math.square on complex, real part

Percentage Accurate: 94.0% → 100.0%

Time: 4.2s

Alternatives: 4

Speedup: 1.0×

• 43.3% 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: 94.0% 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

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 96.9%

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

   1. add-sqr-sqrt 49.9%

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

   2. associate-*l* 49.9%

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

   3. prod-diff 40.9%

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

4. Applied egg-rr 40.9%

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

5. Taylor expanded in im around 0 51.0%

   \[\leadsto \mathsf{fma}\left(re, re, -\left(\sqrt{im} \cdot im\right) \cdot \sqrt{im}\right) + \color{blue}{0} \]
6. Step-by-step derivation

   1. *-commutative 51.0%

      \[\leadsto \mathsf{fma}\left(re, re, -\color{blue}{\left(im \cdot \sqrt{im}\right)} \cdot \sqrt{im}\right) + 0 \]

   2. associate-*r* 51.1%

      \[\leadsto \mathsf{fma}\left(re, re, -\color{blue}{im \cdot \left(\sqrt{im} \cdot \sqrt{im}\right)}\right) + 0 \]

   3. add-sqr-sqrt 99.2%

      \[\leadsto \mathsf{fma}\left(re, re, -im \cdot \color{blue}{im}\right) + 0 \]

   4. fma-neg 96.9%

      \[\leadsto \color{blue}{\left(re \cdot re - im \cdot im\right)} + 0 \]

   5. difference-of-squares 100.0%

      \[\leadsto \color{blue}{\left(re + im\right) \cdot \left(re - im\right)} + 0 \]

7. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\left(re + im\right) \cdot \left(re - im\right)} + 0 \]

8. Final simplification 100.0%

   \[\leadsto \left(re + im\right) \cdot \left(re - im\right) \]
9. Add Preprocessing

Alternative 2: 97.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;re \cdot re \leq 10^{+249}:\\ \;\;\;\;re \cdot re - im \cdot im\\ \mathbf{else}:\\ \;\;\;\;re \cdot \left(re + im \cdot -2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore re_sqr (re im)
 :precision binary64
 (if (<= (* re re) 1e+249) (- (* re re) (* im im)) (* re (+ re (* im -2.0)))))

C

double re_sqr(double re, double im) {
	double tmp;
	if ((re * re) <= 1e+249) {
		tmp = (re * re) - (im * im);
	} else {
		tmp = re * (re + (im * -2.0));
	}
	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) <= 1d+249) then
        tmp = (re * re) - (im * im)
    else
        tmp = re * (re + (im * (-2.0d0)))
    end if
    re_sqr = tmp
end function

Java

public static double re_sqr(double re, double im) {
	double tmp;
	if ((re * re) <= 1e+249) {
		tmp = (re * re) - (im * im);
	} else {
		tmp = re * (re + (im * -2.0));
	}
	return tmp;
}

Python

def re_sqr(re, im):
	tmp = 0
	if (re * re) <= 1e+249:
		tmp = (re * re) - (im * im)
	else:
		tmp = re * (re + (im * -2.0))
	return tmp

Julia

function re_sqr(re, im)
	tmp = 0.0
	if (Float64(re * re) <= 1e+249)
		tmp = Float64(Float64(re * re) - Float64(im * im));
	else
		tmp = Float64(re * Float64(re + Float64(im * -2.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = re_sqr(re, im)
	tmp = 0.0;
	if ((re * re) <= 1e+249)
		tmp = (re * re) - (im * im);
	else
		tmp = re * (re + (im * -2.0));
	end
	tmp_2 = tmp;
end

Wolfram

re$95$sqr[re_, im_] := If[LessEqual[N[(re * re), $MachinePrecision], 1e+249], N[(N[(re * re), $MachinePrecision] - N[(im * im), $MachinePrecision]), $MachinePrecision], N[(re * N[(re + N[(im * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 re re) < 9.9999999999999992e248

   1. Initial program 100.0%

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

   if 9.9999999999999992e248 < (*.f64 re re)

