a parameter of renormalized beta distribution

Percentage Accurate: 99.8% → 99.8%

Time: 8.9s

Alternatives: 10

Speedup: 1.0×

• 41.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

\[\left(0 < m \land 0 < v\right) \land v < 0.25\]

Math

\[\begin{array}{l} \\ \left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \end{array} \]

FPCore

(FPCore (m v) :precision binary64 (* (- (/ (* m (- 1.0 m)) v) 1.0) m))

C

double code(double m, double v) {
	return (((m * (1.0 - m)) / v) - 1.0) * m;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    code = (((m * (1.0d0 - m)) / v) - 1.0d0) * m
end function

Java

public static double code(double m, double v) {
	return (((m * (1.0 - m)) / v) - 1.0) * m;
}

Python

def code(m, v):
	return (((m * (1.0 - m)) / v) - 1.0) * m

Julia

function code(m, v)
	return Float64(Float64(Float64(Float64(m * Float64(1.0 - m)) / v) - 1.0) * m)
end

MATLAB

function tmp = code(m, v)
	tmp = (((m * (1.0 - m)) / v) - 1.0) * m;
end

Wolfram

code[m_, v_] := N[(N[(N[(N[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \end{array} \]

FPCore

(FPCore (m v) :precision binary64 (* (- (/ (* m (- 1.0 m)) v) 1.0) m))

C

double code(double m, double v) {
	return (((m * (1.0 - m)) / v) - 1.0) * m;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    code = (((m * (1.0d0 - m)) / v) - 1.0d0) * m
end function

Java

public static double code(double m, double v) {
	return (((m * (1.0 - m)) / v) - 1.0) * m;
}

Python

def code(m, v):
	return (((m * (1.0 - m)) / v) - 1.0) * m

Julia

function code(m, v)
	return Float64(Float64(Float64(Float64(m * Float64(1.0 - m)) / v) - 1.0) * m)
end

MATLAB

function tmp = code(m, v)
	tmp = (((m * (1.0 - m)) / v) - 1.0) * m;
end

Wolfram

code[m_, v_] := N[(N[(N[(N[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision]

Alternative 1: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{m}{v}, -m\right) \end{array} \]

FPCore

(FPCore (m v) :precision binary64 (fma (fma m (- m) m) (/ m v) (- m)))

C

double code(double m, double v) {
	return fma(fma(m, -m, m), (m / v), -m);
}

Julia

function code(m, v)
	return fma(fma(m, Float64(-m), m), Float64(m / v), Float64(-m))
end

Wolfram

code[m_, v_] := N[(N[(m * (-m) + m), $MachinePrecision] * N[(m / v), $MachinePrecision] + (-m)), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto \left(\frac{m \cdot \color{blue}{\left(1 - m\right)}}{v} - 1\right) \cdot m \]

   2. lift-*.f64 N/A

      \[\leadsto \left(\frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} - 1\right) \cdot m \]

   3. lift-/.f64 N/A

      \[\leadsto \left(\color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} - 1\right) \cdot m \]

   4. lift--.f64 N/A

      \[\leadsto \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \cdot m \]

   5. *-commutative N/A

      \[\leadsto \color{blue}{m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

   6. lift--.f64 N/A

      \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

   7. sub-neg N/A

      \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} + \left(\mathsf{neg}\left(1\right)\right)\right)} \]

   8. distribute-lft-in N/A

      \[\leadsto \color{blue}{m \cdot \frac{m \cdot \left(1 - m\right)}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right)} \]

   9. lift-/.f64 N/A

      \[\leadsto m \cdot \color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

   10. lift-*.f64 N/A

       \[\leadsto m \cdot \frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

   11. *-commutative N/A

       \[\leadsto m \cdot \frac{\color{blue}{\left(1 - m\right) \cdot m}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

   12. associate-/l* N/A

       \[\leadsto m \cdot \color{blue}{\left(\left(1 - m\right) \cdot \frac{m}{v}\right)} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

   13. associate-*r* N/A

       \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

   14. lift-*.f64 N/A

       \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right)} \cdot \frac{m}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

   15. distribute-rgt-neg-in N/A

       \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \color{blue}{\left(\mathsf{neg}\left(m \cdot 1\right)\right)} \]

   16. *-rgt-identity N/A

       \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \left(\mathsf{neg}\left(\color{blue}{m}\right)\right) \]

   17. lower-fma.f64 N/A

       \[\leadsto \color{blue}{\mathsf{fma}\left(m \cdot \left(1 - m\right), \frac{m}{v}, \mathsf{neg}\left(m\right)\right)} \]

4. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{m}{v}, -m\right)} \]
5. Add Preprocessing

Alternative 2: 97.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} + -1\right) \leq -4 \cdot 10^{+58}:\\ \;\;\;\;m \cdot \left(m \cdot \frac{m}{-v}\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{m}{v}, m, -m\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* m (+ (/ (* m (- 1.0 m)) v) -1.0)) -4e+58)
   (* m (* m (/ m (- v))))
   (fma (/ m v) m (- m))))

C

double code(double m, double v) {
	double tmp;
	if ((m * (((m * (1.0 - m)) / v) + -1.0)) <= -4e+58) {
		tmp = m * (m * (m / -v));
	} else {
		tmp = fma((m / v), m, -m);
	}
	return tmp;
}

Julia

function code(m, v)
	tmp = 0.0
	if (Float64(m * Float64(Float64(Float64(m * Float64(1.0 - m)) / v) + -1.0)) <= -4e+58)
		tmp = Float64(m * Float64(m * Float64(m / Float64(-v))));
	else
		tmp = fma(Float64(m / v), m, Float64(-m));
	end
	return tmp
end

