Diagrams.Tangent:$catParam from diagrams-lib-1.3.0.3, E

Percentage Accurate: 99.7% → 99.8%

Time: 5.1s

Alternatives: 7

Speedup: 1.3×

• 24.6% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(3 \cdot \left(2 - x \cdot 3\right)\right) \cdot x \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (* 3.0 (- 2.0 (* x 3.0))) x))

C

double code(double x) {
	return (3.0 * (2.0 - (x * 3.0))) * x;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (3.0d0 * (2.0d0 - (x * 3.0d0))) * x
end function

Java

public static double code(double x) {
	return (3.0 * (2.0 - (x * 3.0))) * x;
}

Python

def code(x):
	return (3.0 * (2.0 - (x * 3.0))) * x

Julia

function code(x)
	return Float64(Float64(3.0 * Float64(2.0 - Float64(x * 3.0))) * x)
end

MATLAB

function tmp = code(x)
	tmp = (3.0 * (2.0 - (x * 3.0))) * x;
end

Wolfram

code[x_] := N[(N[(3.0 * N[(2.0 - N[(x * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]

Initial Program: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(3 \cdot \left(2 - x \cdot 3\right)\right) \cdot x \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (* 3.0 (- 2.0 (* x 3.0))) x))

C

double code(double x) {
	return (3.0 * (2.0 - (x * 3.0))) * x;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (3.0d0 * (2.0d0 - (x * 3.0d0))) * x
end function

Java

public static double code(double x) {
	return (3.0 * (2.0 - (x * 3.0))) * x;
}

Python

def code(x):
	return (3.0 * (2.0 - (x * 3.0))) * x

Julia

function code(x)
	return Float64(Float64(3.0 * Float64(2.0 - Float64(x * 3.0))) * x)
end

MATLAB

function tmp = code(x)
	tmp = (3.0 * (2.0 - (x * 3.0))) * x;
end

Wolfram

code[x_] := N[(N[(3.0 * N[(2.0 - N[(x * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]

Alternative 1: 99.8% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, 6, x \cdot \left(x \cdot -9\right)\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (fma x 6.0 (* x (* x -9.0))))

C

double code(double x) {
	return fma(x, 6.0, (x * (x * -9.0)));
}

Julia

function code(x)
	return fma(x, 6.0, Float64(x * Float64(x * -9.0)))
end

Wolfram

code[x_] := N[(x * 6.0 + N[(x * N[(x * -9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left(3 \cdot \left(2 - x \cdot 3\right)\right) \cdot x \]
2. Step-by-step derivation

   1. *-commutative 99.8%

      \[\leadsto \color{blue}{x \cdot \left(3 \cdot \left(2 - x \cdot 3\right)\right)} \]

   2. sub-neg 99.8%

      \[\leadsto x \cdot \left(3 \cdot \color{blue}{\left(2 + \left(-x \cdot 3\right)\right)}\right) \]

   3. distribute-rgt-in 99.8%

      \[\leadsto x \cdot \color{blue}{\left(2 \cdot 3 + \left(-x \cdot 3\right) \cdot 3\right)} \]

   4. metadata-eval 99.8%

      \[\leadsto x \cdot \left(\color{blue}{6} + \left(-x \cdot 3\right) \cdot 3\right) \]

   5. distribute-rgt-neg-in 99.8%

      \[\leadsto x \cdot \left(6 + \color{blue}{\left(x \cdot \left(-3\right)\right)} \cdot 3\right) \]

   6. associate-*l* 99.8%

      \[\leadsto x \cdot \left(6 + \color{blue}{x \cdot \left(\left(-3\right) \cdot 3\right)}\right) \]

   7. metadata-eval 99.8%

      \[\leadsto x \cdot \left(6 + x \cdot \left(\color{blue}{-3} \cdot 3\right)\right) \]

   8. metadata-eval 99.8%

      \[\leadsto x \cdot \left(6 + x \cdot \color{blue}{-9}\right) \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \left(6 + x \cdot -9\right)} \]
4. Step-by-step derivation

   1. distribute-lft-in 99.8%

      \[\leadsto \color{blue}{x \cdot 6 + x \cdot \left(x \cdot -9\right)} \]

   2. fma-def 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, 6, x \cdot \left(x \cdot -9\right)\right)} \]

