Numeric.SpecFunctions:log1p from math-functions-0.1.5.2, A

Percentage Accurate: 99.9% → 99.9%

Time: 5.6s

Alternatives: 4

Speedup: 1.0×

• 25.4% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x \cdot \left(1 - x \cdot y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (- 1.0 (* x y))))

C

double code(double x, double y) {
	return x * (1.0 - (x * y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (1.0d0 - (x * y))
end function

Java

public static double code(double x, double y) {
	return x * (1.0 - (x * y));
}

Python

def code(x, y):
	return x * (1.0 - (x * y))

Julia

function code(x, y)
	return Float64(x * Float64(1.0 - Float64(x * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = x * (1.0 - (x * y));
end

Wolfram

code[x_, y_] := N[(x * N[(1.0 - N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(1 - x \cdot y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (- 1.0 (* x y))))

C

double code(double x, double y) {
	return x * (1.0 - (x * y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (1.0d0 - (x * y))
end function

Java

public static double code(double x, double y) {
	return x * (1.0 - (x * y));
}

Python

def code(x, y):
	return x * (1.0 - (x * y))

Julia

function code(x, y)
	return Float64(x * Float64(1.0 - Float64(x * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = x * (1.0 - (x * y));
end

Wolfram

code[x_, y_] := N[(x * N[(1.0 - N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(1 - y \cdot x\right) \cdot x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (- 1.0 (* y x)) x))

C

double code(double x, double y) {
	return (1.0 - (y * x)) * x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - (y * x)) * x
end function

Java

public static double code(double x, double y) {
	return (1.0 - (y * x)) * x;
}

Python

def code(x, y):
	return (1.0 - (y * x)) * x

Julia

function code(x, y)
	return Float64(Float64(1.0 - Float64(y * x)) * x)
end

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - (y * x)) * x;
end

Wolfram

code[x_, y_] := N[(N[(1.0 - N[(y * x), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x \cdot \left(1 - x \cdot y\right) \]
2. Add Preprocessing

3. Final simplification 99.9%

   \[\leadsto \left(1 - y \cdot x\right) \cdot x \]
4. Add Preprocessing

Alternative 2: 82.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(1 - y \cdot x\right) \cdot x\\ t_1 := \left(\left(-x\right) \cdot y\right) \cdot x\\ \mathbf{if}\;t\_0 \leq -5 \cdot 10^{+146}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 10^{+34}:\\ \;\;\;\;1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (- 1.0 (* y x)) x)) (t_1 (* (* (- x) y) x)))
   (if (<= t_0 -5e+146) t_1 (if (<= t_0 1e+34) (* 1.0 x) t_1))))

C

double code(double x, double y) {
	double t_0 = (1.0 - (y * x)) * x;
	double t_1 = (-x * y) * x;
	double tmp;
	if (t_0 <= -5e+146) {
		tmp = t_1;
	} else if (t_0 <= 1e+34) {
		tmp = 1.0 * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = (1.0d0 - (y * x)) * x
    t_1 = (-x * y) * x
    if (t_0 <= (-5d+146)) then
        tmp = t_1
    else if (t_0 <= 1d+34) then
        tmp = 1.0d0 * x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (1.0 - (y * x)) * x;
	double t_1 = (-x * y) * x;
	double tmp;
	if (t_0 <= -5e+146) {
		tmp = t_1;
	} else if (t_0 <= 1e+34) {
		tmp = 1.0 * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (1.0 - (y * x)) * x
	t_1 = (-x * y) * x
	tmp = 0
	if t_0 <= -5e+146:
		tmp = t_1
	elif t_0 <= 1e+34:
		tmp = 1.0 * x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(1.0 - Float64(y * x)) * x)
	t_1 = Float64(Float64(Float64(-x) * y) * x)
	tmp = 0.0
	if (t_0 <= -5e+146)
		tmp = t_1;
	elseif (t_0 <= 1e+34)
		tmp = Float64(1.0 * x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (1.0 - (y * x)) * x;
	t_1 = (-x * y) * x;
	tmp = 0.0;
	if (t_0 <= -5e+146)
		tmp = t_1;
	elseif (t_0 <= 1e+34)
		tmp = 1.0 * x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(1.0 - N[(y * x), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, Block[{t$95$1 = N[(N[((-x) * y), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[t$95$0, -5e+146], t$95$1, If[LessEqual[t$95$0, 1e+34], N[(1.0 * x), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x (-.f64 #s(literal 1 binary64) (*.f64 x y))) < -4.9999999999999999e146 or 9.99999999999999946e33 < (*.f64 x (-.f64 #s(literal 1 binary64) (*.f64 x y)))

