Numeric.LinearAlgebra.Util:formatSparse from hmatrix-0.16.1.5

Percentage Accurate: 100.0% → 100.0%

Time: 1.5s

Alternatives: 3

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{\left|x - y\right|}{\left|y\right|} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (fabs (- x y)) (fabs y)))

C

double code(double x, double y) {
	return fabs((x - y)) / fabs(y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = abs((x - y)) / abs(y)
end function

Java

public static double code(double x, double y) {
	return Math.abs((x - y)) / Math.abs(y);
}

Python

def code(x, y):
	return math.fabs((x - y)) / math.fabs(y)

Julia

function code(x, y)
	return Float64(abs(Float64(x - y)) / abs(y))
end

MATLAB

function tmp = code(x, y)
	tmp = abs((x - y)) / abs(y);
end

Wolfram

code[x_, y_] := N[(N[Abs[N[(x - y), $MachinePrecision]], $MachinePrecision] / N[Abs[y], $MachinePrecision]), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\left|x - y\right|}{\left|y\right|} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (fabs (- x y)) (fabs y)))

C

double code(double x, double y) {
	return fabs((x - y)) / fabs(y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = abs((x - y)) / abs(y)
end function

Java

public static double code(double x, double y) {
	return Math.abs((x - y)) / Math.abs(y);
}

Python

def code(x, y):
	return math.fabs((x - y)) / math.fabs(y)

Julia

function code(x, y)
	return Float64(abs(Float64(x - y)) / abs(y))
end

MATLAB

function tmp = code(x, y)
	tmp = abs((x - y)) / abs(y);
end

Wolfram

code[x_, y_] := N[(N[Abs[N[(x - y), $MachinePrecision]], $MachinePrecision] / N[Abs[y], $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\left|x - y\right|}{\left|y\right|} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (fabs (- x y)) (fabs y)))

C

double code(double x, double y) {
	return fabs((x - y)) / fabs(y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = abs((x - y)) / abs(y)
end function

Java

public static double code(double x, double y) {
	return Math.abs((x - y)) / Math.abs(y);
}

Python

def code(x, y):
	return math.fabs((x - y)) / math.fabs(y)

Julia

function code(x, y)
	return Float64(abs(Float64(x - y)) / abs(y))
end

MATLAB

function tmp = code(x, y)
	tmp = abs((x - y)) / abs(y);
end

Wolfram

code[x_, y_] := N[(N[Abs[N[(x - y), $MachinePrecision]], $MachinePrecision] / N[Abs[y], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{\left|x - y\right|}{\left|y\right|} \]

2. Final simplification 100.0%

   \[\leadsto \frac{\left|x - y\right|}{\left|y\right|} \]

Alternative 2: 25.7% accurate, 41.0× speedup

Math

\[\begin{array}{l} \\ \frac{x}{y} + -1 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ (/ x y) -1.0))

C

double code(double x, double y) {
	return (x / y) + -1.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x / y) + (-1.0d0)
end function

Java

public static double code(double x, double y) {
	return (x / y) + -1.0;
}

Python

def code(x, y):
	return (x / y) + -1.0

Julia

function code(x, y)
	return Float64(Float64(x / y) + -1.0)
end

MATLAB

function tmp = code(x, y)
	tmp = (x / y) + -1.0;
end

Wolfram

code[x_, y_] := N[(N[(x / y), $MachinePrecision] + -1.0), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{\left|x - y\right|}{\left|y\right|} \]
2. Step-by-step derivation

   1. fabs-sub 100.0%

      \[\leadsto \frac{\color{blue}{\left|y - x\right|}}{\left|y\right|} \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\frac{\left|y - x\right|}{\left|y\right|}} \]

4. Taylor expanded in y around -inf 100.0%

   \[\leadsto \color{blue}{\frac{\left|-\left(x + -1 \cdot y\right)\right|}{\left|y\right|}} \]
5. Step-by-step derivation

   1. fabs-neg 100.0%

      \[\leadsto \frac{\color{blue}{\left|x + -1 \cdot y\right|}}{\left|y\right|} \]

   2. neg-mul-1 100.0%

      \[\leadsto \frac{\left|x + \color{blue}{\left(-y\right)}\right|}{\left|y\right|} \]

   3. sub-neg 100.0%

      \[\leadsto \frac{\left|\color{blue}{x - y}\right|}{\left|y\right|} \]

   4. fabs-sub 100.0%

      \[\leadsto \frac{\color{blue}{\left|y - x\right|}}{\left|y\right|} \]

   5. fabs-div 100.0%

      \[\leadsto \color{blue}{\left|\frac{y - x}{y}\right|} \]

   6. rem-square-sqrt 78.7%

      \[\leadsto \left|\color{blue}{\sqrt{\frac{y - x}{y}} \cdot \sqrt{\frac{y - x}{y}}}\right| \]

   7. fabs-sqr 78.7%

      \[\leadsto \color{blue}{\sqrt{\frac{y - x}{y}} \cdot \sqrt{\frac{y - x}{y}}} \]

   8. rem-square-sqrt 79.0%

      \[\leadsto \color{blue}{\frac{y - x}{y}} \]

   9. rem-square-sqrt 37.7%

      \[\leadsto \frac{\color{blue}{\sqrt{y - x} \cdot \sqrt{y - x}}}{y} \]

