Diagrams.TwoD.Segment.Bernstein:evaluateBernstein from diagrams-lib-1.3.0.3

Percentage Accurate: 88.6% → 99.9%

Time: 7.2s

Alternatives: 9

Speedup: 1.1×

• 21.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

(FPCore (x y z) :precision binary64 (/ (* x (+ (- y z) 1.0)) z))

C

double code(double x, double y, double z) {
	return (x * ((y - z) + 1.0)) / z;
}

Fortran

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

Java

public static double code(double x, double y, double z) {
	return (x * ((y - z) + 1.0)) / z;
}

Python

def code(x, y, z):
	return (x * ((y - z) + 1.0)) / z

Julia

function code(x, y, z)
	return Float64(Float64(x * Float64(Float64(y - z) + 1.0)) / z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * ((y - z) + 1.0)) / z;
end

Wolfram

code[x_, y_, z_] := N[(N[(x * N[(N[(y - z), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]

Initial Program: 88.6% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 (/ (* x (+ (- y z) 1.0)) z))

C

double code(double x, double y, double z) {
	return (x * ((y - z) + 1.0)) / z;
}

Fortran

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

Java

public static double code(double x, double y, double z) {
	return (x * ((y - z) + 1.0)) / z;
}

Python

def code(x, y, z):
	return (x * ((y - z) + 1.0)) / z

Julia

function code(x, y, z)
	return Float64(Float64(x * Float64(Float64(y - z) + 1.0)) / z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * ((y - z) + 1.0)) / z;
end

Wolfram

code[x_, y_, z_] := N[(N[(x * N[(N[(y - z), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]

Alternative 1: 99.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 4 \cdot 10^{-26}:\\ \;\;\;\;\frac{\mathsf{fma}\left(y, x\_m, x\_m\right)}{z} - x\_m\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{x\_m}{z}, 1 + y, -x\_m\right)\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m y z)
 :precision binary64
 (*
  x_s
  (if (<= x_m 4e-26)
    (- (/ (fma y x_m x_m) z) x_m)
    (fma (/ x_m z) (+ 1.0 y) (- x_m)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m, double y, double z) {
	double tmp;
	if (x_m <= 4e-26) {
		tmp = (fma(y, x_m, x_m) / z) - x_m;
	} else {
		tmp = fma((x_m / z), (1.0 + y), -x_m);
	}
	return x_s * tmp;
}

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m, y, z)
	tmp = 0.0
	if (x_m <= 4e-26)
		tmp = Float64(Float64(fma(y, x_m, x_m) / z) - x_m);
	else
		tmp = fma(Float64(x_m / z), Float64(1.0 + y), Float64(-x_m));
	end
	return Float64(x_s * tmp)
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_, y_, z_] := N[(x$95$s * If[LessEqual[x$95$m, 4e-26], N[(N[(N[(y * x$95$m + x$95$m), $MachinePrecision] / z), $MachinePrecision] - x$95$m), $MachinePrecision], N[(N[(x$95$m / z), $MachinePrecision] * N[(1.0 + y), $MachinePrecision] + (-x$95$m)), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 4.0000000000000002e-26

   1. Initial program 90.5%

      \[\frac{x \cdot \left(\left(y - z\right) + 1\right)}{z} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. distribute-rgt-in N/A

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

      4. *-lft-identity N/A

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

      5. lower-fma.f64 90.5

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

   4. Applied rewrites 90.5%

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

      1. lift-/.f64 N/A

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

      2. lift-fma.f64 N/A

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

      3. distribute-lft1-in N/A

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

      4. metadata-eval N/A

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

      5. sub-neg N/A

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

      6. lift--.f64 N/A

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

      7. *-commutative N/A

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

      8. associate-*l/ N/A

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

      9. lift-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. sub-neg N/A

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

      12. metadata-eval N/A

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

      13. +-commutative N/A

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

      14. lift--.f64 N/A

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

      15. sub-neg N/A

          \[\leadsto \frac{x}{z} \cdot \left(1 + \color{blue}{\left(y + \left(\mathsf{neg}\left(z\right)\right)\right)}\right) \]

      16. associate-+r+ N/A

          \[\leadsto \frac{x}{z} \cdot \color{blue}{\left(\left(1 + y\right) + \left(\mathsf{neg}\left(z\right)\right)\right)} \]

      17. distribute-lft-in N/A

          \[\leadsto \color{blue}{\frac{x}{z} \cdot \left(1 + y\right) + \frac{x}{z} \cdot \left(\mathsf{neg}\left(z\right)\right)} \]

      18. lower-fma.f64 N/A

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

      19. lower-+.f64 N/A

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

      20. lower-*.f64 N/A

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

      21. lower-neg.f64 78.2

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

   6. Applied rewrites 78.2%

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

      1. lift-fma.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-neg.f64 N/A

         \[\leadsto \frac{x}{z} \cdot \left(1 + y\right) + \frac{x}{z} \cdot \color{blue}{\left(\mathsf{neg}\left(z\right)\right)} \]

      4. distribute-rgt-neg-out N/A

         \[\leadsto \frac{x}{z} \cdot \left(1 + y\right) + \color{blue}{\left(\mathsf{neg}\left(\frac{x}{z} \cdot z\right)\right)} \]