Wolfram

code[m_, v_] := If[LessEqual[N[(m * N[(N[(N[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision], -4e+58], N[(m * N[(m * N[(m / (-v)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(m / v), $MachinePrecision] * m + (-m)), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m) < -3.99999999999999978e58

   1. Initial program 99.9%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\frac{m \cdot \color{blue}{\left(1 - m\right)}}{v} - 1\right) \cdot m \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} - 1\right) \cdot m \]

      3. lift-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} - 1\right) \cdot m \]

      4. lift--.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \cdot m \]

      5. *-commutative N/A

         \[\leadsto \color{blue}{m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

      6. lift--.f64 N/A

         \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

      7. sub-neg N/A

         \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} + \left(\mathsf{neg}\left(1\right)\right)\right)} \]

      8. distribute-lft-in N/A

         \[\leadsto \color{blue}{m \cdot \frac{m \cdot \left(1 - m\right)}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right)} \]

      9. lift-/.f64 N/A

         \[\leadsto m \cdot \color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      10. lift-*.f64 N/A

          \[\leadsto m \cdot \frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      11. *-commutative N/A

          \[\leadsto m \cdot \frac{\color{blue}{\left(1 - m\right) \cdot m}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      12. associate-/l* N/A

          \[\leadsto m \cdot \color{blue}{\left(\left(1 - m\right) \cdot \frac{m}{v}\right)} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      13. associate-*r* N/A

          \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      14. lift-*.f64 N/A

          \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right)} \cdot \frac{m}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      15. distribute-rgt-neg-in N/A

          \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \color{blue}{\left(\mathsf{neg}\left(m \cdot 1\right)\right)} \]

      16. *-rgt-identity N/A

          \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \left(\mathsf{neg}\left(\color{blue}{m}\right)\right) \]

      17. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(m \cdot \left(1 - m\right), \frac{m}{v}, \mathsf{neg}\left(m\right)\right)} \]

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{m}{v}, -m\right)} \]
   5. Step-by-step derivation

      1. clear-num N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      2. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      3. lower-/.f64 99.9

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{1}{\color{blue}{\frac{v}{m}}}, -m\right) \]

   6. Applied rewrites 99.9%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, -m\right) \]

   8. Applied rewrites 97.4%

      \[\leadsto \color{blue}{\left(m \cdot \frac{m}{-v}\right) \cdot m} \]

   if -3.99999999999999978e58 < (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m)

   1. Initial program 99.7%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\frac{m \cdot \color{blue}{\left(1 - m\right)}}{v} - 1\right) \cdot m \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} - 1\right) \cdot m \]

      3. lift-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} - 1\right) \cdot m \]

      4. lift--.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \cdot m \]

      5. *-commutative N/A

         \[\leadsto \color{blue}{m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

      6. lift--.f64 N/A

         \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

      7. sub-neg N/A

         \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} + \left(\mathsf{neg}\left(1\right)\right)\right)} \]

      8. distribute-lft-in N/A

         \[\leadsto \color{blue}{m \cdot \frac{m \cdot \left(1 - m\right)}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right)} \]

      9. lift-/.f64 N/A

         \[\leadsto m \cdot \color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      10. lift-*.f64 N/A

          \[\leadsto m \cdot \frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      11. *-commutative N/A

          \[\leadsto m \cdot \frac{\color{blue}{\left(1 - m\right) \cdot m}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      12. associate-/l* N/A

          \[\leadsto m \cdot \color{blue}{\left(\left(1 - m\right) \cdot \frac{m}{v}\right)} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      13. associate-*r* N/A

          \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      14. lift-*.f64 N/A

          \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right)} \cdot \frac{m}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      15. distribute-rgt-neg-in N/A

          \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \color{blue}{\left(\mathsf{neg}\left(m \cdot 1\right)\right)} \]

      16. *-rgt-identity N/A

          \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \left(\mathsf{neg}\left(\color{blue}{m}\right)\right) \]

      17. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(m \cdot \left(1 - m\right), \frac{m}{v}, \mathsf{neg}\left(m\right)\right)} \]

   4. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{m}{v}, -m\right)} \]
   5. Step-by-step derivation

      1. clear-num N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      2. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      3. lower-/.f64 99.8

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{1}{\color{blue}{\frac{v}{m}}}, -m\right) \]

   6. Applied rewrites 99.8%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, -m\right) \]
   7. Step-by-step derivation

      1. div-inv N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \frac{1}{\color{blue}{v \cdot \frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      2. associate-/r* N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      3. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \frac{\color{blue}{\frac{1}{v}}}{\frac{1}{m}}, \mathsf{neg}\left(m\right)\right) \]

      4. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      5. lower-/.f64 99.7

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{\frac{1}{v}}{\color{blue}{\frac{1}{m}}}, -m\right) \]

   8. Applied rewrites 99.7%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, -m\right) \]
   9. Step-by-step derivation

      1. lift-neg.f64 N/A

         \[\leadsto \left(m \cdot \color{blue}{\left(\mathsf{neg}\left(m\right)\right)} + m\right) \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      2. lift-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right)} \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      3. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\frac{1}{v}}{\color{blue}{\frac{1}{m}}} + \left(\mathsf{neg}\left(m\right)\right) \]

      4. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\color{blue}{\frac{1}{v}}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      5. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}} + \left(\mathsf{neg}\left(m\right)\right) \]

