Numeric.SpecFunctions:logGammaL from math-functions-0.1.5.2

Percentage Accurate: 99.6% → 99.6%

Time: 17.2s

Alternatives: 16

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (- (+ (log (+ x y)) (log z)) t) (* (- a 0.5) (log t))))

C

double code(double x, double y, double z, double t, double a) {
	return ((log((x + y)) + log(z)) - t) + ((a - 0.5) * log(t));
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = ((log((x + y)) + log(z)) - t) + ((a - 0.5d0) * log(t))
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return ((Math.log((x + y)) + Math.log(z)) - t) + ((a - 0.5) * Math.log(t));
}

Python

def code(x, y, z, t, a):
	return ((math.log((x + y)) + math.log(z)) - t) + ((a - 0.5) * math.log(t))

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(log(Float64(x + y)) + log(z)) - t) + Float64(Float64(a - 0.5) * log(t)))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = ((log((x + y)) + log(z)) - t) + ((a - 0.5) * log(t));
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (- (+ (log (+ x y)) (log z)) t) (* (- a 0.5) (log t))))

C

double code(double x, double y, double z, double t, double a) {
	return ((log((x + y)) + log(z)) - t) + ((a - 0.5) * log(t));
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = ((log((x + y)) + log(z)) - t) + ((a - 0.5d0) * log(t))
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return ((Math.log((x + y)) + Math.log(z)) - t) + ((a - 0.5) * Math.log(t));
}

Python

def code(x, y, z, t, a):
	return ((math.log((x + y)) + math.log(z)) - t) + ((a - 0.5) * math.log(t))

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(log(Float64(x + y)) + log(z)) - t) + Float64(Float64(a - 0.5) * log(t)))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = ((log((x + y)) + log(z)) - t) + ((a - 0.5) * log(t));
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (- (+ (log (+ x y)) (log z)) t) (* (- a 0.5) (log t))))

C

double code(double x, double y, double z, double t, double a) {
	return ((log((x + y)) + log(z)) - t) + ((a - 0.5) * log(t));
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = ((log((x + y)) + log(z)) - t) + ((a - 0.5d0) * log(t))
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return ((Math.log((x + y)) + Math.log(z)) - t) + ((a - 0.5) * Math.log(t));
}

Python

def code(x, y, z, t, a):
	return ((math.log((x + y)) + math.log(z)) - t) + ((a - 0.5) * math.log(t))

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(log(Float64(x + y)) + log(z)) - t) + Float64(Float64(a - 0.5) * log(t)))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = ((log((x + y)) + log(z)) - t) + ((a - 0.5) * log(t));
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 2: 97.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a - 0.5\right) \cdot \log t\\ t_2 := \log \left(x + y\right)\\ t_3 := \left(\left(t\_2 + \log z\right) - t\right) + t\_1\\ \mathbf{if}\;t\_3 \leq -1000:\\ \;\;\;\;\log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right)\\ \mathbf{elif}\;t\_3 \leq 2000:\\ \;\;\;\;\log z + \mathsf{fma}\left(\log t, -0.5, t\_2\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1 + \left(-t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* (- a 0.5) (log t)))
        (t_2 (log (+ x y)))
        (t_3 (+ (- (+ t_2 (log z)) t) t_1)))
   (if (<= t_3 -1000.0)
     (+ (log z) (fma (log t) (+ a -0.5) (- t)))
     (if (<= t_3 2000.0) (+ (log z) (fma (log t) -0.5 t_2)) (+ t_1 (- t))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (a - 0.5) * log(t);
	double t_2 = log((x + y));
	double t_3 = ((t_2 + log(z)) - t) + t_1;
	double tmp;
	if (t_3 <= -1000.0) {
		tmp = log(z) + fma(log(t), (a + -0.5), -t);
	} else if (t_3 <= 2000.0) {
		tmp = log(z) + fma(log(t), -0.5, t_2);
	} else {
		tmp = t_1 + -t;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(a - 0.5) * log(t))
	t_2 = log(Float64(x + y))
	t_3 = Float64(Float64(Float64(t_2 + log(z)) - t) + t_1)
	tmp = 0.0
	if (t_3 <= -1000.0)
		tmp = Float64(log(z) + fma(log(t), Float64(a + -0.5), Float64(-t)));
	elseif (t_3 <= 2000.0)
		tmp = Float64(log(z) + fma(log(t), -0.5, t_2));
	else
		tmp = Float64(t_1 + Float64(-t));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(a - 0.5), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(t$95$2 + N[Log[z], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision] + t$95$1), $MachinePrecision]}, If[LessEqual[t$95$3, -1000.0], N[(N[Log[z], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * N[(a + -0.5), $MachinePrecision] + (-t)), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, 2000.0], N[(N[Log[z], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * -0.5 + t$95$2), $MachinePrecision]), $MachinePrecision], N[(t$95$1 + (-t)), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t))) < -1e3

   1. Initial program 99.8%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

      2. flip-+ N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}} \]

      3. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      5. clear-num N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}}}} \]

   4. Applied rewrites 73.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{1}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t}}} \]

   5. Taylor expanded in a around inf

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{a \cdot \log t}}} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      3. lower-log.f64 32.1

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t} \cdot a}} \]

   7. Applied rewrites 32.1%

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

   8. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\left(\log z + \left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right)\right) - t} \]
   9. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      3. lower-log.f64 N/A

         \[\leadsto \color{blue}{\log z} + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right) \]

      4. +-commutative N/A

         \[\leadsto \log z + \left(\color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + -1 \cdot \log \left(\frac{1}{y}\right)\right)} - t\right) \]

      5. mul-1-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\left(\mathsf{neg}\left(\log \left(\frac{1}{y}\right)\right)\right)}\right) - t\right) \]

      6. log-rec N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(\log y\right)\right)}\right)\right)\right) - t\right) \]

      7. remove-double-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\log y}\right) - t\right) \]

      8. associate--l+ N/A

         \[\leadsto \log z + \color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\log y - t\right)\right)} \]

      9. lower-fma.f64 N/A

         \[\leadsto \log z + \color{blue}{\mathsf{fma}\left(\log t, a - \frac{1}{2}, \log y - t\right)} \]

      10. lower-log.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\color{blue}{\log t}, a - \frac{1}{2}, \log y - t\right) \]

      11. sub-neg N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log y - t\right) \]

      12. metadata-eval N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \color{blue}{\frac{-1}{2}}, \log y - t\right) \]

      13. lower-+.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \frac{-1}{2}}, \log y - t\right) \]

      14. lower--.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log y - t}\right) \]

      15. lower-log.f64 82.1

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, \color{blue}{\log y} - t\right) \]

   10. Applied rewrites 82.1%

       \[\leadsto \color{blue}{\log z + \mathsf{fma}\left(\log t, a + -0.5, \log y - t\right)} \]

   11. Taylor expanded in t around inf

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, -1 \cdot t\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 98.6%

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right) \]

   if -1e3 < (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t))) < 2e3

   1. Initial program 99.3%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(\log z + \left(\log \left(x + y\right) + \frac{-1}{2} \cdot \log t\right)\right) - t} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \frac{-1}{2} \cdot \log t\right) + \log z\right)} - t \]

      2. associate-+l+ N/A

         \[\leadsto \color{blue}{\left(\log \left(x + y\right) + \left(\frac{-1}{2} \cdot \log t + \log z\right)\right)} - t \]

      3. associate--l+ N/A

         \[\leadsto \color{blue}{\log \left(x + y\right) + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right)} \]

      4. lower-+.f64 N/A

         \[\leadsto \color{blue}{\log \left(x + y\right) + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right)} \]

      5. lower-log.f64 N/A

         \[\leadsto \color{blue}{\log \left(x + y\right)} + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right) \]

      6. +-commutative N/A

         \[\leadsto \log \color{blue}{\left(y + x\right)} + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right) \]

      7. lower-+.f64 N/A

         \[\leadsto \log \color{blue}{\left(y + x\right)} + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right) \]

      8. lower--.f64 N/A

         \[\leadsto \log \left(y + x\right) + \color{blue}{\left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right)} \]

      9. *-commutative N/A

         \[\leadsto \log \left(y + x\right) + \left(\left(\color{blue}{\log t \cdot \frac{-1}{2}} + \log z\right) - t\right) \]