      5. unsub-neg N/A

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

      6. lift-/.f64 N/A

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

      7. div-inv N/A

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

      8. associate-*l* N/A

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

      9. inv-pow N/A

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

      10. pow-plus N/A

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

      11. metadata-eval N/A

          \[\leadsto \frac{x}{z} \cdot \left(1 + y\right) - x \cdot {z}^{\color{blue}{0}} \]

      12. metadata-eval N/A

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

      13. *-rgt-identity N/A

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

      14. lower--.f64 N/A

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

      15. lift-/.f64 N/A

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

      16. associate-*l/ N/A

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

      17. lower-/.f64 N/A

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

      18. lift-+.f64 N/A

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

      19. +-commutative N/A

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

      20. distribute-rgt-in N/A

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

      21. *-lft-identity N/A

          \[\leadsto \frac{y \cdot x + \color{blue}{x}}{z} - x \]

      22. lower-fma.f64 97.0

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

   8. Applied rewrites 97.0%

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

   if 4.0000000000000002e-26 < x

   1. Initial program 79.3%

      \[\frac{x \cdot \left(\left(y - z\right) + 1\right)}{z} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. distribute-rgt-in N/A

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

      4. *-lft-identity N/A

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

      5. lower-fma.f64 79.3

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

   4. Applied rewrites 79.3%

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

      1. lift-/.f64 N/A

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

      2. lift-fma.f64 N/A

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

      3. distribute-lft1-in N/A

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

      4. metadata-eval N/A

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

      5. sub-neg N/A

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

      6. lift--.f64 N/A

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

      7. *-commutative N/A

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

      8. associate-*l/ N/A

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

      9. lift-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. sub-neg N/A

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

      12. metadata-eval N/A

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

      13. +-commutative N/A

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

      14. lift--.f64 N/A

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

      15. sub-neg N/A

          \[\leadsto \frac{x}{z} \cdot \left(1 + \color{blue}{\left(y + \left(\mathsf{neg}\left(z\right)\right)\right)}\right) \]

      16. associate-+r+ N/A

          \[\leadsto \frac{x}{z} \cdot \color{blue}{\left(\left(1 + y\right) + \left(\mathsf{neg}\left(z\right)\right)\right)} \]

      17. distribute-lft-in N/A

          \[\leadsto \color{blue}{\frac{x}{z} \cdot \left(1 + y\right) + \frac{x}{z} \cdot \left(\mathsf{neg}\left(z\right)\right)} \]

      18. lower-fma.f64 N/A

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

      19. lower-+.f64 N/A

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

      20. lower-*.f64 N/A

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

      21. lower-neg.f64 90.5

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

   6. Applied rewrites 90.5%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 90.5

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

      4. lift-*.f64 N/A

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

      5. lift-neg.f64 N/A

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

      6. distribute-rgt-neg-out N/A

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

      7. lift-/.f64 N/A

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

      8. div-inv N/A

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

      9. associate-*l* N/A

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

      10. inv-pow N/A

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

      11. pow-plus N/A

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

      12. metadata-eval N/A

          \[\leadsto \mathsf{fma}\left(\frac{x}{z}, y + 1, \mathsf{neg}\left(x \cdot {z}^{\color{blue}{0}}\right)\right) \]

      13. metadata-eval N/A

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

      14. *-rgt-identity N/A

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

      15. lower-neg.f64 100.0

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

   8. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x}{z}, y + 1, -x\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 4 \cdot 10^{-26}:\\ \;\;\;\;\frac{\mathsf{fma}\left(y, x, x\right)}{z} - x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{z}, 1 + y, -x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 85.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;y \leq -1.3 \cdot 10^{+43}:\\ \;\;\;\;\frac{y \cdot x\_m}{z}\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+36}:\\ \;\;\;\;\frac{x\_m}{z} - x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(y, x\_m, x\_m\right)}{z}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m y z)
 :precision binary64
 (*
  x_s
  (if (<= y -1.3e+43)
    (/ (* y x_m) z)
    (if (<= y 1.55e+36) (- (/ x_m z) x_m) (/ (fma y x_m x_m) z)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m, double y, double z) {
	double tmp;
	if (y <= -1.3e+43) {
		tmp = (y * x_m) / z;
	} else if (y <= 1.55e+36) {
		tmp = (x_m / z) - x_m;
	} else {
		tmp = fma(y, x_m, x_m) / z;
	}
	return x_s * tmp;
}

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m, y, z)
	tmp = 0.0
	if (y <= -1.3e+43)
		tmp = Float64(Float64(y * x_m) / z);
	elseif (y <= 1.55e+36)
		tmp = Float64(Float64(x_m / z) - x_m);
	else
		tmp = Float64(fma(y, x_m, x_m) / z);
	end
	return Float64(x_s * tmp)
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_, y_, z_] := N[(x$95$s * If[LessEqual[y, -1.3e+43], N[(N[(y * x$95$m), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[y, 1.55e+36], N[(N[(x$95$m / z), $MachinePrecision] - x$95$m), $MachinePrecision], N[(N[(y * x$95$m + x$95$m), $MachinePrecision] / z), $MachinePrecision]]]), $MachinePrecision]

