ENA, Section 1.4, Exercise 4b, n=2

Percentage Accurate: 75.1% → 100.0%

Time: 6.1s

Alternatives: 5

Speedup: 17.4×

Specification

\[\left(-1000000000 \leq x \land x \leq 1000000000\right) \land \left(-1 \leq \varepsilon \land \varepsilon \leq 1\right)\]

Math

\[\begin{array}{l} \\ {\left(x + \varepsilon\right)}^{2} - {x}^{2} \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (- (pow (+ x eps) 2.0) (pow x 2.0)))

C

double code(double x, double eps) {
	return pow((x + eps), 2.0) - pow(x, 2.0);
}

Fortran

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

Java

public static double code(double x, double eps) {
	return Math.pow((x + eps), 2.0) - Math.pow(x, 2.0);
}

Python

def code(x, eps):
	return math.pow((x + eps), 2.0) - math.pow(x, 2.0)

Julia

function code(x, eps)
	return Float64((Float64(x + eps) ^ 2.0) - (x ^ 2.0))
end

MATLAB

function tmp = code(x, eps)
	tmp = ((x + eps) ^ 2.0) - (x ^ 2.0);
end

Wolfram

code[x_, eps_] := N[(N[Power[N[(x + eps), $MachinePrecision], 2.0], $MachinePrecision] - N[Power[x, 2.0], $MachinePrecision]), $MachinePrecision]

Initial Program: 75.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ {\left(x + \varepsilon\right)}^{2} - {x}^{2} \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (- (pow (+ x eps) 2.0) (pow x 2.0)))

C

double code(double x, double eps) {
	return pow((x + eps), 2.0) - pow(x, 2.0);
}

Fortran

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

Java

public static double code(double x, double eps) {
	return Math.pow((x + eps), 2.0) - Math.pow(x, 2.0);
}

Python

def code(x, eps):
	return math.pow((x + eps), 2.0) - math.pow(x, 2.0)

Julia

function code(x, eps)
	return Float64((Float64(x + eps) ^ 2.0) - (x ^ 2.0))
end

MATLAB

function tmp = code(x, eps)
	tmp = ((x + eps) ^ 2.0) - (x ^ 2.0);
end

Wolfram

code[x_, eps_] := N[(N[Power[N[(x + eps), $MachinePrecision], 2.0], $MachinePrecision] - N[Power[x, 2.0], $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 12.3× speedup

Math

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

FPCore

(FPCore (x eps) :precision binary64 (fma eps eps (* (* x 2.0) eps)))

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 75.5%

   \[{\left(x + \varepsilon\right)}^{2} - {x}^{2} \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

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

   1. distribute-rgt-in N/A

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

   2. unpow2 N/A

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

   3. *-rgt-identity N/A

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

   4. *-rgt-identity N/A

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

   5. *-inverses N/A

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

   6. associate-/l* N/A

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

   7. unpow2 N/A

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

   8. associate-/l* N/A

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

   9. *-commutative N/A

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

   10. associate-*r/ N/A

       \[\leadsto {\varepsilon}^{2} \cdot 1 + \color{blue}{{\varepsilon}^{2} \cdot \frac{2 \cdot x}{\varepsilon}} \]

   11. associate-*r/ N/A

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

   12. distribute-lft-in N/A

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

   13. unpow2 N/A

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

   14. associate-*l* N/A

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

   15. *-commutative N/A

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

   16. lower-*.f64 N/A

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

5. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, 2, \varepsilon\right) \cdot \varepsilon} \]
6. Step-by-step derivation

7. Applied rewrites 100.0%

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

8. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(\varepsilon, \varepsilon, \left(x \cdot 2\right) \cdot \varepsilon\right) \]
9. Add Preprocessing

Alternative 2: 97.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;{\left(x + \varepsilon\right)}^{2} - {x}^{2} \leq 0:\\ \;\;\;\;\left(x \cdot 2\right) \cdot \varepsilon\\ \mathbf{else}:\\ \;\;\;\;\varepsilon \cdot \varepsilon\\ \end{array} \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (if (<= (- (pow (+ x eps) 2.0) (pow x 2.0)) 0.0)
   (* (* x 2.0) eps)
   (* eps eps)))