      10. lower-fma.f64 N/A

          \[\leadsto \log \left(y + x\right) + \left(\color{blue}{\mathsf{fma}\left(\log t, \frac{-1}{2}, \log z\right)} - t\right) \]

      11. lower-log.f64 N/A

          \[\leadsto \log \left(y + x\right) + \left(\mathsf{fma}\left(\color{blue}{\log t}, \frac{-1}{2}, \log z\right) - t\right) \]

      12. lower-log.f64 98.1

          \[\leadsto \log \left(y + x\right) + \left(\mathsf{fma}\left(\log t, -0.5, \color{blue}{\log z}\right) - t\right) \]

   5. Applied rewrites 98.1%

      \[\leadsto \color{blue}{\log \left(y + x\right) + \left(\mathsf{fma}\left(\log t, -0.5, \log z\right) - t\right)} \]

   6. Taylor expanded in t around 0

      \[\leadsto \log z + \color{blue}{\left(\log \left(x + y\right) + \frac{-1}{2} \cdot \log t\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 97.3%

      \[\leadsto \log z + \color{blue}{\mathsf{fma}\left(\log t, -0.5, \log \left(y + x\right)\right)} \]

   if 2e3 < (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t)))

   1. Initial program 99.6%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf

      \[\leadsto \color{blue}{-1 \cdot t} + \left(a - \frac{1}{2}\right) \cdot \log t \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(t\right)\right)} + \left(a - \frac{1}{2}\right) \cdot \log t \]

      2. lower-neg.f64 99.4

         \[\leadsto \color{blue}{\left(-t\right)} + \left(a - 0.5\right) \cdot \log t \]

   5. Applied rewrites 99.4%

      \[\leadsto \color{blue}{\left(-t\right)} + \left(a - 0.5\right) \cdot \log t \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \leq -1000:\\ \;\;\;\;\log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right)\\ \mathbf{elif}\;\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \leq 2000:\\ \;\;\;\;\log z + \mathsf{fma}\left(\log t, -0.5, \log \left(x + y\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(a - 0.5\right) \cdot \log t + \left(-t\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 93.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t\\ t_2 := \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right)\\ \mathbf{if}\;t\_1 \leq -120000000000:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 1000:\\ \;\;\;\;\mathsf{fma}\left(\log t, -0.5, \log \left(\left(x + y\right) \cdot z\right)\right) - t\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (- (+ (log (+ x y)) (log z)) t) (* (- a 0.5) (log t))))
        (t_2 (+ (log z) (fma (log t) (+ a -0.5) (- t)))))
   (if (<= t_1 -120000000000.0)
     t_2
     (if (<= t_1 1000.0) (- (fma (log t) -0.5 (log (* (+ x y) z))) t) t_2))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = ((log((x + y)) + log(z)) - t) + ((a - 0.5) * log(t));
	double t_2 = log(z) + fma(log(t), (a + -0.5), -t);
	double tmp;
	if (t_1 <= -120000000000.0) {
		tmp = t_2;
	} else if (t_1 <= 1000.0) {
		tmp = fma(log(t), -0.5, log(((x + y) * z))) - t;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(Float64(log(Float64(x + y)) + log(z)) - t) + Float64(Float64(a - 0.5) * log(t)))
	t_2 = Float64(log(z) + fma(log(t), Float64(a + -0.5), Float64(-t)))
	tmp = 0.0
	if (t_1 <= -120000000000.0)
		tmp = t_2;
	elseif (t_1 <= 1000.0)
		tmp = Float64(fma(log(t), -0.5, log(Float64(Float64(x + y) * z))) - t);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(N[(N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[Log[z], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * N[(a + -0.5), $MachinePrecision] + (-t)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -120000000000.0], t$95$2, If[LessEqual[t$95$1, 1000.0], N[(N[(N[Log[t], $MachinePrecision] * -0.5 + N[Log[N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t))) < -1.2e11 or 1e3 < (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t)))

   1. Initial program 99.8%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

      2. flip-+ N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}} \]

      3. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      5. clear-num N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}}}} \]

   4. Applied rewrites 70.4%

      \[\leadsto \color{blue}{\frac{1}{\frac{1}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t}}} \]

   5. Taylor expanded in a around inf

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{a \cdot \log t}}} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      3. lower-log.f64 48.9

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t} \cdot a}} \]

   7. Applied rewrites 48.9%

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

   8. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\left(\log z + \left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right)\right) - t} \]
   9. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      3. lower-log.f64 N/A

         \[\leadsto \color{blue}{\log z} + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right) \]

      4. +-commutative N/A

         \[\leadsto \log z + \left(\color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + -1 \cdot \log \left(\frac{1}{y}\right)\right)} - t\right) \]

      5. mul-1-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\left(\mathsf{neg}\left(\log \left(\frac{1}{y}\right)\right)\right)}\right) - t\right) \]

      6. log-rec N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(\log y\right)\right)}\right)\right)\right) - t\right) \]

      7. remove-double-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\log y}\right) - t\right) \]

      8. associate--l+ N/A

         \[\leadsto \log z + \color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\log y - t\right)\right)} \]

      9. lower-fma.f64 N/A

         \[\leadsto \log z + \color{blue}{\mathsf{fma}\left(\log t, a - \frac{1}{2}, \log y - t\right)} \]

      10. lower-log.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\color{blue}{\log t}, a - \frac{1}{2}, \log y - t\right) \]

      11. sub-neg N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log y - t\right) \]

      12. metadata-eval N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \color{blue}{\frac{-1}{2}}, \log y - t\right) \]

      13. lower-+.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \frac{-1}{2}}, \log y - t\right) \]

      14. lower--.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log y - t}\right) \]

      15. lower-log.f64 80.1

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, \color{blue}{\log y} - t\right) \]

   10. Applied rewrites 80.1%

       \[\leadsto \color{blue}{\log z + \mathsf{fma}\left(\log t, a + -0.5, \log y - t\right)} \]

   11. Taylor expanded in t around inf

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, -1 \cdot t\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 96.2%

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right) \]

   if -1.2e11 < (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t))) < 1e3

   1. Initial program 99.2%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

      2. flip-+ N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}} \]

      3. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      5. clear-num N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}}}} \]

   4. Applied rewrites 89.1%

      \[\leadsto \color{blue}{\frac{1}{\frac{1}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t}}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(\log \left(z \cdot \left(x + y\right)\right) + \frac{-1}{2} \cdot \log t\right) - t} \]
   6. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \color{blue}{\left(\log \left(z \cdot \left(x + y\right)\right) + \frac{-1}{2} \cdot \log t\right) - t} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{-1}{2} \cdot \log t + \log \left(z \cdot \left(x + y\right)\right)\right)} - t \]

      3. *-commutative N/A

         \[\leadsto \left(\color{blue}{\log t \cdot \frac{-1}{2}} + \log \left(z \cdot \left(x + y\right)\right)\right) - t \]

      4. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\log t, \frac{-1}{2}, \log \left(z \cdot \left(x + y\right)\right)\right)} - t \]

      5. lower-log.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\log t}, \frac{-1}{2}, \log \left(z \cdot \left(x + y\right)\right)\right) - t \]

      6. lower-log.f64 N/A

         \[\leadsto \mathsf{fma}\left(\log t, \frac{-1}{2}, \color{blue}{\log \left(z \cdot \left(x + y\right)\right)}\right) - t \]

      7. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\log t, \frac{-1}{2}, \log \color{blue}{\left(z \cdot \left(x + y\right)\right)}\right) - t \]

      8. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\log t, \frac{-1}{2}, \log \left(z \cdot \color{blue}{\left(y + x\right)}\right)\right) - t \]

      9. lower-+.f64 89.2

         \[\leadsto \mathsf{fma}\left(\log t, -0.5, \log \left(z \cdot \color{blue}{\left(y + x\right)}\right)\right) - t \]

   7. Applied rewrites 89.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\log t, -0.5, \log \left(z \cdot \left(y + x\right)\right)\right) - t} \]
3. Recombined 2 regimes into one program.

