Optimisation.CirclePacking:place from circle-packing-0.1.0.4, C

Percentage Accurate: 100.0% → 100.0%

Time: 5.2s

Alternatives: 3

Speedup: 2.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 2.0× speedup

Math

\[\begin{array}{l} [x, y] = \mathsf{sort}([x, y])\\ \\ \sqrt{y - x} \end{array} \]

FPCore

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

C

assert(x < y);
double code(double x, double y) {
	return sqrt((y - x));
}

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 = sqrt((y - x))
end function

Java

assert x < y;
public static double code(double x, double y) {
	return Math.sqrt((y - x));
}

Python

[x, y] = sort([x, y])
def code(x, y):
	return math.sqrt((y - x))

Julia

x, y = sort([x, y])
function code(x, y)
	return sqrt(Float64(y - x))
end

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. fabs-sub 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\sqrt{\left|y - x\right|}} \]
4. Add Preprocessing

5. Taylor expanded in y around -inf 100.0%

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

   1. fabs-neg 100.0%

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

   2. neg-mul-1 100.0%

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

   3. sub-neg 100.0%

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

   4. fabs-sub 100.0%

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

   5. rem-square-sqrt 50.4%

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

   6. fabs-sqr 50.4%

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

   7. rem-square-sqrt 50.4%

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

7. Simplified 50.4%

   \[\leadsto \color{blue}{\sqrt{y - x}} \]
8. Add Preprocessing

Alternative 2: 82.5% accurate, 1.9× speedup

Math

\[\begin{array}{l} [x, y] = \mathsf{sort}([x, y])\\ \\ \begin{array}{l} \mathbf{if}\;x \leq -1.3 \cdot 10^{-66}:\\ \;\;\;\;\sqrt{-x}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{y}\\ \end{array} \end{array} \]

FPCore

NOTE: x and y should be sorted in increasing order before calling this function.
(FPCore (x y) :precision binary64 (if (<= x -1.3e-66) (sqrt (- x)) (sqrt y)))

C

assert(x < y);
double code(double x, double y) {
	double tmp;
	if (x <= -1.3e-66) {
		tmp = sqrt(-x);
	} else {
		tmp = sqrt(y);
	}
	return tmp;
}

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
    real(8) :: tmp
    if (x <= (-1.3d-66)) then
        tmp = sqrt(-x)
    else
        tmp = sqrt(y)
    end if
    code = tmp
end function

Java

assert x < y;
public static double code(double x, double y) {
	double tmp;
	if (x <= -1.3e-66) {
		tmp = Math.sqrt(-x);
	} else {
		tmp = Math.sqrt(y);
	}
	return tmp;
}

Python

[x, y] = sort([x, y])
def code(x, y):
	tmp = 0
	if x <= -1.3e-66:
		tmp = math.sqrt(-x)
	else:
		tmp = math.sqrt(y)
	return tmp

Julia

x, y = sort([x, y])
function code(x, y)
	tmp = 0.0
	if (x <= -1.3e-66)
		tmp = sqrt(Float64(-x));
	else
		tmp = sqrt(y);
	end
	return tmp
end

MATLAB

x, y = num2cell(sort([x, y])){:}
function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -1.3e-66)
		tmp = sqrt(-x);
	else
		tmp = sqrt(y);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x and y should be sorted in increasing order before calling this function.
code[x_, y_] := If[LessEqual[x, -1.3e-66], N[Sqrt[(-x)], $MachinePrecision], N[Sqrt[y], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.2999999999999999e-66

   1. Initial program 100.0%

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

      1. fabs-sub 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\sqrt{\left|y - x\right|}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around -inf 100.0%

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

      1. fabs-neg 100.0%

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

      2. neg-mul-1 100.0%

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

      3. sub-neg 100.0%

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

      4. fabs-sub 100.0%

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

      5. rem-square-sqrt 81.5%

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

      6. fabs-sqr 81.5%

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

      7. rem-square-sqrt 81.5%

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

   7. Simplified 81.5%

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

   8. Taylor expanded in y around 0 64.1%

      \[\leadsto \sqrt{\color{blue}{-1 \cdot x}} \]
   9. Step-by-step derivation

      1. neg-mul-1 64.1%

         \[\leadsto \sqrt{\color{blue}{-x}} \]

   10. Simplified 64.1%

       \[\leadsto \sqrt{\color{blue}{-x}} \]

   if -1.2999999999999999e-66 < x

   1. Initial program 100.0%

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

      1. fabs-sub 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\sqrt{\left|y - x\right|}} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-cbrt-cube 70.3%

         \[\leadsto \color{blue}{\sqrt[3]{\left(\sqrt{\left|y - x\right|} \cdot \sqrt{\left|y - x\right|}\right) \cdot \sqrt{\left|y - x\right|}}} \]

      2. pow1/3 65.6%

         \[\leadsto \color{blue}{{\left(\left(\sqrt{\left|y - x\right|} \cdot \sqrt{\left|y - x\right|}\right) \cdot \sqrt{\left|y - x\right|}\right)}^{0.3333333333333333}} \]

      3. add-sqr-sqrt 65.6%

         \[\leadsto {\left(\color{blue}{\left|y - x\right|} \cdot \sqrt{\left|y - x\right|}\right)}^{0.3333333333333333} \]

      4. pow1 65.6%

         \[\leadsto {\left(\color{blue}{{\left(\left|y - x\right|\right)}^{1}} \cdot \sqrt{\left|y - x\right|}\right)}^{0.3333333333333333} \]

      5. pow1/2 65.6%

         \[\leadsto {\left({\left(\left|y - x\right|\right)}^{1} \cdot \color{blue}{{\left(\left|y - x\right|\right)}^{0.5}}\right)}^{0.3333333333333333} \]

