exp2 (problem 3.3.7)

Average Error: 29.6 → 0.0

Time: 11.0s

Precision: binary64

Cost: 26884

Math

\[\left(e^{x} - 2\right) + e^{-x} \]

↓

\[\begin{array}{l} t_0 := e^{-x}\\ \mathbf{if}\;\left(e^{x} + -2\right) + t_0 \leq 0.0005:\\ \;\;\;\;x \cdot x + \left(0.002777777777777778 \cdot {x}^{6} + 0.08333333333333333 \cdot {x}^{4}\right)\\ \mathbf{else}:\\ \;\;\;\;e^{x} + \left(t_0 + -2\right)\\ \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ (- (exp x) 2.0) (exp (- x))))

↓

(FPCore (x)
 :precision binary64
 (let* ((t_0 (exp (- x))))
   (if (<= (+ (+ (exp x) -2.0) t_0) 0.0005)
     (+
      (* x x)
      (+
       (* 0.002777777777777778 (pow x 6.0))
       (* 0.08333333333333333 (pow x 4.0))))
     (+ (exp x) (+ t_0 -2.0)))))

C

double code(double x) {
	return (exp(x) - 2.0) + exp(-x);
}

↓

double code(double x) {
	double t_0 = exp(-x);
	double tmp;
	if (((exp(x) + -2.0) + t_0) <= 0.0005) {
		tmp = (x * x) + ((0.002777777777777778 * pow(x, 6.0)) + (0.08333333333333333 * pow(x, 4.0)));
	} else {
		tmp = exp(x) + (t_0 + -2.0);
	}
	return tmp;
}

Fortran

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

↓

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = exp(-x)
    if (((exp(x) + (-2.0d0)) + t_0) <= 0.0005d0) then
        tmp = (x * x) + ((0.002777777777777778d0 * (x ** 6.0d0)) + (0.08333333333333333d0 * (x ** 4.0d0)))
    else
        tmp = exp(x) + (t_0 + (-2.0d0))
    end if
    code = tmp
end function

Java

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

↓

public static double code(double x) {
	double t_0 = Math.exp(-x);
	double tmp;
	if (((Math.exp(x) + -2.0) + t_0) <= 0.0005) {
		tmp = (x * x) + ((0.002777777777777778 * Math.pow(x, 6.0)) + (0.08333333333333333 * Math.pow(x, 4.0)));
	} else {
		tmp = Math.exp(x) + (t_0 + -2.0);
	}
	return tmp;
}

Python

def code(x):
	return (math.exp(x) - 2.0) + math.exp(-x)

↓

def code(x):
	t_0 = math.exp(-x)
	tmp = 0
	if ((math.exp(x) + -2.0) + t_0) <= 0.0005:
		tmp = (x * x) + ((0.002777777777777778 * math.pow(x, 6.0)) + (0.08333333333333333 * math.pow(x, 4.0)))
	else:
		tmp = math.exp(x) + (t_0 + -2.0)
	return tmp

Julia

function code(x)
	return Float64(Float64(exp(x) - 2.0) + exp(Float64(-x)))
end

↓

function code(x)
	t_0 = exp(Float64(-x))
	tmp = 0.0
	if (Float64(Float64(exp(x) + -2.0) + t_0) <= 0.0005)
		tmp = Float64(Float64(x * x) + Float64(Float64(0.002777777777777778 * (x ^ 6.0)) + Float64(0.08333333333333333 * (x ^ 4.0))));
	else
		tmp = Float64(exp(x) + Float64(t_0 + -2.0));
	end
	return tmp
end

MATLAB

function tmp = code(x)
	tmp = (exp(x) - 2.0) + exp(-x);
end

↓

function tmp_2 = code(x)
	t_0 = exp(-x);
	tmp = 0.0;
	if (((exp(x) + -2.0) + t_0) <= 0.0005)
		tmp = (x * x) + ((0.002777777777777778 * (x ^ 6.0)) + (0.08333333333333333 * (x ^ 4.0)));
	else
		tmp = exp(x) + (t_0 + -2.0);
	end
	tmp_2 = tmp;
end

