def gm_constrs(t, x_list, p):
 assert is_weight(p)
 w = dyad_completion(p)
 tree = decompose(w)
 d = defaultdict((lambda : lu.create_var(t.size)))
 d[w] = t
 if (len(x_list) < len(w)):
  x_list += [t]
 assert (len(x_list) == len(w))
 for (i, (p, v)) in enumerate(zip(w, x_list)):
  if (p > 0):
   tmp = ([0] * len(w))
   tmp[i] = 1
   d[tuple(tmp)] = v
 constraints = []
 for (elem, children) in tree.items():
  if (1 not in elem):
   constraints += [gm(d[elem], d[children[0]], d[children[1]])]
 return constraints

def gm_constrs ( t , x_list , p ) : assert is_weight ( p ) w = dyad_completion ( p ) tree = decompose ( w ) d = defaultdict ( ( lambda : lu . create_var ( t . size ) ) ) d [ w ] = t if ( len ( x_list ) < len ( w ) ) : x_list += [ t ] assert ( len ( x_list ) == len ( w ) ) for ( i , ( p , v ) ) in enumerate ( zip ( w , x_list ) ) : if ( p > 0 ) : tmp = ( [ 0 ] * len ( w ) ) tmp [ i ] = 1 d [ tuple ( tmp ) ] = v constraints = [ ] for ( elem , children ) in tree . items ( ) : if ( 1 not in elem ) : constraints += [ gm ( d [ elem ] , d [ children [ 0 ] ] , d [ children [ 1 ] ] ) ] return constraints
form the internal cxvpy constraints to form the weighted geometric mean t <= x^p .