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}} \]

   5. Taylor expanded in x around inf

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 76.9

         \[\leadsto \left(1 + \frac{0.5}{x \cdot x}\right) - \frac{1}{x} \]

   7. Applied rewrites 76.9%

      \[\leadsto \left(1 + \frac{0.5}{x \cdot x}\right) - \color{blue}{\frac{1}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 77.7% accurate, 2.5× speedup

Math

\[\begin{array}{l} t\_m = \left|t\right| \\ t\_s = \mathsf{copysign}\left(1, t\right) \\ t\_s \cdot \begin{array}{l} \mathbf{if}\;t\_m \leq 5.1 \cdot 10^{-247}:\\ \;\;\;\;t\_m \cdot \sqrt{\frac{x}{\ell \cdot \ell}}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{1}{x}\\ \end{array} \end{array} \]

FPCore

t\_m = (fabs.f64 t)
t\_s = (copysign.f64 #s(literal 1 binary64) t)
(FPCore (t_s x l t_m)
 :precision binary64
 (* t_s (if (<= t_m 5.1e-247) (* t_m (sqrt (/ x (* l l)))) (- 1.0 (/ 1.0 x)))))

C

t\_m = fabs(t);
t\_s = copysign(1.0, t);
double code(double t_s, double x, double l, double t_m) {
	double tmp;
	if (t_m <= 5.1e-247) {
		tmp = t_m * sqrt((x / (l * l)));
	} else {
		tmp = 1.0 - (1.0 / x);
	}
	return t_s * tmp;
}

Fortran

t\_m =     private
t\_s =     private
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_s, x, l, t_m)
use fmin_fmax_functions
    real(8), intent (in) :: t_s
    real(8), intent (in) :: x
    real(8), intent (in) :: l
    real(8), intent (in) :: t_m
    real(8) :: tmp
    if (t_m <= 5.1d-247) then
        tmp = t_m * sqrt((x / (l * l)))
    else
        tmp = 1.0d0 - (1.0d0 / x)
    end if
    code = t_s * tmp
end function

Java

t\_m = Math.abs(t);
t\_s = Math.copySign(1.0, t);
public static double code(double t_s, double x, double l, double t_m) {
	double tmp;
	if (t_m <= 5.1e-247) {
		tmp = t_m * Math.sqrt((x / (l * l)));
	} else {
		tmp = 1.0 - (1.0 / x);
	}
	return t_s * tmp;
}

Python

t\_m = math.fabs(t)
t\_s = math.copysign(1.0, t)
def code(t_s, x, l, t_m):
	tmp = 0
	if t_m <= 5.1e-247:
		tmp = t_m * math.sqrt((x / (l * l)))
	else:
		tmp = 1.0 - (1.0 / x)
	return t_s * tmp

Julia

t\_m = abs(t)
t\_s = copysign(1.0, t)
function code(t_s, x, l, t_m)
	tmp = 0.0
	if (t_m <= 5.1e-247)
		tmp = Float64(t_m * sqrt(Float64(x / Float64(l * l))));
	else
		tmp = Float64(1.0 - Float64(1.0 / x));
	end
	return Float64(t_s * tmp)
end

MATLAB

t\_m = abs(t);
t\_s = sign(t) * abs(1.0);
function tmp_2 = code(t_s, x, l, t_m)
	tmp = 0.0;
	if (t_m <= 5.1e-247)
		tmp = t_m * sqrt((x / (l * l)));
	else
		tmp = 1.0 - (1.0 / x);
	end
	tmp_2 = t_s * tmp;
end

Wolfram

t\_m = N[Abs[t], $MachinePrecision]
t\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[t]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[t$95$s_, x_, l_, t$95$m_] := N[(t$95$s * If[LessEqual[t$95$m, 5.1e-247], N[(t$95$m * N[Sqrt[N[(x / N[(l * l), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(1.0 - N[(1.0 / x), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if t < 5.1000000000000002e-247

   1. Initial program 32.7%

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

   2. Taylor expanded in t around 0

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

      1. associate-/l* N/A

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

      2. lower-*.f64 N/A

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

      3. sqrt-undiv N/A

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

      4. lower-sqrt.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower--.f64 N/A

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

   4. Applied rewrites 3.0%

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

   5. Taylor expanded in x around inf

      \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]

      2. lower-/.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]

      3. lower--.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{{\ell}^{2} - -1 \cdot {\ell}^{2}}} \]

      4. pow2 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot {\ell}^{2}}} \]

      5. lift-*.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot {\ell}^{2}}} \]

      6. lower-*.f64 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot {\ell}^{2}}} \]

      7. pow2 N/A

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot \left(\ell \cdot \ell\right)}} \]

      8. lift-*.f64 15.5

         \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot \left(\ell \cdot \ell\right)}} \]