   1. Initial program 87.3%

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

      1. add-sqr-sqrt 81.0%

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

      2. pow2 81.0%

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

      3. difference-of-squares 84.1%

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

      4. sqrt-prod 44.4%

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

      5. add-sqr-sqrt 25.4%

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

      6. sqrt-prod 44.4%

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

      7. sqr-neg 44.4%

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

      8. sqrt-unprod 19.0%

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

      9. add-sqr-sqrt 44.4%

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

      10. sub-neg 44.4%

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

      11. add-sqr-sqrt 84.1%

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

      12. add-sqr-sqrt 44.4%

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

      13. add-sqr-sqrt 25.4%

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

      14. difference-of-squares 25.4%

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

      15. unpow-prod-down 25.4%

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

   4. Applied egg-rr 25.4%

      \[\leadsto \color{blue}{{\left(\sqrt{re} + \sqrt{im}\right)}^{2} \cdot {\left(\sqrt{re} - \sqrt{im}\right)}^{2}} \]
   5. Step-by-step derivation

      1. unpow2 25.4%

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

      2. unpow2 25.4%

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

      3. unswap-sqr 25.4%

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

      4. difference-of-squares 25.4%

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

      5. rem-square-sqrt 25.4%

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

      6. rem-square-sqrt 25.4%

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

      7. difference-of-squares 25.4%

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

      8. rem-square-sqrt 47.6%

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

      9. rem-square-sqrt 84.1%

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

   6. Simplified 84.1%

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

   7. Taylor expanded in re around inf 77.8%

      \[\leadsto \color{blue}{-2 \cdot \left(im \cdot re\right) + {re}^{2}} \]
   8. Step-by-step derivation

      1. associate-*r* 77.8%

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

      2. unpow2 77.8%

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

      3. distribute-rgt-out 92.1%

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

      4. *-commutative 92.1%

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

   9. Simplified 92.1%

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

4. Final simplification 98.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;re \cdot re \leq 10^{+249}:\\ \;\;\;\;re \cdot re - im \cdot im\\ \mathbf{else}:\\ \;\;\;\;re \cdot \left(re + im \cdot -2\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 56.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ re \cdot \left(re + im \cdot -2\right) \end{array} \]

FPCore

(FPCore re_sqr (re im) :precision binary64 (* re (+ re (* im -2.0))))

C

double re_sqr(double re, double im) {
	return re * (re + (im * -2.0));
}

Fortran

real(8) function re_sqr(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    re_sqr = re * (re + (im * (-2.0d0)))
end function

Java

public static double re_sqr(double re, double im) {
	return re * (re + (im * -2.0));
}

Python

def re_sqr(re, im):
	return re * (re + (im * -2.0))

Julia

function re_sqr(re, im)
	return Float64(re * Float64(re + Float64(im * -2.0)))
end

MATLAB

function tmp = re_sqr(re, im)
	tmp = re * (re + (im * -2.0));
end

Wolfram

re$95$sqr[re_, im_] := N[(re * N[(re + N[(im * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 96.9%

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

   1. add-sqr-sqrt 49.2%

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

   2. pow2 49.2%

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

   3. difference-of-squares 50.0%

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

   4. sqrt-prod 23.6%

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

   5. add-sqr-sqrt 13.2%

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

   6. sqrt-prod 24.4%

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

   7. sqr-neg 24.4%

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

   8. sqrt-unprod 12.8%

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

   9. add-sqr-sqrt 26.0%

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

   10. sub-neg 26.0%

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

   11. add-sqr-sqrt 50.5%

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

   12. add-sqr-sqrt 24.7%

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

   13. add-sqr-sqrt 13.7%

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

   14. difference-of-squares 13.7%

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

   15. unpow-prod-down 13.7%

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

4. Applied egg-rr 13.7%

   \[\leadsto \color{blue}{{\left(\sqrt{re} + \sqrt{im}\right)}^{2} \cdot {\left(\sqrt{re} - \sqrt{im}\right)}^{2}} \]
5. Step-by-step derivation

