Falkner and Boettcher, Appendix A

Percentage Accurate: 90.5% → 99.8%

Time: 4.6s

Alternatives: 11

Speedup: 1.3×

• 21.7% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \end{array} \]

FPCore

(FPCore (a k m)
 :precision binary64
 (/ (* a (pow k m)) (+ (+ 1.0 (* 10.0 k)) (* k k))))

C

double code(double a, double k, double m) {
	return (a * pow(k, m)) / ((1.0 + (10.0 * k)) + (k * k));
}

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(a, k, m)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: k
    real(8), intent (in) :: m
    code = (a * (k ** m)) / ((1.0d0 + (10.0d0 * k)) + (k * k))
end function

Java

public static double code(double a, double k, double m) {
	return (a * Math.pow(k, m)) / ((1.0 + (10.0 * k)) + (k * k));
}

Python

def code(a, k, m):
	return (a * math.pow(k, m)) / ((1.0 + (10.0 * k)) + (k * k))

Julia

function code(a, k, m)
	return Float64(Float64(a * (k ^ m)) / Float64(Float64(1.0 + Float64(10.0 * k)) + Float64(k * k)))
end

MATLAB

function tmp = code(a, k, m)
	tmp = (a * (k ^ m)) / ((1.0 + (10.0 * k)) + (k * k));
end

Wolfram

code[a_, k_, m_] := N[(N[(a * N[Power[k, m], $MachinePrecision]), $MachinePrecision] / N[(N[(1.0 + N[(10.0 * k), $MachinePrecision]), $MachinePrecision] + N[(k * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 90.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \end{array} \]

FPCore

(FPCore (a k m)
 :precision binary64
 (/ (* a (pow k m)) (+ (+ 1.0 (* 10.0 k)) (* k k))))

C

double code(double a, double k, double m) {
	return (a * pow(k, m)) / ((1.0 + (10.0 * k)) + (k * k));
}

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(a, k, m)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: k
    real(8), intent (in) :: m
    code = (a * (k ** m)) / ((1.0d0 + (10.0d0 * k)) + (k * k))
end function

Java

public static double code(double a, double k, double m) {
	return (a * Math.pow(k, m)) / ((1.0 + (10.0 * k)) + (k * k));
}

Python

def code(a, k, m):
	return (a * math.pow(k, m)) / ((1.0 + (10.0 * k)) + (k * k))

Julia

function code(a, k, m)
	return Float64(Float64(a * (k ^ m)) / Float64(Float64(1.0 + Float64(10.0 * k)) + Float64(k * k)))
end

MATLAB

function tmp = code(a, k, m)
	tmp = (a * (k ^ m)) / ((1.0 + (10.0 * k)) + (k * k));
end

Wolfram

code[a_, k_, m_] := N[(N[(a * N[Power[k, m], $MachinePrecision]), $MachinePrecision] / N[(N[(1.0 + N[(10.0 * k), $MachinePrecision]), $MachinePrecision] + N[(k * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;k \leq 4 \cdot 10^{-62}:\\ \;\;\;\;\frac{1}{\frac{1}{{k}^{m}}} \cdot a\\ \mathbf{else}:\\ \;\;\;\;\frac{a \cdot {k}^{\left(m + -1\right)}}{\left(k - -10\right) - \frac{-1}{k}}\\ \end{array} \end{array} \]

FPCore

(FPCore (a k m)
 :precision binary64
 (if (<= k 4e-62)
   (* (/ 1.0 (/ 1.0 (pow k m))) a)
   (/ (* a (pow k (+ m -1.0))) (- (- k -10.0) (/ -1.0 k)))))

C

double code(double a, double k, double m) {
	double tmp;
	if (k <= 4e-62) {
		tmp = (1.0 / (1.0 / pow(k, m))) * a;
	} else {
		tmp = (a * pow(k, (m + -1.0))) / ((k - -10.0) - (-1.0 / k));
	}
	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(a, k, m)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: k
    real(8), intent (in) :: m
    real(8) :: tmp
    if (k <= 4d-62) then
        tmp = (1.0d0 / (1.0d0 / (k ** m))) * a
    else
        tmp = (a * (k ** (m + (-1.0d0)))) / ((k - (-10.0d0)) - ((-1.0d0) / k))
    end if
    code = tmp
end function

Java

public static double code(double a, double k, double m) {
	double tmp;
	if (k <= 4e-62) {
		tmp = (1.0 / (1.0 / Math.pow(k, m))) * a;
	} else {
		tmp = (a * Math.pow(k, (m + -1.0))) / ((k - -10.0) - (-1.0 / k));
	}
	return tmp;
}

Python

def code(a, k, m):
	tmp = 0
	if k <= 4e-62:
		tmp = (1.0 / (1.0 / math.pow(k, m))) * a
	else:
		tmp = (a * math.pow(k, (m + -1.0))) / ((k - -10.0) - (-1.0 / k))
	return tmp

Julia

function code(a, k, m)
	tmp = 0.0
	if (k <= 4e-62)
		tmp = Float64(Float64(1.0 / Float64(1.0 / (k ^ m))) * a);
	else
		tmp = Float64(Float64(a * (k ^ Float64(m + -1.0))) / Float64(Float64(k - -10.0) - Float64(-1.0 / k)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, k, m)
	tmp = 0.0;
	if (k <= 4e-62)
		tmp = (1.0 / (1.0 / (k ^ m))) * a;
	else
		tmp = (a * (k ^ (m + -1.0))) / ((k - -10.0) - (-1.0 / k));
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, k_, m_] := If[LessEqual[k, 4e-62], N[(N[(1.0 / N[(1.0 / N[Power[k, m], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * a), $MachinePrecision], N[(N[(a * N[Power[k, N[(m + -1.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / N[(N[(k - -10.0), $MachinePrecision] - N[(-1.0 / k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if k < 4.0000000000000002e-62

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{a \cdot {k}^{m}}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{a \cdot \frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot a} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot a} \]

   3. Applied rewrites 90.5%

      \[\leadsto \color{blue}{\frac{{k}^{m}}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a} \]
   4. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\mathsf{fma}\left(k - -10, k, 1\right)}} \cdot a \]

      2. div-flip N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

      3. lower-unsound-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

      4. lower-unsound-/.f64 90.5

         \[\leadsto \frac{1}{\color{blue}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

   5. Applied rewrites 90.5%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

   6. Taylor expanded in k around 0

      \[\leadsto \frac{1}{\frac{\color{blue}{1}}{{k}^{m}}} \cdot a \]
   7. Step-by-step derivation

