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

Percentage Accurate: 99.8% → 99.9%

Time: 5.6s

Alternatives: 9

Speedup: 1.0×

• 25.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ 3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 3.0 (+ (- (* (* x 3.0) x) (* x 4.0)) 1.0)))

C

double code(double x) {
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 * ((((x * 3.0d0) * x) - (x * 4.0d0)) + 1.0d0)
end function

Java

public static double code(double x) {
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
}

Python

def code(x):
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0)

Julia

function code(x)
	return Float64(3.0 * Float64(Float64(Float64(Float64(x * 3.0) * x) - Float64(x * 4.0)) + 1.0))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
end

Wolfram

code[x_] := N[(3.0 * N[(N[(N[(N[(x * 3.0), $MachinePrecision] * x), $MachinePrecision] - N[(x * 4.0), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 3.0 (+ (- (* (* x 3.0) x) (* x 4.0)) 1.0)))

C

double code(double x) {
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 * ((((x * 3.0d0) * x) - (x * 4.0d0)) + 1.0d0)
end function

Java

public static double code(double x) {
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
}

Python

def code(x):
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0)

Julia

function code(x)
	return Float64(3.0 * Float64(Float64(Float64(Float64(x * 3.0) * x) - Float64(x * 4.0)) + 1.0))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
end

Wolfram

code[x_] := N[(3.0 * N[(N[(N[(N[(x * 3.0), $MachinePrecision] * x), $MachinePrecision] - N[(x * 4.0), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, \mathsf{fma}\left(x, 9, -12\right), 3\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (fma x (fma x 9.0 -12.0) 3.0))

C

double code(double x) {
	return fma(x, fma(x, 9.0, -12.0), 3.0);
}

Julia

function code(x)
	return fma(x, fma(x, 9.0, -12.0), 3.0)
end

Wolfram

code[x_] := N[(x * N[(x * 9.0 + -12.0), $MachinePrecision] + 3.0), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]
2. Step-by-step derivation

   1. *-commutative 99.8%

      \[\leadsto \color{blue}{\left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \cdot 3} \]

   2. distribute-lft1-in 99.8%

      \[\leadsto \color{blue}{\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) \cdot 3 + 3} \]

   3. *-commutative 99.8%

      \[\leadsto \left(\color{blue}{x \cdot \left(x \cdot 3\right)} - x \cdot 4\right) \cdot 3 + 3 \]

   4. distribute-lft-out-- 99.9%

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot 3 - 4\right)\right)} \cdot 3 + 3 \]

   5. associate-*l* 99.8%

      \[\leadsto \color{blue}{x \cdot \left(\left(x \cdot 3 - 4\right) \cdot 3\right)} + 3 \]

   6. fma-def 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, \left(x \cdot 3 - 4\right) \cdot 3, 3\right)} \]

   7. *-commutative 99.8%

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{3 \cdot \left(x \cdot 3 - 4\right)}, 3\right) \]

   8. sub-neg 99.8%

      \[\leadsto \mathsf{fma}\left(x, 3 \cdot \color{blue}{\left(x \cdot 3 + \left(-4\right)\right)}, 3\right) \]

   9. distribute-lft-in 99.8%

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{3 \cdot \left(x \cdot 3\right) + 3 \cdot \left(-4\right)}, 3\right) \]

   10. *-commutative 99.8%

       \[\leadsto \mathsf{fma}\left(x, \color{blue}{\left(x \cdot 3\right) \cdot 3} + 3 \cdot \left(-4\right), 3\right) \]

   11. associate-*l* 99.9%

       \[\leadsto \mathsf{fma}\left(x, \color{blue}{x \cdot \left(3 \cdot 3\right)} + 3 \cdot \left(-4\right), 3\right) \]

   12. fma-def 99.9%

       \[\leadsto \mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(x, 3 \cdot 3, 3 \cdot \left(-4\right)\right)}, 3\right) \]

   13. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(x, \mathsf{fma}\left(x, \color{blue}{9}, 3 \cdot \left(-4\right)\right), 3\right) \]