C

double code(double x, double eps) {
	double tmp;
	if ((pow((x + eps), 2.0) - pow(x, 2.0)) <= 0.0) {
		tmp = (x * 2.0) * eps;
	} else {
		tmp = eps * eps;
	}
	return tmp;
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    real(8) :: tmp
    if ((((x + eps) ** 2.0d0) - (x ** 2.0d0)) <= 0.0d0) then
        tmp = (x * 2.0d0) * eps
    else
        tmp = eps * eps
    end if
    code = tmp
end function

Java

public static double code(double x, double eps) {
	double tmp;
	if ((Math.pow((x + eps), 2.0) - Math.pow(x, 2.0)) <= 0.0) {
		tmp = (x * 2.0) * eps;
	} else {
		tmp = eps * eps;
	}
	return tmp;
}

Python

def code(x, eps):
	tmp = 0
	if (math.pow((x + eps), 2.0) - math.pow(x, 2.0)) <= 0.0:
		tmp = (x * 2.0) * eps
	else:
		tmp = eps * eps
	return tmp

Julia

function code(x, eps)
	tmp = 0.0
	if (Float64((Float64(x + eps) ^ 2.0) - (x ^ 2.0)) <= 0.0)
		tmp = Float64(Float64(x * 2.0) * eps);
	else
		tmp = Float64(eps * eps);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, eps)
	tmp = 0.0;
	if ((((x + eps) ^ 2.0) - (x ^ 2.0)) <= 0.0)
		tmp = (x * 2.0) * eps;
	else
		tmp = eps * eps;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, eps_] := If[LessEqual[N[(N[Power[N[(x + eps), $MachinePrecision], 2.0], $MachinePrecision] - N[Power[x, 2.0], $MachinePrecision]), $MachinePrecision], 0.0], N[(N[(x * 2.0), $MachinePrecision] * eps), $MachinePrecision], N[(eps * eps), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (pow.f64 (+.f64 x eps) #s(literal 2 binary64)) (pow.f64 x #s(literal 2 binary64))) < 0.0

   1. Initial program 59.8%

      \[{\left(x + \varepsilon\right)}^{2} - {x}^{2} \]
   2. Add Preprocessing

   3. Taylor expanded in eps around 0

      \[\leadsto \color{blue}{2 \cdot \left(\varepsilon \cdot x\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 98.9

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

   5. Applied rewrites 98.9%

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

   7. Applied rewrites 99.0%

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

   if 0.0 < (-.f64 (pow.f64 (+.f64 x eps) #s(literal 2 binary64)) (pow.f64 x #s(literal 2 binary64)))

   1. Initial program 98.8%

      \[{\left(x + \varepsilon\right)}^{2} - {x}^{2} \]
   2. Add Preprocessing

   3. Taylor expanded in eps around inf

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

      1. unpow2 N/A

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

      2. lower-*.f64 93.8

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

   5. Applied rewrites 93.8%

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

4. Final simplification 96.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;{\left(x + \varepsilon\right)}^{2} - {x}^{2} \leq 0:\\ \;\;\;\;\left(x \cdot 2\right) \cdot \varepsilon\\ \mathbf{else}:\\ \;\;\;\;\varepsilon \cdot \varepsilon\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 100.0% accurate, 17.4× speedup

Math

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

FPCore

(FPCore (x eps) :precision binary64 (* (fma x 2.0 eps) eps))

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 75.5%

   \[{\left(x + \varepsilon\right)}^{2} - {x}^{2} \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

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

   1. distribute-rgt-in N/A

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

   2. unpow2 N/A

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

   3. *-rgt-identity N/A

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

   4. *-rgt-identity N/A

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

   5. *-inverses N/A

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

   6. associate-/l* N/A

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

   7. unpow2 N/A

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

   8. associate-/l* N/A

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

   9. *-commutative N/A

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

   10. associate-*r/ N/A

       \[\leadsto {\varepsilon}^{2} \cdot 1 + \color{blue}{{\varepsilon}^{2} \cdot \frac{2 \cdot x}{\varepsilon}} \]

   11. associate-*r/ N/A

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

   12. distribute-lft-in N/A

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

   13. unpow2 N/A

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

   14. associate-*l* N/A

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

   15. *-commutative N/A

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

   16. lower-*.f64 N/A

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

5. Applied rewrites 100.0%

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

Alternative 4: 72.7% accurate, 34.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, eps_] := N[(eps * eps), $MachinePrecision]

