sintan (problem 3.4.5)

Percentage Accurate: 49.7% → 100.0%

Time: 17.5s

Alternatives: 6

Speedup: 207.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x - \sin x}{x - \tan x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (- x (sin x)) (- x (tan x))))

C

double code(double x) {
	return (x - sin(x)) / (x - tan(x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x - sin(x)) / (x - tan(x))
end function

Java

public static double code(double x) {
	return (x - Math.sin(x)) / (x - Math.tan(x));
}

Python

def code(x):
	return (x - math.sin(x)) / (x - math.tan(x))

Julia

function code(x)
	return Float64(Float64(x - sin(x)) / Float64(x - tan(x)))
end

MATLAB

function tmp = code(x)
	tmp = (x - sin(x)) / (x - tan(x));
end

Wolfram

code[x_] := N[(N[(x - N[Sin[x], $MachinePrecision]), $MachinePrecision] / N[(x - N[Tan[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 49.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - \sin x}{x - \tan x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (- x (sin x)) (- x (tan x))))

C

double code(double x) {
	return (x - sin(x)) / (x - tan(x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x - sin(x)) / (x - tan(x))
end function

Java

public static double code(double x) {
	return (x - Math.sin(x)) / (x - Math.tan(x));
}

Python

def code(x):
	return (x - math.sin(x)) / (x - math.tan(x))

Julia

function code(x)
	return Float64(Float64(x - sin(x)) / Float64(x - tan(x)))
end

MATLAB

function tmp = code(x)
	tmp = (x - sin(x)) / (x - tan(x));
end

Wolfram

code[x_] := N[(N[(x - N[Sin[x], $MachinePrecision]), $MachinePrecision] / N[(x - N[Tan[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} x = |x|\\ \\ \begin{array}{l} \mathbf{if}\;x \leq 0.0255:\\ \;\;\;\;\left(x \cdot \left(x \cdot 0.225\right) + -0.009642857142857142 \cdot {x}^{4}\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x - \sin x}{x - \tan x}\\ \end{array} \end{array} \]

FPCore

NOTE: x should be positive before calling this function
(FPCore (x)
 :precision binary64
 (if (<= x 0.0255)
   (+ (+ (* x (* x 0.225)) (* -0.009642857142857142 (pow x 4.0))) -0.5)
   (/ (- x (sin x)) (- x (tan x)))))

C

x = abs(x);
double code(double x) {
	double tmp;
	if (x <= 0.0255) {
		tmp = ((x * (x * 0.225)) + (-0.009642857142857142 * pow(x, 4.0))) + -0.5;
	} else {
		tmp = (x - sin(x)) / (x - tan(x));
	}
	return tmp;
}

Fortran

NOTE: x should be positive before calling this function
real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 0.0255d0) then
        tmp = ((x * (x * 0.225d0)) + ((-0.009642857142857142d0) * (x ** 4.0d0))) + (-0.5d0)
    else
        tmp = (x - sin(x)) / (x - tan(x))
    end if
    code = tmp
end function

Java

x = Math.abs(x);
public static double code(double x) {
	double tmp;
	if (x <= 0.0255) {
		tmp = ((x * (x * 0.225)) + (-0.009642857142857142 * Math.pow(x, 4.0))) + -0.5;
	} else {
		tmp = (x - Math.sin(x)) / (x - Math.tan(x));
	}
	return tmp;
}

Python

x = abs(x)
def code(x):
	tmp = 0
	if x <= 0.0255:
		tmp = ((x * (x * 0.225)) + (-0.009642857142857142 * math.pow(x, 4.0))) + -0.5
	else:
		tmp = (x - math.sin(x)) / (x - math.tan(x))
	return tmp

Julia

x = abs(x)
function code(x)
	tmp = 0.0
	if (x <= 0.0255)
		tmp = Float64(Float64(Float64(x * Float64(x * 0.225)) + Float64(-0.009642857142857142 * (x ^ 4.0))) + -0.5);
	else
		tmp = Float64(Float64(x - sin(x)) / Float64(x - tan(x)));
	end
	return tmp
end

