Examples.Basics.BasicTests:f2 from sbv-4.4

Percentage Accurate: 92.9% → 100.0%

Time: 2.3s

Alternatives: 3

Speedup: 1.0×

• 44.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 92.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 93.0%

   \[x \cdot x - y \cdot y \]
2. Step-by-step derivation

   1. difference-of-squares 100.0%

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

   2. *-commutative 100.0%

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

3. Applied egg-rr 100.0%

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

4. Final simplification 100.0%

   \[\leadsto \left(x - y\right) \cdot \left(x + y\right) \]

Alternative 2: 76.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot x \leq 4.6 \cdot 10^{-134} \lor \neg \left(x \cdot x \leq 2.2 \cdot 10^{-50}\right) \land x \cdot x \leq 350000000:\\ \;\;\;\;y \cdot \left(-y\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= (* x x) 4.6e-134)
         (and (not (<= (* x x) 2.2e-50)) (<= (* x x) 350000000.0)))
   (* y (- y))
   (* x x)))

C

double code(double x, double y) {
	double tmp;
	if (((x * x) <= 4.6e-134) || (!((x * x) <= 2.2e-50) && ((x * x) <= 350000000.0))) {
		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 * x) <= 4.6d-134) .or. (.not. ((x * x) <= 2.2d-50)) .and. ((x * x) <= 350000000.0d0)) 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 * x) <= 4.6e-134) || (!((x * x) <= 2.2e-50) && ((x * x) <= 350000000.0))) {
		tmp = y * -y;
	} else {
		tmp = x * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((x * x) <= 4.6e-134) or (not ((x * x) <= 2.2e-50) and ((x * x) <= 350000000.0)):
		tmp = y * -y
	else:
		tmp = x * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((Float64(x * x) <= 4.6e-134) || (!(Float64(x * x) <= 2.2e-50) && (Float64(x * x) <= 350000000.0)))
		tmp = Float64(y * Float64(-y));
	else
		tmp = Float64(x * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (((x * x) <= 4.6e-134) || (~(((x * x) <= 2.2e-50)) && ((x * x) <= 350000000.0)))
		tmp = y * -y;
	else
		tmp = x * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[N[(x * x), $MachinePrecision], 4.6e-134], And[N[Not[LessEqual[N[(x * x), $MachinePrecision], 2.2e-50]], $MachinePrecision], LessEqual[N[(x * x), $MachinePrecision], 350000000.0]]], N[(y * (-y)), $MachinePrecision], N[(x * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x x) < 4.6000000000000001e-134 or 2.1999999999999999e-50 < (*.f64 x x) < 3.5e8

   1. Initial program 100.0%

      \[x \cdot x - y \cdot y \]

   2. Taylor expanded in x around 0 88.4%

      \[\leadsto \color{blue}{-1 \cdot {y}^{2}} \]
   3. Step-by-step derivation

      1. mul-1-neg 88.4%

         \[\leadsto \color{blue}{-{y}^{2}} \]

      2. unpow2 88.4%

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

      3. distribute-rgt-neg-out 88.4%

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

   4. Simplified 88.4%

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

   if 4.6000000000000001e-134 < (*.f64 x x) < 2.1999999999999999e-50 or 3.5e8 < (*.f64 x x)

   1. Initial program 87.0%

      \[x \cdot x - y \cdot y \]

   2. Taylor expanded in x around inf 79.4%

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

      1. unpow2 79.4%

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

   4. Simplified 79.4%

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

4. Final simplification 83.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot x \leq 4.6 \cdot 10^{-134} \lor \neg \left(x \cdot x \leq 2.2 \cdot 10^{-50}\right) \land x \cdot x \leq 350000000:\\ \;\;\;\;y \cdot \left(-y\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \]

Alternative 3: 53.6% accurate, 2.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 93.0%

   \[x \cdot x - y \cdot y \]

2. Taylor expanded in x around inf 52.6%

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

   1. unpow2 52.6%

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

4. Simplified 52.6%

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

5. Final simplification 52.6%

   \[\leadsto x \cdot x \]

Reproduce

herbie shell --seed 2023297 
(FPCore (x y)
  :name "Examples.Basics.BasicTests:f2 from sbv-4.4"
  :precision binary64
  (- (* x x) (* y y)))