#13358: package for fast polynomial evaluation
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   Reporter:  gmoroz            |             Owner:  AlexGhitza                
       Type:  enhancement       |            Status:  new                       
   Priority:  major             |         Milestone:  sage-5.3                  
  Component:  basic arithmetic  |          Keywords:  polynomials               
Work issues:                    |   Report Upstream:  N/A                       
  Reviewers:                    |           Authors:                            
  Merged in:                    |      Dependencies:  boost::interval (optional)
   Stopgaps:                    |  
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 The attached package provides conversion of univariate and multivariate
 polynomials into object that are optimized for fast evaluation on python
 object or low-levels c++ classes (see examples at the end).

 It could enhanced the fast_callable function for several types, and also
 enhances in general the evaluation of polynomials on polynomials.

 To test it, you can install it with: ./setup.py install
 This will install the package in $SAGE_ROOT/local/lib/python2.7/site-
 packages/

 Main features:
 * handles univariate and multivariate polynomials
 * specialized for several low-level types (mpfi, mpz, mpq,
 boost::interval)
 * different evaluation layouts (horner, estrin, expanded, ...)
 * easily extensible:
   - add new types (see fast_polynomial/interfaces/README)
   - add new layouts (see docstring of fast_polynomial.method)
 * handles generic python/sage objects
 * can be multi-threaded

 Main limitations:
 * only handles polynomial (no evaluation of trigonometric functions,...)
 * polynomial needs to be converted to a fast callable object before
 evaluation
   (there is room for speed up on conversion time)

 Examples and benchmarks:
 {{{
 from fast_polynomial import *
 R.<x> = ZZ[x]
 p = R.random_element(500,-100,100)

 # evaluation of polynomials
 q = python_polynomial(p, mode='horner')
 r = python_polynomial(p, mode='estrin')
 %timeit p(x+1) #5 loops, best of 3: 40.3 ms per loop
 %timeit q(x+1) #5 loops, best of 3: 40.3 ms per loop
 %timeit r(x+1) #125 loops, best of 3: 2.26 ms per loop
 %timeit python_polynomial(p)(x+1) #125 loops, best of 3: 3.2 ms per loop

 # evaluation of long integers
 q = mpz_polynomial(p, num_threads=1)
 r = mpz_polynomial(p, num_threads=2)
 %timeit p(100) #625 loops, best of 3: 50.4 µs per loop
 %timeit q(100) #625 loops, best of 3: 48.1 µs per loop
 %timeit r(100) #625 loops, best of 3: 34.9 µs per loop


 # evaluation of mpfi interval with precision 1000
 q = mpfi_polynomial(p, 1000)
 e = RealIntervalField(1000)(2^500, 2^500+1)
 cmp(p(e),q(e)) #0
 %timeit p(e)   #125 loops, best of 3: 2.71 ms per loop
 %timeit q(e)   #625 loops, best of 3: 513 µs per loop
 %timeit mpfi_polynomial(p)(e) #125 loops, best of 3: 1.15 ms per loop

 # evaluation of boost interval (précision 53)
 q = boost_polynomial(p, mode='horner')
 r = boost_polynomial(p, mode='balanced', num_threads=2)
 f = fast_callable(p, domain=float)
 e = RIF(0.01)
 %timeit p(e)    #125 loops, best of 3: 2.14 ms per loop
 %timeit f(0.01) #625 loops, best of 3: 9.54 µs per loop
 %timeit q(e)    #625 loops, best of 3: 13.4 µs per loop
 %timeit r(e)    #625 loops, best of 3: 11.7 µs per loop
 # Note that boost_polynomial evaluation offers more guarantees than raw
 float evaluation

 # multivariate polynomials
 R20 = PolynomialRing(QQ, 20,'x')
 p = R20.random_element(5,100)
 q = mpq_polynomial(p)
 %timeit p((2/3,)*20) #125 loops, best of 3: 2.06 ms per loop
 %timeit q((2/3,)*20) #625 loops, best of 3: 178 µs per loop
 %timeit mpq_polynomial(p) #125 loops, best of 3: 1.91 ms per loop
 }}}

-- 
Ticket URL: <http://trac.sagemath.org/sage_trac/ticket/13358>
Sage <http://www.sagemath.org>
Sage: Creating a Viable Open Source Alternative to Magma, Maple, Mathematica, 
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