Derivation

1. Split input into 3 regimes

2. if y < -1.3000000000000001e43

   1. Initial program 84.1%

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

   3. Taylor expanded in y around inf

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

      1. *-commutative N/A

         \[\leadsto \frac{\color{blue}{y \cdot x}}{z} \]

      2. lower-*.f64 81.4

         \[\leadsto \frac{\color{blue}{y \cdot x}}{z} \]

   5. Applied rewrites 81.4%

      \[\leadsto \frac{\color{blue}{y \cdot x}}{z} \]

   if -1.3000000000000001e43 < y < 1.55e36

   1. Initial program 88.4%

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

   3. Taylor expanded in y around 0

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

      1. associate-/l* N/A

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

      2. div-sub N/A

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

      3. sub-neg N/A

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

      4. *-inverses N/A

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

      5. metadata-eval N/A

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

      6. distribute-lft-in N/A

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

      7. associate-/l* N/A

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

      8. *-rgt-identity N/A

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

      9. *-commutative N/A

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

      10. mul-1-neg N/A

          \[\leadsto \frac{x}{z} + \color{blue}{\left(\mathsf{neg}\left(x\right)\right)} \]

      11. unsub-neg N/A

          \[\leadsto \color{blue}{\frac{x}{z} - x} \]

      12. lower--.f64 N/A

          \[\leadsto \color{blue}{\frac{x}{z} - x} \]

      13. lower-/.f64 97.2

          \[\leadsto \color{blue}{\frac{x}{z}} - x \]

   5. Applied rewrites 97.2%

      \[\leadsto \color{blue}{\frac{x}{z} - x} \]

   if 1.55e36 < y

   1. Initial program 88.5%

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

   3. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. distribute-rgt-in N/A

         \[\leadsto \frac{\color{blue}{y \cdot x + 1 \cdot x}}{z} \]

      3. *-lft-identity N/A

         \[\leadsto \frac{y \cdot x + \color{blue}{x}}{z} \]

      4. lower-fma.f64 78.0

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(y, x, x\right)}}{z} \]

   5. Applied rewrites 78.0%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(y, x, x\right)}}{z} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 85.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ \begin{array}{l} t_0 := \frac{y \cdot x\_m}{z}\\ x\_s \cdot \begin{array}{l} \mathbf{if}\;y \leq -1.3 \cdot 10^{+43}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+36}:\\ \;\;\;\;\frac{x\_m}{z} - x\_m\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m y z)
 :precision binary64
 (let* ((t_0 (/ (* y x_m) z)))
   (*
    x_s
    (if (<= y -1.3e+43) t_0 (if (<= y 1.55e+36) (- (/ x_m z) x_m) t_0)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m, double y, double z) {
	double t_0 = (y * x_m) / z;
	double tmp;
	if (y <= -1.3e+43) {
		tmp = t_0;
	} else if (y <= 1.55e+36) {
		tmp = (x_m / z) - x_m;
	} else {
		tmp = t_0;
	}
	return x_s * tmp;
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m, y, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (y * x_m) / z
    if (y <= (-1.3d+43)) then
        tmp = t_0
    else if (y <= 1.55d+36) then
        tmp = (x_m / z) - x_m
    else
        tmp = t_0
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m, double y, double z) {
	double t_0 = (y * x_m) / z;
	double tmp;
	if (y <= -1.3e+43) {
		tmp = t_0;
	} else if (y <= 1.55e+36) {
		tmp = (x_m / z) - x_m;
	} else {
		tmp = t_0;
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m, y, z):
	t_0 = (y * x_m) / z
	tmp = 0
	if y <= -1.3e+43:
		tmp = t_0
	elif y <= 1.55e+36:
		tmp = (x_m / z) - x_m
	else:
		tmp = t_0
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m, y, z)
	t_0 = Float64(Float64(y * x_m) / z)
	tmp = 0.0
	if (y <= -1.3e+43)
		tmp = t_0;
	elseif (y <= 1.55e+36)
		tmp = Float64(Float64(x_m / z) - x_m);
	else
		tmp = t_0;
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m, y, z)
	t_0 = (y * x_m) / z;
	tmp = 0.0;
	if (y <= -1.3e+43)
		tmp = t_0;
	elseif (y <= 1.55e+36)
		tmp = (x_m / z) - x_m;
	else
		tmp = t_0;
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_, y_, z_] := Block[{t$95$0 = N[(N[(y * x$95$m), $MachinePrecision] / z), $MachinePrecision]}, N[(x$95$s * If[LessEqual[y, -1.3e+43], t$95$0, If[LessEqual[y, 1.55e+36], N[(N[(x$95$m / z), $MachinePrecision] - x$95$m), $MachinePrecision], t$95$0]]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.3000000000000001e43 or 1.55e36 < y

   1. Initial program 86.9%

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

   3. Taylor expanded in y around inf

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

      1. *-commutative N/A

         \[\leadsto \frac{\color{blue}{y \cdot x}}{z} \]