Wolfram

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

↓

code[x_] := Block[{t$95$0 = N[Exp[(-x)], $MachinePrecision]}, If[LessEqual[N[(N[(N[Exp[x], $MachinePrecision] + -2.0), $MachinePrecision] + t$95$0), $MachinePrecision], 0.0005], N[(N[(x * x), $MachinePrecision] + N[(N[(0.002777777777777778 * N[Power[x, 6.0], $MachinePrecision]), $MachinePrecision] + N[(0.08333333333333333 * N[Power[x, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Exp[x], $MachinePrecision] + N[(t$95$0 + -2.0), $MachinePrecision]), $MachinePrecision]]]

Target

Original	29.6
Target	0.0
Herbie	0.0

\[4 \cdot {\sinh \left(\frac{x}{2}\right)}^{2} \]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (exp.f64 x) 2) (exp.f64 (neg.f64 x))) < 5.0000000000000001e-4

   1. Initial program 29.9

      \[\left(e^{x} - 2\right) + e^{-x} \]

   2. Simplified 29.9

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

      [Start] 29.9	\[ \left(e^{x} - 2\right) + e^{-x} \]
      associate-+l- [=>] 29.9	\[ \color{blue}{e^{x} - \left(2 - e^{-x}\right)} \]
      sub-neg [=>] 29.9	\[ \color{blue}{e^{x} + \left(-\left(2 - e^{-x}\right)\right)} \]
      neg-sub0 [=>] 29.9	\[ e^{x} + \color{blue}{\left(0 - \left(2 - e^{-x}\right)\right)} \]
      associate--r- [=>] 29.9	\[ e^{x} + \color{blue}{\left(\left(0 - 2\right) + e^{-x}\right)} \]
      metadata-eval [=>] 29.9	\[ e^{x} + \left(\color{blue}{-2} + e^{-x}\right) \]
      metadata-eval [<=] 29.9	\[ e^{x} + \left(\color{blue}{\left(-2\right)} + e^{-x}\right) \]
      +-commutative [=>] 29.9	\[ e^{x} + \color{blue}{\left(e^{-x} + \left(-2\right)\right)} \]
      metadata-eval [=>] 29.9	\[ e^{x} + \left(e^{-x} + \color{blue}{-2}\right) \]

   3. Taylor expanded in x around 0 0.0

      \[\leadsto \color{blue}{0.002777777777777778 \cdot {x}^{6} + \left({x}^{2} + 0.08333333333333333 \cdot {x}^{4}\right)} \]

   4. Simplified 0.0

      \[\leadsto \color{blue}{\mathsf{fma}\left(0.002777777777777778, {x}^{6}, x \cdot x + 0.08333333333333333 \cdot {x}^{4}\right)} \]
      Proof

      [Start] 0.0	\[ 0.002777777777777778 \cdot {x}^{6} + \left({x}^{2} + 0.08333333333333333 \cdot {x}^{4}\right) \]
      fma-def [=>] 0.0	\[ \color{blue}{\mathsf{fma}\left(0.002777777777777778, {x}^{6}, {x}^{2} + 0.08333333333333333 \cdot {x}^{4}\right)} \]
      unpow2 [=>] 0.0	\[ \mathsf{fma}\left(0.002777777777777778, {x}^{6}, \color{blue}{x \cdot x} + 0.08333333333333333 \cdot {x}^{4}\right) \]

   5. Applied egg-rr 0.0

      \[\leadsto \color{blue}{\left(0.08333333333333333 \cdot {x}^{4} + 0.002777777777777778 \cdot {x}^{6}\right) + x \cdot x} \]

   if 5.0000000000000001e-4 < (+.f64 (-.f64 (exp.f64 x) 2) (exp.f64 (neg.f64 x)))

   1. Initial program 2.1

      \[\left(e^{x} - 2\right) + e^{-x} \]

