Graphics.Rendering.Chart.Plot.AreaSpots:renderSpotLegend from Chart-1.5.3

Percentage Accurate: 99.9% → 99.9%

Time: 3.4s

Alternatives: 6

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ x + \frac{\left|y - x\right|}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ (fabs (- y x)) 2.0)))

C

double code(double x, double y) {
	return x + (fabs((y - x)) / 2.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (abs((y - x)) / 2.0d0)
end function

Java

public static double code(double x, double y) {
	return x + (Math.abs((y - x)) / 2.0);
}

Python

def code(x, y):
	return x + (math.fabs((y - x)) / 2.0)

Julia

function code(x, y)
	return Float64(x + Float64(abs(Float64(y - x)) / 2.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (abs((y - x)) / 2.0);
end

Wolfram

code[x_, y_] := N[(x + N[(N[Abs[N[(y - x), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{\left|y - x\right|}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ (fabs (- y x)) 2.0)))

C

double code(double x, double y) {
	return x + (fabs((y - x)) / 2.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (abs((y - x)) / 2.0d0)
end function

Java

public static double code(double x, double y) {
	return x + (Math.abs((y - x)) / 2.0);
}

Python

def code(x, y):
	return x + (math.fabs((y - x)) / 2.0)

Julia

function code(x, y)
	return Float64(x + Float64(abs(Float64(y - x)) / 2.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (abs((y - x)) / 2.0);
end

Wolfram

code[x_, y_] := N[(x + N[(N[Abs[N[(y - x), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{\left|y - x\right|}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ (fabs (- y x)) 2.0)))

C

double code(double x, double y) {
	return x + (fabs((y - x)) / 2.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (abs((y - x)) / 2.0d0)
end function

Java

public static double code(double x, double y) {
	return x + (Math.abs((y - x)) / 2.0);
}

Python

def code(x, y):
	return x + (math.fabs((y - x)) / 2.0)

Julia

function code(x, y)
	return Float64(x + Float64(abs(Float64(y - x)) / 2.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (abs((y - x)) / 2.0);
end

Wolfram

code[x_, y_] := N[(x + N[(N[Abs[N[(y - x), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x + \frac{\left|y - x\right|}{2} \]
2. Add Preprocessing

3. Final simplification 99.9%

   \[\leadsto x + \frac{\left|y - x\right|}{2} \]
4. Add Preprocessing

Alternative 2: 70.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.2 \cdot 10^{-48}:\\ \;\;\;\;\left|y - x\right| \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x + y}{2}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -2.2e-48) (* (fabs (- y x)) 0.5) (/ (+ x y) 2.0)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -2.2e-48) {
		tmp = fabs((y - x)) * 0.5;
	} else {
		tmp = (x + y) / 2.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-2.2d-48)) then
        tmp = abs((y - x)) * 0.5d0
    else
        tmp = (x + y) / 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -2.2e-48) {
		tmp = Math.abs((y - x)) * 0.5;
	} else {
		tmp = (x + y) / 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -2.2e-48:
		tmp = math.fabs((y - x)) * 0.5
	else:
		tmp = (x + y) / 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -2.2e-48)
		tmp = Float64(abs(Float64(y - x)) * 0.5);
	else
		tmp = Float64(Float64(x + y) / 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -2.2e-48)
		tmp = abs((y - x)) * 0.5;
	else
		tmp = (x + y) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -2.2e-48], N[(N[Abs[N[(y - x), $MachinePrecision]], $MachinePrecision] * 0.5), $MachinePrecision], N[(N[(x + y), $MachinePrecision] / 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -2.20000000000000013e-48

   1. Initial program 99.9%

      \[x + \frac{\left|y - x\right|}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 76.6%

      \[\leadsto \color{blue}{0.5 \cdot \left|y - x\right|} \]

   if -2.20000000000000013e-48 < y

   1. Initial program 99.9%

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

      1. +-commutative 99.9%

         \[\leadsto \color{blue}{\frac{\left|y - x\right|}{2} + x} \]

      2. div-inv 99.9%

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

      3. fma-def 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left|y - x\right|, \frac{1}{2}, x\right)} \]

      4. add-sqr-sqrt 61.8%

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

      5. fabs-sqr 61.8%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\sqrt{y - x} \cdot \sqrt{y - x}}, \frac{1}{2}, x\right) \]

      6. add-sqr-sqrt 68.8%

         \[\leadsto \mathsf{fma}\left(\color{blue}{y - x}, \frac{1}{2}, x\right) \]

      7. metadata-eval 68.8%

         \[\leadsto \mathsf{fma}\left(y - x, \color{blue}{0.5}, x\right) \]