4. Final simplification 94.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \leq -120000000000:\\ \;\;\;\;\log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right)\\ \mathbf{elif}\;\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \leq 1000:\\ \;\;\;\;\mathsf{fma}\left(\log t, -0.5, \log \left(\left(x + y\right) \cdot z\right)\right) - t\\ \mathbf{else}:\\ \;\;\;\;\log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 94.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \log \left(x + y\right) + \log z\\ t_2 := \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right)\\ \mathbf{if}\;t\_1 \leq -750:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 690:\\ \;\;\;\;\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (log (+ x y)) (log z)))
        (t_2 (+ (log z) (fma (log t) (+ a -0.5) (- t)))))
   (if (<= t_1 -750.0)
     t_2
     (if (<= t_1 690.0)
       (- (fma (+ a -0.5) (log t) (log (* (+ x y) z))) t)
       t_2))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = log((x + y)) + log(z);
	double t_2 = log(z) + fma(log(t), (a + -0.5), -t);
	double tmp;
	if (t_1 <= -750.0) {
		tmp = t_2;
	} else if (t_1 <= 690.0) {
		tmp = fma((a + -0.5), log(t), log(((x + y) * z))) - t;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(log(Float64(x + y)) + log(z))
	t_2 = Float64(log(z) + fma(log(t), Float64(a + -0.5), Float64(-t)))
	tmp = 0.0
	if (t_1 <= -750.0)
		tmp = t_2;
	elseif (t_1 <= 690.0)
		tmp = Float64(fma(Float64(a + -0.5), log(t), log(Float64(Float64(x + y) * z))) - t);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[Log[z], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * N[(a + -0.5), $MachinePrecision] + (-t)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -750.0], t$95$2, If[LessEqual[t$95$1, 690.0], N[(N[(N[(a + -0.5), $MachinePrecision] * N[Log[t], $MachinePrecision] + N[Log[N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) < -750 or 690 < (+.f64 (log.f64 (+.f64 x y)) (log.f64 z))

   1. Initial program 99.7%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

      2. flip-+ N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}} \]

      3. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      5. clear-num N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}}}} \]

   4. Applied rewrites 7.9%

      \[\leadsto \color{blue}{\frac{1}{\frac{1}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t}}} \]

   5. Taylor expanded in a around inf

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{a \cdot \log t}}} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      3. lower-log.f64 39.1

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t} \cdot a}} \]

   7. Applied rewrites 39.1%

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

   8. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\left(\log z + \left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right)\right) - t} \]
   9. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      3. lower-log.f64 N/A

         \[\leadsto \color{blue}{\log z} + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right) \]

      4. +-commutative N/A

         \[\leadsto \log z + \left(\color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + -1 \cdot \log \left(\frac{1}{y}\right)\right)} - t\right) \]

      5. mul-1-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\left(\mathsf{neg}\left(\log \left(\frac{1}{y}\right)\right)\right)}\right) - t\right) \]

      6. log-rec N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(\log y\right)\right)}\right)\right)\right) - t\right) \]

      7. remove-double-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\log y}\right) - t\right) \]

      8. associate--l+ N/A

         \[\leadsto \log z + \color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\log y - t\right)\right)} \]

      9. lower-fma.f64 N/A

         \[\leadsto \log z + \color{blue}{\mathsf{fma}\left(\log t, a - \frac{1}{2}, \log y - t\right)} \]

      10. lower-log.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\color{blue}{\log t}, a - \frac{1}{2}, \log y - t\right) \]

      11. sub-neg N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log y - t\right) \]

      12. metadata-eval N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \color{blue}{\frac{-1}{2}}, \log y - t\right) \]

      13. lower-+.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \frac{-1}{2}}, \log y - t\right) \]

      14. lower--.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log y - t}\right) \]

      15. lower-log.f64 72.5

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, \color{blue}{\log y} - t\right) \]

   10. Applied rewrites 72.5%

       \[\leadsto \color{blue}{\log z + \mathsf{fma}\left(\log t, a + -0.5, \log y - t\right)} \]

   11. Taylor expanded in t around inf

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, -1 \cdot t\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 83.3%

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right) \]

   if -750 < (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) < 690

   1. Initial program 99.6%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{\left(a - \frac{1}{2}\right) \cdot \log t + \left(\left(\log \left(x + y\right) + \log z\right) - t\right)} \]

      3. lift--.f64 N/A

         \[\leadsto \left(a - \frac{1}{2}\right) \cdot \log t + \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right)} \]

      4. associate-+r- N/A

         \[\leadsto \color{blue}{\left(\left(a - \frac{1}{2}\right) \cdot \log t + \left(\log \left(x + y\right) + \log z\right)\right) - t} \]

      5. lower--.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a - \frac{1}{2}\right) \cdot \log t + \left(\log \left(x + y\right) + \log z\right)\right) - t} \]

      6. lift-*.f64 N/A

         \[\leadsto \left(\color{blue}{\left(a - \frac{1}{2}\right) \cdot \log t} + \left(\log \left(x + y\right) + \log z\right)\right) - t \]

      7. lower-fma.f64 99.6

         \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, \log t, \log \left(x + y\right) + \log z\right)} - t \]

      8. lift--.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{a - \frac{1}{2}}, \log t, \log \left(x + y\right) + \log z\right) - t \]

      9. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log t, \log \left(x + y\right) + \log z\right) - t \]

      10. lower-+.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log t, \log \left(x + y\right) + \log z\right) - t \]

      11. metadata-eval 99.6

          \[\leadsto \mathsf{fma}\left(a + \color{blue}{-0.5}, \log t, \log \left(x + y\right) + \log z\right) - t \]

      12. lift-+.f64 N/A

          \[\leadsto \mathsf{fma}\left(a + \frac{-1}{2}, \log t, \color{blue}{\log \left(x + y\right) + \log z}\right) - t \]

      13. lift-log.f64 N/A

          \[\leadsto \mathsf{fma}\left(a + \frac{-1}{2}, \log t, \color{blue}{\log \left(x + y\right)} + \log z\right) - t \]

      14. lift-log.f64 N/A

          \[\leadsto \mathsf{fma}\left(a + \frac{-1}{2}, \log t, \log \left(x + y\right) + \color{blue}{\log z}\right) - t \]

      15. sum-log N/A

          \[\leadsto \mathsf{fma}\left(a + \frac{-1}{2}, \log t, \color{blue}{\log \left(\left(x + y\right) \cdot z\right)}\right) - t \]

      16. lower-log.f64 N/A

          \[\leadsto \mathsf{fma}\left(a + \frac{-1}{2}, \log t, \color{blue}{\log \left(\left(x + y\right) \cdot z\right)}\right) - t \]

      17. lower-*.f64 99.7

          \[\leadsto \mathsf{fma}\left(a + -0.5, \log t, \log \color{blue}{\left(\left(x + y\right) \cdot z\right)}\right) - t \]

   4. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 68.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \log \left(x + y\right) + \log z\\ t_2 := \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right)\\ \mathbf{if}\;t\_1 \leq -750:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 690:\\ \;\;\;\;\mathsf{fma}\left(\log t, a + -0.5, \log \left(y \cdot z\right)\right) - t\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (log (+ x y)) (log z)))
        (t_2 (+ (log z) (fma (log t) (+ a -0.5) (- t)))))
   (if (<= t_1 -750.0)
     t_2
     (if (<= t_1 690.0) (- (fma (log t) (+ a -0.5) (log (* y z))) t) t_2))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = log((x + y)) + log(z);
	double t_2 = log(z) + fma(log(t), (a + -0.5), -t);
	double tmp;
	if (t_1 <= -750.0) {
		tmp = t_2;
	} else if (t_1 <= 690.0) {
		tmp = fma(log(t), (a + -0.5), log((y * z))) - t;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(log(Float64(x + y)) + log(z))
	t_2 = Float64(log(z) + fma(log(t), Float64(a + -0.5), Float64(-t)))
	tmp = 0.0
	if (t_1 <= -750.0)
		tmp = t_2;
	elseif (t_1 <= 690.0)
		tmp = Float64(fma(log(t), Float64(a + -0.5), log(Float64(y * z))) - t);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[Log[z], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * N[(a + -0.5), $MachinePrecision] + (-t)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -750.0], t$95$2, If[LessEqual[t$95$1, 690.0], N[(N[(N[Log[t], $MachinePrecision] * N[(a + -0.5), $MachinePrecision] + N[Log[N[(y * z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) < -750 or 690 < (+.f64 (log.f64 (+.f64 x y)) (log.f64 z))

