math.square on complex, real part

Percentage Accurate: 93.9% → 100.0%

Time: 4.9s

Alternatives: 5

Speedup: 1.0×

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

Specification

Math

\[\begin{array}{l} \\ re \cdot re - im \cdot im \end{array} \]

FPCore

(FPCore re_sqr (re im) :precision binary64 (- (* re re) (* im im)))

C

double re_sqr(double re, double im) {
	return (re * re) - (im * im);
}

Fortran

real(8) function re_sqr(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    re_sqr = (re * re) - (im * im)
end function

Java

public static double re_sqr(double re, double im) {
	return (re * re) - (im * im);
}

Python

def re_sqr(re, im):
	return (re * re) - (im * im)

Julia

function re_sqr(re, im)
	return Float64(Float64(re * re) - Float64(im * im))
end

MATLAB

function tmp = re_sqr(re, im)
	tmp = (re * re) - (im * im);
end

Wolfram

re$95$sqr[re_, im_] := N[(N[(re * re), $MachinePrecision] - N[(im * im), $MachinePrecision]), $MachinePrecision]

Initial Program: 93.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ re \cdot re - im \cdot im \end{array} \]

FPCore

(FPCore re_sqr (re im) :precision binary64 (- (* re re) (* im im)))

C

double re_sqr(double re, double im) {
	return (re * re) - (im * im);
}

Fortran

real(8) function re_sqr(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    re_sqr = (re * re) - (im * im)
end function

Java

public static double re_sqr(double re, double im) {
	return (re * re) - (im * im);
}

Python

def re_sqr(re, im):
	return (re * re) - (im * im)

Julia

function re_sqr(re, im)
	return Float64(Float64(re * re) - Float64(im * im))
end

MATLAB

function tmp = re_sqr(re, im)
	tmp = (re * re) - (im * im);
end

Wolfram

re$95$sqr[re_, im_] := N[(N[(re * re), $MachinePrecision] - N[(im * im), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

(FPCore re_sqr (re im) :precision binary64 (* (+ im re) (- re im)))

C

double re_sqr(double re, double im) {
	return (im + re) * (re - im);
}

Fortran

real(8) function re_sqr(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    re_sqr = (im + re) * (re - im)
end function

Java

public static double re_sqr(double re, double im) {
	return (im + re) * (re - im);
}

Python

def re_sqr(re, im):
	return (im + re) * (re - im)

Julia

function re_sqr(re, im)
	return Float64(Float64(im + re) * Float64(re - im))
end

MATLAB

function tmp = re_sqr(re, im)
	tmp = (im + re) * (re - im);
end

Wolfram

re$95$sqr[re_, im_] := N[(N[(im + re), $MachinePrecision] * N[(re - im), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 93.7%

   \[re \cdot re - im \cdot im \]
2. Add Preprocessing
3. Step-by-step derivation

   1. pow2 93.7%

      \[\leadsto \color{blue}{{re}^{2}} - im \cdot im \]

   2. add-cbrt-cube 82.6%

      \[\leadsto {\color{blue}{\left(\sqrt[3]{\left(re \cdot re\right) \cdot re}\right)}}^{2} - im \cdot im \]

   3. pow1/3 63.9%

      \[\leadsto {\color{blue}{\left({\left(\left(re \cdot re\right) \cdot re\right)}^{0.3333333333333333}\right)}}^{2} - im \cdot im \]

   4. pow-pow 63.9%

      \[\leadsto \color{blue}{{\left(\left(re \cdot re\right) \cdot re\right)}^{\left(0.3333333333333333 \cdot 2\right)}} - im \cdot im \]

   5. pow3 63.9%

      \[\leadsto {\color{blue}{\left({re}^{3}\right)}}^{\left(0.3333333333333333 \cdot 2\right)} - im \cdot im \]

   6. metadata-eval 63.9%

      \[\leadsto {\left({re}^{3}\right)}^{\color{blue}{0.6666666666666666}} - im \cdot im \]

4. Applied egg-rr 63.9%

   \[\leadsto \color{blue}{{\left({re}^{3}\right)}^{0.6666666666666666}} - im \cdot im \]
5. Step-by-step derivation

   1. pow-pow 93.7%

      \[\leadsto \color{blue}{{re}^{\left(3 \cdot 0.6666666666666666\right)}} - im \cdot im \]

   2. metadata-eval 93.7%

      \[\leadsto {re}^{\color{blue}{2}} - im \cdot im \]

   3. pow2 93.7%

      \[\leadsto \color{blue}{re \cdot re} - im \cdot im \]

