Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2

Average Error: 0.1 → 0.1

Time: 23.0s

Precision: binary64

Cost: 7104

Math

\[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

↓

\[\left(x - \left(\left(y + 0.5\right) \cdot \log y - y\right)\right) - z \]

FPCore

(FPCore (x y z) :precision binary64 (- (+ (- x (* (+ y 0.5) (log y))) y) z))

↓

(FPCore (x y z) :precision binary64 (- (- x (- (* (+ y 0.5) (log y)) y)) z))

C

double code(double x, double y, double z) {
	return ((x - ((y + 0.5) * log(y))) + y) - z;
}

↓

double code(double x, double y, double z) {
	return (x - (((y + 0.5) * log(y)) - y)) - z;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = ((x - ((y + 0.5d0) * log(y))) + y) - z
end function

↓

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (x - (((y + 0.5d0) * log(y)) - y)) - z
end function

Java

public static double code(double x, double y, double z) {
	return ((x - ((y + 0.5) * Math.log(y))) + y) - z;
}

↓

public static double code(double x, double y, double z) {
	return (x - (((y + 0.5) * Math.log(y)) - y)) - z;
}

Python

def code(x, y, z):
	return ((x - ((y + 0.5) * math.log(y))) + y) - z

↓

def code(x, y, z):
	return (x - (((y + 0.5) * math.log(y)) - y)) - z

Julia

function code(x, y, z)
	return Float64(Float64(Float64(x - Float64(Float64(y + 0.5) * log(y))) + y) - z)
end

↓

function code(x, y, z)
	return Float64(Float64(x - Float64(Float64(Float64(y + 0.5) * log(y)) - y)) - z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((x - ((y + 0.5) * log(y))) + y) - z;
end

↓

function tmp = code(x, y, z)
	tmp = (x - (((y + 0.5) * log(y)) - y)) - z;
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(x - N[(N[(y + 0.5), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - z), $MachinePrecision]

↓

code[x_, y_, z_] := N[(N[(x - N[(N[(N[(y + 0.5), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision] - y), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]

Target

Original	0.1
Target	0.1
Herbie	0.1

\[\left(\left(y + x\right) - z\right) - \left(y + 0.5\right) \cdot \log y \]

Derivation

1. Initial program 0.1

   \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

2. Applied egg-rr 0.1

   \[\leadsto \color{blue}{\left(x - \left(\left(y + 0.5\right) \cdot \log y - y\right)\right)} - z \]

3. Final simplification 0.1

   \[\leadsto \left(x - \left(\left(y + 0.5\right) \cdot \log y - y\right)\right) - z \]

Alternatives

Alternative 1
Error	15.2
Cost	7508

\[\begin{array}{l} t_0 := \log y \cdot -0.5 - z\\ \mathbf{if}\;y \leq 2.3 \cdot 10^{-128}:\\ \;\;\;\;x - z\\ \mathbf{elif}\;y \leq 8.6 \cdot 10^{-88}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 8.4 \cdot 10^{-63}:\\ \;\;\;\;x - z\\ \mathbf{elif}\;y \leq 1.16 \cdot 10^{-36}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 7.7 \cdot 10^{+49}:\\ \;\;\;\;x - z\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(1 - \log y\right) - z\\ \end{array} \]

Alternative 2
Error	6.9
Cost	7240

\[\begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{+15}:\\ \;\;\;\;x - z\\ \mathbf{elif}\;x \leq 1.22 \cdot 10^{+64}:\\ \;\;\;\;\left(y - \left(0.5 + y\right) \cdot \log y\right) - z\\ \mathbf{else}:\\ \;\;\;\;x - z\\ \end{array} \]

Alternative 3
Error	0.1
Cost	7104

\[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

Alternative 4
Error	18.9
Cost	6984

\[\begin{array}{l} \mathbf{if}\;x \leq -105:\\ \;\;\;\;x - z\\ \mathbf{elif}\;x \leq 320:\\ \;\;\;\;\log y \cdot -0.5 - z\\ \mathbf{else}:\\ \;\;\;\;x - z\\ \end{array} \]

Alternative 5
Error	6.5
Cost	6980

\[\begin{array}{l} \mathbf{if}\;y \leq 3.5 \cdot 10^{+50}:\\ \;\;\;\;\left(x - 0.5 \cdot \log y\right) - z\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(1 - \log y\right) - z\\ \end{array} \]

Alternative 6
Error	26.5
Cost	192

\[x - z \]

Alternative 7
Error	45.2
Cost	128

\[-z \]

Reproduce

herbie shell --seed 2023077 
(FPCore (x y z)
  :name "Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2"
  :precision binary64

  :herbie-target
  (- (- (+ y x) z) (* (+ y 0.5) (log y)))

  (- (+ (- x (* (+ y 0.5) (log y))) y) z))