   14. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(x, \mathsf{fma}\left(x, 9, 3 \cdot \color{blue}{-4}\right), 3\right) \]

   15. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(x, \mathsf{fma}\left(x, 9, \color{blue}{-12}\right), 3\right) \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 9, -12\right), 3\right)} \]

4. Final simplification 99.9%

   \[\leadsto \mathsf{fma}\left(x, \mathsf{fma}\left(x, 9, -12\right), 3\right) \]

Alternative 2: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 3 \cdot \left(1 + \left(3 \cdot \left(x \cdot x\right) - x \cdot 4\right)\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 3.0 (+ 1.0 (- (* 3.0 (* x x)) (* x 4.0)))))

C

double code(double x) {
	return 3.0 * (1.0 + ((3.0 * (x * x)) - (x * 4.0)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 * (1.0d0 + ((3.0d0 * (x * x)) - (x * 4.0d0)))
end function

Java

public static double code(double x) {
	return 3.0 * (1.0 + ((3.0 * (x * x)) - (x * 4.0)));
}

Python

def code(x):
	return 3.0 * (1.0 + ((3.0 * (x * x)) - (x * 4.0)))

Julia

function code(x)
	return Float64(3.0 * Float64(1.0 + Float64(Float64(3.0 * Float64(x * x)) - Float64(x * 4.0))))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 * (1.0 + ((3.0 * (x * x)) - (x * 4.0)));
end

Wolfram

code[x_] := N[(3.0 * N[(1.0 + N[(N[(3.0 * N[(x * x), $MachinePrecision]), $MachinePrecision] - N[(x * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

2. Taylor expanded in x around 0 99.8%

   \[\leadsto 3 \cdot \left(\left(\color{blue}{3 \cdot {x}^{2}} - x \cdot 4\right) + 1\right) \]
3. Step-by-step derivation

   1. unpow2 99.8%

      \[\leadsto 3 \cdot \left(\left(3 \cdot \color{blue}{\left(x \cdot x\right)} - x \cdot 4\right) + 1\right) \]

4. Simplified 99.8%

   \[\leadsto 3 \cdot \left(\left(\color{blue}{3 \cdot \left(x \cdot x\right)} - x \cdot 4\right) + 1\right) \]

5. Final simplification 99.8%

   \[\leadsto 3 \cdot \left(1 + \left(3 \cdot \left(x \cdot x\right) - x \cdot 4\right)\right) \]

Alternative 3: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 3 \cdot \left(\left(x \cdot \left(x \cdot 3\right) - x \cdot 4\right) + 1\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 3.0 (+ (- (* x (* x 3.0)) (* x 4.0)) 1.0)))

C

double code(double x) {
	return 3.0 * (((x * (x * 3.0)) - (x * 4.0)) + 1.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 * (((x * (x * 3.0d0)) - (x * 4.0d0)) + 1.0d0)
end function

Java

public static double code(double x) {
	return 3.0 * (((x * (x * 3.0)) - (x * 4.0)) + 1.0);
}

Python

def code(x):
	return 3.0 * (((x * (x * 3.0)) - (x * 4.0)) + 1.0)

Julia

function code(x)
	return Float64(3.0 * Float64(Float64(Float64(x * Float64(x * 3.0)) - Float64(x * 4.0)) + 1.0))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 * (((x * (x * 3.0)) - (x * 4.0)) + 1.0);
end

Wolfram

code[x_] := N[(3.0 * N[(N[(N[(x * N[(x * 3.0), $MachinePrecision]), $MachinePrecision] - N[(x * 4.0), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

2. Final simplification 99.8%

   \[\leadsto 3 \cdot \left(\left(x \cdot \left(x \cdot 3\right) - x \cdot 4\right) + 1\right) \]

Alternative 4: 98.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.56 \lor \neg \left(x \leq 0.58\right):\\ \;\;\;\;x \cdot \left(-12 + x \cdot 9\right)\\ \mathbf{else}:\\ \;\;\;\;3 + x \cdot -12\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= x -0.56) (not (<= x 0.58)))
   (* x (+ -12.0 (* x 9.0)))
   (+ 3.0 (* x -12.0))))