   4. Applied egg-rr 68.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y - x, 0.5, x\right)} \]
   5. Step-by-step derivation

      1. fma-udef 68.8%

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

      2. flip-+ 37.7%

         \[\leadsto \color{blue}{\frac{\left(\left(y - x\right) \cdot 0.5\right) \cdot \left(\left(y - x\right) \cdot 0.5\right) - x \cdot x}{\left(y - x\right) \cdot 0.5 - x}} \]

      3. pow2 37.7%

         \[\leadsto \frac{\color{blue}{{\left(\left(y - x\right) \cdot 0.5\right)}^{2}} - x \cdot x}{\left(y - x\right) \cdot 0.5 - x} \]

      4. pow2 37.7%

         \[\leadsto \frac{{\left(\left(y - x\right) \cdot 0.5\right)}^{2} - \color{blue}{{x}^{2}}}{\left(y - x\right) \cdot 0.5 - x} \]

   6. Applied egg-rr 37.7%

      \[\leadsto \color{blue}{\frac{{\left(\left(y - x\right) \cdot 0.5\right)}^{2} - {x}^{2}}{\left(y - x\right) \cdot 0.5 - x}} \]
   7. Step-by-step derivation

      1. unpow2 37.7%

         \[\leadsto \frac{\color{blue}{\left(\left(y - x\right) \cdot 0.5\right) \cdot \left(\left(y - x\right) \cdot 0.5\right)} - {x}^{2}}{\left(y - x\right) \cdot 0.5 - x} \]

      2. unpow2 37.7%

         \[\leadsto \frac{\left(\left(y - x\right) \cdot 0.5\right) \cdot \left(\left(y - x\right) \cdot 0.5\right) - \color{blue}{x \cdot x}}{\left(y - x\right) \cdot 0.5 - x} \]

      3. difference-of-squares 39.9%

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

      4. fma-def 39.9%

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(y - x, 0.5, x\right)} \cdot \left(\left(y - x\right) \cdot 0.5 - x\right)}{\left(y - x\right) \cdot 0.5 - x} \]

      5. associate-/l* 68.8%

         \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(y - x, 0.5, x\right)}{\frac{\left(y - x\right) \cdot 0.5 - x}{\left(y - x\right) \cdot 0.5 - x}}} \]

      6. fma-def 68.8%

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

      7. +-commutative 68.8%

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

      8. *-commutative 68.8%

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

      9. sub-neg 68.8%

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

      10. +-commutative 68.8%

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

      11. distribute-lft-in 68.8%

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

      12. distribute-rgt-neg-in 68.8%

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

      13. distribute-lft-neg-in 68.8%

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

      14. metadata-eval 68.8%

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

      15. associate-+r+ 68.8%

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

      16. distribute-rgt1-in 68.8%

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

      17. metadata-eval 68.8%

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

      18. distribute-lft-out 68.8%

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

      19. *-inverses 68.8%

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

   8. Simplified 68.8%

      \[\leadsto \color{blue}{\frac{x + y}{2}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 71.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.2 \cdot 10^{-48}:\\ \;\;\;\;\left|y - x\right| \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x + y}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 31.4% accurate, 13.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.7 \cdot 10^{-176}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 1.7e-176) x (* y 0.5)))

C

double code(double x, double y) {
	double tmp;
	if (y <= 1.7e-176) {
		tmp = x;
	} else {
		tmp = y * 0.5;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 1.7d-176) then
        tmp = x
    else
        tmp = y * 0.5d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 1.7e-176) {
		tmp = x;
	} else {
		tmp = y * 0.5;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 1.7e-176:
		tmp = x
	else:
		tmp = y * 0.5
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 1.7e-176)
		tmp = x;
	else
		tmp = Float64(y * 0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 1.7e-176)
		tmp = x;
	else
		tmp = y * 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 1.7e-176], x, N[(y * 0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.6999999999999999e-176

   1. Initial program 99.9%

      \[x + \frac{\left|y - x\right|}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 13.2%

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

   if 1.6999999999999999e-176 < y

   1. Initial program 99.9%

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

      1. +-commutative 99.9%

         \[\leadsto \color{blue}{\frac{\left|y - x\right|}{2} + x} \]

      2. div-inv 99.9%

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

      3. fma-def 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left|y - x\right|, \frac{1}{2}, x\right)} \]

      4. add-sqr-sqrt 77.7%

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

      5. fabs-sqr 77.7%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\sqrt{y - x} \cdot \sqrt{y - x}}, \frac{1}{2}, x\right) \]

      6. add-sqr-sqrt 82.1%

         \[\leadsto \mathsf{fma}\left(\color{blue}{y - x}, \frac{1}{2}, x\right) \]

      7. metadata-eval 82.1%

         \[\leadsto \mathsf{fma}\left(y - x, \color{blue}{0.5}, x\right) \]

   4. Applied egg-rr 82.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y - x, 0.5, x\right)} \]

   5. Taylor expanded in y around inf 58.8%

      \[\leadsto \color{blue}{0.5 \cdot y} \]
3. Recombined 2 regimes into one program.