      2. lower-*.f64 79.2

         \[\leadsto \frac{\color{blue}{y \cdot x}}{z} \]

   5. Applied rewrites 79.2%

      \[\leadsto \frac{\color{blue}{y \cdot x}}{z} \]

   if -1.3000000000000001e43 < y < 1.55e36

   1. Initial program 88.4%

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

   3. Taylor expanded in y around 0

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

      1. associate-/l* N/A

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

      2. div-sub N/A

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

      3. sub-neg N/A

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

      4. *-inverses N/A

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

      5. metadata-eval N/A

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

      6. distribute-lft-in N/A

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

      7. associate-/l* N/A

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

      8. *-rgt-identity N/A

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

      9. *-commutative N/A

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

      10. mul-1-neg N/A

          \[\leadsto \frac{x}{z} + \color{blue}{\left(\mathsf{neg}\left(x\right)\right)} \]

      11. unsub-neg N/A

          \[\leadsto \color{blue}{\frac{x}{z} - x} \]

      12. lower--.f64 N/A

          \[\leadsto \color{blue}{\frac{x}{z} - x} \]

      13. lower-/.f64 97.2

          \[\leadsto \color{blue}{\frac{x}{z}} - x \]

   5. Applied rewrites 97.2%

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

Alternative 4: 84.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;y \leq -1.3 \cdot 10^{+43}:\\ \;\;\;\;\frac{x\_m}{z} \cdot y\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+36}:\\ \;\;\;\;\frac{x\_m}{z} - x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{z} \cdot x\_m\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m y z)
 :precision binary64
 (*
  x_s
  (if (<= y -1.3e+43)
    (* (/ x_m z) y)
    (if (<= y 1.55e+36) (- (/ x_m z) x_m) (* (/ y z) x_m)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m, double y, double z) {
	double tmp;
	if (y <= -1.3e+43) {
		tmp = (x_m / z) * y;
	} else if (y <= 1.55e+36) {
		tmp = (x_m / z) - x_m;
	} else {
		tmp = (y / z) * x_m;
	}
	return x_s * tmp;
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m, y, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-1.3d+43)) then
        tmp = (x_m / z) * y
    else if (y <= 1.55d+36) then
        tmp = (x_m / z) - x_m
    else
        tmp = (y / z) * x_m
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m, double y, double z) {
	double tmp;
	if (y <= -1.3e+43) {
		tmp = (x_m / z) * y;
	} else if (y <= 1.55e+36) {
		tmp = (x_m / z) - x_m;
	} else {
		tmp = (y / z) * x_m;
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m, y, z):
	tmp = 0
	if y <= -1.3e+43:
		tmp = (x_m / z) * y
	elif y <= 1.55e+36:
		tmp = (x_m / z) - x_m
	else:
		tmp = (y / z) * x_m
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m, y, z)
	tmp = 0.0
	if (y <= -1.3e+43)
		tmp = Float64(Float64(x_m / z) * y);
	elseif (y <= 1.55e+36)
		tmp = Float64(Float64(x_m / z) - x_m);
	else
		tmp = Float64(Float64(y / z) * x_m);
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m, y, z)
	tmp = 0.0;
	if (y <= -1.3e+43)
		tmp = (x_m / z) * y;
	elseif (y <= 1.55e+36)
		tmp = (x_m / z) - x_m;
	else
		tmp = (y / z) * x_m;
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_, y_, z_] := N[(x$95$s * If[LessEqual[y, -1.3e+43], N[(N[(x$95$m / z), $MachinePrecision] * y), $MachinePrecision], If[LessEqual[y, 1.55e+36], N[(N[(x$95$m / z), $MachinePrecision] - x$95$m), $MachinePrecision], N[(N[(y / z), $MachinePrecision] * x$95$m), $MachinePrecision]]]), $MachinePrecision]

Derivation

1. Split input into 3 regimes

2. if y < -1.3000000000000001e43

   1. Initial program 84.1%

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

   3. Taylor expanded in y around inf

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

      1. associate-/l* N/A

         \[\leadsto \color{blue}{x \cdot \frac{y}{z}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{y}{z} \cdot x} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{y}{z} \cdot x} \]

      4. lower-/.f64 72.0

         \[\leadsto \color{blue}{\frac{y}{z}} \cdot x \]

   5. Applied rewrites 72.0%

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

   7. Applied rewrites 76.2%

      \[\leadsto y \cdot \color{blue}{\frac{x}{z}} \]

   if -1.3000000000000001e43 < y < 1.55e36

   1. Initial program 88.4%

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

   3. Taylor expanded in y around 0

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

      1. associate-/l* N/A

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

      2. div-sub N/A

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

      3. sub-neg N/A

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

      4. *-inverses N/A

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

      5. metadata-eval N/A

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

      6. distribute-lft-in N/A

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

      7. associate-/l* N/A

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

      8. *-rgt-identity N/A

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

      9. *-commutative N/A

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

      10. mul-1-neg N/A

          \[\leadsto \frac{x}{z} + \color{blue}{\left(\mathsf{neg}\left(x\right)\right)} \]

      11. unsub-neg N/A

          \[\leadsto \color{blue}{\frac{x}{z} - x} \]