      6. lift-neg.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \color{blue}{\left(\mathsf{neg}\left(m\right)\right)} \]

   10. Applied rewrites 97.5%

       \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{m}{v}, m, -m\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} + -1\right) \leq -4 \cdot 10^{+58}:\\ \;\;\;\;m \cdot \left(m \cdot \frac{m}{-v}\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{m}{v}, m, -m\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 97.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} + -1\right) \leq -4 \cdot 10^{+58}:\\ \;\;\;\;\frac{m \cdot \left(m \cdot \left(-m\right)\right)}{v}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{m}{v}, m, -m\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* m (+ (/ (* m (- 1.0 m)) v) -1.0)) -4e+58)
   (/ (* m (* m (- m))) v)
   (fma (/ m v) m (- m))))

C

double code(double m, double v) {
	double tmp;
	if ((m * (((m * (1.0 - m)) / v) + -1.0)) <= -4e+58) {
		tmp = (m * (m * -m)) / v;
	} else {
		tmp = fma((m / v), m, -m);
	}
	return tmp;
}

Julia

function code(m, v)
	tmp = 0.0
	if (Float64(m * Float64(Float64(Float64(m * Float64(1.0 - m)) / v) + -1.0)) <= -4e+58)
		tmp = Float64(Float64(m * Float64(m * Float64(-m))) / v);
	else
		tmp = fma(Float64(m / v), m, Float64(-m));
	end
	return tmp
end

Wolfram

code[m_, v_] := If[LessEqual[N[(m * N[(N[(N[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision], -4e+58], N[(N[(m * N[(m * (-m)), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision], N[(N[(m / v), $MachinePrecision] * m + (-m)), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m) < -3.99999999999999978e58

   1. Initial program 99.9%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around inf

      \[\leadsto \color{blue}{-1 \cdot \frac{{m}^{3}}{v}} \]
   4. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{-1 \cdot {m}^{3}}{v}} \]

      2. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{-1}{v} \cdot {m}^{3}} \]

      3. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{\mathsf{neg}\left(1\right)}}{v} \cdot {m}^{3} \]

      4. distribute-neg-frac N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\frac{1}{v}\right)\right)} \cdot {m}^{3} \]

      5. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(\frac{1}{v} \cdot {m}^{3}\right)} \]

      6. *-commutative N/A

         \[\leadsto \mathsf{neg}\left(\color{blue}{{m}^{3} \cdot \frac{1}{v}}\right) \]

      7. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left({m}^{3} \cdot \frac{1}{v}\right)} \]

      8. associate-*r/ N/A

         \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{{m}^{3} \cdot 1}{v}}\right) \]

      9. *-rgt-identity N/A

         \[\leadsto \mathsf{neg}\left(\frac{\color{blue}{{m}^{3}}}{v}\right) \]

      10. lower-/.f64 N/A

          \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{{m}^{3}}{v}}\right) \]

      11. cube-mult N/A

          \[\leadsto \mathsf{neg}\left(\frac{\color{blue}{m \cdot \left(m \cdot m\right)}}{v}\right) \]

      12. unpow2 N/A

          \[\leadsto \mathsf{neg}\left(\frac{m \cdot \color{blue}{{m}^{2}}}{v}\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{neg}\left(\frac{\color{blue}{m \cdot {m}^{2}}}{v}\right) \]

      14. unpow2 N/A

          \[\leadsto \mathsf{neg}\left(\frac{m \cdot \color{blue}{\left(m \cdot m\right)}}{v}\right) \]

      15. lower-*.f64 97.4

          \[\leadsto -\frac{m \cdot \color{blue}{\left(m \cdot m\right)}}{v} \]

   5. Applied rewrites 97.4%

      \[\leadsto \color{blue}{-\frac{m \cdot \left(m \cdot m\right)}{v}} \]

   if -3.99999999999999978e58 < (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m)

   1. Initial program 99.7%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\frac{m \cdot \color{blue}{\left(1 - m\right)}}{v} - 1\right) \cdot m \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} - 1\right) \cdot m \]

      3. lift-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} - 1\right) \cdot m \]

      4. lift--.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \cdot m \]

      5. *-commutative N/A

         \[\leadsto \color{blue}{m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

      6. lift--.f64 N/A

         \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

      7. sub-neg N/A

         \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} + \left(\mathsf{neg}\left(1\right)\right)\right)} \]

      8. distribute-lft-in N/A

         \[\leadsto \color{blue}{m \cdot \frac{m \cdot \left(1 - m\right)}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right)} \]

      9. lift-/.f64 N/A

         \[\leadsto m \cdot \color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      10. lift-*.f64 N/A

          \[\leadsto m \cdot \frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      11. *-commutative N/A

          \[\leadsto m \cdot \frac{\color{blue}{\left(1 - m\right) \cdot m}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      12. associate-/l* N/A

          \[\leadsto m \cdot \color{blue}{\left(\left(1 - m\right) \cdot \frac{m}{v}\right)} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      13. associate-*r* N/A

          \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      14. lift-*.f64 N/A

          \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right)} \cdot \frac{m}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      15. distribute-rgt-neg-in N/A

          \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \color{blue}{\left(\mathsf{neg}\left(m \cdot 1\right)\right)} \]

      16. *-rgt-identity N/A

          \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \left(\mathsf{neg}\left(\color{blue}{m}\right)\right) \]

      17. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(m \cdot \left(1 - m\right), \frac{m}{v}, \mathsf{neg}\left(m\right)\right)} \]