   8. Applied rewrites 82.8%

      \[\leadsto \frac{1}{\frac{\color{blue}{1}}{{k}^{m}}} \cdot a \]

   if 4.0000000000000002e-62 < k

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

      4. mult-flip N/A

         \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

      5. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

      6. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

      7. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   3. Applied rewrites 90.4%

      \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

   5. Applied rewrites 89.8%

      \[\leadsto \color{blue}{\frac{{k}^{m} \cdot \frac{a}{\left(10 - \frac{-1}{k}\right) + k}}{k}} \]
   6. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m} \cdot \frac{a}{\left(10 - \frac{-1}{k}\right) + k}}{k}} \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left({k}^{m} \cdot \frac{a}{\left(10 - \frac{-1}{k}\right) + k}\right) \cdot \frac{1}{k}} \]

      3. metadata-eval N/A

         \[\leadsto \left({k}^{m} \cdot \frac{a}{\left(10 - \frac{-1}{k}\right) + k}\right) \cdot \frac{\color{blue}{\mathsf{neg}\left(-1\right)}}{k} \]

      4. distribute-neg-frac N/A

         \[\leadsto \left({k}^{m} \cdot \frac{a}{\left(10 - \frac{-1}{k}\right) + k}\right) \cdot \color{blue}{\left(\mathsf{neg}\left(\frac{-1}{k}\right)\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto \left({k}^{m} \cdot \frac{a}{\left(10 - \frac{-1}{k}\right) + k}\right) \cdot \left(\mathsf{neg}\left(\color{blue}{\frac{-1}{k}}\right)\right) \]

      6. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left({k}^{m} \cdot \frac{a}{\left(10 - \frac{-1}{k}\right) + k}\right)} \cdot \left(\mathsf{neg}\left(\frac{-1}{k}\right)\right) \]

      7. lift-/.f64 N/A

         \[\leadsto \left({k}^{m} \cdot \color{blue}{\frac{a}{\left(10 - \frac{-1}{k}\right) + k}}\right) \cdot \left(\mathsf{neg}\left(\frac{-1}{k}\right)\right) \]

      8. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{{k}^{m} \cdot a}{\left(10 - \frac{-1}{k}\right) + k}} \cdot \left(\mathsf{neg}\left(\frac{-1}{k}\right)\right) \]

      9. lift-pow.f64 N/A

         \[\leadsto \frac{\color{blue}{{k}^{m}} \cdot a}{\left(10 - \frac{-1}{k}\right) + k} \cdot \left(\mathsf{neg}\left(\frac{-1}{k}\right)\right) \]

      10. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{\left({k}^{m} \cdot a\right) \cdot \left(\mathsf{neg}\left(\frac{-1}{k}\right)\right)}{\left(10 - \frac{-1}{k}\right) + k}} \]

      11. lower-/.f64 N/A

          \[\leadsto \color{blue}{\frac{\left({k}^{m} \cdot a\right) \cdot \left(\mathsf{neg}\left(\frac{-1}{k}\right)\right)}{\left(10 - \frac{-1}{k}\right) + k}} \]

   7. Applied rewrites 83.5%

      \[\leadsto \color{blue}{\frac{a \cdot {k}^{\left(m + -1\right)}}{\left(k - -10\right) - \frac{-1}{k}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 97.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;k \leq 0.00186:\\ \;\;\;\;\frac{1}{\frac{1}{{k}^{m}}} \cdot a\\ \mathbf{else}:\\ \;\;\;\;\frac{{k}^{m} \cdot \frac{a}{k}}{k}\\ \end{array} \end{array} \]

FPCore

(FPCore (a k m)
 :precision binary64
 (if (<= k 0.00186)
   (* (/ 1.0 (/ 1.0 (pow k m))) a)
   (/ (* (pow k m) (/ a k)) k)))

C

double code(double a, double k, double m) {
	double tmp;
	if (k <= 0.00186) {
		tmp = (1.0 / (1.0 / pow(k, m))) * a;
	} else {
		tmp = (pow(k, m) * (a / k)) / k;
	}
	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(a, k, m)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: k
    real(8), intent (in) :: m
    real(8) :: tmp
    if (k <= 0.00186d0) then
        tmp = (1.0d0 / (1.0d0 / (k ** m))) * a
    else
        tmp = ((k ** m) * (a / k)) / k
    end if
    code = tmp
end function

Java

public static double code(double a, double k, double m) {
	double tmp;
	if (k <= 0.00186) {
		tmp = (1.0 / (1.0 / Math.pow(k, m))) * a;
	} else {
		tmp = (Math.pow(k, m) * (a / k)) / k;
	}
	return tmp;
}

Python

def code(a, k, m):
	tmp = 0
	if k <= 0.00186:
		tmp = (1.0 / (1.0 / math.pow(k, m))) * a
	else:
		tmp = (math.pow(k, m) * (a / k)) / k
	return tmp

Julia

function code(a, k, m)
	tmp = 0.0
	if (k <= 0.00186)
		tmp = Float64(Float64(1.0 / Float64(1.0 / (k ^ m))) * a);
	else
		tmp = Float64(Float64((k ^ m) * Float64(a / k)) / k);
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, k, m)
	tmp = 0.0;
	if (k <= 0.00186)
		tmp = (1.0 / (1.0 / (k ^ m))) * a;
	else
		tmp = ((k ^ m) * (a / k)) / k;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, k_, m_] := If[LessEqual[k, 0.00186], N[(N[(1.0 / N[(1.0 / N[Power[k, m], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * a), $MachinePrecision], N[(N[(N[Power[k, m], $MachinePrecision] * N[(a / k), $MachinePrecision]), $MachinePrecision] / k), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if k < 0.0018600000000000001

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{a \cdot {k}^{m}}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{a \cdot \frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot a} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot a} \]

   3. Applied rewrites 90.5%

      \[\leadsto \color{blue}{\frac{{k}^{m}}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a} \]
   4. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\mathsf{fma}\left(k - -10, k, 1\right)}} \cdot a \]

      2. div-flip N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

      3. lower-unsound-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

      4. lower-unsound-/.f64 90.5

         \[\leadsto \frac{1}{\color{blue}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

   5. Applied rewrites 90.5%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

   6. Taylor expanded in k around 0

      \[\leadsto \frac{1}{\frac{\color{blue}{1}}{{k}^{m}}} \cdot a \]
   7. Step-by-step derivation

   8. Applied rewrites 82.8%

      \[\leadsto \frac{1}{\frac{\color{blue}{1}}{{k}^{m}}} \cdot a \]

   if 0.0018600000000000001 < k

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

      4. mult-flip N/A

         \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

      5. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

      6. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

      7. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   3. Applied rewrites 90.4%