   1. Initial program 99.7%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

      2. flip-+ N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}} \]

      3. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      5. clear-num N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}}}} \]

   4. Applied rewrites 7.9%

      \[\leadsto \color{blue}{\frac{1}{\frac{1}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t}}} \]

   5. Taylor expanded in a around inf

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{a \cdot \log t}}} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      3. lower-log.f64 39.1

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t} \cdot a}} \]

   7. Applied rewrites 39.1%

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

   8. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\left(\log z + \left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right)\right) - t} \]
   9. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      3. lower-log.f64 N/A

         \[\leadsto \color{blue}{\log z} + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right) \]

      4. +-commutative N/A

         \[\leadsto \log z + \left(\color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + -1 \cdot \log \left(\frac{1}{y}\right)\right)} - t\right) \]

      5. mul-1-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\left(\mathsf{neg}\left(\log \left(\frac{1}{y}\right)\right)\right)}\right) - t\right) \]

      6. log-rec N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(\log y\right)\right)}\right)\right)\right) - t\right) \]

      7. remove-double-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\log y}\right) - t\right) \]

      8. associate--l+ N/A

         \[\leadsto \log z + \color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\log y - t\right)\right)} \]

      9. lower-fma.f64 N/A

         \[\leadsto \log z + \color{blue}{\mathsf{fma}\left(\log t, a - \frac{1}{2}, \log y - t\right)} \]

      10. lower-log.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\color{blue}{\log t}, a - \frac{1}{2}, \log y - t\right) \]

      11. sub-neg N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log y - t\right) \]

      12. metadata-eval N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \color{blue}{\frac{-1}{2}}, \log y - t\right) \]

      13. lower-+.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \frac{-1}{2}}, \log y - t\right) \]

      14. lower--.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log y - t}\right) \]

      15. lower-log.f64 72.5

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, \color{blue}{\log y} - t\right) \]

   10. Applied rewrites 72.5%

       \[\leadsto \color{blue}{\log z + \mathsf{fma}\left(\log t, a + -0.5, \log y - t\right)} \]

   11. Taylor expanded in t around inf

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, -1 \cdot t\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 83.3%

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right) \]

   if -750 < (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) < 690

   1. Initial program 99.6%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

      2. flip-+ N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}} \]

      3. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      5. clear-num N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}}}} \]

   4. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\frac{1}{\frac{1}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t}}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(\log \left(y \cdot z\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t} \]
   6. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \color{blue}{\left(\log \left(y \cdot z\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + \log \left(y \cdot z\right)\right)} - t \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\log t, a - \frac{1}{2}, \log \left(y \cdot z\right)\right)} - t \]

      4. lower-log.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\log t}, a - \frac{1}{2}, \log \left(y \cdot z\right)\right) - t \]

      5. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\log t, \color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log \left(y \cdot z\right)\right) - t \]

      6. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\log t, a + \color{blue}{\frac{-1}{2}}, \log \left(y \cdot z\right)\right) - t \]

      7. lower-+.f64 N/A

         \[\leadsto \mathsf{fma}\left(\log t, \color{blue}{a + \frac{-1}{2}}, \log \left(y \cdot z\right)\right) - t \]

      8. lower-log.f64 N/A

         \[\leadsto \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log \left(y \cdot z\right)}\right) - t \]

      9. lower-*.f64 71.4

         \[\leadsto \mathsf{fma}\left(\log t, a + -0.5, \log \color{blue}{\left(y \cdot z\right)}\right) - t \]

   7. Applied rewrites 71.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\log t, a + -0.5, \log \left(y \cdot z\right)\right) - t} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 80.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right)\\ \mathbf{if}\;a - 0.5 \leq -1 \cdot 10^{+14}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a - 0.5 \leq -0.5:\\ \;\;\;\;\log y + \left(\mathsf{fma}\left(\log t, -0.5, \log z\right) - t\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (log z) (fma (log t) (+ a -0.5) (- t)))))
   (if (<= (- a 0.5) -1e+14)
     t_1
     (if (<= (- a 0.5) -0.5)
       (+ (log y) (- (fma (log t) -0.5 (log z)) t))
       t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = log(z) + fma(log(t), (a + -0.5), -t);
	double tmp;
	if ((a - 0.5) <= -1e+14) {
		tmp = t_1;
	} else if ((a - 0.5) <= -0.5) {
		tmp = log(y) + (fma(log(t), -0.5, log(z)) - t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(log(z) + fma(log(t), Float64(a + -0.5), Float64(-t)))
	tmp = 0.0
	if (Float64(a - 0.5) <= -1e+14)
		tmp = t_1;
	elseif (Float64(a - 0.5) <= -0.5)
		tmp = Float64(log(y) + Float64(fma(log(t), -0.5, log(z)) - t));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[Log[z], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * N[(a + -0.5), $MachinePrecision] + (-t)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(a - 0.5), $MachinePrecision], -1e+14], t$95$1, If[LessEqual[N[(a - 0.5), $MachinePrecision], -0.5], N[(N[Log[y], $MachinePrecision] + N[(N[(N[Log[t], $MachinePrecision] * -0.5 + N[Log[z], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 a #s(literal 1/2 binary64)) < -1e14 or -0.5 < (-.f64 a #s(literal 1/2 binary64))

   1. Initial program 99.7%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

      2. flip-+ N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}} \]

      3. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      5. clear-num N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}}}} \]

   4. Applied rewrites 73.6%

      \[\leadsto \color{blue}{\frac{1}{\frac{1}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t}}} \]

   5. Taylor expanded in a around inf

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{a \cdot \log t}}} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      3. lower-log.f64 71.6

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t} \cdot a}} \]

   7. Applied rewrites 71.6%

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

   8. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\left(\log z + \left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right)\right) - t} \]
   9. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      3. lower-log.f64 N/A

         \[\leadsto \color{blue}{\log z} + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right) \]

      4. +-commutative N/A

         \[\leadsto \log z + \left(\color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + -1 \cdot \log \left(\frac{1}{y}\right)\right)} - t\right) \]

      5. mul-1-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\left(\mathsf{neg}\left(\log \left(\frac{1}{y}\right)\right)\right)}\right) - t\right) \]

      6. log-rec N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(\log y\right)\right)}\right)\right)\right) - t\right) \]

      7. remove-double-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\log y}\right) - t\right) \]

      8. associate--l+ N/A

         \[\leadsto \log z + \color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\log y - t\right)\right)} \]

      9. lower-fma.f64 N/A

         \[\leadsto \log z + \color{blue}{\mathsf{fma}\left(\log t, a - \frac{1}{2}, \log y - t\right)} \]

      10. lower-log.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\color{blue}{\log t}, a - \frac{1}{2}, \log y - t\right) \]

      11. sub-neg N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log y - t\right) \]

      12. metadata-eval N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \color{blue}{\frac{-1}{2}}, \log y - t\right) \]

      13. lower-+.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \frac{-1}{2}}, \log y - t\right) \]

      14. lower--.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log y - t}\right) \]

      15. lower-log.f64 79.6

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, \color{blue}{\log y} - t\right) \]

   10. Applied rewrites 79.6%

       \[\leadsto \color{blue}{\log z + \mathsf{fma}\left(\log t, a + -0.5, \log y - t\right)} \]

   11. Taylor expanded in t around inf

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, -1 \cdot t\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 99.3%

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right) \]

   if -1e14 < (-.f64 a #s(literal 1/2 binary64)) < -0.5

   1. Initial program 99.6%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(\log z + \left(\log \left(x + y\right) + \frac{-1}{2} \cdot \log t\right)\right) - t} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \frac{-1}{2} \cdot \log t\right) + \log z\right)} - t \]

      2. associate-+l+ N/A

         \[\leadsto \color{blue}{\left(\log \left(x + y\right) + \left(\frac{-1}{2} \cdot \log t + \log z\right)\right)} - t \]

      3. associate--l+ N/A

         \[\leadsto \color{blue}{\log \left(x + y\right) + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right)} \]

      4. lower-+.f64 N/A

         \[\leadsto \color{blue}{\log \left(x + y\right) + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right)} \]

      5. lower-log.f64 N/A

         \[\leadsto \color{blue}{\log \left(x + y\right)} + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right) \]

