Numeric.Log:$clog1p from log-domain-0.10.2.1, A

Percentage Accurate: 99.9% → 100.0%

Time: 5.0s

Alternatives: 7

Speedup: 1.2×

• 42.9% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(x \cdot 2 + x \cdot x\right) + y \cdot y \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot 2 + x \cdot x\right) + y \cdot y \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y, y, x \cdot \left(x + 2\right)\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma y y (* x (+ x 2.0))))

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

   2. distribute-lft-out N/A

      \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

   4. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

   5. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

   6. *-lowering-*.f64 100.0%

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto \left(x \cdot x + x \cdot 2\right) + \color{blue}{y} \cdot y \]

   2. +-commutative N/A

      \[\leadsto \left(x \cdot 2 + x \cdot x\right) + \color{blue}{y} \cdot y \]

   3. +-commutative N/A

      \[\leadsto y \cdot y + \color{blue}{\left(x \cdot 2 + x \cdot x\right)} \]

   4. fma-define N/A

      \[\leadsto \mathsf{fma}\left(y, \color{blue}{y}, x \cdot 2 + x \cdot x\right) \]

   5. fma-lowering-fma.f64 N/A

      \[\leadsto \mathsf{fma.f64}\left(y, \color{blue}{y}, \left(x \cdot 2 + x \cdot x\right)\right) \]

   6. +-commutative N/A

      \[\leadsto \mathsf{fma.f64}\left(y, y, \left(x \cdot x + x \cdot 2\right)\right) \]

   7. distribute-lft-in N/A

      \[\leadsto \mathsf{fma.f64}\left(y, y, \left(x \cdot \left(x + 2\right)\right)\right) \]

   8. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{fma.f64}\left(y, y, \mathsf{*.f64}\left(x, \left(x + 2\right)\right)\right) \]

   9. +-lowering-+.f64 100.0%

      \[\leadsto \mathsf{fma.f64}\left(y, y, \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right)\right) \]

6. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, y, x \cdot \left(x + 2\right)\right)} \]
7. Add Preprocessing

Alternative 2: 71.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.032:\\ \;\;\;\;x \cdot x\\ \mathbf{elif}\;x \leq -7.2 \cdot 10^{-120}:\\ \;\;\;\;x \cdot 2\\ \mathbf{elif}\;x \leq 4300000000:\\ \;\;\;\;y \cdot y\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -0.032)
   (* x x)
   (if (<= x -7.2e-120) (* x 2.0) (if (<= x 4300000000.0) (* y y) (* x x)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -0.032) {
		tmp = x * x;
	} else if (x <= -7.2e-120) {
		tmp = x * 2.0;
	} else if (x <= 4300000000.0) {
		tmp = y * y;
	} else {
		tmp = x * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-0.032d0)) then
        tmp = x * x
    else if (x <= (-7.2d-120)) then
        tmp = x * 2.0d0
    else if (x <= 4300000000.0d0) then
        tmp = y * y
    else
        tmp = x * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -0.032) {
		tmp = x * x;
	} else if (x <= -7.2e-120) {
		tmp = x * 2.0;
	} else if (x <= 4300000000.0) {
		tmp = y * y;
	} else {
		tmp = x * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -0.032:
		tmp = x * x
	elif x <= -7.2e-120:
		tmp = x * 2.0
	elif x <= 4300000000.0:
		tmp = y * y
	else:
		tmp = x * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -0.032)
		tmp = Float64(x * x);
	elseif (x <= -7.2e-120)
		tmp = Float64(x * 2.0);
	elseif (x <= 4300000000.0)
		tmp = Float64(y * y);
	else
		tmp = Float64(x * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -0.032)
		tmp = x * x;
	elseif (x <= -7.2e-120)
		tmp = x * 2.0;
	elseif (x <= 4300000000.0)
		tmp = y * y;
	else
		tmp = x * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -0.032], N[(x * x), $MachinePrecision], If[LessEqual[x, -7.2e-120], N[(x * 2.0), $MachinePrecision], If[LessEqual[x, 4300000000.0], N[(y * y), $MachinePrecision], N[(x * x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.032000000000000001 or 4.3e9 < x

   1. Initial program 100.0%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

      5. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

      6. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2}} \]
   6. Step-by-step derivation

      1. unpow2 N/A

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

      2. *-lowering-*.f64 83.7%

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{x}\right) \]

   7. Simplified 83.7%

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

   if -0.032000000000000001 < x < -7.2000000000000005e-120

   1. Initial program 100.0%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

      5. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

      6. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + 2 \cdot \frac{1}{x}\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

