Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2

Average Error: 0.1 → 0.1

Time: 16.0s

Precision: binary64

Cost: 13632

Math

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

↓

\[\left(\left(x - y \cdot \left(\log y + -1\right)\right) + \log y \cdot -0.5\right) - z \]

FPCore

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

↓

(FPCore (x y z)
 :precision binary64
 (- (+ (- x (* y (+ (log y) -1.0))) (* (log y) -0.5)) 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 * (log(y) + -1.0))) + (log(y) * -0.5)) - 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 * (log(y) + (-1.0d0)))) + (log(y) * (-0.5d0))) - 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 * (Math.log(y) + -1.0))) + (Math.log(y) * -0.5)) - 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 * (math.log(y) + -1.0))) + (math.log(y) * -0.5)) - 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(Float64(x - Float64(y * Float64(log(y) + -1.0))) + Float64(log(y) * -0.5)) - 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 * (log(y) + -1.0))) + (log(y) * -0.5)) - 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[(N[(x - N[(y * N[(N[Log[y], $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[Log[y], $MachinePrecision] * -0.5), $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. Simplified 0.1

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

   [Start] 0.1	\[ \left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
   associate-+l- [=>] 0.1	\[ \color{blue}{\left(x - \left(\left(y + 0.5\right) \cdot \log y - y\right)\right)} - z \]

3. Taylor expanded in y around 0 0.1

   \[\leadsto \color{blue}{\left(\left(\left(1 - \log y\right) \cdot y + x\right) - 0.5 \cdot \log y\right)} - z \]

4. Final simplification 0.1

   \[\leadsto \left(\left(x - y \cdot \left(\log y + -1\right)\right) + \log y \cdot -0.5\right) - z \]

Alternatives

Alternative 1
Error	0.1
Cost	13376

\[x + \mathsf{fma}\left(\log y, -0.5 - y, y - z\right) \]

Alternative 2
Error	16.3
Cost	7509

\[\begin{array}{l} t_0 := \left(y + x\right) - z\\ \mathbf{if}\;y \leq 8.5 \cdot 10^{-116}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{-15}:\\ \;\;\;\;\log y \cdot -0.5 - z\\ \mathbf{elif}\;y \leq 3.2 \cdot 10^{+41}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 9.6 \cdot 10^{+55} \lor \neg \left(y \leq 1.22 \cdot 10^{+100}\right):\\ \;\;\;\;x - y \cdot \left(\log y + -1\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x + -1\right) - z\\ \end{array} \]

Alternative 3
Error	19.6
Cost	7381

\[\begin{array}{l} \mathbf{if}\;y \leq 1.02 \cdot 10^{-115}:\\ \;\;\;\;\left(y + x\right) - z\\ \mathbf{elif}\;y \leq 2.25 \cdot 10^{-15}:\\ \;\;\;\;\log y \cdot -0.5 - z\\ \mathbf{elif}\;y \leq 4.6 \cdot 10^{+106} \lor \neg \left(y \leq 2.3 \cdot 10^{+131}\right) \land y \leq 1.3 \cdot 10^{+150}:\\ \;\;\;\;\left(x + -1\right) - z\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(1 - \log y\right)\\ \end{array} \]

Alternative 4
Error	7.2
Cost	7245

\[\begin{array}{l} \mathbf{if}\;y \leq 3.15 \cdot 10^{+41} \lor \neg \left(y \leq 6.2 \cdot 10^{+55}\right) \land y \leq 1.2 \cdot 10^{+100}:\\ \;\;\;\;\left(x + \log y \cdot -0.5\right) - z\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \left(\log y + -1\right)\\ \end{array} \]

Alternative 5
Error	18.2
Cost	7117

\[\begin{array}{l} \mathbf{if}\;y \leq 1.05 \cdot 10^{+107}:\\ \;\;\;\;\left(y + x\right) - z\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{+131} \lor \neg \left(y \leq 3.8 \cdot 10^{+149}\right):\\ \;\;\;\;y \cdot \left(1 - \log y\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x + -1\right) - z\\ \end{array} \]

Alternative 6
Error	0.1
Cost	7104

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

Alternative 7
Error	0.1
Cost	7104

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

Alternative 8
Error	27.1
Cost	6856

\[\begin{array}{l} \mathbf{if}\;z \leq 1.38 \cdot 10^{-233}:\\ \;\;\;\;\left(x + -1\right) - z\\ \mathbf{elif}\;z \leq 1.04 \cdot 10^{-196}:\\ \;\;\;\;\log y \cdot -0.5\\ \mathbf{else}:\\ \;\;\;\;x - z\\ \end{array} \]

Alternative 9
Error	33.0
Cost	392

\[\begin{array}{l} \mathbf{if}\;z \leq -1.3 \cdot 10^{+54}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq 6 \cdot 10^{+44}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 10
Error	26.7
Cost	320

\[\left(x + -1\right) - z \]

Alternative 11
Error	26.7
Cost	192

\[x - z \]

Alternative 12
Error	44.5
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023034 
(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))