MATLAB

x = abs(x)
function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 0.0255)
		tmp = ((x * (x * 0.225)) + (-0.009642857142857142 * (x ^ 4.0))) + -0.5;
	else
		tmp = (x - sin(x)) / (x - tan(x));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x should be positive before calling this function
code[x_] := If[LessEqual[x, 0.0255], N[(N[(N[(x * N[(x * 0.225), $MachinePrecision]), $MachinePrecision] + N[(-0.009642857142857142 * N[Power[x, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -0.5), $MachinePrecision], N[(N[(x - N[Sin[x], $MachinePrecision]), $MachinePrecision] / N[(x - N[Tan[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 0.0254999999999999984

   1. Initial program 36.7%

      \[\frac{x - \sin x}{x - \tan x} \]

   2. Taylor expanded in x around 0 65.6%

      \[\leadsto \color{blue}{\left(-0.009642857142857142 \cdot {x}^{4} + 0.225 \cdot {x}^{2}\right) - 0.5} \]
   3. Step-by-step derivation

      1. sub-neg 65.6%

         \[\leadsto \color{blue}{\left(-0.009642857142857142 \cdot {x}^{4} + 0.225 \cdot {x}^{2}\right) + \left(-0.5\right)} \]

      2. fma-def 65.6%

         \[\leadsto \color{blue}{\mathsf{fma}\left(-0.009642857142857142, {x}^{4}, 0.225 \cdot {x}^{2}\right)} + \left(-0.5\right) \]

      3. unpow2 65.6%

         \[\leadsto \mathsf{fma}\left(-0.009642857142857142, {x}^{4}, 0.225 \cdot \color{blue}{\left(x \cdot x\right)}\right) + \left(-0.5\right) \]

      4. metadata-eval 65.6%

         \[\leadsto \mathsf{fma}\left(-0.009642857142857142, {x}^{4}, 0.225 \cdot \left(x \cdot x\right)\right) + \color{blue}{-0.5} \]

   4. Simplified 65.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.009642857142857142, {x}^{4}, 0.225 \cdot \left(x \cdot x\right)\right) + -0.5} \]
   5. Step-by-step derivation

      1. fma-udef 65.6%

         \[\leadsto \color{blue}{\left(-0.009642857142857142 \cdot {x}^{4} + 0.225 \cdot \left(x \cdot x\right)\right)} + -0.5 \]

      2. +-commutative 65.6%

         \[\leadsto \color{blue}{\left(0.225 \cdot \left(x \cdot x\right) + -0.009642857142857142 \cdot {x}^{4}\right)} + -0.5 \]

      3. *-commutative 65.6%

         \[\leadsto \left(\color{blue}{\left(x \cdot x\right) \cdot 0.225} + -0.009642857142857142 \cdot {x}^{4}\right) + -0.5 \]

      4. associate-*l* 65.6%

         \[\leadsto \left(\color{blue}{x \cdot \left(x \cdot 0.225\right)} + -0.009642857142857142 \cdot {x}^{4}\right) + -0.5 \]

   6. Applied egg-rr 65.6%

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot 0.225\right) + -0.009642857142857142 \cdot {x}^{4}\right)} + -0.5 \]

   if 0.0254999999999999984 < x

   1. Initial program 100.0%

      \[\frac{x - \sin x}{x - \tan x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 73.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 0.0255:\\ \;\;\;\;\left(x \cdot \left(x \cdot 0.225\right) + -0.009642857142857142 \cdot {x}^{4}\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x - \sin x}{x - \tan x}\\ \end{array} \]

Alternative 2: 98.9% accurate, 1.8× speedup

Math

\[\begin{array}{l} x = |x|\\ \\ \begin{array}{l} \mathbf{if}\;x \leq 2.8:\\ \;\;\;\;\left(x \cdot \left(x \cdot 0.225\right) + -0.009642857142857142 \cdot {x}^{4}\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x - \tan x}\\ \end{array} \end{array} \]