   1. Initial program 99.8%

      \[x \cdot \left(1 - x \cdot y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto x \cdot \color{blue}{\left(-1 \cdot \left(x \cdot y\right)\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

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

      2. lower-*.f64 N/A

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

      3. mul-1-neg N/A

         \[\leadsto x \cdot \left(\color{blue}{\left(\mathsf{neg}\left(x\right)\right)} \cdot y\right) \]

      4. lower-neg.f64 84.4

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

   5. Applied rewrites 84.4%

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

   if -4.9999999999999999e146 < (*.f64 x (-.f64 #s(literal 1 binary64) (*.f64 x y))) < 9.99999999999999946e33

   1. Initial program 99.9%

      \[x \cdot \left(1 - x \cdot y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 83.0%

      \[\leadsto x \cdot \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 83.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(1 - y \cdot x\right) \cdot x \leq -5 \cdot 10^{+146}:\\ \;\;\;\;\left(\left(-x\right) \cdot y\right) \cdot x\\ \mathbf{elif}\;\left(1 - y \cdot x\right) \cdot x \leq 10^{+34}:\\ \;\;\;\;1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-x\right) \cdot y\right) \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 80.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(1 - y \cdot x\right) \cdot x\\ t_1 := \left(\left(-x\right) \cdot x\right) \cdot y\\ \mathbf{if}\;t\_0 \leq -5 \cdot 10^{+146}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 10^{+34}:\\ \;\;\;\;1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (- 1.0 (* y x)) x)) (t_1 (* (* (- x) x) y)))
   (if (<= t_0 -5e+146) t_1 (if (<= t_0 1e+34) (* 1.0 x) t_1))))

C

double code(double x, double y) {
	double t_0 = (1.0 - (y * x)) * x;
	double t_1 = (-x * x) * y;
	double tmp;
	if (t_0 <= -5e+146) {
		tmp = t_1;
	} else if (t_0 <= 1e+34) {
		tmp = 1.0 * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = (1.0d0 - (y * x)) * x
    t_1 = (-x * x) * y
    if (t_0 <= (-5d+146)) then
        tmp = t_1
    else if (t_0 <= 1d+34) then
        tmp = 1.0d0 * x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (1.0 - (y * x)) * x;
	double t_1 = (-x * x) * y;
	double tmp;
	if (t_0 <= -5e+146) {
		tmp = t_1;
	} else if (t_0 <= 1e+34) {
		tmp = 1.0 * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (1.0 - (y * x)) * x
	t_1 = (-x * x) * y
	tmp = 0
	if t_0 <= -5e+146:
		tmp = t_1
	elif t_0 <= 1e+34:
		tmp = 1.0 * x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(1.0 - Float64(y * x)) * x)
	t_1 = Float64(Float64(Float64(-x) * x) * y)
	tmp = 0.0
	if (t_0 <= -5e+146)
		tmp = t_1;
	elseif (t_0 <= 1e+34)
		tmp = Float64(1.0 * x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (1.0 - (y * x)) * x;
	t_1 = (-x * x) * y;
	tmp = 0.0;
	if (t_0 <= -5e+146)
		tmp = t_1;
	elseif (t_0 <= 1e+34)
		tmp = 1.0 * x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(1.0 - N[(y * x), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, Block[{t$95$1 = N[(N[((-x) * x), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[t$95$0, -5e+146], t$95$1, If[LessEqual[t$95$0, 1e+34], N[(1.0 * x), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x (-.f64 #s(literal 1 binary64) (*.f64 x y))) < -4.9999999999999999e146 or 9.99999999999999946e33 < (*.f64 x (-.f64 #s(literal 1 binary64) (*.f64 x y)))

   1. Initial program 99.8%

      \[x \cdot \left(1 - x \cdot y\right) \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift--.f64 N/A