      6. pow-prod-up 65.6%

         \[\leadsto {\color{blue}{\left({\left(\left|y - x\right|\right)}^{\left(1 + 0.5\right)}\right)}}^{0.3333333333333333} \]

      7. add-sqr-sqrt 25.7%

         \[\leadsto {\left({\left(\left|\color{blue}{\sqrt{y - x} \cdot \sqrt{y - x}}\right|\right)}^{\left(1 + 0.5\right)}\right)}^{0.3333333333333333} \]

      8. fabs-sqr 25.7%

         \[\leadsto {\left({\color{blue}{\left(\sqrt{y - x} \cdot \sqrt{y - x}\right)}}^{\left(1 + 0.5\right)}\right)}^{0.3333333333333333} \]

      9. add-sqr-sqrt 25.7%

         \[\leadsto {\left({\color{blue}{\left(y - x\right)}}^{\left(1 + 0.5\right)}\right)}^{0.3333333333333333} \]

      10. metadata-eval 25.7%

          \[\leadsto {\left({\left(y - x\right)}^{\color{blue}{1.5}}\right)}^{0.3333333333333333} \]

   6. Applied egg-rr 25.7%

      \[\leadsto \color{blue}{{\left({\left(y - x\right)}^{1.5}\right)}^{0.3333333333333333}} \]
   7. Step-by-step derivation

      1. unpow1/3 27.5%

         \[\leadsto \color{blue}{\sqrt[3]{{\left(y - x\right)}^{1.5}}} \]

   8. Simplified 27.5%

      \[\leadsto \color{blue}{\sqrt[3]{{\left(y - x\right)}^{1.5}}} \]

   9. Taylor expanded in y around inf 32.5%

      \[\leadsto \color{blue}{\sqrt{y}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 51.0% accurate, 2.0× speedup

Math

\[\begin{array}{l} [x, y] = \mathsf{sort}([x, y])\\ \\ \sqrt{y} \end{array} \]

FPCore

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

C

assert(x < y);
double code(double x, double y) {
	return sqrt(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 = sqrt(y)
end function

Java

assert x < y;
public static double code(double x, double y) {
	return Math.sqrt(y);
}

Python

[x, y] = sort([x, y])
def code(x, y):
	return math.sqrt(y)

Julia

x, y = sort([x, y])
function code(x, y)
	return sqrt(y)
end

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. fabs-sub 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\sqrt{\left|y - x\right|}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. add-cbrt-cube 66.5%

      \[\leadsto \color{blue}{\sqrt[3]{\left(\sqrt{\left|y - x\right|} \cdot \sqrt{\left|y - x\right|}\right) \cdot \sqrt{\left|y - x\right|}}} \]

   2. pow1/3 62.0%

      \[\leadsto \color{blue}{{\left(\left(\sqrt{\left|y - x\right|} \cdot \sqrt{\left|y - x\right|}\right) \cdot \sqrt{\left|y - x\right|}\right)}^{0.3333333333333333}} \]

   3. add-sqr-sqrt 62.0%

      \[\leadsto {\left(\color{blue}{\left|y - x\right|} \cdot \sqrt{\left|y - x\right|}\right)}^{0.3333333333333333} \]

   4. pow1 62.0%

      \[\leadsto {\left(\color{blue}{{\left(\left|y - x\right|\right)}^{1}} \cdot \sqrt{\left|y - x\right|}\right)}^{0.3333333333333333} \]

   5. pow1/2 62.0%

      \[\leadsto {\left({\left(\left|y - x\right|\right)}^{1} \cdot \color{blue}{{\left(\left|y - x\right|\right)}^{0.5}}\right)}^{0.3333333333333333} \]

   6. pow-prod-up 62.0%

      \[\leadsto {\color{blue}{\left({\left(\left|y - x\right|\right)}^{\left(1 + 0.5\right)}\right)}}^{0.3333333333333333} \]

   7. add-sqr-sqrt 31.7%

      \[\leadsto {\left({\left(\left|\color{blue}{\sqrt{y - x} \cdot \sqrt{y - x}}\right|\right)}^{\left(1 + 0.5\right)}\right)}^{0.3333333333333333} \]

   8. fabs-sqr 31.7%

      \[\leadsto {\left({\color{blue}{\left(\sqrt{y - x} \cdot \sqrt{y - x}\right)}}^{\left(1 + 0.5\right)}\right)}^{0.3333333333333333} \]

   9. add-sqr-sqrt 31.7%

      \[\leadsto {\left({\color{blue}{\left(y - x\right)}}^{\left(1 + 0.5\right)}\right)}^{0.3333333333333333} \]

   10. metadata-eval 31.7%

       \[\leadsto {\left({\left(y - x\right)}^{\color{blue}{1.5}}\right)}^{0.3333333333333333} \]

6. Applied egg-rr 31.7%

   \[\leadsto \color{blue}{{\left({\left(y - x\right)}^{1.5}\right)}^{0.3333333333333333}} \]
7. Step-by-step derivation

   1. unpow1/3 34.0%

      \[\leadsto \color{blue}{\sqrt[3]{{\left(y - x\right)}^{1.5}}} \]

8. Simplified 34.0%

   \[\leadsto \color{blue}{\sqrt[3]{{\left(y - x\right)}^{1.5}}} \]

9. Taylor expanded in y around inf 28.5%

   \[\leadsto \color{blue}{\sqrt{y}} \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2024132 
(FPCore (x y)
  :name "Optimisation.CirclePacking:place from circle-packing-0.1.0.4, C"
  :precision binary64
  (sqrt (fabs (- x y))))