   7. Applied rewrites 15.5%

      \[\leadsto t \cdot \sqrt{2 \cdot \frac{x}{\ell \cdot \ell - -1 \cdot \left(\ell \cdot \ell\right)}} \]

   8. Taylor expanded in l around 0

      \[\leadsto t \cdot \sqrt{\frac{x}{{\ell}^{2}}} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto t \cdot \sqrt{\frac{x}{{\ell}^{2}}} \]

      2. pow2 N/A

         \[\leadsto t \cdot \sqrt{\frac{x}{\ell \cdot \ell}} \]

      3. lift-*.f64 15.5

         \[\leadsto t \cdot \sqrt{\frac{x}{\ell \cdot \ell}} \]

   10. Applied rewrites 15.5%

       \[\leadsto t \cdot \sqrt{\frac{x}{\ell \cdot \ell}} \]

   if 5.1000000000000002e-247 < t

   1. Initial program 32.7%

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

   2. Taylor expanded in t around inf

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

      1. sqrt-undiv N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      3. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

      4. div-add-rev N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. div-add-rev N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

      9. +-commutative N/A

         \[\leadsto \sqrt{\frac{2}{\frac{x + 1}{x - 1} \cdot 2}} \]

      10. add-flip N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - \left(\mathsf{neg}\left(1\right)\right)}{x - 1} \cdot 2}} \]

      11. metadata-eval N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      12. lower--.f64 N/A

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

      13. lift--.f64 77.3

          \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

   4. Applied rewrites 77.3%

      \[\leadsto \color{blue}{\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}} \]

   5. Taylor expanded in x around inf

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

      1. lower--.f64 N/A

         \[\leadsto 1 - \frac{1}{\color{blue}{x}} \]

      2. lower-/.f64 76.7

         \[\leadsto 1 - \frac{1}{x} \]

   7. Applied rewrites 76.7%

      \[\leadsto 1 - \color{blue}{\frac{1}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 76.7% accurate, 5.7× speedup

Math

\[\begin{array}{l} t\_m = \left|t\right| \\ t\_s = \mathsf{copysign}\left(1, t\right) \\ t\_s \cdot \left(1 - \frac{1}{x}\right) \end{array} \]

FPCore

t\_m = (fabs.f64 t)
t\_s = (copysign.f64 #s(literal 1 binary64) t)
(FPCore (t_s x l t_m) :precision binary64 (* t_s (- 1.0 (/ 1.0 x))))

C

t\_m = fabs(t);
t\_s = copysign(1.0, t);
double code(double t_s, double x, double l, double t_m) {
	return t_s * (1.0 - (1.0 / x));
}

Fortran

t\_m =     private
t\_s =     private
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_s, x, l, t_m)
use fmin_fmax_functions
    real(8), intent (in) :: t_s
    real(8), intent (in) :: x
    real(8), intent (in) :: l
    real(8), intent (in) :: t_m
    code = t_s * (1.0d0 - (1.0d0 / x))
end function

Java

t\_m = Math.abs(t);
t\_s = Math.copySign(1.0, t);
public static double code(double t_s, double x, double l, double t_m) {
	return t_s * (1.0 - (1.0 / x));
}

Python

t\_m = math.fabs(t)
t\_s = math.copysign(1.0, t)
def code(t_s, x, l, t_m):
	return t_s * (1.0 - (1.0 / x))

Julia

t\_m = abs(t)
t\_s = copysign(1.0, t)
function code(t_s, x, l, t_m)
	return Float64(t_s * Float64(1.0 - Float64(1.0 / x)))
end

MATLAB

t\_m = abs(t);
t\_s = sign(t) * abs(1.0);
function tmp = code(t_s, x, l, t_m)
	tmp = t_s * (1.0 - (1.0 / x));
end

Wolfram

t\_m = N[Abs[t], $MachinePrecision]
t\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[t]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[t$95$s_, x_, l_, t$95$m_] := N[(t$95$s * N[(1.0 - N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 32.7%

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

2. Taylor expanded in t around inf

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

   1. sqrt-undiv N/A

      \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

   2. lower-sqrt.f64 N/A

      \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

   3. lower-/.f64 N/A

      \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

   4. div-add-rev N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

   7. div-add-rev N/A

      \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

   8. lower-/.f64 N/A

      \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

   9. +-commutative N/A

      \[\leadsto \sqrt{\frac{2}{\frac{x + 1}{x - 1} \cdot 2}} \]

   10. add-flip N/A

       \[\leadsto \sqrt{\frac{2}{\frac{x - \left(\mathsf{neg}\left(1\right)\right)}{x - 1} \cdot 2}} \]

   11. metadata-eval N/A

       \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

   12. lower--.f64 N/A

       \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

   13. lift--.f64 77.3

       \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

4. Applied rewrites 77.3%

   \[\leadsto \color{blue}{\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}} \]