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

      2. distribute-rgt-in N/A

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

      3. *-lft-identity N/A

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

      4. associate-*l* N/A

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

      5. fma-define N/A

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

      6. unpow2 N/A

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

      7. associate-*r* N/A

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

      8. lft-mult-inverse N/A

         \[\leadsto \mathsf{fma}\left(2, 1 \cdot x, {x}^{2}\right) \]

      9. *-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(2, x, {x}^{2}\right) \]

      10. fma-define N/A

          \[\leadsto 2 \cdot x + \color{blue}{{x}^{2}} \]

      11. unpow2 N/A

          \[\leadsto 2 \cdot x + x \cdot \color{blue}{x} \]

      12. distribute-rgt-in N/A

          \[\leadsto x \cdot \color{blue}{\left(2 + x\right)} \]

      13. *-lowering-*.f64 N/A

          \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(2 + x\right)}\right) \]

      14. +-lowering-+.f64 71.8%

          \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(2, \color{blue}{x}\right)\right) \]

   7. Simplified 71.8%

      \[\leadsto \color{blue}{x \cdot \left(2 + x\right)} \]

   8. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot x} \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto x \cdot \color{blue}{2} \]

      2. *-lowering-*.f64 71.8%

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{2}\right) \]

   10. Simplified 71.8%

       \[\leadsto \color{blue}{x \cdot 2} \]

   if -7.2000000000000005e-120 < x < 4.3e9

   1. Initial program 100.0%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

      5. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

      6. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

      \[\leadsto \color{blue}{{y}^{2}} \]
   6. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto y \cdot \color{blue}{y} \]

      2. *-lowering-*.f64 73.7%

         \[\leadsto \mathsf{*.f64}\left(y, \color{blue}{y}\right) \]

   7. Simplified 73.7%

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

Alternative 3: 96.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \cdot y \leq 3.8 \cdot 10^{-151}:\\ \;\;\;\;x \cdot \left(x + 2\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot y + x \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* y y) 3.8e-151) (* x (+ x 2.0)) (+ (* y y) (* x x))))

C

double code(double x, double y) {
	double tmp;
	if ((y * y) <= 3.8e-151) {
		tmp = x * (x + 2.0);
	} else {
		tmp = (y * y) + (x * x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y * y) <= 3.8d-151) then
        tmp = x * (x + 2.0d0)
    else
        tmp = (y * y) + (x * x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y * y) <= 3.8e-151) {
		tmp = x * (x + 2.0);
	} else {
		tmp = (y * y) + (x * x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y * y) <= 3.8e-151:
		tmp = x * (x + 2.0)
	else:
		tmp = (y * y) + (x * x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(y * y) <= 3.8e-151)
		tmp = Float64(x * Float64(x + 2.0));
	else
		tmp = Float64(Float64(y * y) + Float64(x * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y * y) <= 3.8e-151)
		tmp = x * (x + 2.0);
	else
		tmp = (y * y) + (x * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(y * y), $MachinePrecision], 3.8e-151], N[(x * N[(x + 2.0), $MachinePrecision]), $MachinePrecision], N[(N[(y * y), $MachinePrecision] + N[(x * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y y) < 3.7999999999999997e-151

   1. Initial program 100.0%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

      5. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

      6. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + 2 \cdot \frac{1}{x}\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

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

      2. distribute-rgt-in N/A

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

      3. *-lft-identity N/A

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

      4. associate-*l* N/A

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

      5. fma-define N/A

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

      6. unpow2 N/A

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

      7. associate-*r* N/A

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

      8. lft-mult-inverse N/A

         \[\leadsto \mathsf{fma}\left(2, 1 \cdot x, {x}^{2}\right) \]

      9. *-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(2, x, {x}^{2}\right) \]

      10. fma-define N/A

          \[\leadsto 2 \cdot x + \color{blue}{{x}^{2}} \]

      11. unpow2 N/A

          \[\leadsto 2 \cdot x + x \cdot \color{blue}{x} \]

      12. distribute-rgt-in N/A

          \[\leadsto x \cdot \color{blue}{\left(2 + x\right)} \]

      13. *-lowering-*.f64 N/A

          \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(2 + x\right)}\right) \]

      14. +-lowering-+.f64 99.0%

          \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(2, \color{blue}{x}\right)\right) \]

   7. Simplified 99.0%

      \[\leadsto \color{blue}{x \cdot \left(2 + x\right)} \]

   if 3.7999999999999997e-151 < (*.f64 y y)

   1. Initial program 100.0%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

      5. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

      6. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

      \[\leadsto \mathsf{+.f64}\left(\color{blue}{\left({x}^{2}\right)}, \mathsf{*.f64}\left(y, y\right)\right) \]
   6. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot x\right), \mathsf{*.f64}\left(\color{blue}{y}, y\right)\right) \]

      2. *-lowering-*.f64 98.2%

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, x\right), \mathsf{*.f64}\left(\color{blue}{y}, y\right)\right) \]