   4. difference-of-squares 100.0%

      \[\leadsto \color{blue}{\left(re + im\right) \cdot \left(re - im\right)} \]

   5. +-commutative 100.0%

      \[\leadsto \color{blue}{\left(im + re\right)} \cdot \left(re - im\right) \]

6. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\left(im + re\right) \cdot \left(re - im\right)} \]
7. Add Preprocessing

Alternative 2: 81.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;re \cdot re \leq 2 \cdot 10^{-69}:\\ \;\;\;\;im \cdot \left(-im\right)\\ \mathbf{else}:\\ \;\;\;\;re \cdot \left(re - im\right)\\ \end{array} \end{array} \]

FPCore

(FPCore re_sqr (re im)
 :precision binary64
 (if (<= (* re re) 2e-69) (* im (- im)) (* re (- re im))))

C

double re_sqr(double re, double im) {
	double tmp;
	if ((re * re) <= 2e-69) {
		tmp = im * -im;
	} else {
		tmp = re * (re - im);
	}
	return tmp;
}

Fortran

real(8) function re_sqr(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if ((re * re) <= 2d-69) then
        tmp = im * -im
    else
        tmp = re * (re - im)
    end if
    re_sqr = tmp
end function

Java

public static double re_sqr(double re, double im) {
	double tmp;
	if ((re * re) <= 2e-69) {
		tmp = im * -im;
	} else {
		tmp = re * (re - im);
	}
	return tmp;
}

Python

def re_sqr(re, im):
	tmp = 0
	if (re * re) <= 2e-69:
		tmp = im * -im
	else:
		tmp = re * (re - im)
	return tmp

Julia

function re_sqr(re, im)
	tmp = 0.0
	if (Float64(re * re) <= 2e-69)
		tmp = Float64(im * Float64(-im));
	else
		tmp = Float64(re * Float64(re - im));
	end
	return tmp
end

MATLAB

function tmp_2 = re_sqr(re, im)
	tmp = 0.0;
	if ((re * re) <= 2e-69)
		tmp = im * -im;
	else
		tmp = re * (re - im);
	end
	tmp_2 = tmp;
end

Wolfram

re$95$sqr[re_, im_] := If[LessEqual[N[(re * re), $MachinePrecision], 2e-69], N[(im * (-im)), $MachinePrecision], N[(re * N[(re - im), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 re re) < 1.9999999999999999e-69

   1. Initial program 100.0%

      \[re \cdot re - im \cdot im \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0 86.0%

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

      1. neg-mul-1 86.0%

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

   5. Simplified 86.0%

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

      1. pow2 86.0%

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

      2. distribute-lft-neg-in 86.0%

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

   7. Applied egg-rr 86.0%

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

   if 1.9999999999999999e-69 < (*.f64 re re)

   1. Initial program 88.6%

      \[re \cdot re - im \cdot im \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. pow2 88.6%

         \[\leadsto \color{blue}{{re}^{2}} - im \cdot im \]

      2. add-cbrt-cube 72.2%

         \[\leadsto {\color{blue}{\left(\sqrt[3]{\left(re \cdot re\right) \cdot re}\right)}}^{2} - im \cdot im \]

      3. pow1/3 49.1%

         \[\leadsto {\color{blue}{\left({\left(\left(re \cdot re\right) \cdot re\right)}^{0.3333333333333333}\right)}}^{2} - im \cdot im \]

      4. pow-pow 49.1%

         \[\leadsto \color{blue}{{\left(\left(re \cdot re\right) \cdot re\right)}^{\left(0.3333333333333333 \cdot 2\right)}} - im \cdot im \]

      5. pow3 49.1%

         \[\leadsto {\color{blue}{\left({re}^{3}\right)}}^{\left(0.3333333333333333 \cdot 2\right)} - im \cdot im \]

      6. metadata-eval 49.1%

         \[\leadsto {\left({re}^{3}\right)}^{\color{blue}{0.6666666666666666}} - im \cdot im \]

   4. Applied egg-rr 49.1%

      \[\leadsto \color{blue}{{\left({re}^{3}\right)}^{0.6666666666666666}} - im \cdot im \]
   5. Step-by-step derivation

      1. pow-pow 88.6%

         \[\leadsto \color{blue}{{re}^{\left(3 \cdot 0.6666666666666666\right)}} - im \cdot im \]

      2. metadata-eval 88.6%

         \[\leadsto {re}^{\color{blue}{2}} - im \cdot im \]

      3. pow2 88.6%

         \[\leadsto \color{blue}{re \cdot re} - im \cdot im \]