      \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

   5. Applied rewrites 89.8%

      \[\leadsto \color{blue}{\frac{{k}^{m} \cdot \frac{a}{\left(10 - \frac{-1}{k}\right) + k}}{k}} \]

   6. Taylor expanded in k around inf

      \[\leadsto \frac{{k}^{m} \cdot \color{blue}{\frac{a}{k}}}{k} \]
   7. Step-by-step derivation

      1. lower-/.f64 75.1

         \[\leadsto \frac{{k}^{m} \cdot \frac{a}{\color{blue}{k}}}{k} \]

   8. Applied rewrites 75.1%

      \[\leadsto \frac{{k}^{m} \cdot \color{blue}{\frac{a}{k}}}{k} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 97.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;k \leq 0.00186:\\ \;\;\;\;\frac{1}{\frac{1}{{k}^{m}}} \cdot a\\ \mathbf{else}:\\ \;\;\;\;\frac{a}{k} \cdot {k}^{\left(m + -1\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (a k m)
 :precision binary64
 (if (<= k 0.00186)
   (* (/ 1.0 (/ 1.0 (pow k m))) a)
   (* (/ a k) (pow k (+ m -1.0)))))

C

double code(double a, double k, double m) {
	double tmp;
	if (k <= 0.00186) {
		tmp = (1.0 / (1.0 / pow(k, m))) * a;
	} else {
		tmp = (a / k) * pow(k, (m + -1.0));
	}
	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(a, k, m)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: k
    real(8), intent (in) :: m
    real(8) :: tmp
    if (k <= 0.00186d0) then
        tmp = (1.0d0 / (1.0d0 / (k ** m))) * a
    else
        tmp = (a / k) * (k ** (m + (-1.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double a, double k, double m) {
	double tmp;
	if (k <= 0.00186) {
		tmp = (1.0 / (1.0 / Math.pow(k, m))) * a;
	} else {
		tmp = (a / k) * Math.pow(k, (m + -1.0));
	}
	return tmp;
}

Python

def code(a, k, m):
	tmp = 0
	if k <= 0.00186:
		tmp = (1.0 / (1.0 / math.pow(k, m))) * a
	else:
		tmp = (a / k) * math.pow(k, (m + -1.0))
	return tmp

Julia

function code(a, k, m)
	tmp = 0.0
	if (k <= 0.00186)
		tmp = Float64(Float64(1.0 / Float64(1.0 / (k ^ m))) * a);
	else
		tmp = Float64(Float64(a / k) * (k ^ Float64(m + -1.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, k, m)
	tmp = 0.0;
	if (k <= 0.00186)
		tmp = (1.0 / (1.0 / (k ^ m))) * a;
	else
		tmp = (a / k) * (k ^ (m + -1.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, k_, m_] := If[LessEqual[k, 0.00186], N[(N[(1.0 / N[(1.0 / N[Power[k, m], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * a), $MachinePrecision], N[(N[(a / k), $MachinePrecision] * N[Power[k, N[(m + -1.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if k < 0.0018600000000000001

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{a \cdot {k}^{m}}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{a \cdot \frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot a} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot a} \]

   3. Applied rewrites 90.5%

      \[\leadsto \color{blue}{\frac{{k}^{m}}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a} \]
   4. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\mathsf{fma}\left(k - -10, k, 1\right)}} \cdot a \]

      2. div-flip N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

      3. lower-unsound-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

      4. lower-unsound-/.f64 90.5

         \[\leadsto \frac{1}{\color{blue}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

   5. Applied rewrites 90.5%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

   6. Taylor expanded in k around 0

      \[\leadsto \frac{1}{\frac{\color{blue}{1}}{{k}^{m}}} \cdot a \]
   7. Step-by-step derivation

   8. Applied rewrites 82.8%

      \[\leadsto \frac{1}{\frac{\color{blue}{1}}{{k}^{m}}} \cdot a \]

   if 0.0018600000000000001 < k

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

      4. mult-flip N/A

         \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

      5. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

      6. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

      7. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   3. Applied rewrites 90.4%

      \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

   5. Applied rewrites 89.8%

      \[\leadsto \color{blue}{\frac{{k}^{m} \cdot \frac{a}{\left(10 - \frac{-1}{k}\right) + k}}{k}} \]

   6. Taylor expanded in k around inf

      \[\leadsto \frac{{k}^{m} \cdot \color{blue}{\frac{a}{k}}}{k} \]
   7. Step-by-step derivation

      1. lower-/.f64 75.1

         \[\leadsto \frac{{k}^{m} \cdot \frac{a}{\color{blue}{k}}}{k} \]

   8. Applied rewrites 75.1%

      \[\leadsto \frac{{k}^{m} \cdot \color{blue}{\frac{a}{k}}}{k} \]
   9. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m} \cdot \frac{a}{k}}{k}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{{k}^{m} \cdot \frac{a}{k}}}{k} \]

      3. *-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{a}{k} \cdot {k}^{m}}}{k} \]

      4. associate-/l* N/A

         \[\leadsto \color{blue}{\frac{a}{k} \cdot \frac{{k}^{m}}{k}} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{a}{k} \cdot \frac{{k}^{m}}{k}} \]

      6. mult-flip-rev N/A

         \[\leadsto \frac{a}{k} \cdot \color{blue}{\left({k}^{m} \cdot \frac{1}{k}\right)} \]

      7. lift-pow.f64 N/A

         \[\leadsto \frac{a}{k} \cdot \left(\color{blue}{{k}^{m}} \cdot \frac{1}{k}\right) \]

      8. inv-pow N/A

         \[\leadsto \frac{a}{k} \cdot \left({k}^{m} \cdot \color{blue}{{k}^{-1}}\right) \]

      9. pow-prod-up N/A

         \[\leadsto \frac{a}{k} \cdot \color{blue}{{k}^{\left(m + -1\right)}} \]

      10. lower-pow.f64 N/A

          \[\leadsto \frac{a}{k} \cdot \color{blue}{{k}^{\left(m + -1\right)}} \]

      11. lower-+.f64 64.4

          \[\leadsto \frac{a}{k} \cdot {k}^{\color{blue}{\left(m + -1\right)}} \]

   10. Applied rewrites 64.4%

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

Alternative 4: 97.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;k \leq 0.00186:\\ \;\;\;\;{k}^{m} \cdot a\\ \mathbf{else}:\\ \;\;\;\;\frac{a}{k} \cdot {k}^{\left(m + -1\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (a k m)
 :precision binary64
 (if (<= k 0.00186) (* (pow k m) a) (* (/ a k) (pow k (+ m -1.0)))))