4. Final simplification 29.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 1.7 \cdot 10^{-176}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot 0.5\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 45.5% accurate, 13.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.95 \cdot 10^{-50}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;y \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 1.95e-50) (* x 0.5) (* y 0.5)))

C

double code(double x, double y) {
	double tmp;
	if (y <= 1.95e-50) {
		tmp = x * 0.5;
	} else {
		tmp = y * 0.5;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 1.95d-50) then
        tmp = x * 0.5d0
    else
        tmp = y * 0.5d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 1.95e-50) {
		tmp = x * 0.5;
	} else {
		tmp = y * 0.5;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 1.95e-50:
		tmp = x * 0.5
	else:
		tmp = y * 0.5
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 1.95e-50)
		tmp = Float64(x * 0.5);
	else
		tmp = Float64(y * 0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 1.95e-50)
		tmp = x * 0.5;
	else
		tmp = y * 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 1.95e-50], N[(x * 0.5), $MachinePrecision], N[(y * 0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.9500000000000001e-50

   1. Initial program 99.9%

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

      1. +-commutative 99.9%

         \[\leadsto \color{blue}{\frac{\left|y - x\right|}{2} + x} \]

      2. div-inv 99.9%

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

      3. fma-def 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left|y - x\right|, \frac{1}{2}, x\right)} \]

      4. add-sqr-sqrt 34.0%

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

      5. fabs-sqr 34.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\sqrt{y - x} \cdot \sqrt{y - x}}, \frac{1}{2}, x\right) \]

      6. add-sqr-sqrt 40.1%

         \[\leadsto \mathsf{fma}\left(\color{blue}{y - x}, \frac{1}{2}, x\right) \]

      7. metadata-eval 40.1%

         \[\leadsto \mathsf{fma}\left(y - x, \color{blue}{0.5}, x\right) \]

   4. Applied egg-rr 40.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y - x, 0.5, x\right)} \]

   5. Taylor expanded in y around 0 36.5%

      \[\leadsto \color{blue}{x + -0.5 \cdot x} \]
   6. Step-by-step derivation

      1. distribute-rgt1-in 36.5%

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

      2. metadata-eval 36.5%

         \[\leadsto \color{blue}{0.5} \cdot x \]

      3. *-commutative 36.5%

         \[\leadsto \color{blue}{x \cdot 0.5} \]

   7. Simplified 36.5%

      \[\leadsto \color{blue}{x \cdot 0.5} \]

   if 1.9500000000000001e-50 < y

   1. Initial program 100.0%

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

      1. +-commutative 100.0%

         \[\leadsto \color{blue}{\frac{\left|y - x\right|}{2} + x} \]

      2. div-inv 100.0%

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

      3. fma-def 100.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left|y - x\right|, \frac{1}{2}, x\right)} \]

      4. add-sqr-sqrt 79.1%

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

      5. fabs-sqr 79.1%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\sqrt{y - x} \cdot \sqrt{y - x}}, \frac{1}{2}, x\right) \]

      6. add-sqr-sqrt 83.3%

         \[\leadsto \mathsf{fma}\left(\color{blue}{y - x}, \frac{1}{2}, x\right) \]

      7. metadata-eval 83.3%

         \[\leadsto \mathsf{fma}\left(y - x, \color{blue}{0.5}, x\right) \]

   4. Applied egg-rr 83.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y - x, 0.5, x\right)} \]

   5. Taylor expanded in y around inf 67.7%

      \[\leadsto \color{blue}{0.5 \cdot y} \]
3. Recombined 2 regimes into one program.

4. Final simplification 44.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 1.95 \cdot 10^{-50}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;y \cdot 0.5\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 54.3% accurate, 21.4× speedup

Math

\[\begin{array}{l} \\ \frac{x + y}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (+ x y) 2.0))

C

double code(double x, double y) {
	return (x + y) / 2.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x + y) / 2.0d0
end function

Java

public static double code(double x, double y) {
	return (x + y) / 2.0;
}

Python

def code(x, y):
	return (x + y) / 2.0

Julia

function code(x, y)
	return Float64(Float64(x + y) / 2.0)
end

MATLAB

function tmp = code(x, y)
	tmp = (x + y) / 2.0;
end

Wolfram

code[x_, y_] := N[(N[(x + y), $MachinePrecision] / 2.0), $MachinePrecision]