5. Applied egg-rr 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, 6, x \cdot \left(x \cdot -9\right)\right)} \]

6. Final simplification 99.9%

   \[\leadsto \mathsf{fma}\left(x, 6, x \cdot \left(x \cdot -9\right)\right) \]

Alternative 2: 97.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.65 \lor \neg \left(x \leq 0.68\right):\\ \;\;\;\;-9 \cdot \left(x \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot 6\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= x -0.65) (not (<= x 0.68))) (* -9.0 (* x x)) (* x 6.0)))

C

double code(double x) {
	double tmp;
	if ((x <= -0.65) || !(x <= 0.68)) {
		tmp = -9.0 * (x * x);
	} else {
		tmp = x * 6.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-0.65d0)) .or. (.not. (x <= 0.68d0))) then
        tmp = (-9.0d0) * (x * x)
    else
        tmp = x * 6.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x <= -0.65) || !(x <= 0.68)) {
		tmp = -9.0 * (x * x);
	} else {
		tmp = x * 6.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x <= -0.65) or not (x <= 0.68):
		tmp = -9.0 * (x * x)
	else:
		tmp = x * 6.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((x <= -0.65) || !(x <= 0.68))
		tmp = Float64(-9.0 * Float64(x * x));
	else
		tmp = Float64(x * 6.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x <= -0.65) || ~((x <= 0.68)))
		tmp = -9.0 * (x * x);
	else
		tmp = x * 6.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[x, -0.65], N[Not[LessEqual[x, 0.68]], $MachinePrecision]], N[(-9.0 * N[(x * x), $MachinePrecision]), $MachinePrecision], N[(x * 6.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -0.650000000000000022 or 0.680000000000000049 < x

   1. Initial program 99.8%

      \[\left(3 \cdot \left(2 - x \cdot 3\right)\right) \cdot x \]
   2. Step-by-step derivation

      1. associate-*l* 99.8%

         \[\leadsto \color{blue}{3 \cdot \left(\left(2 - x \cdot 3\right) \cdot x\right)} \]

      2. *-commutative 99.8%

         \[\leadsto 3 \cdot \left(\left(2 - \color{blue}{3 \cdot x}\right) \cdot x\right) \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{3 \cdot \left(\left(2 - 3 \cdot x\right) \cdot x\right)} \]

   4. Taylor expanded in x around inf 97.7%

      \[\leadsto \color{blue}{-9 \cdot {x}^{2}} \]
   5. Step-by-step derivation

      1. unpow2 97.7%

         \[\leadsto -9 \cdot \color{blue}{\left(x \cdot x\right)} \]

   6. Simplified 97.7%

      \[\leadsto \color{blue}{-9 \cdot \left(x \cdot x\right)} \]

   if -0.650000000000000022 < x < 0.680000000000000049

   1. Initial program 99.8%

      \[\left(3 \cdot \left(2 - x \cdot 3\right)\right) \cdot x \]
   2. Step-by-step derivation

      1. *-commutative 99.8%

         \[\leadsto \color{blue}{x \cdot \left(3 \cdot \left(2 - x \cdot 3\right)\right)} \]

      2. sub-neg 99.8%

         \[\leadsto x \cdot \left(3 \cdot \color{blue}{\left(2 + \left(-x \cdot 3\right)\right)}\right) \]

      3. distribute-rgt-in 99.8%

         \[\leadsto x \cdot \color{blue}{\left(2 \cdot 3 + \left(-x \cdot 3\right) \cdot 3\right)} \]

      4. metadata-eval 99.8%

         \[\leadsto x \cdot \left(\color{blue}{6} + \left(-x \cdot 3\right) \cdot 3\right) \]

      5. distribute-rgt-neg-in 99.8%

         \[\leadsto x \cdot \left(6 + \color{blue}{\left(x \cdot \left(-3\right)\right)} \cdot 3\right) \]

      6. associate-*l* 99.8%

         \[\leadsto x \cdot \left(6 + \color{blue}{x \cdot \left(\left(-3\right) \cdot 3\right)}\right) \]

      7. metadata-eval 99.8%

         \[\leadsto x \cdot \left(6 + x \cdot \left(\color{blue}{-3} \cdot 3\right)\right) \]

