ENA, Section 1.4, Mentioned, A

Percentage Accurate: 53.3% → 100.0%

Time: 5.4s

Alternatives: 4

Speedup: 20.6×

Specification

\[-0.01 \leq x \land x \leq 0.01\]

Math

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

FPCore

(FPCore (x) :precision binary64 (- 1.0 (cos x)))

C

double code(double x) {
	return 1.0 - cos(x);
}

Fortran

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

Java

public static double code(double x) {
	return 1.0 - Math.cos(x);
}

Python

def code(x):
	return 1.0 - math.cos(x)

Julia

function code(x)
	return Float64(1.0 - cos(x))
end

MATLAB

function tmp = code(x)
	tmp = 1.0 - cos(x);
end

Wolfram

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

Initial Program: 53.3% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 (- 1.0 (cos x)))

C

double code(double x) {
	return 1.0 - cos(x);
}

Fortran

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

Java

public static double code(double x) {
	return 1.0 - Math.cos(x);
}

Python

def code(x):
	return 1.0 - math.cos(x)

Julia

function code(x)
	return Float64(1.0 - cos(x))
end

MATLAB

function tmp = code(x)
	tmp = 1.0 - cos(x);
end

Wolfram

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

Alternative 1: 100.0% accurate, 6.1× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot x\right) \cdot \left(0.5 + \left(x \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot 0.001388888888888889 - 0.041666666666666664\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  (* x x)
  (+
   0.5
   (* (* x x) (- (* (* x x) 0.001388888888888889) 0.041666666666666664)))))

C

double code(double x) {
	return (x * x) * (0.5 + ((x * x) * (((x * x) * 0.001388888888888889) - 0.041666666666666664)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * x) * (0.5d0 + ((x * x) * (((x * x) * 0.001388888888888889d0) - 0.041666666666666664d0)))
end function

Java

public static double code(double x) {
	return (x * x) * (0.5 + ((x * x) * (((x * x) * 0.001388888888888889) - 0.041666666666666664)));
}

Python

def code(x):
	return (x * x) * (0.5 + ((x * x) * (((x * x) * 0.001388888888888889) - 0.041666666666666664)))

Julia

function code(x)
	return Float64(Float64(x * x) * Float64(0.5 + Float64(Float64(x * x) * Float64(Float64(Float64(x * x) * 0.001388888888888889) - 0.041666666666666664))))
end

MATLAB

function tmp = code(x)
	tmp = (x * x) * (0.5 + ((x * x) * (((x * x) * 0.001388888888888889) - 0.041666666666666664)));
end

Wolfram

code[x_] := N[(N[(x * x), $MachinePrecision] * N[(0.5 + N[(N[(x * x), $MachinePrecision] * N[(N[(N[(x * x), $MachinePrecision] * 0.001388888888888889), $MachinePrecision] - 0.041666666666666664), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 59.5%

   \[1 - \cos x \]
2. Add Preprocessing

3. Taylor expanded in x around 0 100.0%

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(0.5 + {x}^{2} \cdot \left(0.001388888888888889 \cdot {x}^{2} - 0.041666666666666664\right)\right)} \]
4. Step-by-step derivation

   1. unpow2 100.0%

      \[\leadsto {x}^{2} \cdot \left(0.5 + {x}^{2} \cdot \left(0.001388888888888889 \cdot \color{blue}{\left(x \cdot x\right)} - 0.041666666666666664\right)\right) \]

5. Applied egg-rr 100.0%

   \[\leadsto {x}^{2} \cdot \left(0.5 + {x}^{2} \cdot \left(0.001388888888888889 \cdot \color{blue}{\left(x \cdot x\right)} - 0.041666666666666664\right)\right) \]
6. Step-by-step derivation

   1. unpow2 100.0%

      \[\leadsto {x}^{2} \cdot \left(0.5 + {x}^{2} \cdot \left(0.001388888888888889 \cdot \color{blue}{\left(x \cdot x\right)} - 0.041666666666666664\right)\right) \]

7. Applied egg-rr 100.0%

   \[\leadsto {x}^{2} \cdot \left(0.5 + \color{blue}{\left(x \cdot x\right)} \cdot \left(0.001388888888888889 \cdot \left(x \cdot x\right) - 0.041666666666666664\right)\right) \]
8. Step-by-step derivation

   1. unpow2 100.0%

      \[\leadsto {x}^{2} \cdot \left(0.5 + {x}^{2} \cdot \left(0.001388888888888889 \cdot \color{blue}{\left(x \cdot x\right)} - 0.041666666666666664\right)\right) \]

9. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\left(x \cdot x\right)} \cdot \left(0.5 + \left(x \cdot x\right) \cdot \left(0.001388888888888889 \cdot \left(x \cdot x\right) - 0.041666666666666664\right)\right) \]

10. Final simplification 100.0%

    \[\leadsto \left(x \cdot x\right) \cdot \left(0.5 + \left(x \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot 0.001388888888888889 - 0.041666666666666664\right)\right) \]
11. Add Preprocessing

Alternative 2: 99.5% accurate, 20.6× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(x \cdot 0.5\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x (* x 0.5)))

C

double code(double x) {
	return x * (x * 0.5);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x * (x * 0.5d0)
end function