FPCore

NOTE: x should be positive before calling this function
(FPCore (x)
 :precision binary64
 (if (<= x 2.8)
   (+ (+ (* x (* x 0.225)) (* -0.009642857142857142 (pow x 4.0))) -0.5)
   (/ x (- x (tan x)))))

C

x = abs(x);
double code(double x) {
	double tmp;
	if (x <= 2.8) {
		tmp = ((x * (x * 0.225)) + (-0.009642857142857142 * pow(x, 4.0))) + -0.5;
	} else {
		tmp = x / (x - tan(x));
	}
	return tmp;
}

Fortran

NOTE: x should be positive before calling this function
real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 2.8d0) then
        tmp = ((x * (x * 0.225d0)) + ((-0.009642857142857142d0) * (x ** 4.0d0))) + (-0.5d0)
    else
        tmp = x / (x - tan(x))
    end if
    code = tmp
end function

Java

x = Math.abs(x);
public static double code(double x) {
	double tmp;
	if (x <= 2.8) {
		tmp = ((x * (x * 0.225)) + (-0.009642857142857142 * Math.pow(x, 4.0))) + -0.5;
	} else {
		tmp = x / (x - Math.tan(x));
	}
	return tmp;
}

Python

x = abs(x)
def code(x):
	tmp = 0
	if x <= 2.8:
		tmp = ((x * (x * 0.225)) + (-0.009642857142857142 * math.pow(x, 4.0))) + -0.5
	else:
		tmp = x / (x - math.tan(x))
	return tmp

Julia

x = abs(x)
function code(x)
	tmp = 0.0
	if (x <= 2.8)
		tmp = Float64(Float64(Float64(x * Float64(x * 0.225)) + Float64(-0.009642857142857142 * (x ^ 4.0))) + -0.5);
	else
		tmp = Float64(x / Float64(x - tan(x)));
	end
	return tmp
end

MATLAB

x = abs(x)
function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 2.8)
		tmp = ((x * (x * 0.225)) + (-0.009642857142857142 * (x ^ 4.0))) + -0.5;
	else
		tmp = x / (x - tan(x));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x should be positive before calling this function
code[x_] := If[LessEqual[x, 2.8], N[(N[(N[(x * N[(x * 0.225), $MachinePrecision]), $MachinePrecision] + N[(-0.009642857142857142 * N[Power[x, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -0.5), $MachinePrecision], N[(x / N[(x - N[Tan[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 2.7999999999999998

   1. Initial program 36.7%

      \[\frac{x - \sin x}{x - \tan x} \]

   2. Taylor expanded in x around 0 65.6%

      \[\leadsto \color{blue}{\left(-0.009642857142857142 \cdot {x}^{4} + 0.225 \cdot {x}^{2}\right) - 0.5} \]
   3. Step-by-step derivation

      1. sub-neg 65.6%

         \[\leadsto \color{blue}{\left(-0.009642857142857142 \cdot {x}^{4} + 0.225 \cdot {x}^{2}\right) + \left(-0.5\right)} \]

      2. fma-def 65.6%

         \[\leadsto \color{blue}{\mathsf{fma}\left(-0.009642857142857142, {x}^{4}, 0.225 \cdot {x}^{2}\right)} + \left(-0.5\right) \]

      3. unpow2 65.6%

         \[\leadsto \mathsf{fma}\left(-0.009642857142857142, {x}^{4}, 0.225 \cdot \color{blue}{\left(x \cdot x\right)}\right) + \left(-0.5\right) \]

      4. metadata-eval 65.6%

         \[\leadsto \mathsf{fma}\left(-0.009642857142857142, {x}^{4}, 0.225 \cdot \left(x \cdot x\right)\right) + \color{blue}{-0.5} \]