      6. +-commutative N/A

         \[\leadsto \log \color{blue}{\left(y + x\right)} + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right) \]

      7. lower-+.f64 N/A

         \[\leadsto \log \color{blue}{\left(y + x\right)} + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right) \]

      8. lower--.f64 N/A

         \[\leadsto \log \left(y + x\right) + \color{blue}{\left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right)} \]

      9. *-commutative N/A

         \[\leadsto \log \left(y + x\right) + \left(\left(\color{blue}{\log t \cdot \frac{-1}{2}} + \log z\right) - t\right) \]

      10. lower-fma.f64 N/A

          \[\leadsto \log \left(y + x\right) + \left(\color{blue}{\mathsf{fma}\left(\log t, \frac{-1}{2}, \log z\right)} - t\right) \]

      11. lower-log.f64 N/A

          \[\leadsto \log \left(y + x\right) + \left(\mathsf{fma}\left(\color{blue}{\log t}, \frac{-1}{2}, \log z\right) - t\right) \]

      12. lower-log.f64 99.0

          \[\leadsto \log \left(y + x\right) + \left(\mathsf{fma}\left(\log t, -0.5, \color{blue}{\log z}\right) - t\right) \]

   5. Applied rewrites 99.0%

      \[\leadsto \color{blue}{\log \left(y + x\right) + \left(\mathsf{fma}\left(\log t, -0.5, \log z\right) - t\right)} \]

   6. Taylor expanded in y around inf

      \[\leadsto -1 \cdot \log \left(\frac{1}{y}\right) + \left(\color{blue}{\mathsf{fma}\left(\log t, \frac{-1}{2}, \log z\right)} - t\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 67.9%

      \[\leadsto \log y + \left(\color{blue}{\mathsf{fma}\left(\log t, -0.5, \log z\right)} - t\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 80.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq 2.8 \cdot 10^{-7}:\\ \;\;\;\;\mathsf{fma}\left(\log t, a + -0.5, \log z + \log y\right)\\ \mathbf{else}:\\ \;\;\;\;\log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t 2.8e-7)
   (fma (log t) (+ a -0.5) (+ (log z) (log y)))
   (+ (log z) (fma (log t) (+ a -0.5) (- t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= 2.8e-7) {
		tmp = fma(log(t), (a + -0.5), (log(z) + log(y)));
	} else {
		tmp = log(z) + fma(log(t), (a + -0.5), -t);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= 2.8e-7)
		tmp = fma(log(t), Float64(a + -0.5), Float64(log(z) + log(y)));
	else
		tmp = Float64(log(z) + fma(log(t), Float64(a + -0.5), Float64(-t)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, 2.8e-7], N[(N[Log[t], $MachinePrecision] * N[(a + -0.5), $MachinePrecision] + N[(N[Log[z], $MachinePrecision] + N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Log[z], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * N[(a + -0.5), $MachinePrecision] + (-t)), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < 2.80000000000000019e-7

   1. Initial program 99.4%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\log z + \left(\log \left(x + y\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right)} \]
   4. Step-by-step derivation

      1. associate-+r+ N/A

         \[\leadsto \color{blue}{\left(\log z + \log \left(x + y\right)\right) + \log t \cdot \left(a - \frac{1}{2}\right)} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{\log t \cdot \left(a - \frac{1}{2}\right) + \left(\log z + \log \left(x + y\right)\right)} \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\log t, a - \frac{1}{2}, \log z + \log \left(x + y\right)\right)} \]

      4. lower-log.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\log t}, a - \frac{1}{2}, \log z + \log \left(x + y\right)\right) \]

      5. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\log t, \color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log z + \log \left(x + y\right)\right) \]

      6. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\log t, a + \color{blue}{\frac{-1}{2}}, \log z + \log \left(x + y\right)\right) \]

      7. lower-+.f64 N/A

         \[\leadsto \mathsf{fma}\left(\log t, \color{blue}{a + \frac{-1}{2}}, \log z + \log \left(x + y\right)\right) \]

      8. lower-+.f64 N/A

         \[\leadsto \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log z + \log \left(x + y\right)}\right) \]

      9. lower-log.f64 N/A

         \[\leadsto \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log z} + \log \left(x + y\right)\right) \]

      10. lower-log.f64 N/A

          \[\leadsto \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \log z + \color{blue}{\log \left(x + y\right)}\right) \]

      11. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \log z + \log \color{blue}{\left(y + x\right)}\right) \]

      12. lower-+.f64 99.0

          \[\leadsto \mathsf{fma}\left(\log t, a + -0.5, \log z + \log \color{blue}{\left(y + x\right)}\right) \]

   5. Applied rewrites 99.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\log t, a + -0.5, \log z + \log \left(y + x\right)\right)} \]

   6. Taylor expanded in y around inf

      \[\leadsto \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \log z + -1 \cdot \log \left(\frac{1}{y}\right)\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 65.7%

      \[\leadsto \mathsf{fma}\left(\log t, a + -0.5, \log z + \log y\right) \]

   if 2.80000000000000019e-7 < t

   1. Initial program 99.9%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

      2. flip-+ N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}} \]

      3. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      5. clear-num N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}}}} \]

   4. Applied rewrites 73.4%

      \[\leadsto \color{blue}{\frac{1}{\frac{1}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t}}} \]

   5. Taylor expanded in a around inf

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{a \cdot \log t}}} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      3. lower-log.f64 26.8

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t} \cdot a}} \]

   7. Applied rewrites 26.8%

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

   8. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\left(\log z + \left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right)\right) - t} \]
   9. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      3. lower-log.f64 N/A

         \[\leadsto \color{blue}{\log z} + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right) \]

      4. +-commutative N/A

         \[\leadsto \log z + \left(\color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + -1 \cdot \log \left(\frac{1}{y}\right)\right)} - t\right) \]

      5. mul-1-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\left(\mathsf{neg}\left(\log \left(\frac{1}{y}\right)\right)\right)}\right) - t\right) \]

      6. log-rec N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(\log y\right)\right)}\right)\right)\right) - t\right) \]

      7. remove-double-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\log y}\right) - t\right) \]

      8. associate--l+ N/A

         \[\leadsto \log z + \color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\log y - t\right)\right)} \]

      9. lower-fma.f64 N/A

         \[\leadsto \log z + \color{blue}{\mathsf{fma}\left(\log t, a - \frac{1}{2}, \log y - t\right)} \]

      10. lower-log.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\color{blue}{\log t}, a - \frac{1}{2}, \log y - t\right) \]

      11. sub-neg N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log y - t\right) \]

      12. metadata-eval N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \color{blue}{\frac{-1}{2}}, \log y - t\right) \]

      13. lower-+.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \frac{-1}{2}}, \log y - t\right) \]

      14. lower--.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log y - t}\right) \]

      15. lower-log.f64 82.3

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, \color{blue}{\log y} - t\right) \]

   10. Applied rewrites 82.3%

       \[\leadsto \color{blue}{\log z + \mathsf{fma}\left(\log t, a + -0.5, \log y - t\right)} \]

   11. Taylor expanded in t around inf

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, -1 \cdot t\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 98.8%

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(a + -0.5, \log t, \log \left(x + y\right)\right) + \left(\log z - t\right) \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (fma (+ a -0.5) (log t) (log (+ x y))) (- (log z) t)))

C

double code(double x, double y, double z, double t, double a) {
	return fma((a + -0.5), log(t), log((x + y))) + (log(z) - t);
}

Julia

function code(x, y, z, t, a)
	return Float64(fma(Float64(a + -0.5), log(t), log(Float64(x + y))) + Float64(log(z) - t))
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(a + -0.5), $MachinePrecision] * N[Log[t], $MachinePrecision] + N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + N[(N[Log[z], $MachinePrecision] - t), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

   2. +-commutative N/A

      \[\leadsto \color{blue}{\left(a - \frac{1}{2}\right) \cdot \log t + \left(\left(\log \left(x + y\right) + \log z\right) - t\right)} \]

   3. lift--.f64 N/A

      \[\leadsto \left(a - \frac{1}{2}\right) \cdot \log t + \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right)} \]