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

      4. flip-- N/A

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

      5. associate-*l/ N/A

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

      6. associate-/l* N/A

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

      7. lower-*.f64 N/A

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

      8. metadata-eval N/A

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

      9. lower--.f64 N/A

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

      10. pow2 N/A

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

      11. lower-pow.f64 N/A

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

      12. lift-*.f64 N/A

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

      13. *-commutative N/A

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

      14. lower-*.f64 N/A

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

      15. lower-/.f64 N/A

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

      16. +-commutative N/A

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

      17. lift-*.f64 N/A

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

      18. *-commutative N/A

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

      19. lower-fma.f64 63.6

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

   4. Applied rewrites 63.6%

      \[\leadsto \color{blue}{\left(1 - {\left(y \cdot x\right)}^{2}\right) \cdot \frac{x}{\mathsf{fma}\left(y, x, 1\right)}} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. clear-num N/A

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

      4. un-div-inv N/A

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

      5. lower-/.f64 N/A

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

      6. lower-/.f64 63.6

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

   6. Applied rewrites 63.6%

      \[\leadsto \color{blue}{\frac{1 - {\left(y \cdot x\right)}^{2}}{\frac{\mathsf{fma}\left(y, x, 1\right)}{x}}} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. div-inv N/A

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

      4. +-lft-identity N/A

         \[\leadsto \frac{1 - {\left(y \cdot x\right)}^{2}}{\mathsf{fma}\left(y, x, 1\right) \cdot \frac{1}{\color{blue}{0 + x}}} \]

      5. lift-+.f64 N/A

         \[\leadsto \frac{1 - {\left(y \cdot x\right)}^{2}}{\mathsf{fma}\left(y, x, 1\right) \cdot \frac{1}{\color{blue}{0 + x}}} \]

      6. lift-/.f64 N/A

         \[\leadsto \frac{1 - {\left(y \cdot x\right)}^{2}}{\mathsf{fma}\left(y, x, 1\right) \cdot \color{blue}{\frac{1}{0 + x}}} \]

      7. associate-/r* N/A

         \[\leadsto \color{blue}{\frac{\frac{1 - {\left(y \cdot x\right)}^{2}}{\mathsf{fma}\left(y, x, 1\right)}}{\frac{1}{0 + x}}} \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\frac{1 - {\left(y \cdot x\right)}^{2}}{\mathsf{fma}\left(y, x, 1\right)}}{\frac{1}{0 + x}}} \]

   8. Applied rewrites 99.7%

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

   9. Taylor expanded in x around inf

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

       1. associate-*r* N/A

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

       2. lower-*.f64 N/A

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

       3. unpow2 N/A

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

       4. associate-*r* N/A

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

       5. lower-*.f64 N/A

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

       6. mul-1-neg N/A

          \[\leadsto \left(\color{blue}{\left(\mathsf{neg}\left(x\right)\right)} \cdot x\right) \cdot y \]

       7. lower-neg.f64 82.4

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

   11. Applied rewrites 82.4%

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

   if -4.9999999999999999e146 < (*.f64 x (-.f64 #s(literal 1 binary64) (*.f64 x y))) < 9.99999999999999946e33

   1. Initial program 99.9%

      \[x \cdot \left(1 - x \cdot y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 83.0%

      \[\leadsto x \cdot \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 82.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(1 - y \cdot x\right) \cdot x \leq -5 \cdot 10^{+146}:\\ \;\;\;\;\left(\left(-x\right) \cdot x\right) \cdot y\\ \mathbf{elif}\;\left(1 - y \cdot x\right) \cdot x \leq 10^{+34}:\\ \;\;\;\;1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-x\right) \cdot x\right) \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 51.3% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ 1 \cdot x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 1.0 x))

C

double code(double x, double y) {
	return 1.0 * x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 * x
end function

Java

public static double code(double x, double y) {
	return 1.0 * x;
}

Python

def code(x, y):
	return 1.0 * x

Julia

function code(x, y)
	return Float64(1.0 * x)
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 * x;
end

Wolfram

code[x_, y_] := N[(1.0 * x), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x \cdot \left(1 - x \cdot y\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 53.5%

   \[\leadsto x \cdot \color{blue}{1} \]

6. Final simplification 53.5%

   \[\leadsto 1 \cdot x \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024331 
(FPCore (x y)
  :name "Numeric.SpecFunctions:log1p from math-functions-0.1.5.2, A"
  :precision binary64
  (* x (- 1.0 (* x y))))