C

double code(double x) {
	double tmp;
	if ((x <= -0.56) || !(x <= 0.58)) {
		tmp = x * (-12.0 + (x * 9.0));
	} else {
		tmp = 3.0 + (x * -12.0);
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-0.56d0)) .or. (.not. (x <= 0.58d0))) then
        tmp = x * ((-12.0d0) + (x * 9.0d0))
    else
        tmp = 3.0d0 + (x * (-12.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x <= -0.56) || !(x <= 0.58)) {
		tmp = x * (-12.0 + (x * 9.0));
	} else {
		tmp = 3.0 + (x * -12.0);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x <= -0.56) or not (x <= 0.58):
		tmp = x * (-12.0 + (x * 9.0))
	else:
		tmp = 3.0 + (x * -12.0)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((x <= -0.56) || !(x <= 0.58))
		tmp = Float64(x * Float64(-12.0 + Float64(x * 9.0)));
	else
		tmp = Float64(3.0 + Float64(x * -12.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x <= -0.56) || ~((x <= 0.58)))
		tmp = x * (-12.0 + (x * 9.0));
	else
		tmp = 3.0 + (x * -12.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[x, -0.56], N[Not[LessEqual[x, 0.58]], $MachinePrecision]], N[(x * N[(-12.0 + N[(x * 9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(3.0 + N[(x * -12.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -0.56000000000000005 or 0.57999999999999996 < x

   1. Initial program 99.7%

      \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

   2. Taylor expanded in x around inf 97.3%

      \[\leadsto \color{blue}{-12 \cdot x + 9 \cdot {x}^{2}} \]
   3. Step-by-step derivation

      1. unpow2 97.3%

         \[\leadsto -12 \cdot x + 9 \cdot \color{blue}{\left(x \cdot x\right)} \]

      2. associate-*r* 97.3%

         \[\leadsto -12 \cdot x + \color{blue}{\left(9 \cdot x\right) \cdot x} \]

      3. distribute-rgt-out 98.0%

         \[\leadsto \color{blue}{x \cdot \left(-12 + 9 \cdot x\right)} \]

      4. *-commutative 98.0%

         \[\leadsto x \cdot \left(-12 + \color{blue}{x \cdot 9}\right) \]

      5. +-commutative 98.0%

         \[\leadsto x \cdot \color{blue}{\left(x \cdot 9 + -12\right)} \]

      6. fma-udef 98.0%

         \[\leadsto x \cdot \color{blue}{\mathsf{fma}\left(x, 9, -12\right)} \]

   4. Simplified 98.0%

      \[\leadsto \color{blue}{x \cdot \mathsf{fma}\left(x, 9, -12\right)} \]
   5. Step-by-step derivation

      1. fma-udef 98.0%

         \[\leadsto x \cdot \color{blue}{\left(x \cdot 9 + -12\right)} \]

      2. distribute-lft-in 97.3%

         \[\leadsto \color{blue}{x \cdot \left(x \cdot 9\right) + x \cdot -12} \]

      3. associate-*l* 97.3%

         \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot 9} + x \cdot -12 \]

      4. *-commutative 97.3%

         \[\leadsto \left(x \cdot x\right) \cdot 9 + \color{blue}{-12 \cdot x} \]

      5. fma-def 97.3%

         \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, 9, -12 \cdot x\right)} \]

      6. *-commutative 97.3%

         \[\leadsto \mathsf{fma}\left(x \cdot x, 9, \color{blue}{x \cdot -12}\right) \]

   6. Applied egg-rr 97.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, 9, x \cdot -12\right)} \]
   7. Step-by-step derivation

      1. fma-udef 97.3%

         \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot 9 + x \cdot -12} \]

      2. associate-*r* 97.3%

         \[\leadsto \color{blue}{x \cdot \left(x \cdot 9\right)} + x \cdot -12 \]