   4. lift-+.f64 N/A

      \[\leadsto \left(a - \frac{1}{2}\right) \cdot \log t + \left(\color{blue}{\left(\log \left(x + y\right) + \log z\right)} - t\right) \]

   5. associate--l+ N/A

      \[\leadsto \left(a - \frac{1}{2}\right) \cdot \log t + \color{blue}{\left(\log \left(x + y\right) + \left(\log z - t\right)\right)} \]

   6. associate-+r+ N/A

      \[\leadsto \color{blue}{\left(\left(a - \frac{1}{2}\right) \cdot \log t + \log \left(x + y\right)\right) + \left(\log z - t\right)} \]

   7. lower-+.f64 N/A

      \[\leadsto \color{blue}{\left(\left(a - \frac{1}{2}\right) \cdot \log t + \log \left(x + y\right)\right) + \left(\log z - t\right)} \]

   8. lift-*.f64 N/A

      \[\leadsto \left(\color{blue}{\left(a - \frac{1}{2}\right) \cdot \log t} + \log \left(x + y\right)\right) + \left(\log z - t\right) \]

   9. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(a - \frac{1}{2}, \log t, \log \left(x + y\right)\right)} + \left(\log z - t\right) \]

   10. lift--.f64 N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{a - \frac{1}{2}}, \log t, \log \left(x + y\right)\right) + \left(\log z - t\right) \]

   11. sub-neg N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log t, \log \left(x + y\right)\right) + \left(\log z - t\right) \]

   12. lower-+.f64 N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log t, \log \left(x + y\right)\right) + \left(\log z - t\right) \]

   13. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(a + \color{blue}{\frac{-1}{2}}, \log t, \log \left(x + y\right)\right) + \left(\log z - t\right) \]

   14. lower--.f64 99.6

       \[\leadsto \mathsf{fma}\left(a + -0.5, \log t, \log \left(x + y\right)\right) + \color{blue}{\left(\log z - t\right)} \]

4. Applied rewrites 99.6%

   \[\leadsto \color{blue}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(x + y\right)\right) + \left(\log z - t\right)} \]
5. Add Preprocessing

Alternative 9: 68.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \log y + \mathsf{fma}\left(\log t, a + -0.5, \log z - t\right) \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (log y) (fma (log t) (+ a -0.5) (- (log z) t))))

C

double code(double x, double y, double z, double t, double a) {
	return log(y) + fma(log(t), (a + -0.5), (log(z) - t));
}

Julia

function code(x, y, z, t, a)
	return Float64(log(y) + fma(log(t), Float64(a + -0.5), Float64(log(z) - t)))
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[Log[y], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * N[(a + -0.5), $MachinePrecision] + N[(N[Log[z], $MachinePrecision] - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\left(\log y + \left(\log z + \log t \cdot \left(a - \frac{1}{2}\right)\right)\right) - t} \]
4. Step-by-step derivation

   1. associate--l+ N/A

      \[\leadsto \color{blue}{\log y + \left(\left(\log z + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

   2. lower-+.f64 N/A

      \[\leadsto \color{blue}{\log y + \left(\left(\log z + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

   3. lower-log.f64 N/A

      \[\leadsto \color{blue}{\log y} + \left(\left(\log z + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right) \]

   4. +-commutative N/A

      \[\leadsto \log y + \left(\color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + \log z\right)} - t\right) \]

   5. associate--l+ N/A

      \[\leadsto \log y + \color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\log z - t\right)\right)} \]

   6. lower-fma.f64 N/A

      \[\leadsto \log y + \color{blue}{\mathsf{fma}\left(\log t, a - \frac{1}{2}, \log z - t\right)} \]

   7. lower-log.f64 N/A

      \[\leadsto \log y + \mathsf{fma}\left(\color{blue}{\log t}, a - \frac{1}{2}, \log z - t\right) \]

   8. sub-neg N/A

      \[\leadsto \log y + \mathsf{fma}\left(\log t, \color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log z - t\right) \]

   9. metadata-eval N/A

      \[\leadsto \log y + \mathsf{fma}\left(\log t, a + \color{blue}{\frac{-1}{2}}, \log z - t\right) \]

   10. lower-+.f64 N/A

       \[\leadsto \log y + \mathsf{fma}\left(\log t, \color{blue}{a + \frac{-1}{2}}, \log z - t\right) \]

   11. lower--.f64 N/A

       \[\leadsto \log y + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log z - t}\right) \]

   12. lower-log.f64 74.3

       \[\leadsto \log y + \mathsf{fma}\left(\log t, a + -0.5, \color{blue}{\log z} - t\right) \]

5. Applied rewrites 74.3%

   \[\leadsto \color{blue}{\log y + \mathsf{fma}\left(\log t, a + -0.5, \log z - t\right)} \]
6. Add Preprocessing

Alternative 10: 77.8% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right) \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (log z) (fma (log t) (+ a -0.5) (- t))))

C

double code(double x, double y, double z, double t, double a) {
	return log(z) + fma(log(t), (a + -0.5), -t);
}

Julia

function code(x, y, z, t, a)
	return Float64(log(z) + fma(log(t), Float64(a + -0.5), Float64(-t)))
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[Log[z], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * N[(a + -0.5), $MachinePrecision] + (-t)), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

   2. flip-+ N/A

      \[\leadsto \color{blue}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}} \]

   3. clear-num N/A

      \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

   4. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

   5. clear-num N/A

      \[\leadsto \frac{1}{\color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}}}} \]

4. Applied rewrites 74.5%

   \[\leadsto \color{blue}{\frac{1}{\frac{1}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t}}} \]

5. Taylor expanded in a around inf

   \[\leadsto \frac{1}{\frac{1}{\color{blue}{a \cdot \log t}}} \]
6. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

   3. lower-log.f64 39.0

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t} \cdot a}} \]

7. Applied rewrites 39.0%

   \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

8. Taylor expanded in y around inf

   \[\leadsto \color{blue}{\left(\log z + \left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right)\right) - t} \]
9. Step-by-step derivation

   1. associate--l+ N/A

      \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

   2. lower-+.f64 N/A

      \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

   3. lower-log.f64 N/A

      \[\leadsto \color{blue}{\log z} + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right) \]

   4. +-commutative N/A

      \[\leadsto \log z + \left(\color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + -1 \cdot \log \left(\frac{1}{y}\right)\right)} - t\right) \]

   5. mul-1-neg N/A

      \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\left(\mathsf{neg}\left(\log \left(\frac{1}{y}\right)\right)\right)}\right) - t\right) \]

   6. log-rec N/A

      \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(\log y\right)\right)}\right)\right)\right) - t\right) \]

   7. remove-double-neg N/A

      \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\log y}\right) - t\right) \]

   8. associate--l+ N/A

      \[\leadsto \log z + \color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\log y - t\right)\right)} \]

   9. lower-fma.f64 N/A

      \[\leadsto \log z + \color{blue}{\mathsf{fma}\left(\log t, a - \frac{1}{2}, \log y - t\right)} \]

   10. lower-log.f64 N/A

       \[\leadsto \log z + \mathsf{fma}\left(\color{blue}{\log t}, a - \frac{1}{2}, \log y - t\right) \]

   11. sub-neg N/A

       \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log y - t\right) \]

   12. metadata-eval N/A

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \color{blue}{\frac{-1}{2}}, \log y - t\right) \]

   13. lower-+.f64 N/A

       \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \frac{-1}{2}}, \log y - t\right) \]

   14. lower--.f64 N/A

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log y - t}\right) \]

   15. lower-log.f64 74.3

       \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, \color{blue}{\log y} - t\right) \]

10. Applied rewrites 74.3%

    \[\leadsto \color{blue}{\log z + \mathsf{fma}\left(\log t, a + -0.5, \log y - t\right)} \]

11. Taylor expanded in t around inf

    \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, -1 \cdot t\right) \]
12. Step-by-step derivation

13. Applied rewrites 78.8%

    \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, -t\right) \]
14. Add Preprocessing