C

double code(double a, double k, double m) {
	double tmp;
	if (k <= 0.00186) {
		tmp = pow(k, m) * a;
	} else {
		tmp = (a / k) * pow(k, (m + -1.0));
	}
	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(a, k, m)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: k
    real(8), intent (in) :: m
    real(8) :: tmp
    if (k <= 0.00186d0) then
        tmp = (k ** m) * a
    else
        tmp = (a / k) * (k ** (m + (-1.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double a, double k, double m) {
	double tmp;
	if (k <= 0.00186) {
		tmp = Math.pow(k, m) * a;
	} else {
		tmp = (a / k) * Math.pow(k, (m + -1.0));
	}
	return tmp;
}

Python

def code(a, k, m):
	tmp = 0
	if k <= 0.00186:
		tmp = math.pow(k, m) * a
	else:
		tmp = (a / k) * math.pow(k, (m + -1.0))
	return tmp

Julia

function code(a, k, m)
	tmp = 0.0
	if (k <= 0.00186)
		tmp = Float64((k ^ m) * a);
	else
		tmp = Float64(Float64(a / k) * (k ^ Float64(m + -1.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, k, m)
	tmp = 0.0;
	if (k <= 0.00186)
		tmp = (k ^ m) * a;
	else
		tmp = (a / k) * (k ^ (m + -1.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, k_, m_] := If[LessEqual[k, 0.00186], N[(N[Power[k, m], $MachinePrecision] * a), $MachinePrecision], N[(N[(a / k), $MachinePrecision] * N[Power[k, N[(m + -1.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if k < 0.0018600000000000001

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{a \cdot {k}^{m}}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{a \cdot \frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot a} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot a} \]

   3. Applied rewrites 90.5%

      \[\leadsto \color{blue}{\frac{{k}^{m}}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a} \]
   4. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\mathsf{fma}\left(k - -10, k, 1\right)}} \cdot a \]

      2. div-flip N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

      3. lower-unsound-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

      4. lower-unsound-/.f64 90.5

         \[\leadsto \frac{1}{\color{blue}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

   5. Applied rewrites 90.5%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

   6. Taylor expanded in k around 0

      \[\leadsto \color{blue}{{k}^{m}} \cdot a \]
   7. Step-by-step derivation

      1. lower-pow.f64 82.8

         \[\leadsto {k}^{\color{blue}{m}} \cdot a \]

   8. Applied rewrites 82.8%

      \[\leadsto \color{blue}{{k}^{m}} \cdot a \]

   if 0.0018600000000000001 < k

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

      4. mult-flip N/A

         \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

      5. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

      6. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

      7. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   3. Applied rewrites 90.4%

      \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

   5. Applied rewrites 89.8%

      \[\leadsto \color{blue}{\frac{{k}^{m} \cdot \frac{a}{\left(10 - \frac{-1}{k}\right) + k}}{k}} \]

   6. Taylor expanded in k around inf

      \[\leadsto \frac{{k}^{m} \cdot \color{blue}{\frac{a}{k}}}{k} \]
   7. Step-by-step derivation

      1. lower-/.f64 75.1

         \[\leadsto \frac{{k}^{m} \cdot \frac{a}{\color{blue}{k}}}{k} \]

   8. Applied rewrites 75.1%

      \[\leadsto \frac{{k}^{m} \cdot \color{blue}{\frac{a}{k}}}{k} \]
   9. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m} \cdot \frac{a}{k}}{k}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{{k}^{m} \cdot \frac{a}{k}}}{k} \]

      3. *-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{a}{k} \cdot {k}^{m}}}{k} \]

      4. associate-/l* N/A

         \[\leadsto \color{blue}{\frac{a}{k} \cdot \frac{{k}^{m}}{k}} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{a}{k} \cdot \frac{{k}^{m}}{k}} \]

      6. mult-flip-rev N/A

         \[\leadsto \frac{a}{k} \cdot \color{blue}{\left({k}^{m} \cdot \frac{1}{k}\right)} \]

      7. lift-pow.f64 N/A

         \[\leadsto \frac{a}{k} \cdot \left(\color{blue}{{k}^{m}} \cdot \frac{1}{k}\right) \]

      8. inv-pow N/A

         \[\leadsto \frac{a}{k} \cdot \left({k}^{m} \cdot \color{blue}{{k}^{-1}}\right) \]

      9. pow-prod-up N/A

         \[\leadsto \frac{a}{k} \cdot \color{blue}{{k}^{\left(m + -1\right)}} \]

      10. lower-pow.f64 N/A

          \[\leadsto \frac{a}{k} \cdot \color{blue}{{k}^{\left(m + -1\right)}} \]

      11. lower-+.f64 64.4

          \[\leadsto \frac{a}{k} \cdot {k}^{\color{blue}{\left(m + -1\right)}} \]

   10. Applied rewrites 64.4%

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

Alternative 5: 97.1% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {k}^{m} \cdot a\\ \mathbf{if}\;m \leq -2.55 \cdot 10^{-5}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;m \leq 9.6 \cdot 10^{-7}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (a k m)
 :precision binary64
 (let* ((t_0 (* (pow k m) a)))
   (if (<= m -2.55e-5)
     t_0
     (if (<= m 9.6e-7) (* (/ 1.0 (fma (- k -10.0) k 1.0)) a) t_0))))

C

double code(double a, double k, double m) {
	double t_0 = pow(k, m) * a;
	double tmp;
	if (m <= -2.55e-5) {
		tmp = t_0;
	} else if (m <= 9.6e-7) {
		tmp = (1.0 / fma((k - -10.0), k, 1.0)) * a;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(a, k, m)
	t_0 = Float64((k ^ m) * a)
	tmp = 0.0
	if (m <= -2.55e-5)
		tmp = t_0;
	elseif (m <= 9.6e-7)
		tmp = Float64(Float64(1.0 / fma(Float64(k - -10.0), k, 1.0)) * a);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[a_, k_, m_] := Block[{t$95$0 = N[(N[Power[k, m], $MachinePrecision] * a), $MachinePrecision]}, If[LessEqual[m, -2.55e-5], t$95$0, If[LessEqual[m, 9.6e-7], N[(N[(1.0 / N[(N[(k - -10.0), $MachinePrecision] * k + 1.0), $MachinePrecision]), $MachinePrecision] * a), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if m < -2.54999999999999998e-5 or 9.59999999999999914e-7 < m

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{a \cdot {k}^{m}}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{a \cdot \frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot a} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot a} \]

   3. Applied rewrites 90.5%

      \[\leadsto \color{blue}{\frac{{k}^{m}}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a} \]
   4. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{{k}^{m}}{\mathsf{fma}\left(k - -10, k, 1\right)}} \cdot a \]