      4. difference-of-squares 100.0%

         \[\leadsto \color{blue}{\left(re + im\right) \cdot \left(re - im\right)} \]

      5. +-commutative 100.0%

         \[\leadsto \color{blue}{\left(im + re\right)} \cdot \left(re - im\right) \]

   6. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\left(im + re\right) \cdot \left(re - im\right)} \]

   7. Taylor expanded in im around 0 77.3%

      \[\leadsto \color{blue}{re} \cdot \left(re - im\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 81.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;re \cdot re \leq 2 \cdot 10^{-69}:\\ \;\;\;\;im \cdot \left(-im\right)\\ \mathbf{else}:\\ \;\;\;\;re \cdot \left(re - im\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 77.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;im \cdot im \leq 2 \cdot 10^{-114}:\\ \;\;\;\;re \cdot re\\ \mathbf{else}:\\ \;\;\;\;im \cdot \left(-im\right)\\ \end{array} \end{array} \]

FPCore

(FPCore re_sqr (re im)
 :precision binary64
 (if (<= (* im im) 2e-114) (* re re) (* im (- im))))

C

double re_sqr(double re, double im) {
	double tmp;
	if ((im * im) <= 2e-114) {
		tmp = re * re;
	} else {
		tmp = im * -im;
	}
	return tmp;
}

Fortran

real(8) function re_sqr(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if ((im * im) <= 2d-114) then
        tmp = re * re
    else
        tmp = im * -im
    end if
    re_sqr = tmp
end function

Java

public static double re_sqr(double re, double im) {
	double tmp;
	if ((im * im) <= 2e-114) {
		tmp = re * re;
	} else {
		tmp = im * -im;
	}
	return tmp;
}

Python

def re_sqr(re, im):
	tmp = 0
	if (im * im) <= 2e-114:
		tmp = re * re
	else:
		tmp = im * -im
	return tmp

Julia

function re_sqr(re, im)
	tmp = 0.0
	if (Float64(im * im) <= 2e-114)
		tmp = Float64(re * re);
	else
		tmp = Float64(im * Float64(-im));
	end
	return tmp
end

MATLAB

function tmp_2 = re_sqr(re, im)
	tmp = 0.0;
	if ((im * im) <= 2e-114)
		tmp = re * re;
	else
		tmp = im * -im;
	end
	tmp_2 = tmp;
end

Wolfram

re$95$sqr[re_, im_] := If[LessEqual[N[(im * im), $MachinePrecision], 2e-114], N[(re * re), $MachinePrecision], N[(im * (-im)), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 im im) < 2.0000000000000001e-114

   1. Initial program 99.9%

      \[re \cdot re - im \cdot im \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. difference-of-squares 100.0%

         \[\leadsto \color{blue}{\left(re + im\right) \cdot \left(re - im\right)} \]

      2. sub-neg 100.0%

         \[\leadsto \left(re + im\right) \cdot \color{blue}{\left(re + \left(-im\right)\right)} \]

      3. add-sqr-sqrt 51.8%

         \[\leadsto \left(re + im\right) \cdot \left(re + \color{blue}{\sqrt{-im} \cdot \sqrt{-im}}\right) \]

      4. sqrt-unprod 96.9%

         \[\leadsto \left(re + im\right) \cdot \left(re + \color{blue}{\sqrt{\left(-im\right) \cdot \left(-im\right)}}\right) \]

      5. sqr-neg 96.9%

         \[\leadsto \left(re + im\right) \cdot \left(re + \sqrt{\color{blue}{im \cdot im}}\right) \]

      6. sqrt-prod 45.0%

         \[\leadsto \left(re + im\right) \cdot \left(re + \color{blue}{\sqrt{im} \cdot \sqrt{im}}\right) \]

      7. add-sqr-sqrt 88.0%

         \[\leadsto \left(re + im\right) \cdot \left(re + \color{blue}{im}\right) \]

   4. Applied egg-rr 88.0%

      \[\leadsto \color{blue}{\left(re + im\right) \cdot \left(re + im\right)} \]

   5. Taylor expanded in re around inf 88.3%

      \[\leadsto \left(re + im\right) \cdot \color{blue}{re} \]

   6. Taylor expanded in re around inf 88.5%

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

   if 2.0000000000000001e-114 < (*.f64 im im)

   1. Initial program 89.3%

      \[re \cdot re - im \cdot im \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0 75.6%

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

      1. neg-mul-1 75.6%

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

   5. Simplified 75.6%

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

      1. pow2 75.6%

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

      2. distribute-lft-neg-in 75.6%

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

   7. Applied egg-rr 75.6%

      \[\leadsto \color{blue}{\left(-im\right) \cdot im} \]
3. Recombined 2 regimes into one program.