Derivation

1. Initial program 99.9%

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

   1. +-commutative 99.9%

      \[\leadsto \color{blue}{\frac{\left|y - x\right|}{2} + x} \]

   2. div-inv 99.9%

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

   3. fma-def 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\left|y - x\right|, \frac{1}{2}, x\right)} \]

   4. add-sqr-sqrt 46.1%

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

   5. fabs-sqr 46.1%

      \[\leadsto \mathsf{fma}\left(\color{blue}{\sqrt{y - x} \cdot \sqrt{y - x}}, \frac{1}{2}, x\right) \]

   6. add-sqr-sqrt 51.7%

      \[\leadsto \mathsf{fma}\left(\color{blue}{y - x}, \frac{1}{2}, x\right) \]

   7. metadata-eval 51.7%

      \[\leadsto \mathsf{fma}\left(y - x, \color{blue}{0.5}, x\right) \]

4. Applied egg-rr 51.7%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y - x, 0.5, x\right)} \]
5. Step-by-step derivation

   1. fma-udef 51.7%

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

   2. flip-+ 28.2%

      \[\leadsto \color{blue}{\frac{\left(\left(y - x\right) \cdot 0.5\right) \cdot \left(\left(y - x\right) \cdot 0.5\right) - x \cdot x}{\left(y - x\right) \cdot 0.5 - x}} \]

   3. pow2 28.2%

      \[\leadsto \frac{\color{blue}{{\left(\left(y - x\right) \cdot 0.5\right)}^{2}} - x \cdot x}{\left(y - x\right) \cdot 0.5 - x} \]

   4. pow2 28.2%

      \[\leadsto \frac{{\left(\left(y - x\right) \cdot 0.5\right)}^{2} - \color{blue}{{x}^{2}}}{\left(y - x\right) \cdot 0.5 - x} \]

6. Applied egg-rr 28.2%

   \[\leadsto \color{blue}{\frac{{\left(\left(y - x\right) \cdot 0.5\right)}^{2} - {x}^{2}}{\left(y - x\right) \cdot 0.5 - x}} \]
7. Step-by-step derivation

   1. unpow2 28.2%

      \[\leadsto \frac{\color{blue}{\left(\left(y - x\right) \cdot 0.5\right) \cdot \left(\left(y - x\right) \cdot 0.5\right)} - {x}^{2}}{\left(y - x\right) \cdot 0.5 - x} \]

   2. unpow2 28.2%

      \[\leadsto \frac{\left(\left(y - x\right) \cdot 0.5\right) \cdot \left(\left(y - x\right) \cdot 0.5\right) - \color{blue}{x \cdot x}}{\left(y - x\right) \cdot 0.5 - x} \]

   3. difference-of-squares 30.0%

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

   4. fma-def 30.0%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(y - x, 0.5, x\right)} \cdot \left(\left(y - x\right) \cdot 0.5 - x\right)}{\left(y - x\right) \cdot 0.5 - x} \]

   5. associate-/l* 51.7%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(y - x, 0.5, x\right)}{\frac{\left(y - x\right) \cdot 0.5 - x}{\left(y - x\right) \cdot 0.5 - x}}} \]

   6. fma-def 51.7%

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

   7. +-commutative 51.7%

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

   8. *-commutative 51.7%

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

   9. sub-neg 51.7%

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

   10. +-commutative 51.7%

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

   11. distribute-lft-in 51.7%

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

   12. distribute-rgt-neg-in 51.7%

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

   13. distribute-lft-neg-in 51.7%

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

   14. metadata-eval 51.7%

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

   15. associate-+r+ 51.7%

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

   16. distribute-rgt1-in 51.7%

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

   17. metadata-eval 51.7%

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

   18. distribute-lft-out 51.7%

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

   19. *-inverses 51.7%

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

8. Simplified 51.7%

   \[\leadsto \color{blue}{\frac{x + y}{2}} \]

9. Final simplification 51.7%

   \[\leadsto \frac{x + y}{2} \]
10. Add Preprocessing

Alternative 6: 11.3% accurate, 107.0× speedup

Math

\[\begin{array}{l} \\ x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 x)

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return x

Julia

function code(x, y)
	return x
end

MATLAB

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

Wolfram

code[x_, y_] := x

Derivation

1. Initial program 99.9%

   \[x + \frac{\left|y - x\right|}{2} \]
2. Add Preprocessing

3. Taylor expanded in x around inf 12.0%

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

4. Final simplification 12.0%

   \[\leadsto x \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024036 
(FPCore (x y)
  :name "Graphics.Rendering.Chart.Plot.AreaSpots:renderSpotLegend from Chart-1.5.3"
  :precision binary64
  (+ x (/ (fabs (- y x)) 2.0)))