Diagrams.Tangent:$catParam from diagrams-lib-1.3.0.3, F

Average Error: 0.2 → 0.2

Time: 4.3s

Precision: binary64

Cost: 320

Math

\[\left(x \cdot 3\right) \cdot x \]

↓

\[\left(x \cdot x\right) \cdot 3 \]

FPCore

(FPCore (x) :precision binary64 (* (* x 3.0) x))

↓

(FPCore (x) :precision binary64 (* (* x x) 3.0))

C

double code(double x) {
	return (x * 3.0) * x;
}

↓

double code(double x) {
	return (x * x) * 3.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * 3.0d0) * x
end function

↓

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * x) * 3.0d0
end function

Java

public static double code(double x) {
	return (x * 3.0) * x;
}

↓

public static double code(double x) {
	return (x * x) * 3.0;
}

Python

def code(x):
	return (x * 3.0) * x

↓

def code(x):
	return (x * x) * 3.0

Julia

function code(x)
	return Float64(Float64(x * 3.0) * x)
end

↓

function code(x)
	return Float64(Float64(x * x) * 3.0)
end

MATLAB

function tmp = code(x)
	tmp = (x * 3.0) * x;
end

↓

function tmp = code(x)
	tmp = (x * x) * 3.0;
end

Wolfram

code[x_] := N[(N[(x * 3.0), $MachinePrecision] * x), $MachinePrecision]

↓

code[x_] := N[(N[(x * x), $MachinePrecision] * 3.0), $MachinePrecision]

Derivation

1. Initial program 0.2

   \[\left(x \cdot 3\right) \cdot x \]

2. Applied egg-rr 0.4

   \[\leadsto \color{blue}{{\left(\sqrt{x \cdot \left(x \cdot 3\right)}\right)}^{2}} \]

3. Applied egg-rr 0.2

   \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot 3} \]

4. Final simplification 0.2

   \[\leadsto \left(x \cdot x\right) \cdot 3 \]

Reproduce

herbie shell --seed 2022308 
(FPCore (x)
  :name "Diagrams.Tangent:$catParam from diagrams-lib-1.3.0.3, F"
  :precision binary64
  (* (* x 3.0) x))