qlog (example 3.10)

Percentage Accurate: 40.4% → 88.9%

Time: 35.5s

Alternatives: 5

Speedup: 207.0×

Specification

\[\left|x\right| \leq 1\]

Math

\[\begin{array}{l} \\ \frac{\log \left(1 - x\right)}{\log \left(1 + x\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (log (- 1.0 x)) (log (+ 1.0 x))))

C

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

Fortran

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

Java

public static double code(double x) {
	return Math.log((1.0 - x)) / Math.log((1.0 + x));
}

Python

def code(x):
	return math.log((1.0 - x)) / math.log((1.0 + x))

Julia

function code(x)
	return Float64(log(Float64(1.0 - x)) / log(Float64(1.0 + x)))
end

MATLAB

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

Wolfram

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

Initial Program: 40.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\log \left(1 - x\right)}{\log \left(1 + x\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (log (- 1.0 x)) (log (+ 1.0 x))))

C

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

Fortran

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

Java

public static double code(double x) {
	return Math.log((1.0 - x)) / Math.log((1.0 + x));
}

Python

def code(x):
	return math.log((1.0 - x)) / math.log((1.0 + x))

Julia

function code(x)
	return Float64(log(Float64(1.0 - x)) / log(Float64(1.0 + x)))
end

MATLAB

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

Wolfram

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

Alternative 1: 88.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\mathsf{log1p}\left(-x\right)}{\mathsf{log1p}\left(x\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (log1p (- x)) (log1p x)))

C

double code(double x) {
	return log1p(-x) / log1p(x);
}

Java

public static double code(double x) {
	return Math.log1p(-x) / Math.log1p(x);
}

Python

def code(x):
	return math.log1p(-x) / math.log1p(x)

Julia

function code(x)
	return Float64(log1p(Float64(-x)) / log1p(x))
end

Wolfram

code[x_] := N[(N[Log[1 + (-x)], $MachinePrecision] / N[Log[1 + x], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 45.7%

   \[\frac{\log \left(1 - x\right)}{\log \left(1 + x\right)} \]
2. Step-by-step derivation

   1. sub-neg 45.7%

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

   2. log1p-def 36.6%

      \[\leadsto \frac{\color{blue}{\mathsf{log1p}\left(-x\right)}}{\log \left(1 + x\right)} \]

   3. log1p-def 89.9%

      \[\leadsto \frac{\mathsf{log1p}\left(-x\right)}{\color{blue}{\mathsf{log1p}\left(x\right)}} \]

3. Simplified 89.9%

   \[\leadsto \color{blue}{\frac{\mathsf{log1p}\left(-x\right)}{\mathsf{log1p}\left(x\right)}} \]
4. Add Preprocessing

5. Final simplification 89.9%

   \[\leadsto \frac{\mathsf{log1p}\left(-x\right)}{\mathsf{log1p}\left(x\right)} \]
6. Add Preprocessing

Alternative 2: 84.4% accurate, 15.9× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(x \cdot \left(x \cdot -0.4166666666666667 - 0.5\right)\right) + \left(-1 - x\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (+ (* x (* x (- (* x -0.4166666666666667) 0.5))) (- -1.0 x)))

C

double code(double x) {
	return (x * (x * ((x * -0.4166666666666667) - 0.5))) + (-1.0 - x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * (x * ((x * (-0.4166666666666667d0)) - 0.5d0))) + ((-1.0d0) - x)
end function

Java

public static double code(double x) {
	return (x * (x * ((x * -0.4166666666666667) - 0.5))) + (-1.0 - x);
}

Python

def code(x):
	return (x * (x * ((x * -0.4166666666666667) - 0.5))) + (-1.0 - x)

Julia

function code(x)
	return Float64(Float64(x * Float64(x * Float64(Float64(x * -0.4166666666666667) - 0.5))) + Float64(-1.0 - x))
end

MATLAB

function tmp = code(x)
	tmp = (x * (x * ((x * -0.4166666666666667) - 0.5))) + (-1.0 - x);
end

Wolfram

code[x_] := N[(N[(x * N[(x * N[(N[(x * -0.4166666666666667), $MachinePrecision] - 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(-1.0 - x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 45.7%

   \[\frac{\log \left(1 - x\right)}{\log \left(1 + x\right)} \]
2. Add Preprocessing

3. Taylor expanded in x around 0 84.9%

   \[\leadsto \color{blue}{\left(-1 \cdot x + \left(-0.5 \cdot {x}^{2} + -0.4166666666666667 \cdot {x}^{3}\right)\right) - 1} \]
4. Step-by-step derivation

   1. +-commutative 84.9%

      \[\leadsto \color{blue}{\left(\left(-0.5 \cdot {x}^{2} + -0.4166666666666667 \cdot {x}^{3}\right) + -1 \cdot x\right)} - 1 \]