      8. metadata-eval 99.8%

         \[\leadsto x \cdot \left(6 + x \cdot \color{blue}{-9}\right) \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x \cdot \left(6 + x \cdot -9\right)} \]

   4. Taylor expanded in x around 0 97.1%

      \[\leadsto x \cdot \color{blue}{6} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.65 \lor \neg \left(x \leq 0.68\right):\\ \;\;\;\;-9 \cdot \left(x \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot 6\\ \end{array} \]

Alternative 3: 97.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.65 \lor \neg \left(x \leq 0.68\right):\\ \;\;\;\;x \cdot \left(x \cdot -9\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot 6\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= x -0.65) (not (<= x 0.68))) (* x (* x -9.0)) (* x 6.0)))

C

double code(double x) {
	double tmp;
	if ((x <= -0.65) || !(x <= 0.68)) {
		tmp = x * (x * -9.0);
	} else {
		tmp = x * 6.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-0.65d0)) .or. (.not. (x <= 0.68d0))) then
        tmp = x * (x * (-9.0d0))
    else
        tmp = x * 6.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x <= -0.65) || !(x <= 0.68)) {
		tmp = x * (x * -9.0);
	} else {
		tmp = x * 6.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x <= -0.65) or not (x <= 0.68):
		tmp = x * (x * -9.0)
	else:
		tmp = x * 6.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((x <= -0.65) || !(x <= 0.68))
		tmp = Float64(x * Float64(x * -9.0));
	else
		tmp = Float64(x * 6.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x <= -0.65) || ~((x <= 0.68)))
		tmp = x * (x * -9.0);
	else
		tmp = x * 6.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[x, -0.65], N[Not[LessEqual[x, 0.68]], $MachinePrecision]], N[(x * N[(x * -9.0), $MachinePrecision]), $MachinePrecision], N[(x * 6.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -0.650000000000000022 or 0.680000000000000049 < x

   1. Initial program 99.8%

      \[\left(3 \cdot \left(2 - x \cdot 3\right)\right) \cdot x \]
   2. Step-by-step derivation

      1. associate-*l* 99.8%

         \[\leadsto \color{blue}{3 \cdot \left(\left(2 - x \cdot 3\right) \cdot x\right)} \]

      2. *-commutative 99.8%

         \[\leadsto 3 \cdot \left(\left(2 - \color{blue}{3 \cdot x}\right) \cdot x\right) \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{3 \cdot \left(\left(2 - 3 \cdot x\right) \cdot x\right)} \]

   4. Taylor expanded in x around inf 97.7%

      \[\leadsto \color{blue}{-9 \cdot {x}^{2}} \]
   5. Step-by-step derivation

      1. unpow2 97.7%

         \[\leadsto -9 \cdot \color{blue}{\left(x \cdot x\right)} \]

      2. *-commutative 97.7%

         \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot -9} \]

      3. associate-*r* 97.8%

         \[\leadsto \color{blue}{x \cdot \left(x \cdot -9\right)} \]

   6. Simplified 97.8%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot -9\right)} \]

   if -0.650000000000000022 < x < 0.680000000000000049

   1. Initial program 99.8%

      \[\left(3 \cdot \left(2 - x \cdot 3\right)\right) \cdot x \]
   2. Step-by-step derivation

      1. *-commutative 99.8%

         \[\leadsto \color{blue}{x \cdot \left(3 \cdot \left(2 - x \cdot 3\right)\right)} \]

      2. sub-neg 99.8%

         \[\leadsto x \cdot \left(3 \cdot \color{blue}{\left(2 + \left(-x \cdot 3\right)\right)}\right) \]

      3. distribute-rgt-in 99.8%

         \[\leadsto x \cdot \color{blue}{\left(2 \cdot 3 + \left(-x \cdot 3\right) \cdot 3\right)} \]

      4. metadata-eval 99.8%

         \[\leadsto x \cdot \left(\color{blue}{6} + \left(-x \cdot 3\right) \cdot 3\right) \]

      5. distribute-rgt-neg-in 99.8%

         \[\leadsto x \cdot \left(6 + \color{blue}{\left(x \cdot \left(-3\right)\right)} \cdot 3\right) \]

      6. associate-*l* 99.8%

         \[\leadsto x \cdot \left(6 + \color{blue}{x \cdot \left(\left(-3\right) \cdot 3\right)}\right) \]