   4. Simplified 65.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.009642857142857142, {x}^{4}, 0.225 \cdot \left(x \cdot x\right)\right) + -0.5} \]
   5. Step-by-step derivation

      1. fma-udef 65.6%

         \[\leadsto \color{blue}{\left(-0.009642857142857142 \cdot {x}^{4} + 0.225 \cdot \left(x \cdot x\right)\right)} + -0.5 \]

      2. +-commutative 65.6%

         \[\leadsto \color{blue}{\left(0.225 \cdot \left(x \cdot x\right) + -0.009642857142857142 \cdot {x}^{4}\right)} + -0.5 \]

      3. *-commutative 65.6%

         \[\leadsto \left(\color{blue}{\left(x \cdot x\right) \cdot 0.225} + -0.009642857142857142 \cdot {x}^{4}\right) + -0.5 \]

      4. associate-*l* 65.6%

         \[\leadsto \left(\color{blue}{x \cdot \left(x \cdot 0.225\right)} + -0.009642857142857142 \cdot {x}^{4}\right) + -0.5 \]

   6. Applied egg-rr 65.6%

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot 0.225\right) + -0.009642857142857142 \cdot {x}^{4}\right)} + -0.5 \]

   if 2.7999999999999998 < x

   1. Initial program 100.0%

      \[\frac{x - \sin x}{x - \tan x} \]

   2. Taylor expanded in x around inf 96.9%

      \[\leadsto \frac{\color{blue}{x}}{x - \tan x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 73.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 2.8:\\ \;\;\;\;\left(x \cdot \left(x \cdot 0.225\right) + -0.009642857142857142 \cdot {x}^{4}\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x - \tan x}\\ \end{array} \]

Alternative 3: 98.8% accurate, 1.9× speedup

Math

\[\begin{array}{l} x = |x|\\ \\ \begin{array}{l} \mathbf{if}\;x \leq 2.3:\\ \;\;\;\;x \cdot \left(x \cdot 0.225\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x - \tan x}\\ \end{array} \end{array} \]

FPCore

NOTE: x should be positive before calling this function
(FPCore (x)
 :precision binary64
 (if (<= x 2.3) (+ (* x (* x 0.225)) -0.5) (/ x (- x (tan x)))))

C

x = abs(x);
double code(double x) {
	double tmp;
	if (x <= 2.3) {
		tmp = (x * (x * 0.225)) + -0.5;
	} else {
		tmp = x / (x - tan(x));
	}
	return tmp;
}

Fortran

NOTE: x should be positive before calling this function
real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 2.3d0) then
        tmp = (x * (x * 0.225d0)) + (-0.5d0)
    else
        tmp = x / (x - tan(x))
    end if
    code = tmp
end function

Java

x = Math.abs(x);
public static double code(double x) {
	double tmp;
	if (x <= 2.3) {
		tmp = (x * (x * 0.225)) + -0.5;
	} else {
		tmp = x / (x - Math.tan(x));
	}
	return tmp;
}

Python

x = abs(x)
def code(x):
	tmp = 0
	if x <= 2.3:
		tmp = (x * (x * 0.225)) + -0.5
	else:
		tmp = x / (x - math.tan(x))
	return tmp

Julia

x = abs(x)
function code(x)
	tmp = 0.0
	if (x <= 2.3)
		tmp = Float64(Float64(x * Float64(x * 0.225)) + -0.5);
	else
		tmp = Float64(x / Float64(x - tan(x)));
	end
	return tmp
end

MATLAB

x = abs(x)
function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 2.3)
		tmp = (x * (x * 0.225)) + -0.5;
	else
		tmp = x / (x - tan(x));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x should be positive before calling this function
code[x_] := If[LessEqual[x, 2.3], N[(N[(x * N[(x * 0.225), $MachinePrecision]), $MachinePrecision] + -0.5), $MachinePrecision], N[(x / N[(x - N[Tan[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 2.2999999999999998

   1. Initial program 36.7%

      \[\frac{x - \sin x}{x - \tan x} \]