   4. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{m}{v}, -m\right)} \]
   5. Step-by-step derivation

      1. clear-num N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      2. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      3. lower-/.f64 99.8

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{1}{\color{blue}{\frac{v}{m}}}, -m\right) \]

   6. Applied rewrites 99.8%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, -m\right) \]
   7. Step-by-step derivation

      1. div-inv N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \frac{1}{\color{blue}{v \cdot \frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      2. associate-/r* N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      3. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \frac{\color{blue}{\frac{1}{v}}}{\frac{1}{m}}, \mathsf{neg}\left(m\right)\right) \]

      4. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      5. lower-/.f64 99.7

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{\frac{1}{v}}{\color{blue}{\frac{1}{m}}}, -m\right) \]

   8. Applied rewrites 99.7%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, -m\right) \]
   9. Step-by-step derivation

      1. lift-neg.f64 N/A

         \[\leadsto \left(m \cdot \color{blue}{\left(\mathsf{neg}\left(m\right)\right)} + m\right) \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      2. lift-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right)} \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      3. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\frac{1}{v}}{\color{blue}{\frac{1}{m}}} + \left(\mathsf{neg}\left(m\right)\right) \]

      4. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\color{blue}{\frac{1}{v}}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      5. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}} + \left(\mathsf{neg}\left(m\right)\right) \]

      6. lift-neg.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \color{blue}{\left(\mathsf{neg}\left(m\right)\right)} \]

   10. Applied rewrites 97.5%

       \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{m}{v}, m, -m\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} + -1\right) \leq -4 \cdot 10^{+58}:\\ \;\;\;\;\frac{m \cdot \left(m \cdot \left(-m\right)\right)}{v}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{m}{v}, m, -m\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 51.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} + -1\right) \leq -4 \cdot 10^{+58}:\\ \;\;\;\;-m\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{m}{v}, m, -m\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* m (+ (/ (* m (- 1.0 m)) v) -1.0)) -4e+58)
   (- m)
   (fma (/ m v) m (- m))))

C

double code(double m, double v) {
	double tmp;
	if ((m * (((m * (1.0 - m)) / v) + -1.0)) <= -4e+58) {
		tmp = -m;
	} else {
		tmp = fma((m / v), m, -m);
	}
	return tmp;
}

Julia

function code(m, v)
	tmp = 0.0
	if (Float64(m * Float64(Float64(Float64(m * Float64(1.0 - m)) / v) + -1.0)) <= -4e+58)
		tmp = Float64(-m);
	else
		tmp = fma(Float64(m / v), m, Float64(-m));
	end
	return tmp
end

Wolfram

code[m_, v_] := If[LessEqual[N[(m * N[(N[(N[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision], -4e+58], (-m), N[(N[(m / v), $MachinePrecision] * m + (-m)), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m) < -3.99999999999999978e58

   1. Initial program 99.9%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around 0

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

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(m\right)} \]

      2. lower-neg.f64 5.8

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

   5. Applied rewrites 5.8%

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

   if -3.99999999999999978e58 < (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m)

   1. Initial program 99.7%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\frac{m \cdot \color{blue}{\left(1 - m\right)}}{v} - 1\right) \cdot m \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} - 1\right) \cdot m \]

      3. lift-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} - 1\right) \cdot m \]

      4. lift--.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \cdot m \]

      5. *-commutative N/A

         \[\leadsto \color{blue}{m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

      6. lift--.f64 N/A

         \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

      7. sub-neg N/A

         \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} + \left(\mathsf{neg}\left(1\right)\right)\right)} \]

      8. distribute-lft-in N/A

         \[\leadsto \color{blue}{m \cdot \frac{m \cdot \left(1 - m\right)}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right)} \]

      9. lift-/.f64 N/A

         \[\leadsto m \cdot \color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      10. lift-*.f64 N/A

          \[\leadsto m \cdot \frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      11. *-commutative N/A

          \[\leadsto m \cdot \frac{\color{blue}{\left(1 - m\right) \cdot m}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      12. associate-/l* N/A

          \[\leadsto m \cdot \color{blue}{\left(\left(1 - m\right) \cdot \frac{m}{v}\right)} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      13. associate-*r* N/A

          \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      14. lift-*.f64 N/A

          \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right)} \cdot \frac{m}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      15. distribute-rgt-neg-in N/A

          \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \color{blue}{\left(\mathsf{neg}\left(m \cdot 1\right)\right)} \]

      16. *-rgt-identity N/A

          \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \left(\mathsf{neg}\left(\color{blue}{m}\right)\right) \]

      17. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(m \cdot \left(1 - m\right), \frac{m}{v}, \mathsf{neg}\left(m\right)\right)} \]

   4. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{m}{v}, -m\right)} \]
   5. Step-by-step derivation

      1. clear-num N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      2. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      3. lower-/.f64 99.8

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{1}{\color{blue}{\frac{v}{m}}}, -m\right) \]

   6. Applied rewrites 99.8%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, -m\right) \]
   7. Step-by-step derivation

      1. div-inv N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \frac{1}{\color{blue}{v \cdot \frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      2. associate-/r* N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      3. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \frac{\color{blue}{\frac{1}{v}}}{\frac{1}{m}}, \mathsf{neg}\left(m\right)\right) \]

      4. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      5. lower-/.f64 99.7

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{\frac{1}{v}}{\color{blue}{\frac{1}{m}}}, -m\right) \]

   8. Applied rewrites 99.7%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, -m\right) \]
   9. Step-by-step derivation

