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

Percentage Accurate: 74.5% → 100.0%

Time: 3.9s

Alternatives: 3

Speedup: 29.6×

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: 74.5% 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, 29.6× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

public static double code(double x, double eps) {
	return eps * (eps - (x * -2.0));
}

Python

def code(x, eps):
	return eps * (eps - (x * -2.0))

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 75.3%

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

   1. +-commutative 75.3%

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

   2. unpow2 75.3%

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

   3. unpow2 75.3%

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

   4. difference-of-squares 75.3%

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

   5. *-commutative 75.3%

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

   6. associate--l+ 100.0%

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

   7. +-inverses 100.0%

      \[\leadsto \left(\varepsilon + \color{blue}{0}\right) \cdot \left(\left(\varepsilon + x\right) + x\right) \]

   8. add0 100.0%

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

   9. associate-+l+ 100.0%

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

   10. remove-double-neg 100.0%

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

   11. distribute-neg-out 100.0%

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

   12. sub-neg 100.0%

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

   13. neg-mul-1 100.0%

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

   14. neg-mul-1 100.0%

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

   15. distribute-rgt-out 100.0%

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

   16. metadata-eval 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\varepsilon \cdot \left(\varepsilon - x \cdot -2\right)} \]
4. Add Preprocessing

5. Final simplification 100.0%

   \[\leadsto \varepsilon \cdot \left(\varepsilon - x \cdot -2\right) \]
6. Add Preprocessing

Alternative 2: 64.4% accurate, 41.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x, eps):
	return 2.0 * (eps * x)

Julia

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

MATLAB

function tmp = code(x, eps)
	tmp = 2.0 * (eps * x);
end

Wolfram

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

Derivation

1. Initial program 75.3%

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

   1. +-commutative 75.3%

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

   2. unpow2 75.3%

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

   3. unpow2 75.3%

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

   4. difference-of-squares 75.3%

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

   5. *-commutative 75.3%

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

   6. associate--l+ 100.0%

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

   7. +-inverses 100.0%

      \[\leadsto \left(\varepsilon + \color{blue}{0}\right) \cdot \left(\left(\varepsilon + x\right) + x\right) \]

   8. add0 100.0%

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

   9. associate-+l+ 100.0%

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

   10. remove-double-neg 100.0%

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

   11. distribute-neg-out 100.0%

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

   12. sub-neg 100.0%

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

   13. neg-mul-1 100.0%

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

   14. neg-mul-1 100.0%

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

   15. distribute-rgt-out 100.0%

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

   16. metadata-eval 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\varepsilon \cdot \left(\varepsilon - x \cdot -2\right)} \]
4. Add Preprocessing

5. Taylor expanded in eps around 0 63.5%

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

6. Final simplification 63.5%

   \[\leadsto 2 \cdot \left(\varepsilon \cdot x\right) \]
7. Add Preprocessing

Alternative 3: 64.4% accurate, 41.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x, eps):
	return x * (eps * 2.0)

Julia

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

MATLAB

function tmp = code(x, eps)
	tmp = x * (eps * 2.0);
end

Wolfram

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

Derivation

1. Initial program 75.3%

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

   1. +-commutative 75.3%

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

   2. unpow2 75.3%

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

   3. unpow2 75.3%

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

   4. difference-of-squares 75.3%

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

   5. *-commutative 75.3%

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

   6. associate--l+ 100.0%

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

   7. +-inverses 100.0%

      \[\leadsto \left(\varepsilon + \color{blue}{0}\right) \cdot \left(\left(\varepsilon + x\right) + x\right) \]

   8. add0 100.0%

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

   9. associate-+l+ 100.0%

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

   10. remove-double-neg 100.0%

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

   11. distribute-neg-out 100.0%

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

   12. sub-neg 100.0%

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

   13. neg-mul-1 100.0%

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

   14. neg-mul-1 100.0%

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

   15. distribute-rgt-out 100.0%

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

   16. metadata-eval 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\varepsilon \cdot \left(\varepsilon - x \cdot -2\right)} \]
4. Add Preprocessing

5. Taylor expanded in eps around 0 63.5%

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

   1. associate-*r* 63.5%

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

   2. *-commutative 63.5%

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

7. Simplified 63.5%

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

8. Final simplification 63.5%

   \[\leadsto x \cdot \left(\varepsilon \cdot 2\right) \]
9. Add Preprocessing

Reproduce

herbie shell --seed 2024046 
(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)))