Maksimov and Kolovsky, Equation (4)

Average Accuracy: 72.9% → 99.9%

Time: 11.3s

Precision: binary64

Cost: 13504

Math

\[\left(J \cdot \left(e^{\ell} - e^{-\ell}\right)\right) \cdot \cos \left(\frac{K}{2}\right) + U \]

↓

\[J \cdot \left(2 \cdot \left(\sinh \ell \cdot \cos \left(0.5 \cdot K\right)\right)\right) + U \]

FPCore

(FPCore (J l K U)
 :precision binary64
 (+ (* (* J (- (exp l) (exp (- l)))) (cos (/ K 2.0))) U))

↓

(FPCore (J l K U)
 :precision binary64
 (+ (* J (* 2.0 (* (sinh l) (cos (* 0.5 K))))) U))

C

double code(double J, double l, double K, double U) {
	return ((J * (exp(l) - exp(-l))) * cos((K / 2.0))) + U;
}

↓

double code(double J, double l, double K, double U) {
	return (J * (2.0 * (sinh(l) * cos((0.5 * K))))) + U;
}

Fortran

real(8) function code(j, l, k, u)
    real(8), intent (in) :: j
    real(8), intent (in) :: l
    real(8), intent (in) :: k
    real(8), intent (in) :: u
    code = ((j * (exp(l) - exp(-l))) * cos((k / 2.0d0))) + u
end function

↓

real(8) function code(j, l, k, u)
    real(8), intent (in) :: j
    real(8), intent (in) :: l
    real(8), intent (in) :: k
    real(8), intent (in) :: u
    code = (j * (2.0d0 * (sinh(l) * cos((0.5d0 * k))))) + u
end function

Java

public static double code(double J, double l, double K, double U) {
	return ((J * (Math.exp(l) - Math.exp(-l))) * Math.cos((K / 2.0))) + U;
}

↓

public static double code(double J, double l, double K, double U) {
	return (J * (2.0 * (Math.sinh(l) * Math.cos((0.5 * K))))) + U;
}

Python

def code(J, l, K, U):
	return ((J * (math.exp(l) - math.exp(-l))) * math.cos((K / 2.0))) + U

↓

def code(J, l, K, U):
	return (J * (2.0 * (math.sinh(l) * math.cos((0.5 * K))))) + U

Julia

function code(J, l, K, U)
	return Float64(Float64(Float64(J * Float64(exp(l) - exp(Float64(-l)))) * cos(Float64(K / 2.0))) + U)
end

↓

function code(J, l, K, U)
	return Float64(Float64(J * Float64(2.0 * Float64(sinh(l) * cos(Float64(0.5 * K))))) + U)
end

MATLAB

function tmp = code(J, l, K, U)
	tmp = ((J * (exp(l) - exp(-l))) * cos((K / 2.0))) + U;
end

↓

function tmp = code(J, l, K, U)
	tmp = (J * (2.0 * (sinh(l) * cos((0.5 * K))))) + U;
end

Wolfram

code[J_, l_, K_, U_] := N[(N[(N[(J * N[(N[Exp[l], $MachinePrecision] - N[Exp[(-l)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Cos[N[(K / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + U), $MachinePrecision]

↓

code[J_, l_, K_, U_] := N[(N[(J * N[(2.0 * N[(N[Sinh[l], $MachinePrecision] * N[Cos[N[(0.5 * K), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + U), $MachinePrecision]

Derivation

1. Initial program 72.9%

   \[\left(J \cdot \left(e^{\ell} - e^{-\ell}\right)\right) \cdot \cos \left(\frac{K}{2}\right) + U \]

2. Applied egg-rr 99.9%

   \[\leadsto \color{blue}{{\left(J \cdot \left(\left(2 \cdot \sinh \ell\right) \cdot \cos \left(K \cdot 0.5\right)\right)\right)}^{1}} + U \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{J \cdot \left(2 \cdot \left(\sinh \ell \cdot \cos \left(0.5 \cdot K\right)\right)\right)} + U \]
   Proof

   [Start] 99.9	\[ {\left(J \cdot \left(\left(2 \cdot \sinh \ell\right) \cdot \cos \left(K \cdot 0.5\right)\right)\right)}^{1} + U \]
   unpow1 [=>] 99.9	\[ \color{blue}{J \cdot \left(\left(2 \cdot \sinh \ell\right) \cdot \cos \left(K \cdot 0.5\right)\right)} + U \]
   associate-*l* [=>] 99.9	\[ J \cdot \color{blue}{\left(2 \cdot \left(\sinh \ell \cdot \cos \left(K \cdot 0.5\right)\right)\right)} + U \]
   *-commutative [=>] 99.9	\[ J \cdot \left(2 \cdot \left(\sinh \ell \cdot \cos \color{blue}{\left(0.5 \cdot K\right)}\right)\right) + U \]

4. Final simplification 99.9%

   \[\leadsto J \cdot \left(2 \cdot \left(\sinh \ell \cdot \cos \left(0.5 \cdot K\right)\right)\right) + U \]

Alternatives

Alternative 1
Accuracy	98.9%
Cost	13376

\[\mathsf{fma}\left(\ell + \ell, J \cdot \cos \left(\frac{K}{2}\right), U\right) \]

Alternative 2
Accuracy	86.9%
Cost	7108

\[\begin{array}{l} \mathbf{if}\;J \leq -1.65 \cdot 10^{+233}:\\ \;\;\;\;2 \cdot \left(J \cdot \left(\ell \cdot \cos \left(0.5 \cdot K\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;U + J \cdot \left(2 \cdot \sinh \ell\right)\\ \end{array} \]

Alternative 3
Accuracy	98.9%
Cost	7104

\[U + \cos \left(\frac{K}{2}\right) \cdot \left(J \cdot \left(\ell + \ell\right)\right) \]

Alternative 4
Accuracy	86.6%
Cost	6848

\[U + J \cdot \left(2 \cdot \sinh \ell\right) \]

Alternative 5
Accuracy	86.2%
Cost	6720

\[\mathsf{fma}\left(\ell + \ell, J, U\right) \]

Alternative 6
Accuracy	71.4%
Cost	584

\[\begin{array}{l} \mathbf{if}\;U \leq -2.4 \cdot 10^{-214}:\\ \;\;\;\;U\\ \mathbf{elif}\;U \leq 6.8 \cdot 10^{-260}:\\ \;\;\;\;\ell \cdot \left(J \cdot 2\right)\\ \mathbf{else}:\\ \;\;\;\;U\\ \end{array} \]

Alternative 7
Accuracy	86.2%
Cost	448

\[U + J \cdot \left(2 \cdot \ell\right) \]

Alternative 8
Accuracy	70.9%
Cost	64

\[U \]

Reproduce

herbie shell --seed 2023125 
(FPCore (J l K U)
  :name "Maksimov and Kolovsky, Equation (4)"
  :precision binary64
  (+ (* (* J (- (exp l) (exp (- l)))) (cos (/ K 2.0))) U))