      7. metadata-eval 99.8%

         \[\leadsto x \cdot \left(6 + x \cdot \left(\color{blue}{-3} \cdot 3\right)\right) \]

      8. metadata-eval 99.8%

         \[\leadsto x \cdot \left(6 + x \cdot \color{blue}{-9}\right) \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x \cdot \left(6 + x \cdot -9\right)} \]

   4. Taylor expanded in x around 0 97.1%

      \[\leadsto x \cdot \color{blue}{6} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.65 \lor \neg \left(x \leq 0.68\right):\\ \;\;\;\;x \cdot \left(x \cdot -9\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot 6\\ \end{array} \]

Alternative 4: 99.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(6 + x \cdot -9\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x (+ 6.0 (* x -9.0))))

C

double code(double x) {
	return x * (6.0 + (x * -9.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x * (6.0d0 + (x * (-9.0d0)))
end function

Java

public static double code(double x) {
	return x * (6.0 + (x * -9.0));
}

Python

def code(x):
	return x * (6.0 + (x * -9.0))

Julia

function code(x)
	return Float64(x * Float64(6.0 + Float64(x * -9.0)))
end

MATLAB

function tmp = code(x)
	tmp = x * (6.0 + (x * -9.0));
end

Wolfram

code[x_] := N[(x * N[(6.0 + N[(x * -9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left(3 \cdot \left(2 - x \cdot 3\right)\right) \cdot x \]
2. Step-by-step derivation

   1. *-commutative 99.8%

      \[\leadsto \color{blue}{x \cdot \left(3 \cdot \left(2 - x \cdot 3\right)\right)} \]

   2. sub-neg 99.8%

      \[\leadsto x \cdot \left(3 \cdot \color{blue}{\left(2 + \left(-x \cdot 3\right)\right)}\right) \]

   3. distribute-rgt-in 99.8%

      \[\leadsto x \cdot \color{blue}{\left(2 \cdot 3 + \left(-x \cdot 3\right) \cdot 3\right)} \]

   4. metadata-eval 99.8%

      \[\leadsto x \cdot \left(\color{blue}{6} + \left(-x \cdot 3\right) \cdot 3\right) \]

   5. distribute-rgt-neg-in 99.8%

      \[\leadsto x \cdot \left(6 + \color{blue}{\left(x \cdot \left(-3\right)\right)} \cdot 3\right) \]

   6. associate-*l* 99.8%

      \[\leadsto x \cdot \left(6 + \color{blue}{x \cdot \left(\left(-3\right) \cdot 3\right)}\right) \]

   7. metadata-eval 99.8%

      \[\leadsto x \cdot \left(6 + x \cdot \left(\color{blue}{-3} \cdot 3\right)\right) \]

   8. metadata-eval 99.8%

      \[\leadsto x \cdot \left(6 + x \cdot \color{blue}{-9}\right) \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \left(6 + x \cdot -9\right)} \]

4. Final simplification 99.8%

   \[\leadsto x \cdot \left(6 + x \cdot -9\right) \]

Alternative 5: 3.8% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ x \cdot -6 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x -6.0))

C

double code(double x) {
	return x * -6.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x * (-6.0d0)
end function

Java

public static double code(double x) {
	return x * -6.0;
}

Python

def code(x):
	return x * -6.0

Julia

function code(x)
	return Float64(x * -6.0)
end

MATLAB

function tmp = code(x)
	tmp = x * -6.0;
end

Wolfram

code[x_] := N[(x * -6.0), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left(3 \cdot \left(2 - x \cdot 3\right)\right) \cdot x \]
2. Step-by-step derivation

   1. *-commutative 99.8%

      \[\leadsto \color{blue}{x \cdot \left(3 \cdot \left(2 - x \cdot 3\right)\right)} \]

   2. sub-neg 99.8%

      \[\leadsto x \cdot \left(3 \cdot \color{blue}{\left(2 + \left(-x \cdot 3\right)\right)}\right) \]

   3. distribute-rgt-in 99.8%

      \[\leadsto x \cdot \color{blue}{\left(2 \cdot 3 + \left(-x \cdot 3\right) \cdot 3\right)} \]

   4. metadata-eval 99.8%

      \[\leadsto x \cdot \left(\color{blue}{6} + \left(-x \cdot 3\right) \cdot 3\right) \]