      2. div-flip N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

      3. lower-unsound-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

      4. lower-unsound-/.f64 90.5

         \[\leadsto \frac{1}{\color{blue}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

   5. Applied rewrites 90.5%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(k - -10, k, 1\right)}{{k}^{m}}}} \cdot a \]

   6. Taylor expanded in k around 0

      \[\leadsto \color{blue}{{k}^{m}} \cdot a \]
   7. Step-by-step derivation

      1. lower-pow.f64 82.8

         \[\leadsto {k}^{\color{blue}{m}} \cdot a \]

   8. Applied rewrites 82.8%

      \[\leadsto \color{blue}{{k}^{m}} \cdot a \]

   if -2.54999999999999998e-5 < m < 9.59999999999999914e-7

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

      4. mult-flip N/A

         \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

      5. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

      6. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

      7. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   3. Applied rewrites 90.4%

      \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

   4. Taylor expanded in m around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{a}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{a}{1 + k \cdot \color{blue}{\left(10 + k\right)}} \]

      4. lower-+.f64 44.9

         \[\leadsto \frac{a}{1 + k \cdot \left(10 + \color{blue}{k}\right)} \]

   6. Applied rewrites 44.9%

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

      1. lift-/.f64 N/A

         \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

      2. mult-flip N/A

         \[\leadsto a \cdot \color{blue}{\frac{1}{1 + k \cdot \left(10 + k\right)}} \]

      3. lift-+.f64 N/A

         \[\leadsto a \cdot \frac{1}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

      4. +-commutative N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + \color{blue}{1}} \]

      5. lift-*.f64 N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + 1} \]

      6. lift-+.f64 N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + 1} \]

      7. +-commutative N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k + 10\right) + 1} \]

      8. metadata-eval N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k + \left(\mathsf{neg}\left(-10\right)\right)\right) + 1} \]

      9. sub-flip N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k - -10\right) + 1} \]

      10. lift--.f64 N/A

          \[\leadsto a \cdot \frac{1}{k \cdot \left(k - -10\right) + 1} \]

      11. *-commutative N/A

          \[\leadsto a \cdot \frac{1}{\left(k - -10\right) \cdot k + 1} \]

      12. lift-fma.f64 N/A

          \[\leadsto a \cdot \frac{1}{\mathsf{fma}\left(k - -10, \color{blue}{k}, 1\right)} \]

      13. *-commutative N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

      14. lower-*.f64 N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

      15. lower-/.f64 44.8

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a \]

   8. Applied rewrites 44.8%

      \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 53.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \leq 10^{+207}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a\\ \mathbf{else}:\\ \;\;\;\;\left(1 + k \cdot \left(99 \cdot k - 10\right)\right) \cdot a\\ \end{array} \end{array} \]

FPCore

(FPCore (a k m)
 :precision binary64
 (if (<= (/ (* a (pow k m)) (+ (+ 1.0 (* 10.0 k)) (* k k))) 1e+207)
   (* (/ 1.0 (fma (- k -10.0) k 1.0)) a)
   (* (+ 1.0 (* k (- (* 99.0 k) 10.0))) a)))

C

double code(double a, double k, double m) {
	double tmp;
	if (((a * pow(k, m)) / ((1.0 + (10.0 * k)) + (k * k))) <= 1e+207) {
		tmp = (1.0 / fma((k - -10.0), k, 1.0)) * a;
	} else {
		tmp = (1.0 + (k * ((99.0 * k) - 10.0))) * a;
	}
	return tmp;
}

Julia

function code(a, k, m)
	tmp = 0.0
	if (Float64(Float64(a * (k ^ m)) / Float64(Float64(1.0 + Float64(10.0 * k)) + Float64(k * k))) <= 1e+207)
		tmp = Float64(Float64(1.0 / fma(Float64(k - -10.0), k, 1.0)) * a);
	else
		tmp = Float64(Float64(1.0 + Float64(k * Float64(Float64(99.0 * k) - 10.0))) * a);
	end
	return tmp
end

Wolfram

code[a_, k_, m_] := If[LessEqual[N[(N[(a * N[Power[k, m], $MachinePrecision]), $MachinePrecision] / N[(N[(1.0 + N[(10.0 * k), $MachinePrecision]), $MachinePrecision] + N[(k * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1e+207], N[(N[(1.0 / N[(N[(k - -10.0), $MachinePrecision] * k + 1.0), $MachinePrecision]), $MachinePrecision] * a), $MachinePrecision], N[(N[(1.0 + N[(k * N[(N[(99.0 * k), $MachinePrecision] - 10.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * a), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 a (pow.f64 k m)) (+.f64 (+.f64 #s(literal 1 binary64) (*.f64 #s(literal 10 binary64) k)) (*.f64 k k))) < 1e207

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

      4. mult-flip N/A

         \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

      5. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

      6. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

      7. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   3. Applied rewrites 90.4%

      \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

   4. Taylor expanded in m around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{a}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{a}{1 + k \cdot \color{blue}{\left(10 + k\right)}} \]

      4. lower-+.f64 44.9

         \[\leadsto \frac{a}{1 + k \cdot \left(10 + \color{blue}{k}\right)} \]

   6. Applied rewrites 44.9%

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

      1. lift-/.f64 N/A

         \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

      2. mult-flip N/A

         \[\leadsto a \cdot \color{blue}{\frac{1}{1 + k \cdot \left(10 + k\right)}} \]

      3. lift-+.f64 N/A

         \[\leadsto a \cdot \frac{1}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

      4. +-commutative N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + \color{blue}{1}} \]

      5. lift-*.f64 N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + 1} \]

      6. lift-+.f64 N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + 1} \]

      7. +-commutative N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k + 10\right) + 1} \]

      8. metadata-eval N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k + \left(\mathsf{neg}\left(-10\right)\right)\right) + 1} \]

      9. sub-flip N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k - -10\right) + 1} \]

      10. lift--.f64 N/A

          \[\leadsto a \cdot \frac{1}{k \cdot \left(k - -10\right) + 1} \]

      11. *-commutative N/A

          \[\leadsto a \cdot \frac{1}{\left(k - -10\right) \cdot k + 1} \]

      12. lift-fma.f64 N/A

          \[\leadsto a \cdot \frac{1}{\mathsf{fma}\left(k - -10, \color{blue}{k}, 1\right)} \]

      13. *-commutative N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

      14. lower-*.f64 N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

      15. lower-/.f64 44.8

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a \]

   8. Applied rewrites 44.8%

      \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

   if 1e207 < (/.f64 (*.f64 a (pow.f64 k m)) (+.f64 (+.f64 #s(literal 1 binary64) (*.f64 #s(literal 10 binary64) k)) (*.f64 k k)))