      1. lift-neg.f64 N/A

         \[\leadsto \left(m \cdot \color{blue}{\left(\mathsf{neg}\left(m\right)\right)} + m\right) \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      2. lift-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right)} \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      3. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\frac{1}{v}}{\color{blue}{\frac{1}{m}}} + \left(\mathsf{neg}\left(m\right)\right) \]

      4. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\color{blue}{\frac{1}{v}}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      5. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}} + \left(\mathsf{neg}\left(m\right)\right) \]

      6. lift-neg.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \color{blue}{\left(\mathsf{neg}\left(m\right)\right)} \]

   10. Applied rewrites 97.5%

       \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{m}{v}, m, -m\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 53.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} + -1\right) \leq -4 \cdot 10^{+58}:\\ \;\;\;\;-m\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{m}{v}, m, -m\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 51.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} + -1\right) \leq -4 \cdot 10^{+58}:\\ \;\;\;\;-m\\ \mathbf{else}:\\ \;\;\;\;m \cdot \left(\frac{m}{v} + -1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* m (+ (/ (* m (- 1.0 m)) v) -1.0)) -4e+58)
   (- m)
   (* m (+ (/ m v) -1.0))))

C

double code(double m, double v) {
	double tmp;
	if ((m * (((m * (1.0 - m)) / v) + -1.0)) <= -4e+58) {
		tmp = -m;
	} else {
		tmp = m * ((m / v) + -1.0);
	}
	return tmp;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    real(8) :: tmp
    if ((m * (((m * (1.0d0 - m)) / v) + (-1.0d0))) <= (-4d+58)) then
        tmp = -m
    else
        tmp = m * ((m / v) + (-1.0d0))
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	double tmp;
	if ((m * (((m * (1.0 - m)) / v) + -1.0)) <= -4e+58) {
		tmp = -m;
	} else {
		tmp = m * ((m / v) + -1.0);
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if (m * (((m * (1.0 - m)) / v) + -1.0)) <= -4e+58:
		tmp = -m
	else:
		tmp = m * ((m / v) + -1.0)
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (Float64(m * Float64(Float64(Float64(m * Float64(1.0 - m)) / v) + -1.0)) <= -4e+58)
		tmp = Float64(-m);
	else
		tmp = Float64(m * Float64(Float64(m / v) + -1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(m, v)
	tmp = 0.0;
	if ((m * (((m * (1.0 - m)) / v) + -1.0)) <= -4e+58)
		tmp = -m;
	else
		tmp = m * ((m / v) + -1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[N[(m * N[(N[(N[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision], -4e+58], (-m), N[(m * N[(N[(m / v), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m) < -3.99999999999999978e58

   1. Initial program 99.9%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around 0

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

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(m\right)} \]

      2. lower-neg.f64 5.8

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

   5. Applied rewrites 5.8%

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

   if -3.99999999999999978e58 < (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m)

   1. Initial program 99.7%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around 0

      \[\leadsto \left(\color{blue}{\frac{m}{v}} - 1\right) \cdot m \]
   4. Step-by-step derivation

      1. lower-/.f64 97.5

         \[\leadsto \left(\color{blue}{\frac{m}{v}} - 1\right) \cdot m \]

   5. Applied rewrites 97.5%

      \[\leadsto \left(\color{blue}{\frac{m}{v}} - 1\right) \cdot m \]
3. Recombined 2 regimes into one program.

4. Final simplification 53.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} + -1\right) \leq -4 \cdot 10^{+58}:\\ \;\;\;\;-m\\ \mathbf{else}:\\ \;\;\;\;m \cdot \left(\frac{m}{v} + -1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 99.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;m \leq 10^{-38}:\\ \;\;\;\;\mathsf{fma}\left(\frac{m}{v}, m, -m\right)\\ \mathbf{else}:\\ \;\;\;\;m \cdot \frac{m - m \cdot m}{v}\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= m 1e-38) (fma (/ m v) m (- m)) (* m (/ (- m (* m m)) v))))

C

double code(double m, double v) {
	double tmp;
	if (m <= 1e-38) {
		tmp = fma((m / v), m, -m);
	} else {
		tmp = m * ((m - (m * m)) / v);
	}
	return tmp;
}

Julia

function code(m, v)
	tmp = 0.0
	if (m <= 1e-38)
		tmp = fma(Float64(m / v), m, Float64(-m));
	else
		tmp = Float64(m * Float64(Float64(m - Float64(m * m)) / v));
	end
	return tmp
end

Wolfram

code[m_, v_] := If[LessEqual[m, 1e-38], N[(N[(m / v), $MachinePrecision] * m + (-m)), $MachinePrecision], N[(m * N[(N[(m - N[(m * m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if m < 9.9999999999999996e-39

   1. Initial program 99.7%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\frac{m \cdot \color{blue}{\left(1 - m\right)}}{v} - 1\right) \cdot m \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} - 1\right) \cdot m \]

      3. lift-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} - 1\right) \cdot m \]

      4. lift--.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \cdot m \]

      5. *-commutative N/A

         \[\leadsto \color{blue}{m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

      6. lift--.f64 N/A

         \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right)} \]

      7. sub-neg N/A

         \[\leadsto m \cdot \color{blue}{\left(\frac{m \cdot \left(1 - m\right)}{v} + \left(\mathsf{neg}\left(1\right)\right)\right)} \]

      8. distribute-lft-in N/A

         \[\leadsto \color{blue}{m \cdot \frac{m \cdot \left(1 - m\right)}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right)} \]