Alternative 11: 64.9% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \log t\\ \mathbf{if}\;a - 0.5 \leq -1 \cdot 10^{+26}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a - 0.5 \leq 5 \cdot 10^{+78}:\\ \;\;\;\;\log \left(x + y\right) + \left(-t\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* a (log t))))
   (if (<= (- a 0.5) -1e+26)
     t_1
     (if (<= (- a 0.5) 5e+78) (+ (log (+ x y)) (- t)) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = a * log(t);
	double tmp;
	if ((a - 0.5) <= -1e+26) {
		tmp = t_1;
	} else if ((a - 0.5) <= 5e+78) {
		tmp = log((x + y)) + -t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a * log(t)
    if ((a - 0.5d0) <= (-1d+26)) then
        tmp = t_1
    else if ((a - 0.5d0) <= 5d+78) then
        tmp = log((x + y)) + -t
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = a * Math.log(t);
	double tmp;
	if ((a - 0.5) <= -1e+26) {
		tmp = t_1;
	} else if ((a - 0.5) <= 5e+78) {
		tmp = Math.log((x + y)) + -t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = a * math.log(t)
	tmp = 0
	if (a - 0.5) <= -1e+26:
		tmp = t_1
	elif (a - 0.5) <= 5e+78:
		tmp = math.log((x + y)) + -t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(a * log(t))
	tmp = 0.0
	if (Float64(a - 0.5) <= -1e+26)
		tmp = t_1;
	elseif (Float64(a - 0.5) <= 5e+78)
		tmp = Float64(log(Float64(x + y)) + Float64(-t));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = a * log(t);
	tmp = 0.0;
	if ((a - 0.5) <= -1e+26)
		tmp = t_1;
	elseif ((a - 0.5) <= 5e+78)
		tmp = log((x + y)) + -t;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(a * N[Log[t], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(a - 0.5), $MachinePrecision], -1e+26], t$95$1, If[LessEqual[N[(a - 0.5), $MachinePrecision], 5e+78], N[(N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision] + (-t)), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 a #s(literal 1/2 binary64)) < -1.00000000000000005e26 or 4.99999999999999984e78 < (-.f64 a #s(literal 1/2 binary64))

   1. Initial program 99.6%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \log t} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\log t \cdot a} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\log t \cdot a} \]

      3. lower-log.f64 78.9

         \[\leadsto \color{blue}{\log t} \cdot a \]

   5. Applied rewrites 78.9%

      \[\leadsto \color{blue}{\log t \cdot a} \]

   if -1.00000000000000005e26 < (-.f64 a #s(literal 1/2 binary64)) < 4.99999999999999984e78

   1. Initial program 99.7%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\left(\log z + \left(\log \left(x + y\right) + \frac{-1}{2} \cdot \log t\right)\right) - t} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \frac{-1}{2} \cdot \log t\right) + \log z\right)} - t \]

      2. associate-+l+ N/A

         \[\leadsto \color{blue}{\left(\log \left(x + y\right) + \left(\frac{-1}{2} \cdot \log t + \log z\right)\right)} - t \]

      3. associate--l+ N/A

         \[\leadsto \color{blue}{\log \left(x + y\right) + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right)} \]

      4. lower-+.f64 N/A

         \[\leadsto \color{blue}{\log \left(x + y\right) + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right)} \]

      5. lower-log.f64 N/A

         \[\leadsto \color{blue}{\log \left(x + y\right)} + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right) \]

      6. +-commutative N/A

         \[\leadsto \log \color{blue}{\left(y + x\right)} + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right) \]

      7. lower-+.f64 N/A

         \[\leadsto \log \color{blue}{\left(y + x\right)} + \left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right) \]

      8. lower--.f64 N/A

         \[\leadsto \log \left(y + x\right) + \color{blue}{\left(\left(\frac{-1}{2} \cdot \log t + \log z\right) - t\right)} \]

      9. *-commutative N/A

         \[\leadsto \log \left(y + x\right) + \left(\left(\color{blue}{\log t \cdot \frac{-1}{2}} + \log z\right) - t\right) \]

      10. lower-fma.f64 N/A

          \[\leadsto \log \left(y + x\right) + \left(\color{blue}{\mathsf{fma}\left(\log t, \frac{-1}{2}, \log z\right)} - t\right) \]

      11. lower-log.f64 N/A

          \[\leadsto \log \left(y + x\right) + \left(\mathsf{fma}\left(\color{blue}{\log t}, \frac{-1}{2}, \log z\right) - t\right) \]

      12. lower-log.f64 95.5

          \[\leadsto \log \left(y + x\right) + \left(\mathsf{fma}\left(\log t, -0.5, \color{blue}{\log z}\right) - t\right) \]

   5. Applied rewrites 95.5%

      \[\leadsto \color{blue}{\log \left(y + x\right) + \left(\mathsf{fma}\left(\log t, -0.5, \log z\right) - t\right)} \]

   6. Taylor expanded in t around inf

      \[\leadsto \log \left(y + x\right) + -1 \cdot \color{blue}{t} \]
   7. Step-by-step derivation

   8. Applied rewrites 58.1%

      \[\leadsto \log \left(y + x\right) + \left(-t\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 67.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a - 0.5 \leq -1 \cdot 10^{+26}:\\ \;\;\;\;a \cdot \log t\\ \mathbf{elif}\;a - 0.5 \leq 5 \cdot 10^{+78}:\\ \;\;\;\;\log \left(x + y\right) + \left(-t\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \log t\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 77.6% accurate, 2.8× speedup

Math

\[\begin{array}{l} \\ \left(a - 0.5\right) \cdot \log t + \left(-t\right) \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (+ (* (- a 0.5) (log t)) (- t)))

C

double code(double x, double y, double z, double t, double a) {
	return ((a - 0.5) * log(t)) + -t;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = ((a - 0.5d0) * log(t)) + -t
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return ((a - 0.5) * Math.log(t)) + -t;
}

Python

def code(x, y, z, t, a):
	return ((a - 0.5) * math.log(t)) + -t

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(a - 0.5) * log(t)) + Float64(-t))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = ((a - 0.5) * log(t)) + -t;
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(a - 0.5), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision] + (-t)), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
2. Add Preprocessing

3. Taylor expanded in t around inf

   \[\leadsto \color{blue}{-1 \cdot t} + \left(a - \frac{1}{2}\right) \cdot \log t \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(t\right)\right)} + \left(a - \frac{1}{2}\right) \cdot \log t \]

   2. lower-neg.f64 78.4

      \[\leadsto \color{blue}{\left(-t\right)} + \left(a - 0.5\right) \cdot \log t \]

5. Applied rewrites 78.4%

   \[\leadsto \color{blue}{\left(-t\right)} + \left(a - 0.5\right) \cdot \log t \]

6. Final simplification 78.4%

   \[\leadsto \left(a - 0.5\right) \cdot \log t + \left(-t\right) \]
7. Add Preprocessing

Alternative 13: 61.5% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq 1.55 \cdot 10^{+62}:\\ \;\;\;\;a \cdot \log t\\ \mathbf{else}:\\ \;\;\;\;\log z + \left(-t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t 1.55e+62) (* a (log t)) (+ (log z) (- t))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= 1.55e+62) {
		tmp = a * log(t);
	} else {
		tmp = log(z) + -t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (t <= 1.55d+62) then
        tmp = a * log(t)
    else
        tmp = log(z) + -t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= 1.55e+62) {
		tmp = a * Math.log(t);
	} else {
		tmp = Math.log(z) + -t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= 1.55e+62:
		tmp = a * math.log(t)
	else:
		tmp = math.log(z) + -t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= 1.55e+62)
		tmp = Float64(a * log(t));
	else
		tmp = Float64(log(z) + Float64(-t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= 1.55e+62)
		tmp = a * log(t);
	else
		tmp = log(z) + -t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, 1.55e+62], N[(a * N[Log[t], $MachinePrecision]), $MachinePrecision], N[(N[Log[z], $MachinePrecision] + (-t)), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < 1.55000000000000007e62

   1. Initial program 99.4%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \log t} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\log t \cdot a} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\log t \cdot a} \]

      3. lower-log.f64 54.0

         \[\leadsto \color{blue}{\log t} \cdot a \]

   5. Applied rewrites 54.0%

      \[\leadsto \color{blue}{\log t \cdot a} \]

   if 1.55000000000000007e62 < t

   1. Initial program 99.9%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t} \]

      2. flip-+ N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}} \]

      3. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}}} \]