Derivation

1. Initial program 75.5%

   \[{\left(x + \varepsilon\right)}^{2} - {x}^{2} \]
2. Add Preprocessing

3. Taylor expanded in eps around inf

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

   1. unpow2 N/A

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

   2. lower-*.f64 72.6

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

5. Applied rewrites 72.6%

   \[\leadsto \color{blue}{\varepsilon \cdot \varepsilon} \]
6. Add Preprocessing

Alternative 5: 38.6% accurate, 209.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 0.0)

C

double code(double x, double eps) {
	return 0.0;
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    code = 0.0d0
end function

Java

public static double code(double x, double eps) {
	return 0.0;
}

Python

def code(x, eps):
	return 0.0

Julia

function code(x, eps)
	return 0.0
end

MATLAB

function tmp = code(x, eps)
	tmp = 0.0;
end

Wolfram

code[x_, eps_] := 0.0

Derivation

1. Initial program 75.5%

   \[{\left(x + \varepsilon\right)}^{2} - {x}^{2} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto \color{blue}{{\left(x + \varepsilon\right)}^{2} - {x}^{2}} \]

   2. sub-neg N/A

      \[\leadsto \color{blue}{{\left(x + \varepsilon\right)}^{2} + \left(\mathsf{neg}\left({x}^{2}\right)\right)} \]

   3. lift-pow.f64 N/A

      \[\leadsto \color{blue}{{\left(x + \varepsilon\right)}^{2}} + \left(\mathsf{neg}\left({x}^{2}\right)\right) \]

   4. unpow2 N/A

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

   5. lift-+.f64 N/A

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

   6. distribute-lft-in N/A

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

   7. associate-+l+ N/A

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

   8. *-commutative N/A

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

   9. lift-+.f64 N/A

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

   10. +-commutative N/A

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

   11. distribute-rgt-in N/A

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

   12. lower-fma.f64 N/A

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

   13. lift-+.f64 N/A

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

   14. +-commutative N/A

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

   15. lower-+.f64 N/A

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

   16. distribute-rgt-in N/A

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

   17. +-commutative N/A

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

   18. lift-+.f64 N/A

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

   19. *-commutative N/A

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

   20. lower-fma.f64 N/A

       \[\leadsto \mathsf{fma}\left(\varepsilon + x, x, \color{blue}{\mathsf{fma}\left(x + \varepsilon, \varepsilon, \mathsf{neg}\left({x}^{2}\right)\right)}\right) \]

   21. lift-+.f64 N/A

       \[\leadsto \mathsf{fma}\left(\varepsilon + x, x, \mathsf{fma}\left(\color{blue}{x + \varepsilon}, \varepsilon, \mathsf{neg}\left({x}^{2}\right)\right)\right) \]

   22. +-commutative N/A

       \[\leadsto \mathsf{fma}\left(\varepsilon + x, x, \mathsf{fma}\left(\color{blue}{\varepsilon + x}, \varepsilon, \mathsf{neg}\left({x}^{2}\right)\right)\right) \]

   23. lower-+.f64 N/A

       \[\leadsto \mathsf{fma}\left(\varepsilon + x, x, \mathsf{fma}\left(\color{blue}{\varepsilon + x}, \varepsilon, \mathsf{neg}\left({x}^{2}\right)\right)\right) \]

   24. lift-pow.f64 N/A

       \[\leadsto \mathsf{fma}\left(\varepsilon + x, x, \mathsf{fma}\left(\varepsilon + x, \varepsilon, \mathsf{neg}\left(\color{blue}{{x}^{2}}\right)\right)\right) \]

   25. unpow2 N/A

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

   26. distribute-lft-neg-in N/A

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

   27. lower-*.f64 N/A

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

4. Applied rewrites 62.6%

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

5. Taylor expanded in eps around 0

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

   1. distribute-lft1-in N/A

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

   2. metadata-eval N/A

      \[\leadsto \color{blue}{0} \cdot {x}^{2} \]

   3. mul0-lft 37.9

      \[\leadsto \color{blue}{0} \]

7. Applied rewrites 37.9%

   \[\leadsto \color{blue}{0} \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2024277 
(FPCore (x eps)
  :name "ENA, Section 1.4, Exercise 4b, n=2"
  :precision binary64
  :pre (and (and (<= -1000000000.0 x) (<= x 1000000000.0)) (and (<= -1.0 eps) (<= eps 1.0)))
  (- (pow (+ x eps) 2.0) (pow x 2.0)))