      9. lift-/.f64 N/A

         \[\leadsto m \cdot \color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      10. lift-*.f64 N/A

          \[\leadsto m \cdot \frac{\color{blue}{m \cdot \left(1 - m\right)}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      11. *-commutative N/A

          \[\leadsto m \cdot \frac{\color{blue}{\left(1 - m\right) \cdot m}}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      12. associate-/l* N/A

          \[\leadsto m \cdot \color{blue}{\left(\left(1 - m\right) \cdot \frac{m}{v}\right)} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      13. associate-*r* N/A

          \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v}} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      14. lift-*.f64 N/A

          \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right)} \cdot \frac{m}{v} + m \cdot \left(\mathsf{neg}\left(1\right)\right) \]

      15. distribute-rgt-neg-in N/A

          \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \color{blue}{\left(\mathsf{neg}\left(m \cdot 1\right)\right)} \]

      16. *-rgt-identity N/A

          \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} + \left(\mathsf{neg}\left(\color{blue}{m}\right)\right) \]

      17. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(m \cdot \left(1 - m\right), \frac{m}{v}, \mathsf{neg}\left(m\right)\right)} \]

   4. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{m}{v}, -m\right)} \]
   5. Step-by-step derivation

      1. clear-num N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      2. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      3. lower-/.f64 99.8

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{1}{\color{blue}{\frac{v}{m}}}, -m\right) \]

   6. Applied rewrites 99.8%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \color{blue}{\frac{1}{\frac{v}{m}}}, -m\right) \]
   7. Step-by-step derivation

      1. div-inv N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \frac{1}{\color{blue}{v \cdot \frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      2. associate-/r* N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      3. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \frac{\color{blue}{\frac{1}{v}}}{\frac{1}{m}}, \mathsf{neg}\left(m\right)\right) \]

      4. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, \mathsf{neg}\left(m\right)\right) \]

      5. lower-/.f64 99.7

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \frac{\frac{1}{v}}{\color{blue}{\frac{1}{m}}}, -m\right) \]

   8. Applied rewrites 99.7%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(m, -m, m\right), \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}}, -m\right) \]
   9. Step-by-step derivation

      1. lift-neg.f64 N/A

         \[\leadsto \left(m \cdot \color{blue}{\left(\mathsf{neg}\left(m\right)\right)} + m\right) \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      2. lift-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right)} \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      3. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\frac{1}{v}}{\color{blue}{\frac{1}{m}}} + \left(\mathsf{neg}\left(m\right)\right) \]

      4. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\color{blue}{\frac{1}{v}}}{\frac{1}{m}} + \left(\mathsf{neg}\left(m\right)\right) \]

      5. lift-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \color{blue}{\frac{\frac{1}{v}}{\frac{1}{m}}} + \left(\mathsf{neg}\left(m\right)\right) \]

      6. lift-neg.f64 N/A

         \[\leadsto \mathsf{fma}\left(m, \mathsf{neg}\left(m\right), m\right) \cdot \frac{\frac{1}{v}}{\frac{1}{m}} + \color{blue}{\left(\mathsf{neg}\left(m\right)\right)} \]

   10. Applied rewrites 99.8%

       \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{m}{v}, m, -m\right)} \]

   if 9.9999999999999996e-39 < m

   1. Initial program 99.9%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around inf

      \[\leadsto \color{blue}{\left({m}^{2} \cdot \left(\frac{1}{m \cdot v} - \frac{1}{v}\right)\right)} \cdot m \]
   4. Step-by-step derivation

      1. distribute-lft-out-- N/A

         \[\leadsto \color{blue}{\left({m}^{2} \cdot \frac{1}{m \cdot v} - {m}^{2} \cdot \frac{1}{v}\right)} \cdot m \]

      2. associate-/r* N/A

         \[\leadsto \left({m}^{2} \cdot \color{blue}{\frac{\frac{1}{m}}{v}} - {m}^{2} \cdot \frac{1}{v}\right) \cdot m \]

      3. associate-*r/ N/A

         \[\leadsto \left(\color{blue}{\frac{{m}^{2} \cdot \frac{1}{m}}{v}} - {m}^{2} \cdot \frac{1}{v}\right) \cdot m \]

      4. unpow2 N/A

         \[\leadsto \left(\frac{\color{blue}{\left(m \cdot m\right)} \cdot \frac{1}{m}}{v} - {m}^{2} \cdot \frac{1}{v}\right) \cdot m \]

      5. associate-*l* N/A

         \[\leadsto \left(\frac{\color{blue}{m \cdot \left(m \cdot \frac{1}{m}\right)}}{v} - {m}^{2} \cdot \frac{1}{v}\right) \cdot m \]

      6. rgt-mult-inverse N/A

         \[\leadsto \left(\frac{m \cdot \color{blue}{1}}{v} - {m}^{2} \cdot \frac{1}{v}\right) \cdot m \]

      7. *-rgt-identity N/A

         \[\leadsto \left(\frac{\color{blue}{m}}{v} - {m}^{2} \cdot \frac{1}{v}\right) \cdot m \]

      8. *-rgt-identity N/A

         \[\leadsto \left(\frac{\color{blue}{m \cdot 1}}{v} - {m}^{2} \cdot \frac{1}{v}\right) \cdot m \]

      9. associate-*r/ N/A

         \[\leadsto \left(\color{blue}{m \cdot \frac{1}{v}} - {m}^{2} \cdot \frac{1}{v}\right) \cdot m \]