      12. lower--.f64 N/A

          \[\leadsto \color{blue}{\frac{x}{z} - x} \]

      13. lower-/.f64 97.2

          \[\leadsto \color{blue}{\frac{x}{z}} - x \]

   5. Applied rewrites 97.2%

      \[\leadsto \color{blue}{\frac{x}{z} - x} \]

   if 1.55e36 < y

   1. Initial program 88.5%

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

   3. Taylor expanded in y around inf

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

      1. associate-/l* N/A

         \[\leadsto \color{blue}{x \cdot \frac{y}{z}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{y}{z} \cdot x} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{y}{z} \cdot x} \]

      4. lower-/.f64 73.0

         \[\leadsto \color{blue}{\frac{y}{z}} \cdot x \]

   5. Applied rewrites 73.0%

      \[\leadsto \color{blue}{\frac{y}{z} \cdot x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 86.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.3 \cdot 10^{+43}:\\ \;\;\;\;\frac{x}{z} \cdot y\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+36}:\\ \;\;\;\;\frac{x}{z} - x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{z} \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 85.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ \begin{array}{l} t_0 := \frac{x\_m}{z} \cdot y\\ x\_s \cdot \begin{array}{l} \mathbf{if}\;y \leq -1.3 \cdot 10^{+43}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+36}:\\ \;\;\;\;\frac{x\_m}{z} - x\_m\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m y z)
 :precision binary64
 (let* ((t_0 (* (/ x_m z) y)))
   (*
    x_s
    (if (<= y -1.3e+43) t_0 (if (<= y 1.55e+36) (- (/ x_m z) x_m) t_0)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m, double y, double z) {
	double t_0 = (x_m / z) * y;
	double tmp;
	if (y <= -1.3e+43) {
		tmp = t_0;
	} else if (y <= 1.55e+36) {
		tmp = (x_m / z) - x_m;
	} else {
		tmp = t_0;
	}
	return x_s * tmp;
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m, y, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x_m / z) * y
    if (y <= (-1.3d+43)) then
        tmp = t_0
    else if (y <= 1.55d+36) then
        tmp = (x_m / z) - x_m
    else
        tmp = t_0
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m, double y, double z) {
	double t_0 = (x_m / z) * y;
	double tmp;
	if (y <= -1.3e+43) {
		tmp = t_0;
	} else if (y <= 1.55e+36) {
		tmp = (x_m / z) - x_m;
	} else {
		tmp = t_0;
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m, y, z):
	t_0 = (x_m / z) * y
	tmp = 0
	if y <= -1.3e+43:
		tmp = t_0
	elif y <= 1.55e+36:
		tmp = (x_m / z) - x_m
	else:
		tmp = t_0
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m, y, z)
	t_0 = Float64(Float64(x_m / z) * y)
	tmp = 0.0
	if (y <= -1.3e+43)
		tmp = t_0;
	elseif (y <= 1.55e+36)
		tmp = Float64(Float64(x_m / z) - x_m);
	else
		tmp = t_0;
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m, y, z)
	t_0 = (x_m / z) * y;
	tmp = 0.0;
	if (y <= -1.3e+43)
		tmp = t_0;
	elseif (y <= 1.55e+36)
		tmp = (x_m / z) - x_m;
	else
		tmp = t_0;
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_, y_, z_] := Block[{t$95$0 = N[(N[(x$95$m / z), $MachinePrecision] * y), $MachinePrecision]}, N[(x$95$s * If[LessEqual[y, -1.3e+43], t$95$0, If[LessEqual[y, 1.55e+36], N[(N[(x$95$m / z), $MachinePrecision] - x$95$m), $MachinePrecision], t$95$0]]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.3000000000000001e43 or 1.55e36 < y

   1. Initial program 86.9%

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

   3. Taylor expanded in y around inf

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

      1. associate-/l* N/A

         \[\leadsto \color{blue}{x \cdot \frac{y}{z}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{y}{z} \cdot x} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{y}{z} \cdot x} \]

      4. lower-/.f64 72.6

         \[\leadsto \color{blue}{\frac{y}{z}} \cdot x \]

   5. Applied rewrites 72.6%

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

   7. Applied rewrites 73.4%

      \[\leadsto y \cdot \color{blue}{\frac{x}{z}} \]

   if -1.3000000000000001e43 < y < 1.55e36

   1. Initial program 88.4%

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

   3. Taylor expanded in y around 0

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

      1. associate-/l* N/A

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

      2. div-sub N/A

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

      3. sub-neg N/A

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

      4. *-inverses N/A

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

      5. metadata-eval N/A

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

      6. distribute-lft-in N/A

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

      7. associate-/l* N/A

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

      8. *-rgt-identity N/A

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

      9. *-commutative N/A

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

      10. mul-1-neg N/A

          \[\leadsto \frac{x}{z} + \color{blue}{\left(\mathsf{neg}\left(x\right)\right)} \]

      11. unsub-neg N/A

          \[\leadsto \color{blue}{\frac{x}{z} - x} \]

      12. lower--.f64 N/A

          \[\leadsto \color{blue}{\frac{x}{z} - x} \]