   5. distribute-rgt-neg-in 99.8%

      \[\leadsto x \cdot \left(6 + \color{blue}{\left(x \cdot \left(-3\right)\right)} \cdot 3\right) \]

   6. associate-*l* 99.8%

      \[\leadsto x \cdot \left(6 + \color{blue}{x \cdot \left(\left(-3\right) \cdot 3\right)}\right) \]

   7. metadata-eval 99.8%

      \[\leadsto x \cdot \left(6 + x \cdot \left(\color{blue}{-3} \cdot 3\right)\right) \]

   8. metadata-eval 99.8%

      \[\leadsto x \cdot \left(6 + x \cdot \color{blue}{-9}\right) \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \left(6 + x \cdot -9\right)} \]
4. Step-by-step derivation

   1. *-commutative 99.8%

      \[\leadsto \color{blue}{\left(6 + x \cdot -9\right) \cdot x} \]

   2. flip-+ 99.8%

      \[\leadsto \color{blue}{\frac{6 \cdot 6 - \left(x \cdot -9\right) \cdot \left(x \cdot -9\right)}{6 - x \cdot -9}} \cdot x \]

   3. associate-*l/ 91.1%

      \[\leadsto \color{blue}{\frac{\left(6 \cdot 6 - \left(x \cdot -9\right) \cdot \left(x \cdot -9\right)\right) \cdot x}{6 - x \cdot -9}} \]

   4. metadata-eval 91.1%

      \[\leadsto \frac{\left(\color{blue}{36} - \left(x \cdot -9\right) \cdot \left(x \cdot -9\right)\right) \cdot x}{6 - x \cdot -9} \]

   5. pow2 91.1%

      \[\leadsto \frac{\left(36 - \color{blue}{{\left(x \cdot -9\right)}^{2}}\right) \cdot x}{6 - x \cdot -9} \]

5. Applied egg-rr 91.1%

   \[\leadsto \color{blue}{\frac{\left(36 - {\left(x \cdot -9\right)}^{2}\right) \cdot x}{6 - x \cdot -9}} \]

6. Taylor expanded in x around 0 52.4%

   \[\leadsto \frac{\color{blue}{36 \cdot x}}{6 - x \cdot -9} \]
7. Step-by-step derivation

   1. *-commutative 52.4%

      \[\leadsto \frac{\color{blue}{x \cdot 36}}{6 - x \cdot -9} \]

8. Simplified 52.4%

   \[\leadsto \frac{\color{blue}{x \cdot 36}}{6 - x \cdot -9} \]

9. Taylor expanded in x around 0 52.9%

   \[\leadsto \frac{x \cdot 36}{\color{blue}{6}} \]
10. Step-by-step derivation

    1. frac-2neg 52.9%

       \[\leadsto \color{blue}{\frac{-x \cdot 36}{-6}} \]

    2. metadata-eval 52.9%

       \[\leadsto \frac{-x \cdot 36}{\color{blue}{-6}} \]

    3. div-inv 52.8%

       \[\leadsto \color{blue}{\left(-x \cdot 36\right) \cdot \frac{1}{-6}} \]

    4. distribute-lft-neg-in 52.8%

       \[\leadsto \color{blue}{\left(\left(-x\right) \cdot 36\right)} \cdot \frac{1}{-6} \]

    5. add-sqr-sqrt 23.7%

       \[\leadsto \left(\color{blue}{\left(\sqrt{-x} \cdot \sqrt{-x}\right)} \cdot 36\right) \cdot \frac{1}{-6} \]

    6. sqrt-unprod 42.4%

       \[\leadsto \left(\color{blue}{\sqrt{\left(-x\right) \cdot \left(-x\right)}} \cdot 36\right) \cdot \frac{1}{-6} \]

    7. sqr-neg 42.4%

       \[\leadsto \left(\sqrt{\color{blue}{x \cdot x}} \cdot 36\right) \cdot \frac{1}{-6} \]

    8. sqrt-unprod 2.6%

       \[\leadsto \left(\color{blue}{\left(\sqrt{x} \cdot \sqrt{x}\right)} \cdot 36\right) \cdot \frac{1}{-6} \]

    9. add-sqr-sqrt 3.7%

       \[\leadsto \left(\color{blue}{x} \cdot 36\right) \cdot \frac{1}{-6} \]