      10. unpow2 N/A

          \[\leadsto \left(m \cdot \frac{1}{v} - \color{blue}{\left(m \cdot m\right)} \cdot \frac{1}{v}\right) \cdot m \]

      11. associate-*r* N/A

          \[\leadsto \left(m \cdot \frac{1}{v} - \color{blue}{m \cdot \left(m \cdot \frac{1}{v}\right)}\right) \cdot m \]

      12. associate-*r/ N/A

          \[\leadsto \left(m \cdot \frac{1}{v} - m \cdot \color{blue}{\frac{m \cdot 1}{v}}\right) \cdot m \]

      13. *-rgt-identity N/A

          \[\leadsto \left(m \cdot \frac{1}{v} - m \cdot \frac{\color{blue}{m}}{v}\right) \cdot m \]

      14. distribute-lft-out-- N/A

          \[\leadsto \color{blue}{\left(m \cdot \left(\frac{1}{v} - \frac{m}{v}\right)\right)} \cdot m \]

      15. div-sub N/A

          \[\leadsto \left(m \cdot \color{blue}{\frac{1 - m}{v}}\right) \cdot m \]

      16. associate-/l* N/A

          \[\leadsto \color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} \cdot m \]

      17. lower-/.f64 N/A

          \[\leadsto \color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} \cdot m \]

      18. distribute-rgt-out-- N/A

          \[\leadsto \frac{\color{blue}{1 \cdot m - m \cdot m}}{v} \cdot m \]

      19. *-lft-identity N/A

          \[\leadsto \frac{\color{blue}{m} - m \cdot m}{v} \cdot m \]

      20. unpow2 N/A

          \[\leadsto \frac{m - \color{blue}{{m}^{2}}}{v} \cdot m \]

      21. lower--.f64 N/A

          \[\leadsto \frac{\color{blue}{m - {m}^{2}}}{v} \cdot m \]

      22. unpow2 N/A

          \[\leadsto \frac{m - \color{blue}{m \cdot m}}{v} \cdot m \]

      23. lower-*.f64 99.9

          \[\leadsto \frac{m - \color{blue}{m \cdot m}}{v} \cdot m \]

   5. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\frac{m - m \cdot m}{v}} \cdot m \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;m \leq 10^{-38}:\\ \;\;\;\;\mathsf{fma}\left(\frac{m}{v}, m, -m\right)\\ \mathbf{else}:\\ \;\;\;\;m \cdot \frac{m - m \cdot m}{v}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} + -1\right) \end{array} \]

FPCore

(FPCore (m v) :precision binary64 (* m (+ (/ (* m (- 1.0 m)) v) -1.0)))

C

double code(double m, double v) {
	return m * (((m * (1.0 - m)) / v) + -1.0);
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    code = m * (((m * (1.0d0 - m)) / v) + (-1.0d0))
end function

Java

public static double code(double m, double v) {
	return m * (((m * (1.0 - m)) / v) + -1.0);
}

Python

def code(m, v):
	return m * (((m * (1.0 - m)) / v) + -1.0)

Julia

function code(m, v)
	return Float64(m * Float64(Float64(Float64(m * Float64(1.0 - m)) / v) + -1.0))
end

MATLAB

function tmp = code(m, v)
	tmp = m * (((m * (1.0 - m)) / v) + -1.0);
end

Wolfram

code[m_, v_] := N[(m * N[(N[(N[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
2. Add Preprocessing

3. Final simplification 99.8%

   \[\leadsto m \cdot \left(\frac{m \cdot \left(1 - m\right)}{v} + -1\right) \]
4. Add Preprocessing

Alternative 8: 45.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;m \leq 0.004:\\ \;\;\;\;\frac{m \cdot m}{v} - m\\ \mathbf{else}:\\ \;\;\;\;-m\\ \end{array} \end{array} \]

FPCore

(FPCore (m v) :precision binary64 (if (<= m 0.004) (- (/ (* m m) v) m) (- m)))

C

double code(double m, double v) {
	double tmp;
	if (m <= 0.004) {
		tmp = ((m * m) / v) - m;
	} else {
		tmp = -m;
	}
	return tmp;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    real(8) :: tmp
    if (m <= 0.004d0) then
        tmp = ((m * m) / v) - m
    else
        tmp = -m
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	double tmp;
	if (m <= 0.004) {
		tmp = ((m * m) / v) - m;
	} else {
		tmp = -m;
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if m <= 0.004:
		tmp = ((m * m) / v) - m
	else:
		tmp = -m
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (m <= 0.004)
		tmp = Float64(Float64(Float64(m * m) / v) - m);
	else
		tmp = Float64(-m);
	end
	return tmp
end

MATLAB

function tmp_2 = code(m, v)
	tmp = 0.0;
	if (m <= 0.004)
		tmp = ((m * m) / v) - m;
	else
		tmp = -m;
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[m, 0.004], N[(N[(N[(m * m), $MachinePrecision] / v), $MachinePrecision] - m), $MachinePrecision], (-m)]

Derivation

1. Split input into 2 regimes

2. if m < 0.0040000000000000001

   1. Initial program 99.7%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around 0

      \[\leadsto \color{blue}{m \cdot \left(\frac{m}{v} - 1\right)} \]
   4. Step-by-step derivation

      1. distribute-lft-out-- N/A

         \[\leadsto \color{blue}{m \cdot \frac{m}{v} - m \cdot 1} \]

      2. associate-/l* N/A

         \[\leadsto \color{blue}{\frac{m \cdot m}{v}} - m \cdot 1 \]