      13. lower-/.f64 97.2

          \[\leadsto \color{blue}{\frac{x}{z}} - x \]

   5. Applied rewrites 97.2%

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

4. Final simplification 86.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.3 \cdot 10^{+43}:\\ \;\;\;\;\frac{x}{z} \cdot y\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+36}:\\ \;\;\;\;\frac{x}{z} - x\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{z} \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 99.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 2 \cdot 10^{+69}:\\ \;\;\;\;\frac{\mathsf{fma}\left(y, x\_m, x\_m\right)}{z} - x\_m\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y - z\right) - -1\right) \cdot \frac{x\_m}{z}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m y z)
 :precision binary64
 (*
  x_s
  (if (<= x_m 2e+69)
    (- (/ (fma y x_m x_m) z) x_m)
    (* (- (- y z) -1.0) (/ x_m z)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m, double y, double z) {
	double tmp;
	if (x_m <= 2e+69) {
		tmp = (fma(y, x_m, x_m) / z) - x_m;
	} else {
		tmp = ((y - z) - -1.0) * (x_m / z);
	}
	return x_s * tmp;
}

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m, y, z)
	tmp = 0.0
	if (x_m <= 2e+69)
		tmp = Float64(Float64(fma(y, x_m, x_m) / z) - x_m);
	else
		tmp = Float64(Float64(Float64(y - z) - -1.0) * Float64(x_m / z));
	end
	return Float64(x_s * tmp)
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_, y_, z_] := N[(x$95$s * If[LessEqual[x$95$m, 2e+69], N[(N[(N[(y * x$95$m + x$95$m), $MachinePrecision] / z), $MachinePrecision] - x$95$m), $MachinePrecision], N[(N[(N[(y - z), $MachinePrecision] - -1.0), $MachinePrecision] * N[(x$95$m / z), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 2.0000000000000001e69

   1. Initial program 90.9%

      \[\frac{x \cdot \left(\left(y - z\right) + 1\right)}{z} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. distribute-rgt-in N/A

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

      4. *-lft-identity N/A

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

      5. lower-fma.f64 90.9

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

   4. Applied rewrites 90.9%

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

      1. lift-/.f64 N/A

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

      2. lift-fma.f64 N/A

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

      3. distribute-lft1-in N/A

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

      4. metadata-eval N/A

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

      5. sub-neg N/A

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

      6. lift--.f64 N/A

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

      7. *-commutative N/A

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

      8. associate-*l/ N/A

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

      9. lift-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. sub-neg N/A

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

      12. metadata-eval N/A

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

      13. +-commutative N/A

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

      14. lift--.f64 N/A

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

      15. sub-neg N/A

          \[\leadsto \frac{x}{z} \cdot \left(1 + \color{blue}{\left(y + \left(\mathsf{neg}\left(z\right)\right)\right)}\right) \]

      16. associate-+r+ N/A

          \[\leadsto \frac{x}{z} \cdot \color{blue}{\left(\left(1 + y\right) + \left(\mathsf{neg}\left(z\right)\right)\right)} \]

      17. distribute-lft-in N/A

          \[\leadsto \color{blue}{\frac{x}{z} \cdot \left(1 + y\right) + \frac{x}{z} \cdot \left(\mathsf{neg}\left(z\right)\right)} \]

      18. lower-fma.f64 N/A

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

      19. lower-+.f64 N/A

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

      20. lower-*.f64 N/A

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

      21. lower-neg.f64 80.2

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

   6. Applied rewrites 80.2%

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

      1. lift-fma.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-neg.f64 N/A

         \[\leadsto \frac{x}{z} \cdot \left(1 + y\right) + \frac{x}{z} \cdot \color{blue}{\left(\mathsf{neg}\left(z\right)\right)} \]

      4. distribute-rgt-neg-out N/A

         \[\leadsto \frac{x}{z} \cdot \left(1 + y\right) + \color{blue}{\left(\mathsf{neg}\left(\frac{x}{z} \cdot z\right)\right)} \]

      5. unsub-neg N/A

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

      6. lift-/.f64 N/A

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

      7. div-inv N/A

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

      8. associate-*l* N/A

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

      9. inv-pow N/A

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

      10. pow-plus N/A

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

      11. metadata-eval N/A

          \[\leadsto \frac{x}{z} \cdot \left(1 + y\right) - x \cdot {z}^{\color{blue}{0}} \]

      12. metadata-eval N/A

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

      13. *-rgt-identity N/A

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

      14. lower--.f64 N/A

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

      15. lift-/.f64 N/A

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

      16. associate-*l/ N/A

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

      17. lower-/.f64 N/A

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

      18. lift-+.f64 N/A

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

      19. +-commutative N/A

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

      20. distribute-rgt-in N/A

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

      21. *-lft-identity N/A

          \[\leadsto \frac{y \cdot x + \color{blue}{x}}{z} - x \]

      22. lower-fma.f64 97.2

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

   8. Applied rewrites 97.2%

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

   if 2.0000000000000001e69 < x

   1. Initial program 72.8%

      \[\frac{x \cdot \left(\left(y - z\right) + 1\right)}{z} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. associate-/l* N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 99.9