    10. *-commutative 3.7%

        \[\leadsto \color{blue}{\left(36 \cdot x\right)} \cdot \frac{1}{-6} \]

    11. metadata-eval 3.7%

        \[\leadsto \left(36 \cdot x\right) \cdot \color{blue}{-0.16666666666666666} \]

11. Applied egg-rr 3.7%

    \[\leadsto \color{blue}{\left(36 \cdot x\right) \cdot -0.16666666666666666} \]
12. Step-by-step derivation

    1. *-commutative 3.7%

       \[\leadsto \color{blue}{\left(x \cdot 36\right)} \cdot -0.16666666666666666 \]

    2. associate-*l* 3.7%

       \[\leadsto \color{blue}{x \cdot \left(36 \cdot -0.16666666666666666\right)} \]

    3. metadata-eval 3.7%

       \[\leadsto x \cdot \color{blue}{-6} \]

13. Simplified 3.7%

    \[\leadsto \color{blue}{x \cdot -6} \]

14. Final simplification 3.7%

    \[\leadsto x \cdot -6 \]

Alternative 6: 51.2% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ x \cdot 6 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x 6.0))

C

double code(double x) {
	return x * 6.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x * 6.0d0
end function

Java

public static double code(double x) {
	return x * 6.0;
}

Python

def code(x):
	return x * 6.0

Julia

function code(x)
	return Float64(x * 6.0)
end

MATLAB

function tmp = code(x)
	tmp = x * 6.0;
end

Wolfram

code[x_] := N[(x * 6.0), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left(3 \cdot \left(2 - x \cdot 3\right)\right) \cdot x \]
2. Step-by-step derivation

   1. *-commutative 99.8%

      \[\leadsto \color{blue}{x \cdot \left(3 \cdot \left(2 - x \cdot 3\right)\right)} \]

   2. sub-neg 99.8%

      \[\leadsto x \cdot \left(3 \cdot \color{blue}{\left(2 + \left(-x \cdot 3\right)\right)}\right) \]

   3. distribute-rgt-in 99.8%

      \[\leadsto x \cdot \color{blue}{\left(2 \cdot 3 + \left(-x \cdot 3\right) \cdot 3\right)} \]

   4. metadata-eval 99.8%

      \[\leadsto x \cdot \left(\color{blue}{6} + \left(-x \cdot 3\right) \cdot 3\right) \]

   5. distribute-rgt-neg-in 99.8%

      \[\leadsto x \cdot \left(6 + \color{blue}{\left(x \cdot \left(-3\right)\right)} \cdot 3\right) \]

   6. associate-*l* 99.8%

      \[\leadsto x \cdot \left(6 + \color{blue}{x \cdot \left(\left(-3\right) \cdot 3\right)}\right) \]

   7. metadata-eval 99.8%

      \[\leadsto x \cdot \left(6 + x \cdot \left(\color{blue}{-3} \cdot 3\right)\right) \]

   8. metadata-eval 99.8%

      \[\leadsto x \cdot \left(6 + x \cdot \color{blue}{-9}\right) \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \left(6 + x \cdot -9\right)} \]

4. Taylor expanded in x around 0 53.0%

   \[\leadsto x \cdot \color{blue}{6} \]

5. Final simplification 53.0%

   \[\leadsto x \cdot 6 \]

Alternative 7: 2.4% accurate, 9.0× speedup

Math

\[\begin{array}{l} \\ 4 \end{array} \]

FPCore

(FPCore (x) :precision binary64 4.0)

C

double code(double x) {
	return 4.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 4.0d0
end function

Java

public static double code(double x) {
	return 4.0;
}

Python

def code(x):
	return 4.0

Julia

function code(x)
	return 4.0
end

MATLAB

function tmp = code(x)
	tmp = 4.0;
end

Wolfram

code[x_] := 4.0

Derivation

1. Initial program 99.8%

   \[\left(3 \cdot \left(2 - x \cdot 3\right)\right) \cdot x \]
2. Step-by-step derivation

   1. *-commutative 99.8%

      \[\leadsto \color{blue}{x \cdot \left(3 \cdot \left(2 - x \cdot 3\right)\right)} \]