4. Final simplification 81.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;im \cdot im \leq 2 \cdot 10^{-114}:\\ \;\;\;\;re \cdot re\\ \mathbf{else}:\\ \;\;\;\;im \cdot \left(-im\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 55.0% accurate, 2.3× speedup

Math

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

FPCore

(FPCore re_sqr (re im) :precision binary64 (* re re))

C

double re_sqr(double re, double im) {
	return re * re;
}

Fortran

real(8) function re_sqr(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    re_sqr = re * re
end function

Java

public static double re_sqr(double re, double im) {
	return re * re;
}

Python

def re_sqr(re, im):
	return re * re

Julia

function re_sqr(re, im)
	return Float64(re * re)
end

MATLAB

function tmp = re_sqr(re, im)
	tmp = re * re;
end

Wolfram

re$95$sqr[re_, im_] := N[(re * re), $MachinePrecision]

Derivation

1. Initial program 93.7%

   \[re \cdot re - im \cdot im \]
2. Add Preprocessing
3. Step-by-step derivation

   1. difference-of-squares 100.0%

      \[\leadsto \color{blue}{\left(re + im\right) \cdot \left(re - im\right)} \]

   2. sub-neg 100.0%

      \[\leadsto \left(re + im\right) \cdot \color{blue}{\left(re + \left(-im\right)\right)} \]

   3. add-sqr-sqrt 45.6%

      \[\leadsto \left(re + im\right) \cdot \left(re + \color{blue}{\sqrt{-im} \cdot \sqrt{-im}}\right) \]

   4. sqrt-unprod 71.1%

      \[\leadsto \left(re + im\right) \cdot \left(re + \color{blue}{\sqrt{\left(-im\right) \cdot \left(-im\right)}}\right) \]

   5. sqr-neg 71.1%

      \[\leadsto \left(re + im\right) \cdot \left(re + \sqrt{\color{blue}{im \cdot im}}\right) \]

   6. sqrt-prod 26.6%

      \[\leadsto \left(re + im\right) \cdot \left(re + \color{blue}{\sqrt{im} \cdot \sqrt{im}}\right) \]

   7. add-sqr-sqrt 50.4%

      \[\leadsto \left(re + im\right) \cdot \left(re + \color{blue}{im}\right) \]

4. Applied egg-rr 50.4%

   \[\leadsto \color{blue}{\left(re + im\right) \cdot \left(re + im\right)} \]

5. Taylor expanded in re around inf 56.1%

   \[\leadsto \left(re + im\right) \cdot \color{blue}{re} \]

6. Taylor expanded in re around inf 51.1%

   \[\leadsto \color{blue}{re} \cdot re \]
7. Add Preprocessing

Alternative 5: 11.7% accurate, 2.3× speedup

Math

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

FPCore

(FPCore re_sqr (re im) :precision binary64 (* im im))

C

double re_sqr(double re, double im) {
	return im * im;
}

Fortran

real(8) function re_sqr(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    re_sqr = im * im
end function

Java

public static double re_sqr(double re, double im) {
	return im * im;
}

Python

def re_sqr(re, im):
	return im * im

Julia

function re_sqr(re, im)
	return Float64(im * im)
end

MATLAB

function tmp = re_sqr(re, im)
	tmp = im * im;
end

Wolfram

re$95$sqr[re_, im_] := N[(im * im), $MachinePrecision]

Derivation

1. Initial program 93.7%

   \[re \cdot re - im \cdot im \]
2. Add Preprocessing

3. Taylor expanded in re around 0 55.6%

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

   1. neg-mul-1 55.6%

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

5. Simplified 55.6%

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

   1. pow2 55.6%

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

   2. add-sqr-sqrt 6.5%

      \[\leadsto \color{blue}{\sqrt{-im \cdot im} \cdot \sqrt{-im \cdot im}} \]

   3. sqrt-unprod 13.6%

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

   4. sqr-neg 13.6%

      \[\leadsto \sqrt{\color{blue}{\left(im \cdot im\right) \cdot \left(im \cdot im\right)}} \]

   5. sqrt-unprod 11.1%

      \[\leadsto \color{blue}{\sqrt{im \cdot im} \cdot \sqrt{im \cdot im}} \]

   6. add-sqr-sqrt 11.1%

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

7. Applied egg-rr 11.1%

   \[\leadsto \color{blue}{im \cdot im} \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2024172 
(FPCore re_sqr (re im)
  :name "math.square on complex, real part"
  :precision binary64
  (- (* re re) (* im im)))