5. Taylor expanded in x around inf

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

   1. lower--.f64 N/A

      \[\leadsto 1 - \frac{1}{\color{blue}{x}} \]

   2. lower-/.f64 76.7

      \[\leadsto 1 - \frac{1}{x} \]

7. Applied rewrites 76.7%

   \[\leadsto 1 - \color{blue}{\frac{1}{x}} \]
8. Add Preprocessing

Alternative 8: 76.0% accurate, 39.7× speedup

Math

\[\begin{array}{l} t\_m = \left|t\right| \\ t\_s = \mathsf{copysign}\left(1, t\right) \\ t\_s \cdot 1 \end{array} \]

FPCore

t\_m = (fabs.f64 t)
t\_s = (copysign.f64 #s(literal 1 binary64) t)
(FPCore (t_s x l t_m) :precision binary64 (* t_s 1.0))

C

t\_m = fabs(t);
t\_s = copysign(1.0, t);
double code(double t_s, double x, double l, double t_m) {
	return t_s * 1.0;
}

Fortran

t\_m =     private
t\_s =     private
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_s, x, l, t_m)
use fmin_fmax_functions
    real(8), intent (in) :: t_s
    real(8), intent (in) :: x
    real(8), intent (in) :: l
    real(8), intent (in) :: t_m
    code = t_s * 1.0d0
end function

Java

t\_m = Math.abs(t);
t\_s = Math.copySign(1.0, t);
public static double code(double t_s, double x, double l, double t_m) {
	return t_s * 1.0;
}

Python

t\_m = math.fabs(t)
t\_s = math.copysign(1.0, t)
def code(t_s, x, l, t_m):
	return t_s * 1.0

Julia

t\_m = abs(t)
t\_s = copysign(1.0, t)
function code(t_s, x, l, t_m)
	return Float64(t_s * 1.0)
end

MATLAB

t\_m = abs(t);
t\_s = sign(t) * abs(1.0);
function tmp = code(t_s, x, l, t_m)
	tmp = t_s * 1.0;
end

Wolfram

t\_m = N[Abs[t], $MachinePrecision]
t\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[t]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[t$95$s_, x_, l_, t$95$m_] := N[(t$95$s * 1.0), $MachinePrecision]

Derivation

1. Initial program 32.7%

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

2. Taylor expanded in t around inf

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

   1. sqrt-undiv N/A

      \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

   2. lower-sqrt.f64 N/A

      \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

   3. lower-/.f64 N/A

      \[\leadsto \sqrt{\frac{2}{2 \cdot \frac{1 + x}{x - 1}}} \]

   4. div-add-rev N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

   7. div-add-rev N/A

      \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

   8. lower-/.f64 N/A

      \[\leadsto \sqrt{\frac{2}{\frac{1 + x}{x - 1} \cdot 2}} \]

   9. +-commutative N/A

      \[\leadsto \sqrt{\frac{2}{\frac{x + 1}{x - 1} \cdot 2}} \]

   10. add-flip N/A

       \[\leadsto \sqrt{\frac{2}{\frac{x - \left(\mathsf{neg}\left(1\right)\right)}{x - 1} \cdot 2}} \]

   11. metadata-eval N/A

       \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

   12. lower--.f64 N/A

       \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

   13. lift--.f64 77.3

       \[\leadsto \sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}} \]

4. Applied rewrites 77.3%

   \[\leadsto \color{blue}{\sqrt{\frac{2}{\frac{x - -1}{x - 1} \cdot 2}}} \]

5. Taylor expanded in x around 0

   \[\leadsto \sqrt{-1} \]
6. Step-by-step derivation

7. Applied rewrites 0.0%

   \[\leadsto \sqrt{-1} \]

8. Taylor expanded in x around inf

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

   1. sqrt-div N/A

      \[\leadsto 1 - \frac{\color{blue}{1}}{x} \]

   2. lower--.f64 N/A

      \[\leadsto 1 - \frac{1}{\color{blue}{x}} \]

   3. lift-/.f64 76.7

      \[\leadsto 1 - \frac{1}{x} \]

10. Applied rewrites 76.7%

    \[\leadsto 1 - \color{blue}{\frac{1}{x}} \]

11. Taylor expanded in x around inf

    \[\leadsto 1 \]
12. Step-by-step derivation

13. Applied rewrites 76.0%

    \[\leadsto 1 \]
14. Add Preprocessing

Reproduce

herbie shell --seed 2025139 
(FPCore (x l t)
  :name "Toniolo and Linder, Equation (7)"
  :precision binary64
  (/ (* (sqrt 2.0) t) (sqrt (- (* (/ (+ x 1.0) (- x 1.0)) (+ (* l l) (* 2.0 (* t t)))) (* l l)))))