   2. Taylor expanded in x around 0 66.9%

      \[\leadsto \color{blue}{0.225 \cdot {x}^{2} - 0.5} \]
   3. Step-by-step derivation

      1. fma-neg 66.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(0.225, {x}^{2}, -0.5\right)} \]

      2. unpow2 66.9%

         \[\leadsto \mathsf{fma}\left(0.225, \color{blue}{x \cdot x}, -0.5\right) \]

      3. metadata-eval 66.9%

         \[\leadsto \mathsf{fma}\left(0.225, x \cdot x, \color{blue}{-0.5}\right) \]

   4. Simplified 66.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(0.225, x \cdot x, -0.5\right)} \]
   5. Step-by-step derivation

      1. fma-udef 66.9%

         \[\leadsto \color{blue}{0.225 \cdot \left(x \cdot x\right) + -0.5} \]

      2. *-commutative 66.9%

         \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot 0.225} + -0.5 \]

      3. associate-*l* 66.9%

         \[\leadsto \color{blue}{x \cdot \left(x \cdot 0.225\right)} + -0.5 \]

   6. Applied egg-rr 66.9%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot 0.225\right) + -0.5} \]

   if 2.2999999999999998 < x

   1. Initial program 100.0%

      \[\frac{x - \sin x}{x - \tan x} \]

   2. Taylor expanded in x around inf 96.9%

      \[\leadsto \frac{\color{blue}{x}}{x - \tan x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 74.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 2.3:\\ \;\;\;\;x \cdot \left(x \cdot 0.225\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x - \tan x}\\ \end{array} \]

Alternative 4: 98.8% accurate, 22.8× speedup

Math

\[\begin{array}{l} x = |x|\\ \\ \begin{array}{l} \mathbf{if}\;x \leq 2.6:\\ \;\;\;\;x \cdot \left(x \cdot 0.225\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

NOTE: x should be positive before calling this function
(FPCore (x) :precision binary64 (if (<= x 2.6) (+ (* x (* x 0.225)) -0.5) 1.0))

C

x = abs(x);
double code(double x) {
	double tmp;
	if (x <= 2.6) {
		tmp = (x * (x * 0.225)) + -0.5;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

NOTE: x should be positive before calling this function
real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 2.6d0) then
        tmp = (x * (x * 0.225d0)) + (-0.5d0)
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

x = Math.abs(x);
public static double code(double x) {
	double tmp;
	if (x <= 2.6) {
		tmp = (x * (x * 0.225)) + -0.5;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

x = abs(x)
def code(x):
	tmp = 0
	if x <= 2.6:
		tmp = (x * (x * 0.225)) + -0.5
	else:
		tmp = 1.0
	return tmp

Julia

x = abs(x)
function code(x)
	tmp = 0.0
	if (x <= 2.6)
		tmp = Float64(Float64(x * Float64(x * 0.225)) + -0.5);
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

x = abs(x)
function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 2.6)
		tmp = (x * (x * 0.225)) + -0.5;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x should be positive before calling this function
code[x_] := If[LessEqual[x, 2.6], N[(N[(x * N[(x * 0.225), $MachinePrecision]), $MachinePrecision] + -0.5), $MachinePrecision], 1.0]

Derivation

1. Split input into 2 regimes

2. if x < 2.60000000000000009

   1. Initial program 36.7%

      \[\frac{x - \sin x}{x - \tan x} \]

   2. Taylor expanded in x around 0 66.9%

      \[\leadsto \color{blue}{0.225 \cdot {x}^{2} - 0.5} \]
   3. Step-by-step derivation

      1. fma-neg 66.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(0.225, {x}^{2}, -0.5\right)} \]

      2. unpow2 66.9%

         \[\leadsto \mathsf{fma}\left(0.225, \color{blue}{x \cdot x}, -0.5\right) \]

      3. metadata-eval 66.9%

         \[\leadsto \mathsf{fma}\left(0.225, x \cdot x, \color{blue}{-0.5}\right) \]