   2. sub-neg 99.8%

      \[\leadsto x \cdot \left(3 \cdot \color{blue}{\left(2 + \left(-x \cdot 3\right)\right)}\right) \]

   3. distribute-rgt-in 99.8%

      \[\leadsto x \cdot \color{blue}{\left(2 \cdot 3 + \left(-x \cdot 3\right) \cdot 3\right)} \]

   4. metadata-eval 99.8%

      \[\leadsto x \cdot \left(\color{blue}{6} + \left(-x \cdot 3\right) \cdot 3\right) \]

   5. distribute-rgt-neg-in 99.8%

      \[\leadsto x \cdot \left(6 + \color{blue}{\left(x \cdot \left(-3\right)\right)} \cdot 3\right) \]

   6. associate-*l* 99.8%

      \[\leadsto x \cdot \left(6 + \color{blue}{x \cdot \left(\left(-3\right) \cdot 3\right)}\right) \]

   7. metadata-eval 99.8%

      \[\leadsto x \cdot \left(6 + x \cdot \left(\color{blue}{-3} \cdot 3\right)\right) \]

   8. metadata-eval 99.8%

      \[\leadsto x \cdot \left(6 + x \cdot \color{blue}{-9}\right) \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \left(6 + x \cdot -9\right)} \]
4. Step-by-step derivation

   1. *-commutative 99.8%

      \[\leadsto \color{blue}{\left(6 + x \cdot -9\right) \cdot x} \]

   2. flip-+ 99.8%

      \[\leadsto \color{blue}{\frac{6 \cdot 6 - \left(x \cdot -9\right) \cdot \left(x \cdot -9\right)}{6 - x \cdot -9}} \cdot x \]

   3. associate-*l/ 91.1%

      \[\leadsto \color{blue}{\frac{\left(6 \cdot 6 - \left(x \cdot -9\right) \cdot \left(x \cdot -9\right)\right) \cdot x}{6 - x \cdot -9}} \]

   4. metadata-eval 91.1%

      \[\leadsto \frac{\left(\color{blue}{36} - \left(x \cdot -9\right) \cdot \left(x \cdot -9\right)\right) \cdot x}{6 - x \cdot -9} \]

   5. pow2 91.1%

      \[\leadsto \frac{\left(36 - \color{blue}{{\left(x \cdot -9\right)}^{2}}\right) \cdot x}{6 - x \cdot -9} \]

5. Applied egg-rr 91.1%

   \[\leadsto \color{blue}{\frac{\left(36 - {\left(x \cdot -9\right)}^{2}\right) \cdot x}{6 - x \cdot -9}} \]

6. Taylor expanded in x around 0 52.4%

   \[\leadsto \frac{\color{blue}{36 \cdot x}}{6 - x \cdot -9} \]
7. Step-by-step derivation

   1. *-commutative 52.4%

      \[\leadsto \frac{\color{blue}{x \cdot 36}}{6 - x \cdot -9} \]

8. Simplified 52.4%

   \[\leadsto \frac{\color{blue}{x \cdot 36}}{6 - x \cdot -9} \]

9. Taylor expanded in x around inf 2.5%

   \[\leadsto \color{blue}{4} \]

10. Final simplification 2.5%

    \[\leadsto 4 \]

Developer target: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 6 \cdot x - 9 \cdot \left(x \cdot x\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- (* 6.0 x) (* 9.0 (* x x))))

C

double code(double x) {
	return (6.0 * x) - (9.0 * (x * x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (6.0d0 * x) - (9.0d0 * (x * x))
end function

Java

public static double code(double x) {
	return (6.0 * x) - (9.0 * (x * x));
}

Python

def code(x):
	return (6.0 * x) - (9.0 * (x * x))

Julia

function code(x)
	return Float64(Float64(6.0 * x) - Float64(9.0 * Float64(x * x)))
end

MATLAB

function tmp = code(x)
	tmp = (6.0 * x) - (9.0 * (x * x));
end

Wolfram

code[x_] := N[(N[(6.0 * x), $MachinePrecision] - N[(9.0 * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023215 
(FPCore (x)
  :name "Diagrams.Tangent:$catParam from diagrams-lib-1.3.0.3, E"
  :precision binary64

  :herbie-target
  (- (* 6.0 x) (* 9.0 (* x x)))

  (* (* 3.0 (- 2.0 (* x 3.0))) x))