   1. unpow2 13.7%

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

   2. unpow2 13.7%

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

   3. unswap-sqr 13.7%

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

   4. difference-of-squares 13.7%

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

   5. rem-square-sqrt 13.7%

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

   6. rem-square-sqrt 13.7%

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

   7. difference-of-squares 13.7%

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

   8. rem-square-sqrt 27.8%

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

   9. rem-square-sqrt 50.5%

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

6. Simplified 50.5%

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

7. Taylor expanded in re around inf 51.5%

   \[\leadsto \color{blue}{-2 \cdot \left(im \cdot re\right) + {re}^{2}} \]
8. Step-by-step derivation

   1. associate-*r* 51.5%

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

   2. unpow2 51.5%

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

   3. distribute-rgt-out 55.0%

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

   4. *-commutative 55.0%

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

9. Simplified 55.0%

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

10. Final simplification 55.0%

    \[\leadsto re \cdot \left(re + im \cdot -2\right) \]
11. Add Preprocessing

Alternative 4: 14.6% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ -2 \cdot \left(re \cdot im\right) \end{array} \]

FPCore

(FPCore re_sqr (re im) :precision binary64 (* -2.0 (* re im)))

C

double re_sqr(double re, double im) {
	return -2.0 * (re * im);
}

Fortran

real(8) function re_sqr(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    re_sqr = (-2.0d0) * (re * im)
end function

Java

public static double re_sqr(double re, double im) {
	return -2.0 * (re * im);
}

Python

def re_sqr(re, im):
	return -2.0 * (re * im)

Julia

function re_sqr(re, im)
	return Float64(-2.0 * Float64(re * im))
end

MATLAB

function tmp = re_sqr(re, im)
	tmp = -2.0 * (re * im);
end

Wolfram

re$95$sqr[re_, im_] := N[(-2.0 * N[(re * im), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 96.9%

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

   1. add-sqr-sqrt 49.2%

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

   2. pow2 49.2%

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

   3. difference-of-squares 50.0%

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

   4. sqrt-prod 23.6%

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

   5. add-sqr-sqrt 13.2%

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

   6. sqrt-prod 24.4%

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

   7. sqr-neg 24.4%

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

   8. sqrt-unprod 12.8%

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

   9. add-sqr-sqrt 26.0%

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

   10. sub-neg 26.0%

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

   11. add-sqr-sqrt 50.5%

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

   12. add-sqr-sqrt 24.7%

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

   13. add-sqr-sqrt 13.7%

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

   14. difference-of-squares 13.7%

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

   15. unpow-prod-down 13.7%

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

4. Applied egg-rr 13.7%

   \[\leadsto \color{blue}{{\left(\sqrt{re} + \sqrt{im}\right)}^{2} \cdot {\left(\sqrt{re} - \sqrt{im}\right)}^{2}} \]
5. Step-by-step derivation

   1. unpow2 13.7%

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

   2. unpow2 13.7%

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

   3. unswap-sqr 13.7%

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

   4. difference-of-squares 13.7%

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

   5. rem-square-sqrt 13.7%

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

   6. rem-square-sqrt 13.7%

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

   7. difference-of-squares 13.7%

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

   8. rem-square-sqrt 27.8%

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

   9. rem-square-sqrt 50.5%

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

6. Simplified 50.5%

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

7. Taylor expanded in re around inf 51.5%

   \[\leadsto \color{blue}{-2 \cdot \left(im \cdot re\right) + {re}^{2}} \]
8. Step-by-step derivation

   1. associate-*r* 51.5%

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

   2. unpow2 51.5%

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

   3. distribute-rgt-out 55.0%

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

   4. *-commutative 55.0%

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

9. Simplified 55.0%

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

10. Taylor expanded in re around 0 13.2%

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

11. Final simplification 13.2%

    \[\leadsto -2 \cdot \left(re \cdot im\right) \]
12. Add Preprocessing

Reproduce

herbie shell --seed 2024053 
(FPCore re_sqr (re im)
  :name "math.square on complex, real part"
  :precision binary64
  (- (* re re) (* im im)))