      3. unpow2 N/A

         \[\leadsto \frac{\color{blue}{{m}^{2}}}{v} - m \cdot 1 \]

      4. *-rgt-identity N/A

         \[\leadsto \frac{{m}^{2}}{v} - \color{blue}{m} \]

      5. lower--.f64 N/A

         \[\leadsto \color{blue}{\frac{{m}^{2}}{v} - m} \]

      6. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{{m}^{2}}{v}} - m \]

      7. unpow2 N/A

         \[\leadsto \frac{\color{blue}{m \cdot m}}{v} - m \]

      8. lower-*.f64 83.6

         \[\leadsto \frac{\color{blue}{m \cdot m}}{v} - m \]

   5. Applied rewrites 83.6%

      \[\leadsto \color{blue}{\frac{m \cdot m}{v} - m} \]

   if 0.0040000000000000001 < m

   1. Initial program 99.9%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around 0

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

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(m\right)} \]

      2. lower-neg.f64 5.8

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

   5. Applied rewrites 5.8%

      \[\leadsto \color{blue}{-m} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 99.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{m}{v} \cdot \left(m - \mathsf{fma}\left(m, m, v\right)\right) \end{array} \]

FPCore

(FPCore (m v) :precision binary64 (* (/ m v) (- m (fma m m v))))

C

double code(double m, double v) {
	return (m / v) * (m - fma(m, m, v));
}

Julia

function code(m, v)
	return Float64(Float64(m / v) * Float64(m - fma(m, m, v)))
end

Wolfram

code[m_, v_] := N[(N[(m / v), $MachinePrecision] * N[(m - N[(m * m + v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
2. Add Preprocessing

3. Taylor expanded in m around 0

   \[\leadsto \color{blue}{m \cdot \left(m \cdot \left(-1 \cdot \frac{m}{v} + \frac{1}{v}\right) - 1\right)} \]
4. Step-by-step derivation

   1. distribute-lft-out-- N/A

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

   2. +-commutative N/A

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

   3. mul-1-neg N/A

      \[\leadsto m \cdot \left(m \cdot \left(\frac{1}{v} + \color{blue}{\left(\mathsf{neg}\left(\frac{m}{v}\right)\right)}\right)\right) - m \cdot 1 \]

   4. unsub-neg N/A

      \[\leadsto m \cdot \left(m \cdot \color{blue}{\left(\frac{1}{v} - \frac{m}{v}\right)}\right) - m \cdot 1 \]

   5. div-sub N/A

      \[\leadsto m \cdot \left(m \cdot \color{blue}{\frac{1 - m}{v}}\right) - m \cdot 1 \]

   6. associate-/l* N/A

      \[\leadsto m \cdot \color{blue}{\frac{m \cdot \left(1 - m\right)}{v}} - m \cdot 1 \]

   7. *-commutative N/A

      \[\leadsto m \cdot \frac{\color{blue}{\left(1 - m\right) \cdot m}}{v} - m \cdot 1 \]

   8. associate-/l* N/A

      \[\leadsto m \cdot \color{blue}{\left(\left(1 - m\right) \cdot \frac{m}{v}\right)} - m \cdot 1 \]

   9. associate-*r* N/A

      \[\leadsto \color{blue}{\left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v}} - m \cdot 1 \]

   10. *-inverses N/A

       \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} - m \cdot \color{blue}{\frac{v}{v}} \]

   11. associate-/l* N/A

       \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} - \color{blue}{\frac{m \cdot v}{v}} \]

   12. *-commutative N/A

       \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} - \frac{\color{blue}{v \cdot m}}{v} \]

   13. associate-/l* N/A

       \[\leadsto \left(m \cdot \left(1 - m\right)\right) \cdot \frac{m}{v} - \color{blue}{v \cdot \frac{m}{v}} \]

   14. distribute-rgt-out-- N/A

       \[\leadsto \color{blue}{\frac{m}{v} \cdot \left(m \cdot \left(1 - m\right) - v\right)} \]

   15. unsub-neg N/A

       \[\leadsto \frac{m}{v} \cdot \color{blue}{\left(m \cdot \left(1 - m\right) + \left(\mathsf{neg}\left(v\right)\right)\right)} \]

   16. mul-1-neg N/A

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

   17. +-commutative N/A

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

   18. lower-*.f64 N/A

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

   19. lower-/.f64 N/A

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

   20. +-commutative N/A

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

5. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\frac{m}{v} \cdot \left(m - \mathsf{fma}\left(m, m, v\right)\right)} \]
6. Add Preprocessing

Alternative 10: 27.4% accurate, 9.3× speedup

Math

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

FPCore

(FPCore (m v) :precision binary64 (- m))

C

double code(double m, double v) {
	return -m;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    code = -m
end function

Java

public static double code(double m, double v) {
	return -m;
}

Python

def code(m, v):
	return -m

Julia

function code(m, v)
	return Float64(-m)
end

MATLAB

function tmp = code(m, v)
	tmp = -m;
end

Wolfram

code[m_, v_] := (-m)

Derivation

1. Initial program 99.8%

   \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
2. Add Preprocessing

3. Taylor expanded in m around 0

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

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(m\right)} \]

   2. lower-neg.f64 29.6

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

5. Applied rewrites 29.6%

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

Reproduce

herbie shell --seed 2024212 
(FPCore (m v)
  :name "a parameter of renormalized beta distribution"
  :precision binary64
  :pre (and (and (< 0.0 m) (< 0.0 v)) (< v 0.25))
  (* (- (/ (* m (- 1.0 m)) v) 1.0) m))