   2. associate--l+ 84.9%

      \[\leadsto \color{blue}{\left(-0.5 \cdot {x}^{2} + -0.4166666666666667 \cdot {x}^{3}\right) + \left(-1 \cdot x - 1\right)} \]

   3. *-commutative 84.9%

      \[\leadsto \left(\color{blue}{{x}^{2} \cdot -0.5} + -0.4166666666666667 \cdot {x}^{3}\right) + \left(-1 \cdot x - 1\right) \]

   4. *-commutative 84.9%

      \[\leadsto \left({x}^{2} \cdot -0.5 + \color{blue}{{x}^{3} \cdot -0.4166666666666667}\right) + \left(-1 \cdot x - 1\right) \]

   5. unpow3 84.9%

      \[\leadsto \left({x}^{2} \cdot -0.5 + \color{blue}{\left(\left(x \cdot x\right) \cdot x\right)} \cdot -0.4166666666666667\right) + \left(-1 \cdot x - 1\right) \]

   6. unpow2 84.9%

      \[\leadsto \left({x}^{2} \cdot -0.5 + \left(\color{blue}{{x}^{2}} \cdot x\right) \cdot -0.4166666666666667\right) + \left(-1 \cdot x - 1\right) \]

   7. associate-*l* 84.9%

      \[\leadsto \left({x}^{2} \cdot -0.5 + \color{blue}{{x}^{2} \cdot \left(x \cdot -0.4166666666666667\right)}\right) + \left(-1 \cdot x - 1\right) \]

   8. distribute-lft-out 84.9%

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right)} + \left(-1 \cdot x - 1\right) \]

   9. sub-neg 84.9%

      \[\leadsto {x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \color{blue}{\left(-1 \cdot x + \left(-1\right)\right)} \]

   10. metadata-eval 84.9%

       \[\leadsto {x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \left(-1 \cdot x + \color{blue}{-1}\right) \]

   11. +-commutative 84.9%

       \[\leadsto {x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \color{blue}{\left(-1 + -1 \cdot x\right)} \]

   12. mul-1-neg 84.9%

       \[\leadsto {x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \left(-1 + \color{blue}{\left(-x\right)}\right) \]

   13. unsub-neg 84.9%

       \[\leadsto {x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \color{blue}{\left(-1 - x\right)} \]

5. Simplified 84.9%

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \left(-1 - x\right)} \]
6. Step-by-step derivation

   1. expm1-log1p-u 84.9%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left({x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right)\right)\right)} + \left(-1 - x\right) \]

   2. expm1-udef 84.9%

      \[\leadsto \color{blue}{\left(e^{\mathsf{log1p}\left({x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right)\right)} - 1\right)} + \left(-1 - x\right) \]

   3. +-commutative 84.9%

      \[\leadsto \left(e^{\mathsf{log1p}\left({x}^{2} \cdot \color{blue}{\left(x \cdot -0.4166666666666667 + -0.5\right)}\right)} - 1\right) + \left(-1 - x\right) \]

   4. fma-def 84.9%

      \[\leadsto \left(e^{\mathsf{log1p}\left({x}^{2} \cdot \color{blue}{\mathsf{fma}\left(x, -0.4166666666666667, -0.5\right)}\right)} - 1\right) + \left(-1 - x\right) \]

7. Applied egg-rr 84.9%

   \[\leadsto \color{blue}{\left(e^{\mathsf{log1p}\left({x}^{2} \cdot \mathsf{fma}\left(x, -0.4166666666666667, -0.5\right)\right)} - 1\right)} + \left(-1 - x\right) \]
8. Step-by-step derivation

   1. expm1-def 84.9%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left({x}^{2} \cdot \mathsf{fma}\left(x, -0.4166666666666667, -0.5\right)\right)\right)} + \left(-1 - x\right) \]

   2. expm1-log1p-u 84.9%

      \[\leadsto \color{blue}{{x}^{2} \cdot \mathsf{fma}\left(x, -0.4166666666666667, -0.5\right)} + \left(-1 - x\right) \]

   3. *-commutative 84.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, -0.4166666666666667, -0.5\right) \cdot {x}^{2}} + \left(-1 - x\right) \]

   4. unpow2 84.9%

      \[\leadsto \mathsf{fma}\left(x, -0.4166666666666667, -0.5\right) \cdot \color{blue}{\left(x \cdot x\right)} + \left(-1 - x\right) \]

   5. associate-*r* 84.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(x, -0.4166666666666667, -0.5\right) \cdot x\right) \cdot x} + \left(-1 - x\right) \]

9. Applied egg-rr 84.9%

   \[\leadsto \color{blue}{\left(\mathsf{fma}\left(x, -0.4166666666666667, -0.5\right) \cdot x\right) \cdot x} + \left(-1 - x\right) \]