   7. Simplified 98.2%

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

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot y \leq 3.8 \cdot 10^{-151}:\\ \;\;\;\;x \cdot \left(x + 2\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot y + x \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 59.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.032:\\ \;\;\;\;x \cdot x\\ \mathbf{elif}\;x \leq 2:\\ \;\;\;\;x \cdot 2\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -0.032) (* x x) (if (<= x 2.0) (* x 2.0) (* x x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -0.032) {
		tmp = x * x;
	} else if (x <= 2.0) {
		tmp = x * 2.0;
	} else {
		tmp = x * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-0.032d0)) then
        tmp = x * x
    else if (x <= 2.0d0) then
        tmp = x * 2.0d0
    else
        tmp = x * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -0.032) {
		tmp = x * x;
	} else if (x <= 2.0) {
		tmp = x * 2.0;
	} else {
		tmp = x * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -0.032:
		tmp = x * x
	elif x <= 2.0:
		tmp = x * 2.0
	else:
		tmp = x * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -0.032)
		tmp = Float64(x * x);
	elseif (x <= 2.0)
		tmp = Float64(x * 2.0);
	else
		tmp = Float64(x * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -0.032)
		tmp = x * x;
	elseif (x <= 2.0)
		tmp = x * 2.0;
	else
		tmp = x * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -0.032], N[(x * x), $MachinePrecision], If[LessEqual[x, 2.0], N[(x * 2.0), $MachinePrecision], N[(x * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -0.032000000000000001 or 2 < x

   1. Initial program 100.0%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

      5. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

      6. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2}} \]
   6. Step-by-step derivation

      1. unpow2 N/A

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

      2. *-lowering-*.f64 82.3%

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{x}\right) \]

   7. Simplified 82.3%

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

   if -0.032000000000000001 < x < 2

   1. Initial program 100.0%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

      5. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

      6. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + 2 \cdot \frac{1}{x}\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

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

      2. distribute-rgt-in N/A

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

      3. *-lft-identity N/A

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

      4. associate-*l* N/A

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

      5. fma-define N/A

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

      6. unpow2 N/A

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

      7. associate-*r* N/A

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

      8. lft-mult-inverse N/A

         \[\leadsto \mathsf{fma}\left(2, 1 \cdot x, {x}^{2}\right) \]

      9. *-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(2, x, {x}^{2}\right) \]

      10. fma-define N/A

          \[\leadsto 2 \cdot x + \color{blue}{{x}^{2}} \]

      11. unpow2 N/A

          \[\leadsto 2 \cdot x + x \cdot \color{blue}{x} \]

      12. distribute-rgt-in N/A

          \[\leadsto x \cdot \color{blue}{\left(2 + x\right)} \]

      13. *-lowering-*.f64 N/A

          \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(2 + x\right)}\right) \]

      14. +-lowering-+.f64 37.5%

          \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(2, \color{blue}{x}\right)\right) \]

   7. Simplified 37.5%

      \[\leadsto \color{blue}{x \cdot \left(2 + x\right)} \]

   8. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot x} \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto x \cdot \color{blue}{2} \]

      2. *-lowering-*.f64 36.7%

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{2}\right) \]

   10. Simplified 36.7%

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

Alternative 5: 83.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \cdot y \leq 4.8 \cdot 10^{-84}:\\ \;\;\;\;x \cdot \left(x + 2\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* y y) 4.8e-84) (* x (+ x 2.0)) (* y y)))

C

double code(double x, double y) {
	double tmp;
	if ((y * y) <= 4.8e-84) {
		tmp = x * (x + 2.0);
	} else {
		tmp = y * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y * y) <= 4.8d-84) then
        tmp = x * (x + 2.0d0)
    else
        tmp = y * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y * y) <= 4.8e-84) {
		tmp = x * (x + 2.0);
	} else {
		tmp = y * y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y * y) <= 4.8e-84:
		tmp = x * (x + 2.0)
	else:
		tmp = y * y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(y * y) <= 4.8e-84)
		tmp = Float64(x * Float64(x + 2.0));
	else
		tmp = Float64(y * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y * y) <= 4.8e-84)
		tmp = x * (x + 2.0);
	else
		tmp = y * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(y * y), $MachinePrecision], 4.8e-84], N[(x * N[(x + 2.0), $MachinePrecision]), $MachinePrecision], N[(y * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y y) < 4.80000000000000035e-84

   1. Initial program 100.0%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

      5. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

      6. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + 2 \cdot \frac{1}{x}\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

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

      2. distribute-rgt-in N/A

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

      3. *-lft-identity N/A

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

      4. associate-*l* N/A

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

      5. fma-define N/A

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

      6. unpow2 N/A

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

      7. associate-*r* N/A

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

      8. lft-mult-inverse N/A

         \[\leadsto \mathsf{fma}\left(2, 1 \cdot x, {x}^{2}\right) \]

