#12418: adding Delsarte bound for codes
-----------------------------+----------------------------------------------
   Reporter:  dimpase        |          Owner:  wdj     
       Type:  enhancement    |         Status:  new     
   Priority:  major          |      Milestone:  sage-5.0
  Component:  coding theory  |       Keywords:          
Work_issues:                 |       Upstream:  N/A     
   Reviewer:                 |         Author:          
     Merged:                 |   Dependencies:          
-----------------------------+----------------------------------------------
 Delsarte bound for codes, aka Linear Programming bound, is easy to
 implement in Sage.
 Here is a quick and dirty code that does it; so the problem would be to
 integrate this properly.
 {{{
 def Kra(n,q,l,i): # K^{n,q}_l(i), i.e Krawtchouk polynomial
    return sum([((-1)**j)*((q-1)**(l-j))*binomial(i,j)*binomial(n-i,l-j)
                               for j in range(l+1)])

 def roundres(x): # this is a quick and unsafe way to round the result...
    import math
    tol = 0.0001
    if math.ceil(x)-x < tol:
       return int(math.ceil(x))
    if x-math.floor(x) < tol:
       return int(math.floor(x))
    return x

 # @cached_function
 def delsarte_bound(n, q, d, d_star=1, q_base=0, return_log=True,\
                     isinteger=False, return_data=False):
    p = MixedIntegerLinearProgram(maximization=True)
    A = p.new_variable(integer=isinteger) # A>=0 is assumed
    p.set_objective(sum([A[r] for r in range(n+1)]))
    p.add_constraint(A[0]==1)
    for i in range(1,d):
       p.add_constraint(A[i]==0)
    for j in range(1,n+1):
       rhs = sum([Kra(n,q,j,r)*A[r] for r in range(n+1)])
       if j >= d_star:
         p.add_constraint(0*A[0] <= rhs)
       else: # rhs is proportional to j-th weight of the dual code
         p.add_constraint(0*A[0] == rhs)
    try:
       bd=p.solve()
    except sage.numerical.mip.MIPSolverException, exc:
       print "Solver exception: ", exc, exc.args
       if return_data:
          return A,p,False
       return False
    if q_base > 0: # rounding the bound down to the nearest power of
 q_base,
                   # for q=q_base^m
 #      qb = factor(q).radical()
 #      if len(qb) == 1:
 #         base = qb.expand()
 #         bd = base^int(log(bd, base=base))
          if return_log:
 #            bd = int(log(roundres(bd), base=q_base)) # unsafe:
                                                       # loss of precision
             bd = roundres(log(bd, base=q_base))
          else:
 #            bd = q_base^int(log(roundres(bd), base=q_base))
             bd = q_base^roundres(log(bd, base=q_base))
    if return_data:
       return A,p,bd
    return bd
 }}}

 d_star is the minimal distance  of the dual code (if it exists at all)
 If q_base is 0, just compute the upper bound.
 If q_base is >0, it is assumed to be a prime power, and the code assumed
 to be additive over this field (i.e. the dual code exists,and its weight
 enumerator is
 obtained by applying {{{MacWilliams transform}}} --- the matrix A of the
 LP times the
 weight enumerator of our code), then the output is the corr. dimension,
 i.e.
 {{{floor(log(bound, q_base))}}}.

-- 
Ticket URL: <http://trac.sagemath.org/sage_trac/ticket/12418>
Sage <http://www.sagemath.org>
Sage: Creating a Viable Open Source Alternative to Magma, Maple, Mathematica, 
and MATLAB

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