Diagrams.Solve.Polynomial:cubForm from diagrams-solve-0.1, F

Percentage Accurate: 99.6% → 99.6%

Time: 1.2s

Alternatives: 3

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \left(x \cdot 27\right) \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (* x 27.0) y))

C

double code(double x, double y) {
	return (x * 27.0) * y;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * 27.0d0) * y
end function

Java

public static double code(double x, double y) {
	return (x * 27.0) * y;
}

Python

def code(x, y):
	return (x * 27.0) * y

Julia

function code(x, y)
	return Float64(Float64(x * 27.0) * y)
end

MATLAB

function tmp = code(x, y)
	tmp = (x * 27.0) * y;
end

Wolfram

code[x_, y_] := N[(N[(x * 27.0), $MachinePrecision] * y), $MachinePrecision]

Initial Program: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot 27\right) \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (* x 27.0) y))

C

double code(double x, double y) {
	return (x * 27.0) * y;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * 27.0d0) * y
end function

Java

public static double code(double x, double y) {
	return (x * 27.0) * y;
}

Python

def code(x, y):
	return (x * 27.0) * y

Julia

function code(x, y)
	return Float64(Float64(x * 27.0) * y)
end

MATLAB

function tmp = code(x, y)
	tmp = (x * 27.0) * y;
end

Wolfram

code[x_, y_] := N[(N[(x * 27.0), $MachinePrecision] * y), $MachinePrecision]

Alternative 1: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} [x, y] = \mathsf{sort}([x, y])\\ \\ \left(x \cdot 27\right) \cdot y \end{array} \]

FPCore

NOTE: x and y should be sorted in increasing order before calling this function.
(FPCore (x y) :precision binary64 (* (* x 27.0) y))

C

assert(x < y);
double code(double x, double y) {
	return (x * 27.0) * y;
}

Fortran

NOTE: x and y should be sorted in increasing order before calling this function.
real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * 27.0d0) * y
end function

Java

assert x < y;
public static double code(double x, double y) {
	return (x * 27.0) * y;
}

Python

[x, y] = sort([x, y])
def code(x, y):
	return (x * 27.0) * y

Julia

x, y = sort([x, y])
function code(x, y)
	return Float64(Float64(x * 27.0) * y)
end

MATLAB

x, y = num2cell(sort([x, y])){:}
function tmp = code(x, y)
	tmp = (x * 27.0) * y;
end

Wolfram

NOTE: x and y should be sorted in increasing order before calling this function.
code[x_, y_] := N[(N[(x * 27.0), $MachinePrecision] * y), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(x \cdot 27\right) \cdot y \]

2. Final simplification 99.7%

   \[\leadsto \left(x \cdot 27\right) \cdot y \]

Alternative 2: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} [x, y] = \mathsf{sort}([x, y])\\ \\ 27 \cdot \left(x \cdot y\right) \end{array} \]

FPCore

NOTE: x and y should be sorted in increasing order before calling this function.
(FPCore (x y) :precision binary64 (* 27.0 (* x y)))

C

assert(x < y);
double code(double x, double y) {
	return 27.0 * (x * y);
}

Fortran

NOTE: x and y should be sorted in increasing order before calling this function.
real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 27.0d0 * (x * y)
end function

Java

assert x < y;
public static double code(double x, double y) {
	return 27.0 * (x * y);
}

Python

[x, y] = sort([x, y])
def code(x, y):
	return 27.0 * (x * y)

Julia

x, y = sort([x, y])
function code(x, y)
	return Float64(27.0 * Float64(x * y))
end

MATLAB

x, y = num2cell(sort([x, y])){:}
function tmp = code(x, y)
	tmp = 27.0 * (x * y);
end

Wolfram

NOTE: x and y should be sorted in increasing order before calling this function.
code[x_, y_] := N[(27.0 * N[(x * y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(x \cdot 27\right) \cdot y \]
2. Step-by-step derivation

   1. *-commutative 99.7%

      \[\leadsto \color{blue}{\left(27 \cdot x\right)} \cdot y \]

   2. associate-*l* 99.6%

      \[\leadsto \color{blue}{27 \cdot \left(x \cdot y\right)} \]

3. Simplified 99.6%

   \[\leadsto \color{blue}{27 \cdot \left(x \cdot y\right)} \]

4. Final simplification 99.6%

   \[\leadsto 27 \cdot \left(x \cdot y\right) \]

Alternative 3: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} [x, y] = \mathsf{sort}([x, y])\\ \\ x \cdot \left(27 \cdot y\right) \end{array} \]

FPCore

NOTE: x and y should be sorted in increasing order before calling this function.
(FPCore (x y) :precision binary64 (* x (* 27.0 y)))

C

assert(x < y);
double code(double x, double y) {
	return x * (27.0 * y);
}

Fortran

NOTE: x and y should be sorted in increasing order before calling this function.
real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (27.0d0 * y)
end function

Java

assert x < y;
public static double code(double x, double y) {
	return x * (27.0 * y);
}

Python

[x, y] = sort([x, y])
def code(x, y):
	return x * (27.0 * y)

Julia

x, y = sort([x, y])
function code(x, y)
	return Float64(x * Float64(27.0 * y))
end

MATLAB

x, y = num2cell(sort([x, y])){:}
function tmp = code(x, y)
	tmp = x * (27.0 * y);
end

Wolfram

NOTE: x and y should be sorted in increasing order before calling this function.
code[x_, y_] := N[(x * N[(27.0 * y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(x \cdot 27\right) \cdot y \]
2. Step-by-step derivation

   1. associate-*l* 99.8%

      \[\leadsto \color{blue}{x \cdot \left(27 \cdot y\right)} \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \left(27 \cdot y\right)} \]

4. Final simplification 99.8%

   \[\leadsto x \cdot \left(27 \cdot y\right) \]

Reproduce

herbie shell --seed 2023305 
(FPCore (x y)
  :name "Diagrams.Solve.Polynomial:cubForm  from diagrams-solve-0.1, F"
  :precision binary64
  (* (* x 27.0) y))