      9. *-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(2, x, {x}^{2}\right) \]

      10. fma-define N/A

          \[\leadsto 2 \cdot x + \color{blue}{{x}^{2}} \]

      11. unpow2 N/A

          \[\leadsto 2 \cdot x + x \cdot \color{blue}{x} \]

      12. distribute-rgt-in N/A

          \[\leadsto x \cdot \color{blue}{\left(2 + x\right)} \]

      13. *-lowering-*.f64 N/A

          \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(2 + x\right)}\right) \]

      14. +-lowering-+.f64 95.8%

          \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(2, \color{blue}{x}\right)\right) \]

   7. Simplified 95.8%

      \[\leadsto \color{blue}{x \cdot \left(2 + x\right)} \]

   if 4.80000000000000035e-84 < (*.f64 y y)

   1. Initial program 100.0%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

      5. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

      6. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

      \[\leadsto \color{blue}{{y}^{2}} \]
   6. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto y \cdot \color{blue}{y} \]

      2. *-lowering-*.f64 80.6%

         \[\leadsto \mathsf{*.f64}\left(y, \color{blue}{y}\right) \]

   7. Simplified 80.6%

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

4. Final simplification 87.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot y \leq 4.8 \cdot 10^{-84}:\\ \;\;\;\;x \cdot \left(x + 2\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 100.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(x + 2\right) + y \cdot y \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

   2. distribute-lft-out N/A

      \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

   4. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

   5. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

   6. *-lowering-*.f64 100.0%

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 7: 21.0% accurate, 3.7× speedup

Math

\[\begin{array}{l} \\ x \cdot 2 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x 2.0))

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return x * 2.0

Julia

function code(x, y)
	return Float64(x * 2.0)
end

MATLAB

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

Wolfram

code[x_, y_] := N[(x * 2.0), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

   1. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(x \cdot 2 + x \cdot x\right), \color{blue}{\left(y \cdot y\right)}\right) \]

   2. distribute-lft-out N/A

      \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(2 + x\right)\right), \left(\color{blue}{y} \cdot y\right)\right) \]

   4. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \left(x + 2\right)\right), \left(y \cdot y\right)\right) \]

   5. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \left(y \cdot y\right)\right) \]

   6. *-lowering-*.f64 100.0%

      \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \mathsf{+.f64}\left(x, 2\right)\right), \mathsf{*.f64}\left(y, \color{blue}{y}\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{x \cdot \left(x + 2\right) + y \cdot y} \]
4. Add Preprocessing

5. Taylor expanded in x around inf

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + 2 \cdot \frac{1}{x}\right)} \]
6. Step-by-step derivation

   1. +-commutative N/A

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

   2. distribute-rgt-in N/A

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

   3. *-lft-identity N/A

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

   4. associate-*l* N/A

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

   5. fma-define N/A

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

   6. unpow2 N/A

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

   7. associate-*r* N/A

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

   8. lft-mult-inverse N/A

      \[\leadsto \mathsf{fma}\left(2, 1 \cdot x, {x}^{2}\right) \]

   9. *-lft-identity N/A

      \[\leadsto \mathsf{fma}\left(2, x, {x}^{2}\right) \]

   10. fma-define N/A

       \[\leadsto 2 \cdot x + \color{blue}{{x}^{2}} \]

   11. unpow2 N/A

       \[\leadsto 2 \cdot x + x \cdot \color{blue}{x} \]

   12. distribute-rgt-in N/A

       \[\leadsto x \cdot \color{blue}{\left(2 + x\right)} \]

   13. *-lowering-*.f64 N/A

       \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(2 + x\right)}\right) \]

   14. +-lowering-+.f64 63.1%

       \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(2, \color{blue}{x}\right)\right) \]

7. Simplified 63.1%

   \[\leadsto \color{blue}{x \cdot \left(2 + x\right)} \]

8. Taylor expanded in x around 0

   \[\leadsto \color{blue}{2 \cdot x} \]
9. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto x \cdot \color{blue}{2} \]

   2. *-lowering-*.f64 18.6%

      \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{2}\right) \]

10. Simplified 18.6%

    \[\leadsto \color{blue}{x \cdot 2} \]
11. Add Preprocessing

Developer Target 1: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ y \cdot y + \left(2 \cdot x + x \cdot x\right) \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024150 
(FPCore (x y)
  :name "Numeric.Log:$clog1p from log-domain-0.10.2.1, A"
  :precision binary64

  :alt
  (! :herbie-platform default (+ (* y y) (+ (* 2 x) (* x x))))

  (+ (+ (* x 2.0) (* x x)) (* y y)))