      3. distribute-lft-out 98.0%

         \[\leadsto \color{blue}{x \cdot \left(x \cdot 9 + -12\right)} \]

   8. Applied egg-rr 98.0%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot 9 + -12\right)} \]

   if -0.56000000000000005 < x < 0.57999999999999996

   1. Initial program 100.0%

      \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

   2. Taylor expanded in x around 0 99.8%

      \[\leadsto \color{blue}{-12 \cdot x + 3} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.56 \lor \neg \left(x \leq 0.58\right):\\ \;\;\;\;x \cdot \left(-12 + x \cdot 9\right)\\ \mathbf{else}:\\ \;\;\;\;3 + x \cdot -12\\ \end{array} \]

Alternative 5: 97.6% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.56 \lor \neg \left(x \leq 1.7\right):\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;3\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= x -0.56) (not (<= x 1.7))) (* 9.0 (* x x)) 3.0))

C

double code(double x) {
	double tmp;
	if ((x <= -0.56) || !(x <= 1.7)) {
		tmp = 9.0 * (x * x);
	} else {
		tmp = 3.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-0.56d0)) .or. (.not. (x <= 1.7d0))) then
        tmp = 9.0d0 * (x * x)
    else
        tmp = 3.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x <= -0.56) || !(x <= 1.7)) {
		tmp = 9.0 * (x * x);
	} else {
		tmp = 3.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x <= -0.56) or not (x <= 1.7):
		tmp = 9.0 * (x * x)
	else:
		tmp = 3.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((x <= -0.56) || !(x <= 1.7))
		tmp = Float64(9.0 * Float64(x * x));
	else
		tmp = 3.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x <= -0.56) || ~((x <= 1.7)))
		tmp = 9.0 * (x * x);
	else
		tmp = 3.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[x, -0.56], N[Not[LessEqual[x, 1.7]], $MachinePrecision]], N[(9.0 * N[(x * x), $MachinePrecision]), $MachinePrecision], 3.0]

Derivation

1. Split input into 2 regimes

2. if x < -0.56000000000000005 or 1.69999999999999996 < x

   1. Initial program 99.7%

      \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

   2. Taylor expanded in x around inf 95.8%

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

      1. unpow2 95.8%

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

   4. Simplified 95.8%

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

   if -0.56000000000000005 < x < 1.69999999999999996

   1. Initial program 100.0%

      \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

   2. Taylor expanded in x around 0 99.1%

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

4. Final simplification 97.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.56 \lor \neg \left(x \leq 1.7\right):\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;3\\ \end{array} \]

Alternative 6: 97.6% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.56:\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \mathbf{elif}\;x \leq 1.7:\\ \;\;\;\;3\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(x \cdot 9\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -0.56) (* 9.0 (* x x)) (if (<= x 1.7) 3.0 (* x (* x 9.0)))))

C

double code(double x) {
	double tmp;
	if (x <= -0.56) {
		tmp = 9.0 * (x * x);
	} else if (x <= 1.7) {
		tmp = 3.0;
	} else {
		tmp = x * (x * 9.0);
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-0.56d0)) then
        tmp = 9.0d0 * (x * x)
    else if (x <= 1.7d0) then
        tmp = 3.0d0
    else
        tmp = x * (x * 9.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -0.56) {
		tmp = 9.0 * (x * x);
	} else if (x <= 1.7) {
		tmp = 3.0;
	} else {
		tmp = x * (x * 9.0);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -0.56:
		tmp = 9.0 * (x * x)
	elif x <= 1.7:
		tmp = 3.0
	else:
		tmp = x * (x * 9.0)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -0.56)
		tmp = Float64(9.0 * Float64(x * x));
	elseif (x <= 1.7)
		tmp = 3.0;
	else
		tmp = Float64(x * Float64(x * 9.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -0.56)
		tmp = 9.0 * (x * x);
	elseif (x <= 1.7)
		tmp = 3.0;
	else
		tmp = x * (x * 9.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -0.56], N[(9.0 * N[(x * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.7], 3.0, N[(x * N[(x * 9.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.56000000000000005