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

      4. mult-flip N/A

         \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

      5. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

      6. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

      7. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   3. Applied rewrites 90.4%

      \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

   4. Taylor expanded in m around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{a}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{a}{1 + k \cdot \color{blue}{\left(10 + k\right)}} \]

      4. lower-+.f64 44.9

         \[\leadsto \frac{a}{1 + k \cdot \left(10 + \color{blue}{k}\right)} \]

   6. Applied rewrites 44.9%

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

      1. lift-/.f64 N/A

         \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

      2. mult-flip N/A

         \[\leadsto a \cdot \color{blue}{\frac{1}{1 + k \cdot \left(10 + k\right)}} \]

      3. lift-+.f64 N/A

         \[\leadsto a \cdot \frac{1}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

      4. +-commutative N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + \color{blue}{1}} \]

      5. lift-*.f64 N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + 1} \]

      6. lift-+.f64 N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + 1} \]

      7. +-commutative N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k + 10\right) + 1} \]

      8. metadata-eval N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k + \left(\mathsf{neg}\left(-10\right)\right)\right) + 1} \]

      9. sub-flip N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k - -10\right) + 1} \]

      10. lift--.f64 N/A

          \[\leadsto a \cdot \frac{1}{k \cdot \left(k - -10\right) + 1} \]

      11. *-commutative N/A

          \[\leadsto a \cdot \frac{1}{\left(k - -10\right) \cdot k + 1} \]

      12. lift-fma.f64 N/A

          \[\leadsto a \cdot \frac{1}{\mathsf{fma}\left(k - -10, \color{blue}{k}, 1\right)} \]

      13. *-commutative N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

      14. lower-*.f64 N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

      15. lower-/.f64 44.8

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a \]

   8. Applied rewrites 44.8%

      \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

   9. Taylor expanded in k around 0

      \[\leadsto \left(1 + k \cdot \left(99 \cdot k - 10\right)\right) \cdot a \]
   10. Step-by-step derivation

       1. lower-+.f64 N/A

          \[\leadsto \left(1 + k \cdot \left(99 \cdot k - 10\right)\right) \cdot a \]

       2. lower-*.f64 N/A

          \[\leadsto \left(1 + k \cdot \left(99 \cdot k - 10\right)\right) \cdot a \]

       3. lower--.f64 N/A

          \[\leadsto \left(1 + k \cdot \left(99 \cdot k - 10\right)\right) \cdot a \]

       4. lower-*.f64 28.8

          \[\leadsto \left(1 + k \cdot \left(99 \cdot k - 10\right)\right) \cdot a \]

   11. Applied rewrites 28.8%

       \[\leadsto \left(1 + k \cdot \left(99 \cdot k - 10\right)\right) \cdot a \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 44.9% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a \end{array} \]

FPCore

(FPCore (a k m) :precision binary64 (* (/ 1.0 (fma (- k -10.0) k 1.0)) a))

C

double code(double a, double k, double m) {
	return (1.0 / fma((k - -10.0), k, 1.0)) * a;
}

Julia

function code(a, k, m)
	return Float64(Float64(1.0 / fma(Float64(k - -10.0), k, 1.0)) * a)
end

Wolfram

code[a_, k_, m_] := N[(N[(1.0 / N[(N[(k - -10.0), $MachinePrecision] * k + 1.0), $MachinePrecision]), $MachinePrecision] * a), $MachinePrecision]

Derivation

1. Initial program 90.5%

   \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

   2. mult-flip N/A

      \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

   3. *-commutative N/A

      \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

   4. mult-flip N/A

      \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

   5. metadata-eval N/A

      \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

   6. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

   7. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   8. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

3. Applied rewrites 90.4%

   \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

4. Taylor expanded in m around 0

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

   1. lower-/.f64 N/A

      \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

   2. lower-+.f64 N/A

      \[\leadsto \frac{a}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

   3. lower-*.f64 N/A

      \[\leadsto \frac{a}{1 + k \cdot \color{blue}{\left(10 + k\right)}} \]

   4. lower-+.f64 44.9

      \[\leadsto \frac{a}{1 + k \cdot \left(10 + \color{blue}{k}\right)} \]

6. Applied rewrites 44.9%

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

   1. lift-/.f64 N/A

      \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

   2. mult-flip N/A

      \[\leadsto a \cdot \color{blue}{\frac{1}{1 + k \cdot \left(10 + k\right)}} \]

   3. lift-+.f64 N/A

      \[\leadsto a \cdot \frac{1}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

   4. +-commutative N/A

      \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + \color{blue}{1}} \]

   5. lift-*.f64 N/A

      \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + 1} \]

   6. lift-+.f64 N/A

      \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + 1} \]

   7. +-commutative N/A

      \[\leadsto a \cdot \frac{1}{k \cdot \left(k + 10\right) + 1} \]

   8. metadata-eval N/A

      \[\leadsto a \cdot \frac{1}{k \cdot \left(k + \left(\mathsf{neg}\left(-10\right)\right)\right) + 1} \]

   9. sub-flip N/A

      \[\leadsto a \cdot \frac{1}{k \cdot \left(k - -10\right) + 1} \]

   10. lift--.f64 N/A

       \[\leadsto a \cdot \frac{1}{k \cdot \left(k - -10\right) + 1} \]

   11. *-commutative N/A

       \[\leadsto a \cdot \frac{1}{\left(k - -10\right) \cdot k + 1} \]

   12. lift-fma.f64 N/A

       \[\leadsto a \cdot \frac{1}{\mathsf{fma}\left(k - -10, \color{blue}{k}, 1\right)} \]

   13. *-commutative N/A

       \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

   14. lower-*.f64 N/A

       \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

   15. lower-/.f64 44.8

       \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a \]

8. Applied rewrites 44.8%

   \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]
9. Add Preprocessing

Alternative 8: 44.8% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \frac{a}{\mathsf{fma}\left(k - -10, k, 1\right)} \end{array} \]

FPCore

(FPCore (a k m) :precision binary64 (/ a (fma (- k -10.0) k 1.0)))

C

double code(double a, double k, double m) {
	return a / fma((k - -10.0), k, 1.0);
}

Julia

function code(a, k, m)
	return Float64(a / fma(Float64(k - -10.0), k, 1.0))
end

Wolfram

code[a_, k_, m_] := N[(a / N[(N[(k - -10.0), $MachinePrecision] * k + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 90.5%

   \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

   2. mult-flip N/A

      \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

   3. *-commutative N/A

      \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

   4. mult-flip N/A

      \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

   5. metadata-eval N/A

      \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

   6. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

   7. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   8. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

