ENA, Section 1.4, Mentioned, B

Percentage Accurate: 87.8% → 99.6%

Time: 9.5s

Alternatives: 6

Speedup: 1.1×

Specification

\[0.999 \leq x \land x \leq 1.001\]

Math

\[\begin{array}{l} \\ \frac{10}{1 - x \cdot x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 10.0 (- 1.0 (* x x))))

C

double code(double x) {
	return 10.0 / (1.0 - (x * x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 10.0d0 / (1.0d0 - (x * x))
end function

Java

public static double code(double x) {
	return 10.0 / (1.0 - (x * x));
}

Python

def code(x):
	return 10.0 / (1.0 - (x * x))

Julia

function code(x)
	return Float64(10.0 / Float64(1.0 - Float64(x * x)))
end

MATLAB

function tmp = code(x)
	tmp = 10.0 / (1.0 - (x * x));
end

Wolfram

code[x_] := N[(10.0 / N[(1.0 - N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 87.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{10}{1 - x \cdot x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 10.0 (- 1.0 (* x x))))

C

double code(double x) {
	return 10.0 / (1.0 - (x * x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 10.0d0 / (1.0d0 - (x * x))
end function

Java

public static double code(double x) {
	return 10.0 / (1.0 - (x * x));
}

Python

def code(x):
	return 10.0 / (1.0 - (x * x))

Julia

function code(x)
	return Float64(10.0 / Float64(1.0 - Float64(x * x)))
end

MATLAB

function tmp = code(x)
	tmp = 10.0 / (1.0 - (x * x));
end

Wolfram

code[x_] := N[(10.0 / N[(1.0 - N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{-10}{\mathsf{fma}\left(x, x, -1\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ -10.0 (fma x x -1.0)))

C

double code(double x) {
	return -10.0 / fma(x, x, -1.0);
}

Julia

function code(x)
	return Float64(-10.0 / fma(x, x, -1.0))
end

Wolfram

code[x_] := N[(-10.0 / N[(x * x + -1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 87.8%

   \[\frac{10}{1 - x \cdot x} \]
2. Add Preprocessing

4. Applied rewrites 99.7%

   \[\leadsto \color{blue}{\frac{-10}{\mathsf{fma}\left(x, x, -1\right)}} \]
5. Add Preprocessing

Alternative 2: 13.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{10}{1 - x \cdot x} \leq -5000:\\ \;\;\;\;\mathsf{fma}\left(-10, x \cdot x, -10\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(x \cdot x, 10, 10\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (/ 10.0 (- 1.0 (* x x))) -5000.0)
   (fma -10.0 (* x x) -10.0)
   (fma (* x x) 10.0 10.0)))

C

double code(double x) {
	double tmp;
	if ((10.0 / (1.0 - (x * x))) <= -5000.0) {
		tmp = fma(-10.0, (x * x), -10.0);
	} else {
		tmp = fma((x * x), 10.0, 10.0);
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (Float64(10.0 / Float64(1.0 - Float64(x * x))) <= -5000.0)
		tmp = fma(-10.0, Float64(x * x), -10.0);
	else
		tmp = fma(Float64(x * x), 10.0, 10.0);
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[N[(10.0 / N[(1.0 - N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -5000.0], N[(-10.0 * N[(x * x), $MachinePrecision] + -10.0), $MachinePrecision], N[(N[(x * x), $MachinePrecision] * 10.0 + 10.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 #s(literal 10 binary64) (-.f64 #s(literal 1 binary64) (*.f64 x x))) < -5e3

   1. Initial program 87.0%

      \[\frac{10}{1 - x \cdot x} \]
   2. Add Preprocessing

   4. Applied rewrites 1.5%

      \[\leadsto \color{blue}{{\left(\left(-0.1 \cdot \mathsf{fma}\left(x, x, -1\right)\right) \cdot \left(-0.1 \cdot \mathsf{fma}\left(x, x, -1\right)\right)\right)}^{-0.5}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-10 \cdot {x}^{2} - 10} \]
   6. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto \color{blue}{-10 \cdot {x}^{2} + \left(\mathsf{neg}\left(10\right)\right)} \]

      2. metadata-eval N/A

         \[\leadsto -10 \cdot {x}^{2} + \color{blue}{-10} \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(-10, {x}^{2}, -10\right)} \]

      4. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(-10, \color{blue}{x \cdot x}, -10\right) \]

      5. lower-*.f64 13.7

         \[\leadsto \mathsf{fma}\left(-10, \color{blue}{x \cdot x}, -10\right) \]

   7. Applied rewrites 13.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-10, x \cdot x, -10\right)} \]

   if -5e3 < (/.f64 #s(literal 10 binary64) (-.f64 #s(literal 1 binary64) (*.f64 x x)))

