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

Percentage Accurate: 74.7% → 100.0%

Time: 5.7s

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: 74.7% 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 73.2%

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

   1. unpow2 N/A

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

   2. unpow2 N/A

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

   3. difference-of-squares N/A

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

   4. *-commutative N/A

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

   5. +-commutative N/A

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

   6. associate--l+ N/A

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

   7. +-inverses N/A

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

   8. +-rgt-identity N/A

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

   9. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \color{blue}{\left(\left(x + \varepsilon\right) + x\right)}\right) \]

   10. +-commutative N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \left(\left(\varepsilon + x\right) + x\right)\right) \]

   11. associate-+l+ N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \left(\varepsilon + \color{blue}{\left(x + x\right)}\right)\right) \]

   12. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \color{blue}{\left(x + x\right)}\right)\right) \]

   13. count-2 N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \left(2 \cdot \color{blue}{x}\right)\right)\right) \]

   14. *-commutative N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \left(x \cdot \color{blue}{2}\right)\right)\right) \]

   15. *-lowering-*.f64 100.0%

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \mathsf{*.f64}\left(x, \color{blue}{2}\right)\right)\right) \]

3. Simplified 100.0%

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

Alternative 2: 91.0% accurate, 13.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \varepsilon \cdot \left(x \cdot 2\right)\\ \mathbf{if}\;x \leq -9.5 \cdot 10^{-112}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 6.6 \cdot 10^{-101}:\\ \;\;\;\;\varepsilon \cdot \varepsilon\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (let* ((t_0 (* eps (* x 2.0))))
   (if (<= x -9.5e-112) t_0 (if (<= x 6.6e-101) (* eps eps) t_0))))

C

double code(double x, double eps) {
	double t_0 = eps * (x * 2.0);
	double tmp;
	if (x <= -9.5e-112) {
		tmp = t_0;
	} else if (x <= 6.6e-101) {
		tmp = eps * eps;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    real(8) :: t_0
    real(8) :: tmp
    t_0 = eps * (x * 2.0d0)
    if (x <= (-9.5d-112)) then
        tmp = t_0
    else if (x <= 6.6d-101) then
        tmp = eps * eps
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double eps) {
	double t_0 = eps * (x * 2.0);
	double tmp;
	if (x <= -9.5e-112) {
		tmp = t_0;
	} else if (x <= 6.6e-101) {
		tmp = eps * eps;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, eps):
	t_0 = eps * (x * 2.0)
	tmp = 0
	if x <= -9.5e-112:
		tmp = t_0
	elif x <= 6.6e-101:
		tmp = eps * eps
	else:
		tmp = t_0
	return tmp

Julia

function code(x, eps)
	t_0 = Float64(eps * Float64(x * 2.0))
	tmp = 0.0
	if (x <= -9.5e-112)
		tmp = t_0;
	elseif (x <= 6.6e-101)
		tmp = Float64(eps * eps);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, eps)
	t_0 = eps * (x * 2.0);
	tmp = 0.0;
	if (x <= -9.5e-112)
		tmp = t_0;
	elseif (x <= 6.6e-101)
		tmp = eps * eps;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, eps_] := Block[{t$95$0 = N[(eps * N[(x * 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -9.5e-112], t$95$0, If[LessEqual[x, 6.6e-101], N[(eps * eps), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -9.50000000000000056e-112 or 6.59999999999999968e-101 < x

   1. Initial program 26.4%

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

      1. unpow2 N/A

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

      2. unpow2 N/A

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

      3. difference-of-squares N/A

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

      4. *-commutative N/A

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

      5. +-commutative N/A

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

      6. associate--l+ N/A

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

      7. +-inverses N/A

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

      8. +-rgt-identity N/A

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

      9. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(\varepsilon, \color{blue}{\left(\left(x + \varepsilon\right) + x\right)}\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \left(\left(\varepsilon + x\right) + x\right)\right) \]

      11. associate-+l+ N/A

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \left(\varepsilon + \color{blue}{\left(x + x\right)}\right)\right) \]