10. Taylor expanded in x around 0 84.9%

    \[\leadsto \left(\color{blue}{\left(-0.4166666666666667 \cdot x - 0.5\right)} \cdot x\right) \cdot x + \left(-1 - x\right) \]

11. Final simplification 84.9%

    \[\leadsto x \cdot \left(x \cdot \left(x \cdot -0.4166666666666667 - 0.5\right)\right) + \left(-1 - x\right) \]
12. Add Preprocessing

Alternative 3: 82.7% accurate, 23.0× speedup

Math

\[\begin{array}{l} \\ \left(-1 - x\right) + x \cdot \left(x \cdot -0.5\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ (- -1.0 x) (* x (* x -0.5))))

C

double code(double x) {
	return (-1.0 - x) + (x * (x * -0.5));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((-1.0d0) - x) + (x * (x * (-0.5d0)))
end function

Java

public static double code(double x) {
	return (-1.0 - x) + (x * (x * -0.5));
}

Python

def code(x):
	return (-1.0 - x) + (x * (x * -0.5))

Julia

function code(x)
	return Float64(Float64(-1.0 - x) + Float64(x * Float64(x * -0.5)))
end

MATLAB

function tmp = code(x)
	tmp = (-1.0 - x) + (x * (x * -0.5));
end

Wolfram

code[x_] := N[(N[(-1.0 - x), $MachinePrecision] + N[(x * N[(x * -0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 45.7%

   \[\frac{\log \left(1 - x\right)}{\log \left(1 + x\right)} \]
2. Add Preprocessing

3. Taylor expanded in x around 0 84.9%

   \[\leadsto \color{blue}{\left(-1 \cdot x + \left(-0.5 \cdot {x}^{2} + -0.4166666666666667 \cdot {x}^{3}\right)\right) - 1} \]
4. Step-by-step derivation

   1. +-commutative 84.9%

      \[\leadsto \color{blue}{\left(\left(-0.5 \cdot {x}^{2} + -0.4166666666666667 \cdot {x}^{3}\right) + -1 \cdot x\right)} - 1 \]

   2. associate--l+ 84.9%

      \[\leadsto \color{blue}{\left(-0.5 \cdot {x}^{2} + -0.4166666666666667 \cdot {x}^{3}\right) + \left(-1 \cdot x - 1\right)} \]

   3. *-commutative 84.9%

      \[\leadsto \left(\color{blue}{{x}^{2} \cdot -0.5} + -0.4166666666666667 \cdot {x}^{3}\right) + \left(-1 \cdot x - 1\right) \]

   4. *-commutative 84.9%

      \[\leadsto \left({x}^{2} \cdot -0.5 + \color{blue}{{x}^{3} \cdot -0.4166666666666667}\right) + \left(-1 \cdot x - 1\right) \]

   5. unpow3 84.9%

      \[\leadsto \left({x}^{2} \cdot -0.5 + \color{blue}{\left(\left(x \cdot x\right) \cdot x\right)} \cdot -0.4166666666666667\right) + \left(-1 \cdot x - 1\right) \]

   6. unpow2 84.9%

      \[\leadsto \left({x}^{2} \cdot -0.5 + \left(\color{blue}{{x}^{2}} \cdot x\right) \cdot -0.4166666666666667\right) + \left(-1 \cdot x - 1\right) \]

   7. associate-*l* 84.9%

      \[\leadsto \left({x}^{2} \cdot -0.5 + \color{blue}{{x}^{2} \cdot \left(x \cdot -0.4166666666666667\right)}\right) + \left(-1 \cdot x - 1\right) \]

   8. distribute-lft-out 84.9%

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right)} + \left(-1 \cdot x - 1\right) \]

   9. sub-neg 84.9%

      \[\leadsto {x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \color{blue}{\left(-1 \cdot x + \left(-1\right)\right)} \]

   10. metadata-eval 84.9%

       \[\leadsto {x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \left(-1 \cdot x + \color{blue}{-1}\right) \]

   11. +-commutative 84.9%

       \[\leadsto {x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \color{blue}{\left(-1 + -1 \cdot x\right)} \]

   12. mul-1-neg 84.9%

       \[\leadsto {x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \left(-1 + \color{blue}{\left(-x\right)}\right) \]

   13. unsub-neg 84.9%

       \[\leadsto {x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \color{blue}{\left(-1 - x\right)} \]

5. Simplified 84.9%

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right) + \left(-1 - x\right)} \]
6. Step-by-step derivation

   1. expm1-log1p-u 84.9%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left({x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right)\right)\right)} + \left(-1 - x\right) \]

   2. expm1-udef 84.9%

      \[\leadsto \color{blue}{\left(e^{\mathsf{log1p}\left({x}^{2} \cdot \left(-0.5 + x \cdot -0.4166666666666667\right)\right)} - 1\right)} + \left(-1 - x\right) \]