   1. Initial program 88.1%

      \[\frac{10}{1 - x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{10 + 10 \cdot {x}^{2}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{10 \cdot {x}^{2} + 10} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot 10} + 10 \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, 10, 10\right)} \]

      4. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, 10, 10\right) \]

      5. lower-*.f64 13.7

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, 10, 10\right) \]

   5. Applied rewrites 13.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, 10, 10\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 13.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{10}{1 - x \cdot x} \leq -5000:\\ \;\;\;\;-10\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(x \cdot x, 10, 10\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (/ 10.0 (- 1.0 (* x x))) -5000.0) -10.0 (fma (* x x) 10.0 10.0)))

C

double code(double x) {
	double tmp;
	if ((10.0 / (1.0 - (x * x))) <= -5000.0) {
		tmp = -10.0;
	} else {
		tmp = fma((x * x), 10.0, 10.0);
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (Float64(10.0 / Float64(1.0 - Float64(x * x))) <= -5000.0)
		tmp = -10.0;
	else
		tmp = fma(Float64(x * x), 10.0, 10.0);
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[N[(10.0 / N[(1.0 - N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -5000.0], -10.0, N[(N[(x * x), $MachinePrecision] * 10.0 + 10.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 #s(literal 10 binary64) (-.f64 #s(literal 1 binary64) (*.f64 x x))) < -5e3

   1. Initial program 87.0%

      \[\frac{10}{1 - x \cdot x} \]
   2. Add Preprocessing

   4. Applied rewrites 1.5%

      \[\leadsto \color{blue}{{\left(\left(-0.1 \cdot \mathsf{fma}\left(x, x, -1\right)\right) \cdot \left(-0.1 \cdot \mathsf{fma}\left(x, x, -1\right)\right)\right)}^{-0.5}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-10} \]
   6. Step-by-step derivation

   7. Applied rewrites 13.5%

      \[\leadsto \color{blue}{-10} \]

   if -5e3 < (/.f64 #s(literal 10 binary64) (-.f64 #s(literal 1 binary64) (*.f64 x x)))

   1. Initial program 88.1%

      \[\frac{10}{1 - x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{10 + 10 \cdot {x}^{2}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{10 \cdot {x}^{2} + 10} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot 10} + 10 \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, 10, 10\right)} \]

      4. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, 10, 10\right) \]

      5. lower-*.f64 13.7

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, 10, 10\right) \]

   5. Applied rewrites 13.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, 10, 10\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 13.5% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;1 - x \cdot x \leq -1 \cdot 10^{-310}:\\ \;\;\;\;-10\\ \mathbf{else}:\\ \;\;\;\;10\\ \end{array} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (if (<= (- 1.0 (* x x)) -1e-310) -10.0 10.0))

C

double code(double x) {
	double tmp;
	if ((1.0 - (x * x)) <= -1e-310) {
		tmp = -10.0;
	} else {
		tmp = 10.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((1.0d0 - (x * x)) <= (-1d-310)) then
        tmp = -10.0d0
    else
        tmp = 10.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((1.0 - (x * x)) <= -1e-310) {
		tmp = -10.0;
	} else {
		tmp = 10.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (1.0 - (x * x)) <= -1e-310:
		tmp = -10.0
	else:
		tmp = 10.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (Float64(1.0 - Float64(x * x)) <= -1e-310)
		tmp = -10.0;
	else
		tmp = 10.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((1.0 - (x * x)) <= -1e-310)
		tmp = -10.0;
	else
		tmp = 10.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[N[(1.0 - N[(x * x), $MachinePrecision]), $MachinePrecision], -1e-310], -10.0, 10.0]

Derivation

1. Split input into 2 regimes

2. if (-.f64 #s(literal 1 binary64) (*.f64 x x)) < -9.999999999999969e-311

   1. Initial program 87.0%

      \[\frac{10}{1 - x \cdot x} \]
   2. Add Preprocessing

   4. Applied rewrites 1.5%

      \[\leadsto \color{blue}{{\left(\left(-0.1 \cdot \mathsf{fma}\left(x, x, -1\right)\right) \cdot \left(-0.1 \cdot \mathsf{fma}\left(x, x, -1\right)\right)\right)}^{-0.5}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-10} \]
   6. Step-by-step derivation

   7. Applied rewrites 13.5%

      \[\leadsto \color{blue}{-10} \]

   if -9.999999999999969e-311 < (-.f64 #s(literal 1 binary64) (*.f64 x x))

   1. Initial program 88.1%

      \[\frac{10}{1 - x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{10} \]
   4. Step-by-step derivation

   5. Applied rewrites 13.5%

      \[\leadsto \color{blue}{10} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 18.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \frac{-10}{x - 1} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ -10.0 (- x 1.0)))