   10. fabs-sqr 37.7%

       \[\leadsto \frac{\color{blue}{\left|\sqrt{y - x} \cdot \sqrt{y - x}\right|}}{y} \]

   11. rem-square-sqrt 50.6%

       \[\leadsto \frac{\left|\color{blue}{y - x}\right|}{y} \]

   12. fabs-sub 50.6%

       \[\leadsto \frac{\color{blue}{\left|x - y\right|}}{y} \]

   13. unpow1 50.6%

       \[\leadsto \frac{\left|\color{blue}{{\left(x - y\right)}^{1}}\right|}{y} \]

   14. sqr-pow 12.6%

       \[\leadsto \frac{\left|\color{blue}{{\left(x - y\right)}^{\left(\frac{1}{2}\right)} \cdot {\left(x - y\right)}^{\left(\frac{1}{2}\right)}}\right|}{y} \]

   15. fabs-sqr 12.6%

       \[\leadsto \frac{\color{blue}{{\left(x - y\right)}^{\left(\frac{1}{2}\right)} \cdot {\left(x - y\right)}^{\left(\frac{1}{2}\right)}}}{y} \]

   16. sqr-pow 22.1%

       \[\leadsto \frac{\color{blue}{{\left(x - y\right)}^{1}}}{y} \]

   17. unpow1 22.1%

       \[\leadsto \frac{\color{blue}{x - y}}{y} \]

   18. div-sub 22.1%

       \[\leadsto \color{blue}{\frac{x}{y} - \frac{y}{y}} \]

   19. *-inverses 22.1%

       \[\leadsto \frac{x}{y} - \color{blue}{1} \]

   20. sub-neg 22.1%

       \[\leadsto \color{blue}{\frac{x}{y} + \left(-1\right)} \]

   21. metadata-eval 22.1%

       \[\leadsto \frac{x}{y} + \color{blue}{-1} \]

   22. +-commutative 22.1%

       \[\leadsto \color{blue}{-1 + \frac{x}{y}} \]

6. Simplified 22.1%

   \[\leadsto \color{blue}{-1 + \frac{x}{y}} \]

7. Final simplification 22.1%

   \[\leadsto \frac{x}{y} + -1 \]

Alternative 3: 75.4% accurate, 41.0× speedup

Math

\[\begin{array}{l} \\ 1 - \frac{x}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- 1.0 (/ x y)))

C

double code(double x, double y) {
	return 1.0 - (x / y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 - (x / y)
end function

Java

public static double code(double x, double y) {
	return 1.0 - (x / y);
}

Python

def code(x, y):
	return 1.0 - (x / y)

Julia

function code(x, y)
	return Float64(1.0 - Float64(x / y))
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 - (x / y);
end

Wolfram

code[x_, y_] := N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{\left|x - y\right|}{\left|y\right|} \]
2. Step-by-step derivation

   1. fabs-sub 100.0%

      \[\leadsto \frac{\color{blue}{\left|y - x\right|}}{\left|y\right|} \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\frac{\left|y - x\right|}{\left|y\right|}} \]

4. Taylor expanded in y around -inf 100.0%

   \[\leadsto \color{blue}{\frac{\left|-\left(x + -1 \cdot y\right)\right|}{\left|y\right|}} \]
5. Step-by-step derivation

   1. fabs-neg 100.0%

      \[\leadsto \frac{\color{blue}{\left|x + -1 \cdot y\right|}}{\left|y\right|} \]

   2. neg-mul-1 100.0%

      \[\leadsto \frac{\left|x + \color{blue}{\left(-y\right)}\right|}{\left|y\right|} \]

   3. sub-neg 100.0%

      \[\leadsto \frac{\left|\color{blue}{x - y}\right|}{\left|y\right|} \]

   4. fabs-sub 100.0%

      \[\leadsto \frac{\color{blue}{\left|y - x\right|}}{\left|y\right|} \]

   5. fabs-div 100.0%

      \[\leadsto \color{blue}{\left|\frac{y - x}{y}\right|} \]

   6. rem-square-sqrt 78.7%

      \[\leadsto \left|\color{blue}{\sqrt{\frac{y - x}{y}} \cdot \sqrt{\frac{y - x}{y}}}\right| \]

   7. fabs-sqr 78.7%

      \[\leadsto \color{blue}{\sqrt{\frac{y - x}{y}} \cdot \sqrt{\frac{y - x}{y}}} \]

   8. rem-square-sqrt 79.0%

      \[\leadsto \color{blue}{\frac{y - x}{y}} \]

   9. div-sub 79.1%

      \[\leadsto \color{blue}{\frac{y}{y} - \frac{x}{y}} \]

   10. *-inverses 79.1%

       \[\leadsto \color{blue}{1} - \frac{x}{y} \]

6. Simplified 79.1%

   \[\leadsto \color{blue}{1 - \frac{x}{y}} \]

7. Final simplification 79.1%

   \[\leadsto 1 - \frac{x}{y} \]

Reproduce

herbie shell --seed 2023320 
(FPCore (x y)
  :name "Numeric.LinearAlgebra.Util:formatSparse from hmatrix-0.16.1.5"
  :precision binary64
  (/ (fabs (- x y)) (fabs y)))