  Three algorithms looking for pretty large partial Hadamard matrices are
described. Here "large" means that hopefully about a third of a Hadamard matrix
(which is the best asymptotic result known so far, [dLa00]) is achieved. The
first one performs some kind of local exhaustive search, and consequently is
expensive from the time consuming point of view. The second one comes from the
adaptation of the best genetic algorithm known so far searching for cliques in
a graph, due to Singh and Gupta [SG06]. The last one consists in another
heuristic search, which prioritizes the required processing time better than
the final size of the partial Hadamard matrix to be obtained.
  In all cases, the key idea is characterizing the adjacency properties of
vertices in a particular subgraph G_t of Ito's Hadamard Graph Delta (4t)
[Ito85], since cliques of order m in G_t can be seen as (m+3)*4t partial
Hadamard matrices.