   4. Simplified 66.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(0.225, x \cdot x, -0.5\right)} \]
   5. Step-by-step derivation

      1. fma-udef 66.9%

         \[\leadsto \color{blue}{0.225 \cdot \left(x \cdot x\right) + -0.5} \]

      2. *-commutative 66.9%

         \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot 0.225} + -0.5 \]

      3. associate-*l* 66.9%

         \[\leadsto \color{blue}{x \cdot \left(x \cdot 0.225\right)} + -0.5 \]

   6. Applied egg-rr 66.9%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot 0.225\right) + -0.5} \]

   if 2.60000000000000009 < x

   1. Initial program 100.0%

      \[\frac{x - \sin x}{x - \tan x} \]

   2. Taylor expanded in x around inf 96.9%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 74.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 2.6:\\ \;\;\;\;x \cdot \left(x \cdot 0.225\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 5: 98.5% accurate, 67.6× speedup

Math

\[\begin{array}{l} x = |x|\\ \\ \begin{array}{l} \mathbf{if}\;x \leq 1.6:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

NOTE: x should be positive before calling this function
(FPCore (x) :precision binary64 (if (<= x 1.6) -0.5 1.0))

C

x = abs(x);
double code(double x) {
	double tmp;
	if (x <= 1.6) {
		tmp = -0.5;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

NOTE: x should be positive before calling this function
real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 1.6d0) then
        tmp = -0.5d0
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

x = Math.abs(x);
public static double code(double x) {
	double tmp;
	if (x <= 1.6) {
		tmp = -0.5;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

x = abs(x)
def code(x):
	tmp = 0
	if x <= 1.6:
		tmp = -0.5
	else:
		tmp = 1.0
	return tmp

Julia

x = abs(x)
function code(x)
	tmp = 0.0
	if (x <= 1.6)
		tmp = -0.5;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

x = abs(x)
function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 1.6)
		tmp = -0.5;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x should be positive before calling this function
code[x_] := If[LessEqual[x, 1.6], -0.5, 1.0]

Derivation

1. Split input into 2 regimes

2. if x < 1.6000000000000001

   1. Initial program 36.7%

      \[\frac{x - \sin x}{x - \tan x} \]

   2. Taylor expanded in x around 0 65.0%

      \[\leadsto \color{blue}{-0.5} \]

   if 1.6000000000000001 < x

   1. Initial program 100.0%

      \[\frac{x - \sin x}{x - \tan x} \]

   2. Taylor expanded in x around inf 96.9%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 72.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 1.6:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 6: 51.4% accurate, 207.0× speedup

Math

\[\begin{array}{l} x = |x|\\ \\ -0.5 \end{array} \]

FPCore

NOTE: x should be positive before calling this function
(FPCore (x) :precision binary64 -0.5)

C

x = abs(x);
double code(double x) {
	return -0.5;
}

Fortran

NOTE: x should be positive before calling this function
real(8) function code(x)
    real(8), intent (in) :: x
    code = -0.5d0
end function

Java

x = Math.abs(x);
public static double code(double x) {
	return -0.5;
}

Python

x = abs(x)
def code(x):
	return -0.5

Julia

x = abs(x)
function code(x)
	return -0.5
end

MATLAB

x = abs(x)
function tmp = code(x)
	tmp = -0.5;
end

Wolfram

NOTE: x should be positive before calling this function
code[x_] := -0.5

Derivation

1. Initial program 52.0%

   \[\frac{x - \sin x}{x - \tan x} \]

2. Taylor expanded in x around 0 49.7%

   \[\leadsto \color{blue}{-0.5} \]

3. Final simplification 49.7%

   \[\leadsto -0.5 \]

Reproduce

herbie shell --seed 2023275 
(FPCore (x)
  :name "sintan (problem 3.4.5)"
  :precision binary64
  (/ (- x (sin x)) (- x (tan x))))