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

      8. lift-+.f64 N/A

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

      9. lift--.f64 N/A

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

      10. associate-+l- N/A

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

      11. sub-neg N/A

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

      12. metadata-eval N/A

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

      13. associate--r+ N/A

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

      14. lift--.f64 N/A

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

      15. lower--.f64 99.9

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

   4. Applied rewrites 99.9%

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

4. Final simplification 97.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 2 \cdot 10^{+69}:\\ \;\;\;\;\frac{\mathsf{fma}\left(y, x, x\right)}{z} - x\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y - z\right) - -1\right) \cdot \frac{x}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 96.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \left(\frac{\mathsf{fma}\left(y, x\_m, x\_m\right)}{z} - x\_m\right) \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m y z)
 :precision binary64
 (* x_s (- (/ (fma y x_m x_m) z) x_m)))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m, double y, double z) {
	return x_s * ((fma(y, x_m, x_m) / z) - x_m);
}

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m, y, z)
	return Float64(x_s * Float64(Float64(fma(y, x_m, x_m) / z) - x_m))
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_, y_, z_] := N[(x$95$s * N[(N[(N[(y * x$95$m + x$95$m), $MachinePrecision] / z), $MachinePrecision] - x$95$m), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 87.7%

   \[\frac{x \cdot \left(\left(y - z\right) + 1\right)}{z} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. distribute-rgt-in N/A

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

   4. *-lft-identity N/A

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

   5. lower-fma.f64 87.7

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

4. Applied rewrites 87.7%

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

   1. lift-/.f64 N/A

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

   2. lift-fma.f64 N/A

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

   3. distribute-lft1-in N/A

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

   4. metadata-eval N/A

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

   5. sub-neg N/A

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

   6. lift--.f64 N/A

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

   7. *-commutative N/A

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

   8. associate-*l/ N/A

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

   9. lift-/.f64 N/A

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

   10. lift--.f64 N/A

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

   11. sub-neg N/A

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

   12. metadata-eval N/A

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

   13. +-commutative N/A

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

   14. lift--.f64 N/A

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

   15. sub-neg N/A

       \[\leadsto \frac{x}{z} \cdot \left(1 + \color{blue}{\left(y + \left(\mathsf{neg}\left(z\right)\right)\right)}\right) \]

   16. associate-+r+ N/A

       \[\leadsto \frac{x}{z} \cdot \color{blue}{\left(\left(1 + y\right) + \left(\mathsf{neg}\left(z\right)\right)\right)} \]

   17. distribute-lft-in N/A

       \[\leadsto \color{blue}{\frac{x}{z} \cdot \left(1 + y\right) + \frac{x}{z} \cdot \left(\mathsf{neg}\left(z\right)\right)} \]

   18. lower-fma.f64 N/A

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

   19. lower-+.f64 N/A

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

   20. lower-*.f64 N/A

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

   21. lower-neg.f64 81.3

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

6. Applied rewrites 81.3%

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

   1. lift-fma.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. lift-neg.f64 N/A

      \[\leadsto \frac{x}{z} \cdot \left(1 + y\right) + \frac{x}{z} \cdot \color{blue}{\left(\mathsf{neg}\left(z\right)\right)} \]

   4. distribute-rgt-neg-out N/A

      \[\leadsto \frac{x}{z} \cdot \left(1 + y\right) + \color{blue}{\left(\mathsf{neg}\left(\frac{x}{z} \cdot z\right)\right)} \]

   5. unsub-neg N/A

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

   6. lift-/.f64 N/A

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

   7. div-inv N/A

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

   8. associate-*l* N/A

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

   9. inv-pow N/A

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

   10. pow-plus N/A

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

   11. metadata-eval N/A

       \[\leadsto \frac{x}{z} \cdot \left(1 + y\right) - x \cdot {z}^{\color{blue}{0}} \]

   12. metadata-eval N/A

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

   13. *-rgt-identity N/A

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

   14. lower--.f64 N/A

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

   15. lift-/.f64 N/A

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

   16. associate-*l/ N/A

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

   17. lower-/.f64 N/A

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

   18. lift-+.f64 N/A

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

   19. +-commutative N/A

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

   20. distribute-rgt-in N/A

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

   21. *-lft-identity N/A

       \[\leadsto \frac{y \cdot x + \color{blue}{x}}{z} - x \]

   22. lower-fma.f64 95.8

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

8. Applied rewrites 95.8%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(y, x, x\right)}{z} - x} \]
9. Add Preprocessing

Alternative 8: 65.1% accurate, 1.5× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \left(\frac{x\_m}{z} - x\_m\right) \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m y z) :precision binary64 (* x_s (- (/ x_m z) x_m)))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m, double y, double z) {
	return x_s * ((x_m / z) - x_m);
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m, y, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x_s * ((x_m / z) - x_m)
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m, double y, double z) {
	return x_s * ((x_m / z) - x_m);
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m, y, z):
	return x_s * ((x_m / z) - x_m)