3. Applied rewrites 90.4%

   \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

4. Taylor expanded in m around 0

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

   1. lower-/.f64 N/A

      \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

   2. lower-+.f64 N/A

      \[\leadsto \frac{a}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

   3. lower-*.f64 N/A

      \[\leadsto \frac{a}{1 + k \cdot \color{blue}{\left(10 + k\right)}} \]

   4. lower-+.f64 44.9

      \[\leadsto \frac{a}{1 + k \cdot \left(10 + \color{blue}{k}\right)} \]

6. Applied rewrites 44.9%

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

   1. lift-+.f64 N/A

      \[\leadsto \frac{a}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

   2. +-commutative N/A

      \[\leadsto \frac{a}{k \cdot \left(10 + k\right) + \color{blue}{1}} \]

   3. lift-*.f64 N/A

      \[\leadsto \frac{a}{k \cdot \left(10 + k\right) + 1} \]

   4. lift-+.f64 N/A

      \[\leadsto \frac{a}{k \cdot \left(10 + k\right) + 1} \]

   5. +-commutative N/A

      \[\leadsto \frac{a}{k \cdot \left(k + 10\right) + 1} \]

   6. metadata-eval N/A

      \[\leadsto \frac{a}{k \cdot \left(k + \left(\mathsf{neg}\left(-10\right)\right)\right) + 1} \]

   7. sub-flip N/A

      \[\leadsto \frac{a}{k \cdot \left(k - -10\right) + 1} \]

   8. lift--.f64 N/A

      \[\leadsto \frac{a}{k \cdot \left(k - -10\right) + 1} \]

   9. *-commutative N/A

      \[\leadsto \frac{a}{\left(k - -10\right) \cdot k + 1} \]

   10. lift-fma.f64 44.9

       \[\leadsto \frac{a}{\mathsf{fma}\left(k - -10, \color{blue}{k}, 1\right)} \]

8. Applied rewrites 44.9%

   \[\leadsto \color{blue}{\frac{a}{\mathsf{fma}\left(k - -10, k, 1\right)}} \]
9. Add Preprocessing

Alternative 9: 29.7% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;m \leq 1.9 \cdot 10^{+34}:\\ \;\;\;\;\frac{a}{1 + k \cdot 10}\\ \mathbf{else}:\\ \;\;\;\;\left(1 + -10 \cdot k\right) \cdot a\\ \end{array} \end{array} \]

FPCore

(FPCore (a k m)
 :precision binary64
 (if (<= m 1.9e+34) (/ a (+ 1.0 (* k 10.0))) (* (+ 1.0 (* -10.0 k)) a)))

C

double code(double a, double k, double m) {
	double tmp;
	if (m <= 1.9e+34) {
		tmp = a / (1.0 + (k * 10.0));
	} else {
		tmp = (1.0 + (-10.0 * k)) * a;
	}
	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(a, k, m)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: k
    real(8), intent (in) :: m
    real(8) :: tmp
    if (m <= 1.9d+34) then
        tmp = a / (1.0d0 + (k * 10.0d0))
    else
        tmp = (1.0d0 + ((-10.0d0) * k)) * a
    end if
    code = tmp
end function

Java

public static double code(double a, double k, double m) {
	double tmp;
	if (m <= 1.9e+34) {
		tmp = a / (1.0 + (k * 10.0));
	} else {
		tmp = (1.0 + (-10.0 * k)) * a;
	}
	return tmp;
}

Python

def code(a, k, m):
	tmp = 0
	if m <= 1.9e+34:
		tmp = a / (1.0 + (k * 10.0))
	else:
		tmp = (1.0 + (-10.0 * k)) * a
	return tmp

Julia

function code(a, k, m)
	tmp = 0.0
	if (m <= 1.9e+34)
		tmp = Float64(a / Float64(1.0 + Float64(k * 10.0)));
	else
		tmp = Float64(Float64(1.0 + Float64(-10.0 * k)) * a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, k, m)
	tmp = 0.0;
	if (m <= 1.9e+34)
		tmp = a / (1.0 + (k * 10.0));
	else
		tmp = (1.0 + (-10.0 * k)) * a;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, k_, m_] := If[LessEqual[m, 1.9e+34], N[(a / N[(1.0 + N[(k * 10.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 + N[(-10.0 * k), $MachinePrecision]), $MachinePrecision] * a), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if m < 1.9000000000000001e34

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

      4. mult-flip N/A

         \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

      5. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

      6. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

      7. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   3. Applied rewrites 90.4%

      \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

   4. Taylor expanded in m around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{a}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{a}{1 + k \cdot \color{blue}{\left(10 + k\right)}} \]

      4. lower-+.f64 44.9

         \[\leadsto \frac{a}{1 + k \cdot \left(10 + \color{blue}{k}\right)} \]

   6. Applied rewrites 44.9%

      \[\leadsto \color{blue}{\frac{a}{1 + k \cdot \left(10 + k\right)}} \]

   7. Taylor expanded in k around 0

      \[\leadsto \frac{a}{1 + k \cdot 10} \]
   8. Step-by-step derivation

   9. Applied rewrites 28.1%

      \[\leadsto \frac{a}{1 + k \cdot 10} \]

   if 1.9000000000000001e34 < m

   1. Initial program 90.5%

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

      4. mult-flip N/A

         \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

      5. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

      6. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

      7. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   3. Applied rewrites 90.4%

      \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

   4. Taylor expanded in m around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{a}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{a}{1 + k \cdot \color{blue}{\left(10 + k\right)}} \]

      4. lower-+.f64 44.9

         \[\leadsto \frac{a}{1 + k \cdot \left(10 + \color{blue}{k}\right)} \]

   6. Applied rewrites 44.9%

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

      1. lift-/.f64 N/A

         \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

      2. mult-flip N/A

         \[\leadsto a \cdot \color{blue}{\frac{1}{1 + k \cdot \left(10 + k\right)}} \]

      3. lift-+.f64 N/A

         \[\leadsto a \cdot \frac{1}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

      4. +-commutative N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + \color{blue}{1}} \]

      5. lift-*.f64 N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + 1} \]

      6. lift-+.f64 N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(10 + k\right) + 1} \]

      7. +-commutative N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k + 10\right) + 1} \]

      8. metadata-eval N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k + \left(\mathsf{neg}\left(-10\right)\right)\right) + 1} \]

      9. sub-flip N/A

         \[\leadsto a \cdot \frac{1}{k \cdot \left(k - -10\right) + 1} \]

      10. lift--.f64 N/A

          \[\leadsto a \cdot \frac{1}{k \cdot \left(k - -10\right) + 1} \]