   3. +-commutative 84.9%

      \[\leadsto \left(e^{\mathsf{log1p}\left({x}^{2} \cdot \color{blue}{\left(x \cdot -0.4166666666666667 + -0.5\right)}\right)} - 1\right) + \left(-1 - x\right) \]

   4. fma-def 84.9%

      \[\leadsto \left(e^{\mathsf{log1p}\left({x}^{2} \cdot \color{blue}{\mathsf{fma}\left(x, -0.4166666666666667, -0.5\right)}\right)} - 1\right) + \left(-1 - x\right) \]

7. Applied egg-rr 84.9%

   \[\leadsto \color{blue}{\left(e^{\mathsf{log1p}\left({x}^{2} \cdot \mathsf{fma}\left(x, -0.4166666666666667, -0.5\right)\right)} - 1\right)} + \left(-1 - x\right) \]
8. Step-by-step derivation

   1. expm1-def 84.9%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left({x}^{2} \cdot \mathsf{fma}\left(x, -0.4166666666666667, -0.5\right)\right)\right)} + \left(-1 - x\right) \]

   2. expm1-log1p-u 84.9%

      \[\leadsto \color{blue}{{x}^{2} \cdot \mathsf{fma}\left(x, -0.4166666666666667, -0.5\right)} + \left(-1 - x\right) \]

   3. *-commutative 84.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, -0.4166666666666667, -0.5\right) \cdot {x}^{2}} + \left(-1 - x\right) \]

   4. unpow2 84.9%

      \[\leadsto \mathsf{fma}\left(x, -0.4166666666666667, -0.5\right) \cdot \color{blue}{\left(x \cdot x\right)} + \left(-1 - x\right) \]

   5. associate-*r* 84.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(x, -0.4166666666666667, -0.5\right) \cdot x\right) \cdot x} + \left(-1 - x\right) \]

9. Applied egg-rr 84.9%

   \[\leadsto \color{blue}{\left(\mathsf{fma}\left(x, -0.4166666666666667, -0.5\right) \cdot x\right) \cdot x} + \left(-1 - x\right) \]

10. Taylor expanded in x around 0 82.7%

    \[\leadsto \color{blue}{\left(-0.5 \cdot x\right)} \cdot x + \left(-1 - x\right) \]

11. Final simplification 82.7%

    \[\leadsto \left(-1 - x\right) + x \cdot \left(x \cdot -0.5\right) \]
12. Add Preprocessing

Alternative 4: 79.3% accurate, 69.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x):
	return -1.0 - x

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 45.7%

   \[\frac{\log \left(1 - x\right)}{\log \left(1 + x\right)} \]
2. Add Preprocessing

3. Taylor expanded in x around 0 78.7%

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

   1. sub-neg 78.7%

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

   2. metadata-eval 78.7%

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

   3. +-commutative 78.7%

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

   4. mul-1-neg 78.7%

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

   5. unsub-neg 78.7%

      \[\leadsto \color{blue}{-1 - x} \]

5. Simplified 78.7%

   \[\leadsto \color{blue}{-1 - x} \]

6. Final simplification 78.7%

   \[\leadsto -1 - x \]
7. Add Preprocessing

Alternative 5: 68.6% accurate, 207.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 -1.0)

C

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

Fortran

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

Java

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

Python

def code(x):
	return -1.0

Julia

function code(x)
	return -1.0
end

MATLAB

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

Wolfram

code[x_] := -1.0

Derivation

1. Initial program 45.7%

   \[\frac{\log \left(1 - x\right)}{\log \left(1 + x\right)} \]
2. Add Preprocessing

3. Taylor expanded in x around 0 66.9%

   \[\leadsto \color{blue}{-1} \]

4. Final simplification 66.9%

   \[\leadsto -1 \]
5. Add Preprocessing

Developer target: 88.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\mathsf{log1p}\left(-x\right)}{\mathsf{log1p}\left(x\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (log1p (- x)) (log1p x)))

C

double code(double x) {
	return log1p(-x) / log1p(x);
}

Java

public static double code(double x) {
	return Math.log1p(-x) / Math.log1p(x);
}

Python

def code(x):
	return math.log1p(-x) / math.log1p(x)

Julia

function code(x)
	return Float64(log1p(Float64(-x)) / log1p(x))
end

Wolfram

code[x_] := N[(N[Log[1 + (-x)], $MachinePrecision] / N[Log[1 + x], $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024031 
(FPCore (x)
  :name "qlog (example 3.10)"
  :precision binary64
  :pre (<= (fabs x) 1.0)

  :herbie-target
  (/ (log1p (- x)) (log1p x))

  (/ (log (- 1.0 x)) (log (+ 1.0 x))))