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m, y, z)
	return Float64(x_s * Float64(Float64(x_m / z) - x_m))
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m, y, z)
	tmp = x_s * ((x_m / z) - x_m);
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_, y_, z_] := N[(x$95$s * N[(N[(x$95$m / z), $MachinePrecision] - x$95$m), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 87.7%

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

3. Taylor expanded in y around 0

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

   1. associate-/l* N/A

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

   2. div-sub N/A

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

   3. sub-neg N/A

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

   4. *-inverses N/A

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

   5. metadata-eval N/A

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

   6. distribute-lft-in N/A

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

   7. associate-/l* N/A

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

   8. *-rgt-identity N/A

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

   9. *-commutative N/A

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

   10. mul-1-neg N/A

       \[\leadsto \frac{x}{z} + \color{blue}{\left(\mathsf{neg}\left(x\right)\right)} \]

   11. unsub-neg N/A

       \[\leadsto \color{blue}{\frac{x}{z} - x} \]

   12. lower--.f64 N/A

       \[\leadsto \color{blue}{\frac{x}{z} - x} \]

   13. lower-/.f64 67.4

       \[\leadsto \color{blue}{\frac{x}{z}} - x \]

5. Applied rewrites 67.4%

   \[\leadsto \color{blue}{\frac{x}{z} - x} \]
6. Add Preprocessing

Alternative 9: 38.7% accurate, 7.7× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \left(-x\_m\right) \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m y z) :precision binary64 (* x_s (- x_m)))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m, double y, double z) {
	return x_s * -x_m;
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m, y, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x_s * -x_m
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m, double y, double z) {
	return x_s * -x_m;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m, y, z):
	return x_s * -x_m

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m, y, z)
	return Float64(x_s * Float64(-x_m))
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m, y, z)
	tmp = x_s * -x_m;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_, y_, z_] := N[(x$95$s * (-x$95$m)), $MachinePrecision]

Derivation

1. Initial program 87.7%

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

3. Taylor expanded in z around inf

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

   1. mul-1-neg N/A

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

   2. lower-neg.f64 37.6

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

5. Applied rewrites 37.6%

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

Developer Target 1: 99.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(1 + y\right) \cdot \frac{x}{z} - x\\ \mathbf{if}\;x < -2.71483106713436 \cdot 10^{-162}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x < 3.874108816439546 \cdot 10^{-197}:\\ \;\;\;\;\left(x \cdot \left(\left(y - z\right) + 1\right)\right) \cdot \frac{1}{z}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- (* (+ 1.0 y) (/ x z)) x)))
   (if (< x -2.71483106713436e-162)
     t_0
     (if (< x 3.874108816439546e-197)
       (* (* x (+ (- y z) 1.0)) (/ 1.0 z))
       t_0))))

C

double code(double x, double y, double z) {
	double t_0 = ((1.0 + y) * (x / z)) - x;
	double tmp;
	if (x < -2.71483106713436e-162) {
		tmp = t_0;
	} else if (x < 3.874108816439546e-197) {
		tmp = (x * ((y - z) + 1.0)) * (1.0 / z);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((1.0d0 + y) * (x / z)) - x
    if (x < (-2.71483106713436d-162)) then
        tmp = t_0
    else if (x < 3.874108816439546d-197) then
        tmp = (x * ((y - z) + 1.0d0)) * (1.0d0 / z)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = ((1.0 + y) * (x / z)) - x;
	double tmp;
	if (x < -2.71483106713436e-162) {
		tmp = t_0;
	} else if (x < 3.874108816439546e-197) {
		tmp = (x * ((y - z) + 1.0)) * (1.0 / z);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = ((1.0 + y) * (x / z)) - x
	tmp = 0
	if x < -2.71483106713436e-162:
		tmp = t_0
	elif x < 3.874108816439546e-197:
		tmp = (x * ((y - z) + 1.0)) * (1.0 / z)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(1.0 + y) * Float64(x / z)) - x)
	tmp = 0.0
	if (x < -2.71483106713436e-162)
		tmp = t_0;
	elseif (x < 3.874108816439546e-197)
		tmp = Float64(Float64(x * Float64(Float64(y - z) + 1.0)) * Float64(1.0 / z));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = ((1.0 + y) * (x / z)) - x;
	tmp = 0.0;
	if (x < -2.71483106713436e-162)
		tmp = t_0;
	elseif (x < 3.874108816439546e-197)
		tmp = (x * ((y - z) + 1.0)) * (1.0 / z);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(1.0 + y), $MachinePrecision] * N[(x / z), $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision]}, If[Less[x, -2.71483106713436e-162], t$95$0, If[Less[x, 3.874108816439546e-197], N[(N[(x * N[(N[(y - z), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision] * N[(1.0 / z), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Reproduce

herbie shell --seed 2024285 
(FPCore (x y z)
  :name "Diagrams.TwoD.Segment.Bernstein:evaluateBernstein from diagrams-lib-1.3.0.3"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< x -67870776678359/25000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (- (* (+ 1 y) (/ x z)) x) (if (< x 1937054408219773/50000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (* (* x (+ (- y z) 1)) (/ 1 z)) (- (* (+ 1 y) (/ x z)) x))))

  (/ (* x (+ (- y z) 1.0)) z))