      12. +-lowering-+.f64 N/A

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \color{blue}{\left(x + x\right)}\right)\right) \]

      13. count-2 N/A

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \left(2 \cdot \color{blue}{x}\right)\right)\right) \]

      14. *-commutative N/A

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \left(x \cdot \color{blue}{2}\right)\right)\right) \]

      15. *-lowering-*.f64 100.0%

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \mathsf{*.f64}\left(x, \color{blue}{2}\right)\right)\right) \]

   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

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \color{blue}{\left(2 \cdot x\right)}\right) \]
   6. Step-by-step derivation

      1. *-lowering-*.f64 91.4%

         \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{*.f64}\left(2, \color{blue}{x}\right)\right) \]

   7. Simplified 91.4%

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

   if -9.50000000000000056e-112 < x < 6.59999999999999968e-101

   1. Initial program 97.4%

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

      1. unpow2 N/A

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

      2. unpow2 N/A

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

      3. difference-of-squares N/A

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

      4. *-commutative N/A

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

      5. +-commutative N/A

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

      6. associate--l+ N/A

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

      7. +-inverses N/A

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

      8. +-rgt-identity N/A

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

      9. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(\varepsilon, \color{blue}{\left(\left(x + \varepsilon\right) + x\right)}\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \left(\left(\varepsilon + x\right) + x\right)\right) \]

      11. associate-+l+ N/A

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \left(\varepsilon + \color{blue}{\left(x + x\right)}\right)\right) \]

      12. +-lowering-+.f64 N/A

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \color{blue}{\left(x + x\right)}\right)\right) \]

      13. count-2 N/A

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \left(2 \cdot \color{blue}{x}\right)\right)\right) \]

      14. *-commutative N/A

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \left(x \cdot \color{blue}{2}\right)\right)\right) \]

      15. *-lowering-*.f64 100.0%

          \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \mathsf{*.f64}\left(x, \color{blue}{2}\right)\right)\right) \]

   3. Simplified 100.0%

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

   5. Taylor expanded in eps around inf

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

      1. unpow2 N/A

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

      2. *-lowering-*.f64 95.7%

         \[\leadsto \mathsf{*.f64}\left(\varepsilon, \color{blue}{\varepsilon}\right) \]

   7. Simplified 95.7%

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

4. Final simplification 94.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9.5 \cdot 10^{-112}:\\ \;\;\;\;\varepsilon \cdot \left(x \cdot 2\right)\\ \mathbf{elif}\;x \leq 6.6 \cdot 10^{-101}:\\ \;\;\;\;\varepsilon \cdot \varepsilon\\ \mathbf{else}:\\ \;\;\;\;\varepsilon \cdot \left(x \cdot 2\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 72.7% accurate, 69.0× 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 73.2%

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

   1. unpow2 N/A

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

   2. unpow2 N/A

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

   3. difference-of-squares N/A

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

   4. *-commutative N/A

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

   5. +-commutative N/A

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

   6. associate--l+ N/A

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

   7. +-inverses N/A

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

   8. +-rgt-identity N/A

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

   9. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \color{blue}{\left(\left(x + \varepsilon\right) + x\right)}\right) \]

   10. +-commutative N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \left(\left(\varepsilon + x\right) + x\right)\right) \]

   11. associate-+l+ N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \left(\varepsilon + \color{blue}{\left(x + x\right)}\right)\right) \]

   12. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \color{blue}{\left(x + x\right)}\right)\right) \]

   13. count-2 N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \left(2 \cdot \color{blue}{x}\right)\right)\right) \]

   14. *-commutative N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \left(x \cdot \color{blue}{2}\right)\right)\right) \]

   15. *-lowering-*.f64 100.0%

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\varepsilon, \mathsf{*.f64}\left(x, \color{blue}{2}\right)\right)\right) \]

3. Simplified 100.0%

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

5. Taylor expanded in eps around inf

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

   1. unpow2 N/A

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

   2. *-lowering-*.f64 70.6%

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \color{blue}{\varepsilon}\right) \]

7. Simplified 70.6%

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

Reproduce

herbie shell --seed 2024139 
(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)))