Java

public static double code(double x) {
	return x * (x * 0.5);
}

Python

def code(x):
	return x * (x * 0.5)

Julia

function code(x)
	return Float64(x * Float64(x * 0.5))
end

MATLAB

function tmp = code(x)
	tmp = x * (x * 0.5);
end

Wolfram

code[x_] := N[(x * N[(x * 0.5), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 59.5%

   \[1 - \cos x \]
2. Add Preprocessing
3. Step-by-step derivation

   1. flip-- 59.5%

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

   2. div-inv 59.5%

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

   3. metadata-eval 59.5%

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

   4. 1-sub-cos 100.0%

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

   5. pow2 100.0%

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

4. Applied egg-rr 100.0%

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

   1. associate-*r/ 100.0%

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

   2. *-rgt-identity 100.0%

      \[\leadsto \frac{\color{blue}{{\sin x}^{2}}}{1 + \cos x} \]

   3. unpow2 100.0%

      \[\leadsto \frac{\color{blue}{\sin x \cdot \sin x}}{1 + \cos x} \]

   4. associate-/l* 100.0%

      \[\leadsto \color{blue}{\sin x \cdot \frac{\sin x}{1 + \cos x}} \]

   5. hang-0p-tan 100.0%

      \[\leadsto \sin x \cdot \color{blue}{\tan \left(\frac{x}{2}\right)} \]

6. Simplified 100.0%

   \[\leadsto \color{blue}{\sin x \cdot \tan \left(\frac{x}{2}\right)} \]

7. Taylor expanded in x around 0 99.6%

   \[\leadsto \sin x \cdot \color{blue}{\left(0.5 \cdot x\right)} \]

8. Taylor expanded in x around 0 99.6%

   \[\leadsto \color{blue}{x} \cdot \left(0.5 \cdot x\right) \]

9. Final simplification 99.6%

   \[\leadsto x \cdot \left(x \cdot 0.5\right) \]
10. Add Preprocessing

Alternative 3: 99.5% accurate, 20.6× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot x\right) \cdot 0.5 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (* x x) 0.5))

C

double code(double x) {
	return (x * x) * 0.5;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * x) * 0.5d0
end function

Java

public static double code(double x) {
	return (x * x) * 0.5;
}

Python

def code(x):
	return (x * x) * 0.5

Julia

function code(x)
	return Float64(Float64(x * x) * 0.5)
end

MATLAB

function tmp = code(x)
	tmp = (x * x) * 0.5;
end

Wolfram

code[x_] := N[(N[(x * x), $MachinePrecision] * 0.5), $MachinePrecision]

Derivation

1. Initial program 59.5%

   \[1 - \cos x \]
2. Add Preprocessing

3. Taylor expanded in x around 0 99.6%

   \[\leadsto \color{blue}{0.5 \cdot {x}^{2}} \]
4. Step-by-step derivation

   1. unpow2 100.0%

      \[\leadsto {x}^{2} \cdot \left(0.5 + {x}^{2} \cdot \left(0.001388888888888889 \cdot \color{blue}{\left(x \cdot x\right)} - 0.041666666666666664\right)\right) \]

5. Applied egg-rr 99.6%

   \[\leadsto 0.5 \cdot \color{blue}{\left(x \cdot x\right)} \]

6. Final simplification 99.6%

   \[\leadsto \left(x \cdot x\right) \cdot 0.5 \]
7. Add Preprocessing

Alternative 4: 52.3% accurate, 103.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 0.0)

C

double code(double x) {
	return 0.0;
}

Fortran

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

Java

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

Python

def code(x):
	return 0.0

Julia

function code(x)
	return 0.0
end

MATLAB

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

Wolfram

code[x_] := 0.0

Derivation

1. Initial program 59.5%

   \[1 - \cos x \]
2. Add Preprocessing

3. Taylor expanded in x around 0 58.9%

   \[\leadsto 1 - \color{blue}{1} \]
4. Step-by-step derivation

   1. metadata-eval 58.9%

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

5. Applied egg-rr 58.9%

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

Developer Target 1: 100.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \frac{\sin x \cdot \sin x}{1 + \cos x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (* (sin x) (sin x)) (+ 1.0 (cos x))))

C

double code(double x) {
	return (sin(x) * sin(x)) / (1.0 + cos(x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (sin(x) * sin(x)) / (1.0d0 + cos(x))
end function

Java

public static double code(double x) {
	return (Math.sin(x) * Math.sin(x)) / (1.0 + Math.cos(x));
}

Python

def code(x):
	return (math.sin(x) * math.sin(x)) / (1.0 + math.cos(x))

Julia

function code(x)
	return Float64(Float64(sin(x) * sin(x)) / Float64(1.0 + cos(x)))
end

MATLAB

function tmp = code(x)
	tmp = (sin(x) * sin(x)) / (1.0 + cos(x));
end

Wolfram

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

Reproduce

herbie shell --seed 2024180 
(FPCore (x)
  :name "ENA, Section 1.4, Mentioned, A"
  :precision binary64
  :pre (and (<= -0.01 x) (<= x 0.01))

  :alt
  (! :herbie-platform default (/ (* (sin x) (sin x)) (+ 1 (cos x))))

  (- 1.0 (cos x)))