C

double code(double x) {
	return -10.0 / (x - 1.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (-10.0d0) / (x - 1.0d0)
end function

Java

public static double code(double x) {
	return -10.0 / (x - 1.0);
}

Python

def code(x):
	return -10.0 / (x - 1.0)

Julia

function code(x)
	return Float64(-10.0 / Float64(x - 1.0))
end

MATLAB

function tmp = code(x)
	tmp = -10.0 / (x - 1.0);
end

Wolfram

code[x_] := N[(-10.0 / N[(x - 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 87.8%

   \[\frac{10}{1 - x \cdot x} \]
2. Add Preprocessing

4. Applied rewrites 99.7%

   \[\leadsto \color{blue}{\frac{-10}{\mathsf{fma}\left(x, x, -1\right)}} \]
5. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{-10}{\mathsf{fma}\left(x, x, -1\right)}} \]

   2. metadata-eval N/A

      \[\leadsto \frac{\color{blue}{\frac{1}{\frac{-1}{10}}}}{\mathsf{fma}\left(x, x, -1\right)} \]

   3. associate-/r* N/A

      \[\leadsto \color{blue}{\frac{1}{\frac{-1}{10} \cdot \mathsf{fma}\left(x, x, -1\right)}} \]

   4. lift-fma.f64 N/A

      \[\leadsto \frac{1}{\frac{-1}{10} \cdot \color{blue}{\left(x \cdot x + -1\right)}} \]

   5. difference-of-sqr--1 N/A

      \[\leadsto \frac{1}{\frac{-1}{10} \cdot \color{blue}{\left(\left(x + 1\right) \cdot \left(x - 1\right)\right)}} \]

   6. associate-*r* N/A

      \[\leadsto \frac{1}{\color{blue}{\left(\frac{-1}{10} \cdot \left(x + 1\right)\right) \cdot \left(x - 1\right)}} \]

   7. associate-/r* N/A

      \[\leadsto \color{blue}{\frac{\frac{1}{\frac{-1}{10} \cdot \left(x + 1\right)}}{x - 1}} \]

   8. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\frac{1}{\frac{-1}{10} \cdot \left(x + 1\right)}}{x - 1}} \]

   9. lower-/.f64 N/A

      \[\leadsto \frac{\color{blue}{\frac{1}{\frac{-1}{10} \cdot \left(x + 1\right)}}}{x - 1} \]

   10. distribute-lft-in N/A

       \[\leadsto \frac{\frac{1}{\color{blue}{\frac{-1}{10} \cdot x + \frac{-1}{10} \cdot 1}}}{x - 1} \]

   11. metadata-eval N/A

       \[\leadsto \frac{\frac{1}{\frac{-1}{10} \cdot x + \color{blue}{\frac{-1}{10}}}}{x - 1} \]

   12. lower-fma.f64 N/A

       \[\leadsto \frac{\frac{1}{\color{blue}{\mathsf{fma}\left(\frac{-1}{10}, x, \frac{-1}{10}\right)}}}{x - 1} \]

   13. lower--.f64 99.3

       \[\leadsto \frac{\frac{1}{\mathsf{fma}\left(-0.1, x, -0.1\right)}}{\color{blue}{x - 1}} \]

6. Applied rewrites 99.3%

   \[\leadsto \color{blue}{\frac{\frac{1}{\mathsf{fma}\left(-0.1, x, -0.1\right)}}{x - 1}} \]

7. Taylor expanded in x around 0

   \[\leadsto \frac{\color{blue}{-10}}{x - 1} \]
8. Step-by-step derivation

9. Applied rewrites 18.8%

   \[\leadsto \frac{\color{blue}{-10}}{x - 1} \]
10. Add Preprocessing

Alternative 6: 9.5% accurate, 20.0× speedup

Math

\[\begin{array}{l} \\ 10 \end{array} \]

FPCore

(FPCore (x) :precision binary64 10.0)

C

double code(double x) {
	return 10.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 10.0d0
end function

Java

public static double code(double x) {
	return 10.0;
}

Python

def code(x):
	return 10.0

Julia

function code(x)
	return 10.0
end

MATLAB

function tmp = code(x)
	tmp = 10.0;
end

Wolfram

code[x_] := 10.0

Derivation

1. Initial program 87.8%

   \[\frac{10}{1 - x \cdot x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{10} \]
4. Step-by-step derivation

5. Applied rewrites 9.3%

   \[\leadsto \color{blue}{10} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024273 
(FPCore (x)
  :name "ENA, Section 1.4, Mentioned, B"
  :precision binary64
  :pre (and (<= 0.999 x) (<= x 1.001))
  (/ 10.0 (- 1.0 (* x x))))