      5. clear-num N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{1}{\frac{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) \cdot \left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(\left(a - \frac{1}{2}\right) \cdot \log t\right) \cdot \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)}{\left(\left(\log \left(x + y\right) + \log z\right) - t\right) - \left(a - \frac{1}{2}\right) \cdot \log t}}}} \]

   4. Applied rewrites 72.0%

      \[\leadsto \color{blue}{\frac{1}{\frac{1}{\mathsf{fma}\left(a + -0.5, \log t, \log \left(\left(x + y\right) \cdot z\right)\right) - t}}} \]

   5. Taylor expanded in a around inf

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{a \cdot \log t}}} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

      3. lower-log.f64 19.2

         \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t} \cdot a}} \]

   7. Applied rewrites 19.2%

      \[\leadsto \frac{1}{\frac{1}{\color{blue}{\log t \cdot a}}} \]

   8. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\left(\log z + \left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right)\right) - t} \]
   9. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \color{blue}{\log z + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right)} \]

      3. lower-log.f64 N/A

         \[\leadsto \color{blue}{\log z} + \left(\left(-1 \cdot \log \left(\frac{1}{y}\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\right) \]

      4. +-commutative N/A

         \[\leadsto \log z + \left(\color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + -1 \cdot \log \left(\frac{1}{y}\right)\right)} - t\right) \]

      5. mul-1-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\left(\mathsf{neg}\left(\log \left(\frac{1}{y}\right)\right)\right)}\right) - t\right) \]

      6. log-rec N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(\log y\right)\right)}\right)\right)\right) - t\right) \]

      7. remove-double-neg N/A

         \[\leadsto \log z + \left(\left(\log t \cdot \left(a - \frac{1}{2}\right) + \color{blue}{\log y}\right) - t\right) \]

      8. associate--l+ N/A

         \[\leadsto \log z + \color{blue}{\left(\log t \cdot \left(a - \frac{1}{2}\right) + \left(\log y - t\right)\right)} \]

      9. lower-fma.f64 N/A

         \[\leadsto \log z + \color{blue}{\mathsf{fma}\left(\log t, a - \frac{1}{2}, \log y - t\right)} \]

      10. lower-log.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\color{blue}{\log t}, a - \frac{1}{2}, \log y - t\right) \]

      11. sub-neg N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, \log y - t\right) \]

      12. metadata-eval N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \color{blue}{\frac{-1}{2}}, \log y - t\right) \]

      13. lower-+.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, \color{blue}{a + \frac{-1}{2}}, \log y - t\right) \]

      14. lower--.f64 N/A

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + \frac{-1}{2}, \color{blue}{\log y - t}\right) \]

      15. lower-log.f64 81.8

          \[\leadsto \log z + \mathsf{fma}\left(\log t, a + -0.5, \color{blue}{\log y} - t\right) \]

   10. Applied rewrites 81.8%

       \[\leadsto \color{blue}{\log z + \mathsf{fma}\left(\log t, a + -0.5, \log y - t\right)} \]

   11. Taylor expanded in t around inf

       \[\leadsto \log z + -1 \cdot \color{blue}{t} \]
   12. Step-by-step derivation

   13. Applied rewrites 81.5%

       \[\leadsto \log z + \left(-t\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 65.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq 1.55 \cdot 10^{+62}:\\ \;\;\;\;a \cdot \log t\\ \mathbf{else}:\\ \;\;\;\;\log z + \left(-t\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 61.5% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq 1.55 \cdot 10^{+62}:\\ \;\;\;\;a \cdot \log t\\ \mathbf{else}:\\ \;\;\;\;-t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t 1.55e+62) (* a (log t)) (- t)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= 1.55e+62) {
		tmp = a * log(t);
	} else {
		tmp = -t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (t <= 1.55d+62) then
        tmp = a * log(t)
    else
        tmp = -t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= 1.55e+62) {
		tmp = a * Math.log(t);
	} else {
		tmp = -t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= 1.55e+62:
		tmp = a * math.log(t)
	else:
		tmp = -t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= 1.55e+62)
		tmp = Float64(a * log(t));
	else
		tmp = Float64(-t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= 1.55e+62)
		tmp = a * log(t);
	else
		tmp = -t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, 1.55e+62], N[(a * N[Log[t], $MachinePrecision]), $MachinePrecision], (-t)]

Derivation

1. Split input into 2 regimes

2. if t < 1.55000000000000007e62

   1. Initial program 99.4%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \log t} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\log t \cdot a} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\log t \cdot a} \]

      3. lower-log.f64 54.0

         \[\leadsto \color{blue}{\log t} \cdot a \]

   5. Applied rewrites 54.0%

      \[\leadsto \color{blue}{\log t \cdot a} \]

   if 1.55000000000000007e62 < t

   1. Initial program 99.9%

      \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 81.5

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

   5. Applied rewrites 81.5%

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

4. Final simplification 65.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq 1.55 \cdot 10^{+62}:\\ \;\;\;\;a \cdot \log t\\ \mathbf{else}:\\ \;\;\;\;-t\\ \end{array} \]
5. Add Preprocessing

Alternative 15: 37.3% accurate, 107.0× speedup

Math

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

FPCore

(FPCore (x y z t a) :precision binary64 (- t))

C

double code(double x, double y, double z, double t, double a) {
	return -t;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = -t
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return -t;
}

Python

def code(x, y, z, t, a):
	return -t

Julia

function code(x, y, z, t, a)
	return Float64(-t)
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = -t;
end

Wolfram

code[x_, y_, z_, t_, a_] := (-t)

Derivation

1. Initial program 99.6%

   \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
2. Add Preprocessing

3. Taylor expanded in t around inf

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

   1. mul-1-neg N/A

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

   2. lower-neg.f64 38.7

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

5. Applied rewrites 38.7%

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

Alternative 16: 2.5% accurate, 321.0× speedup

Math

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

FPCore

(FPCore (x y z t a) :precision binary64 t)

C

double code(double x, double y, double z, double t, double a) {
	return t;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = t
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return t;
}

Python

def code(x, y, z, t, a):
	return t

Julia

function code(x, y, z, t, a)
	return t
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = t;
end

Wolfram

code[x_, y_, z_, t_, a_] := t

Derivation

1. Initial program 99.6%

   \[\left(\left(\log \left(x + y\right) + \log z\right) - t\right) + \left(a - 0.5\right) \cdot \log t \]
2. Add Preprocessing

3. Taylor expanded in t around inf

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

   1. mul-1-neg N/A

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

   2. lower-neg.f64 38.7

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

5. Applied rewrites 38.7%

   \[\leadsto \color{blue}{-t} \]
6. Step-by-step derivation

7. Applied rewrites 15.5%

   \[\leadsto \frac{0 - t \cdot t}{\color{blue}{0 + t}} \]
8. Step-by-step derivation

9. Applied rewrites 2.4%

   \[\leadsto \color{blue}{t} \]
10. Add Preprocessing

Developer Target 1: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \log \left(x + y\right) + \left(\left(\log z - t\right) + \left(a - 0.5\right) \cdot \log t\right) \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (log (+ x y)) (+ (- (log z) t) (* (- a 0.5) (log t)))))

C

double code(double x, double y, double z, double t, double a) {
	return log((x + y)) + ((log(z) - t) + ((a - 0.5) * log(t)));
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = log((x + y)) + ((log(z) - t) + ((a - 0.5d0) * log(t)))
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return Math.log((x + y)) + ((Math.log(z) - t) + ((a - 0.5) * Math.log(t)));
}

Python

def code(x, y, z, t, a):
	return math.log((x + y)) + ((math.log(z) - t) + ((a - 0.5) * math.log(t)))

Julia

function code(x, y, z, t, a)
	return Float64(log(Float64(x + y)) + Float64(Float64(log(z) - t) + Float64(Float64(a - 0.5) * log(t))))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = log((x + y)) + ((log(z) - t) + ((a - 0.5) * log(t)));
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision] + N[(N[(N[Log[z], $MachinePrecision] - t), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024220 
(FPCore (x y z t a)
  :name "Numeric.SpecFunctions:logGammaL from math-functions-0.1.5.2"
  :precision binary64

  :alt
  (! :herbie-platform default (+ (log (+ x y)) (+ (- (log z) t) (* (- a 1/2) (log t)))))

  (+ (- (+ (log (+ x y)) (log z)) t) (* (- a 0.5) (log t))))