   1. Initial program 99.7%

      \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

   2. Taylor expanded in x around inf 96.5%

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

      1. unpow2 96.5%

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

   4. Simplified 96.5%

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

   if -0.56000000000000005 < x < 1.69999999999999996

   1. Initial program 100.0%

      \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

   2. Taylor expanded in x around 0 99.1%

      \[\leadsto \color{blue}{3} \]

   if 1.69999999999999996 < x

   1. Initial program 99.7%

      \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

   2. Taylor expanded in x around inf 95.1%

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

      1. unpow2 95.1%

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

      2. *-commutative 95.1%

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

      3. associate-*l* 95.2%

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

   4. Simplified 95.2%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot 9\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 97.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.56:\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \mathbf{elif}\;x \leq 1.7:\\ \;\;\;\;3\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(x \cdot 9\right)\\ \end{array} \]

Alternative 7: 98.2% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.55:\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;3 + x \cdot -12\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(x \cdot 9\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.55)
   (* 9.0 (* x x))
   (if (<= x 1.0) (+ 3.0 (* x -12.0)) (* x (* x 9.0)))))

C

double code(double x) {
	double tmp;
	if (x <= -1.55) {
		tmp = 9.0 * (x * x);
	} else if (x <= 1.0) {
		tmp = 3.0 + (x * -12.0);
	} else {
		tmp = x * (x * 9.0);
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-1.55d0)) then
        tmp = 9.0d0 * (x * x)
    else if (x <= 1.0d0) then
        tmp = 3.0d0 + (x * (-12.0d0))
    else
        tmp = x * (x * 9.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -1.55) {
		tmp = 9.0 * (x * x);
	} else if (x <= 1.0) {
		tmp = 3.0 + (x * -12.0);
	} else {
		tmp = x * (x * 9.0);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -1.55:
		tmp = 9.0 * (x * x)
	elif x <= 1.0:
		tmp = 3.0 + (x * -12.0)
	else:
		tmp = x * (x * 9.0)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -1.55)
		tmp = Float64(9.0 * Float64(x * x));
	elseif (x <= 1.0)
		tmp = Float64(3.0 + Float64(x * -12.0));
	else
		tmp = Float64(x * Float64(x * 9.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -1.55)
		tmp = 9.0 * (x * x);
	elseif (x <= 1.0)
		tmp = 3.0 + (x * -12.0);
	else
		tmp = x * (x * 9.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -1.55], N[(9.0 * N[(x * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.0], N[(3.0 + N[(x * -12.0), $MachinePrecision]), $MachinePrecision], N[(x * N[(x * 9.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.55000000000000004

   1. Initial program 99.7%

      \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

   2. Taylor expanded in x around inf 97.6%

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

      1. unpow2 97.6%

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

   4. Simplified 97.6%

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

   if -1.55000000000000004 < x < 1

   1. Initial program 100.0%

      \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

   2. Taylor expanded in x around 0 99.2%

      \[\leadsto \color{blue}{-12 \cdot x + 3} \]

   if 1 < x

   1. Initial program 99.7%

      \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

   2. Taylor expanded in x around inf 95.1%

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

      1. unpow2 95.1%

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

      2. *-commutative 95.1%

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

      3. associate-*l* 95.2%

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

   4. Simplified 95.2%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot 9\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 97.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.55:\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;3 + x \cdot -12\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(x \cdot 9\right)\\ \end{array} \]

Alternative 8: 97.6% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ 3 + x \cdot \left(x \cdot 9\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ 3.0 (* x (* x 9.0))))

C

double code(double x) {
	return 3.0 + (x * (x * 9.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 + (x * (x * 9.0d0))
end function

Java

public static double code(double x) {
	return 3.0 + (x * (x * 9.0));
}

Python

def code(x):
	return 3.0 + (x * (x * 9.0))

Julia

function code(x)
	return Float64(3.0 + Float64(x * Float64(x * 9.0)))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 + (x * (x * 9.0));
end