      11. *-commutative N/A

          \[\leadsto a \cdot \frac{1}{\left(k - -10\right) \cdot k + 1} \]

      12. lift-fma.f64 N/A

          \[\leadsto a \cdot \frac{1}{\mathsf{fma}\left(k - -10, \color{blue}{k}, 1\right)} \]

      13. *-commutative N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

      14. lower-*.f64 N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

      15. lower-/.f64 44.8

          \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot a \]

   8. Applied rewrites 44.8%

      \[\leadsto \frac{1}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \color{blue}{a} \]

   9. Taylor expanded in k around 0

      \[\leadsto \left(1 + -10 \cdot k\right) \cdot a \]
   10. Step-by-step derivation

       1. lower-+.f64 N/A

          \[\leadsto \left(1 + -10 \cdot k\right) \cdot a \]

       2. lower-*.f64 20.7

          \[\leadsto \left(1 + -10 \cdot k\right) \cdot a \]

   11. Applied rewrites 20.7%

       \[\leadsto \left(1 + -10 \cdot k\right) \cdot a \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 20.7% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ a + -10 \cdot \left(a \cdot k\right) \end{array} \]

FPCore

(FPCore (a k m) :precision binary64 (+ a (* -10.0 (* a k))))

C

double code(double a, double k, double m) {
	return a + (-10.0 * (a * k));
}

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(a, k, m)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: k
    real(8), intent (in) :: m
    code = a + ((-10.0d0) * (a * k))
end function

Java

public static double code(double a, double k, double m) {
	return a + (-10.0 * (a * k));
}

Python

def code(a, k, m):
	return a + (-10.0 * (a * k))

Julia

function code(a, k, m)
	return Float64(a + Float64(-10.0 * Float64(a * k)))
end

MATLAB

function tmp = code(a, k, m)
	tmp = a + (-10.0 * (a * k));
end

Wolfram

code[a_, k_, m_] := N[(a + N[(-10.0 * N[(a * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 90.5%

   \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

   2. mult-flip N/A

      \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

   3. *-commutative N/A

      \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

   4. mult-flip N/A

      \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

   5. metadata-eval N/A

      \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

   6. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

   7. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   8. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

3. Applied rewrites 90.4%

   \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

4. Taylor expanded in m around 0

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

   1. lower-/.f64 N/A

      \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

   2. lower-+.f64 N/A

      \[\leadsto \frac{a}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

   3. lower-*.f64 N/A

      \[\leadsto \frac{a}{1 + k \cdot \color{blue}{\left(10 + k\right)}} \]

   4. lower-+.f64 44.9

      \[\leadsto \frac{a}{1 + k \cdot \left(10 + \color{blue}{k}\right)} \]

6. Applied rewrites 44.9%

   \[\leadsto \color{blue}{\frac{a}{1 + k \cdot \left(10 + k\right)}} \]

7. Taylor expanded in k around 0

   \[\leadsto a + \color{blue}{-10 \cdot \left(a \cdot k\right)} \]
8. Step-by-step derivation

   1. lower-+.f64 N/A

      \[\leadsto a + -10 \cdot \color{blue}{\left(a \cdot k\right)} \]

   2. lower-*.f64 N/A

      \[\leadsto a + -10 \cdot \left(a \cdot \color{blue}{k}\right) \]

   3. lower-*.f64 20.7

      \[\leadsto a + -10 \cdot \left(a \cdot k\right) \]

9. Applied rewrites 20.7%

   \[\leadsto a + \color{blue}{-10 \cdot \left(a \cdot k\right)} \]
10. Add Preprocessing

Alternative 11: 19.9% accurate, 7.9× speedup

Math

\[\begin{array}{l} \\ \frac{a}{1} \end{array} \]

FPCore

(FPCore (a k m) :precision binary64 (/ a 1.0))

C

double code(double a, double k, double m) {
	return a / 1.0;
}

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(a, k, m)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: k
    real(8), intent (in) :: m
    code = a / 1.0d0
end function

Java

public static double code(double a, double k, double m) {
	return a / 1.0;
}

Python

def code(a, k, m):
	return a / 1.0

Julia

function code(a, k, m)
	return Float64(a / 1.0)
end

MATLAB

function tmp = code(a, k, m)
	tmp = a / 1.0;
end

Wolfram

code[a_, k_, m_] := N[(a / 1.0), $MachinePrecision]

Derivation

1. Initial program 90.5%

   \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

   2. mult-flip N/A

      \[\leadsto \color{blue}{\left(a \cdot {k}^{m}\right) \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}} \]

   3. *-commutative N/A

      \[\leadsto \color{blue}{\frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k} \cdot \left(a \cdot {k}^{m}\right)} \]

   4. mult-flip N/A

      \[\leadsto \color{blue}{\left(1 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right)} \cdot \left(a \cdot {k}^{m}\right) \]

   5. metadata-eval N/A

      \[\leadsto \left(\color{blue}{\frac{2}{2}} \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right) \]

   6. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}}{2}} \cdot \left(a \cdot {k}^{m}\right) \]

   7. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

   8. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\left(2 \cdot \frac{1}{\left(1 + 10 \cdot k\right) + k \cdot k}\right) \cdot \left(a \cdot {k}^{m}\right)}{2}} \]

3. Applied rewrites 90.4%

   \[\leadsto \color{blue}{\frac{\frac{2}{\mathsf{fma}\left(k - -10, k, 1\right)} \cdot \left({k}^{m} \cdot a\right)}{2}} \]

4. Taylor expanded in m around 0

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

   1. lower-/.f64 N/A

      \[\leadsto \frac{a}{\color{blue}{1 + k \cdot \left(10 + k\right)}} \]

   2. lower-+.f64 N/A

      \[\leadsto \frac{a}{1 + \color{blue}{k \cdot \left(10 + k\right)}} \]

   3. lower-*.f64 N/A

      \[\leadsto \frac{a}{1 + k \cdot \color{blue}{\left(10 + k\right)}} \]

   4. lower-+.f64 44.9

      \[\leadsto \frac{a}{1 + k \cdot \left(10 + \color{blue}{k}\right)} \]

6. Applied rewrites 44.9%

   \[\leadsto \color{blue}{\frac{a}{1 + k \cdot \left(10 + k\right)}} \]

7. Taylor expanded in k around 0

   \[\leadsto \frac{a}{1} \]
8. Step-by-step derivation

9. Applied rewrites 19.9%

   \[\leadsto \frac{a}{1} \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2025154 
(FPCore (a k m)
  :name "Falkner and Boettcher, Appendix A"
  :precision binary64
  (/ (* a (pow k m)) (+ (+ 1.0 (* 10.0 k)) (* k k))))