   2. Simplified 2.1

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

      [Start] 2.1	\[ \left(e^{x} - 2\right) + e^{-x} \]
      associate-+l- [=>] 2.1	\[ \color{blue}{e^{x} - \left(2 - e^{-x}\right)} \]
      sub-neg [=>] 2.1	\[ \color{blue}{e^{x} + \left(-\left(2 - e^{-x}\right)\right)} \]
      neg-sub0 [=>] 2.1	\[ e^{x} + \color{blue}{\left(0 - \left(2 - e^{-x}\right)\right)} \]
      associate--r- [=>] 2.1	\[ e^{x} + \color{blue}{\left(\left(0 - 2\right) + e^{-x}\right)} \]
      metadata-eval [=>] 2.1	\[ e^{x} + \left(\color{blue}{-2} + e^{-x}\right) \]
      metadata-eval [<=] 2.1	\[ e^{x} + \left(\color{blue}{\left(-2\right)} + e^{-x}\right) \]
      +-commutative [=>] 2.1	\[ e^{x} + \color{blue}{\left(e^{-x} + \left(-2\right)\right)} \]
      metadata-eval [=>] 2.1	\[ e^{x} + \left(e^{-x} + \color{blue}{-2}\right) \]

3. Recombined 2 regimes into one program.

4. Final simplification 0.0

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{x} + -2\right) + e^{-x} \leq 0.0005:\\ \;\;\;\;x \cdot x + \left(0.002777777777777778 \cdot {x}^{6} + 0.08333333333333333 \cdot {x}^{4}\right)\\ \mathbf{else}:\\ \;\;\;\;e^{x} + \left(e^{-x} + -2\right)\\ \end{array} \]

Alternatives

Alternative 1
Error	0.4
Cost	39168

\[\mathsf{fma}\left(0.002777777777777778, {x}^{6}, \mathsf{fma}\left(x, x, \mathsf{fma}\left(0.08333333333333333, {x}^{4}, 4.96031746031746 \cdot 10^{-5} \cdot {x}^{8}\right)\right)\right) \]

Alternative 2
Error	0.1
Cost	26436

\[\begin{array}{l} t_0 := e^{-x}\\ \mathbf{if}\;\left(e^{x} + -2\right) + t_0 \leq 10^{-6}:\\ \;\;\;\;x \cdot x + \left(x \cdot x\right) \cdot \left(0.08333333333333333 \cdot \left(x \cdot x\right)\right)\\ \mathbf{else}:\\ \;\;\;\;e^{x} + \left(t_0 + -2\right)\\ \end{array} \]

Alternative 3
Error	0.1
Cost	26436

\[\begin{array}{l} t_0 := e^{-x}\\ \mathbf{if}\;\left(e^{x} + -2\right) + t_0 \leq 10^{-6}:\\ \;\;\;\;\mathsf{fma}\left(x, x, 0.08333333333333333 \cdot {x}^{4}\right)\\ \mathbf{else}:\\ \;\;\;\;e^{x} + \left(t_0 + -2\right)\\ \end{array} \]

Alternative 4
Error	0.4
Cost	20352

\[4.96031746031746 \cdot 10^{-5} \cdot {x}^{8} + \left(\left(x \cdot x + 0.002777777777777778 \cdot {x}^{6}\right) + 0.08333333333333333 \cdot {x}^{4}\right) \]

Alternative 5
Error	0.6
Cost	832

\[x \cdot x + \left(x \cdot x\right) \cdot \left(0.08333333333333333 \cdot \left(x \cdot x\right)\right) \]

Alternative 6
Error	0.6
Cost	704

\[\left(x \cdot x\right) \cdot \left(0.08333333333333333 \cdot \left(x \cdot x\right) + 1\right) \]

Alternative 7
Error	1.0
Cost	192

\[x \cdot x \]

Alternative 8
Error	60.2
Cost	128

\[-x \]

Alternative 9
Error	60.2
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023066 
(FPCore (x)
  :name "exp2 (problem 3.3.7)"
  :precision binary64

  :herbie-target
  (* 4.0 (pow (sinh (/ x 2.0)) 2.0))

  (+ (- (exp x) 2.0) (exp (- x))))