Wolfram

code[x_] := N[(3.0 + N[(x * N[(x * 9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]
2. Step-by-step derivation

   1. distribute-lft-in 99.8%

      \[\leadsto \color{blue}{3 \cdot \left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 3 \cdot 1} \]

   2. metadata-eval 99.8%

      \[\leadsto 3 \cdot \left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + \color{blue}{3} \]

   3. fma-def 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(3, \left(x \cdot 3\right) \cdot x - x \cdot 4, 3\right)} \]

   4. *-commutative 99.8%

      \[\leadsto \mathsf{fma}\left(3, \color{blue}{x \cdot \left(x \cdot 3\right)} - x \cdot 4, 3\right) \]

   5. distribute-lft-out-- 99.8%

      \[\leadsto \mathsf{fma}\left(3, \color{blue}{x \cdot \left(x \cdot 3 - 4\right)}, 3\right) \]

   6. *-commutative 99.8%

      \[\leadsto \mathsf{fma}\left(3, x \cdot \left(\color{blue}{3 \cdot x} - 4\right), 3\right) \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left(3, x \cdot \left(3 \cdot x - 4\right), 3\right)} \]

4. Taylor expanded in x around inf 97.3%

   \[\leadsto \mathsf{fma}\left(3, x \cdot \color{blue}{\left(3 \cdot x\right)}, 3\right) \]
5. Step-by-step derivation

   1. fma-udef 97.3%

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

   2. associate-*r* 97.3%

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

   3. swap-sqr 97.4%

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

   4. metadata-eval 97.4%

      \[\leadsto \color{blue}{9} \cdot \left(x \cdot x\right) + 3 \]

   5. *-commutative 97.4%

      \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot 9} + 3 \]

   6. associate-*l* 97.4%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot 9\right)} + 3 \]

6. Applied egg-rr 97.4%

   \[\leadsto \color{blue}{x \cdot \left(x \cdot 9\right) + 3} \]

7. Final simplification 97.4%

   \[\leadsto 3 + x \cdot \left(x \cdot 9\right) \]

Alternative 9: 51.0% accurate, 13.0× speedup

Math

\[\begin{array}{l} \\ 3 \end{array} \]

FPCore

(FPCore (x) :precision binary64 3.0)

C

double code(double x) {
	return 3.0;
}

Fortran

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

Java

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

Python

def code(x):
	return 3.0

Julia

function code(x)
	return 3.0
end

MATLAB

function tmp = code(x)
	tmp = 3.0;
end

Wolfram

code[x_] := 3.0

Derivation

1. Initial program 99.8%

   \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

2. Taylor expanded in x around 0 48.5%

   \[\leadsto \color{blue}{3} \]

3. Final simplification 48.5%

   \[\leadsto 3 \]

Developer target: 99.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ 3 + \left(\left(9 \cdot x\right) \cdot x - 12 \cdot x\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ 3.0 (- (* (* 9.0 x) x) (* 12.0 x))))

C

double code(double x) {
	return 3.0 + (((9.0 * x) * x) - (12.0 * x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 + (((9.0d0 * x) * x) - (12.0d0 * x))
end function

Java

public static double code(double x) {
	return 3.0 + (((9.0 * x) * x) - (12.0 * x));
}

Python

def code(x):
	return 3.0 + (((9.0 * x) * x) - (12.0 * x))

Julia

function code(x)
	return Float64(3.0 + Float64(Float64(Float64(9.0 * x) * x) - Float64(12.0 * x)))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 + (((9.0 * x) * x) - (12.0 * x));
end

Wolfram

code[x_] := N[(3.0 + N[(N[(N[(9.0 * x), $MachinePrecision] * x), $MachinePrecision] - N[(12.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023257 
(FPCore (x)
  :name "Diagrams.Tangent:$catParam from diagrams-lib-1.3.0.3, D"
  :precision binary64

  :herbie-target
  (+ 3.0 (- (* (* 9.0 x) x) (* 12.0 x)))

  (* 3.0 (+ (- (* (* x 3.0) x) (* x 4.0)) 1.0)))