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which are slightly smaller than their corresponding sparse vectors.  So I changed
the requires clause of the dot(sparse,dense) and dot(dense,sparse) functions to not
require that the dense vector's size > sparse vector's size.  Now any combination
of sizes is legal.

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assigning from sparse to sparse.

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Prior to this change, different function objects declared their return type in different ways,
now this has all been reconciled.  Now they all declare it as a public typedef named result_type.

I also simplified the cross_validate_multiclass_trainer(), cross_validate_trainer(),
test_binary_decision_function(), and test_multiclass_decision_function().  They now always
return double matrices regardless of any other consideration.

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and related routines.

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best point seen so far.  This change will allow me to use the optimizer in conjunction
with methods for quickly approximating subgradients and risk values without needing
to worry about the optimizer permanently fixating on what it erroneously thinks
is the best point.  Now the optimizer will just keep solving the cutting plane
subproblem and will use that solution as the current iterate and best known
solution.

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max_index_plus_one() so that it uses the same name as the essentially identical
function for working with sparse vectors.

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implementation and into the sparse_vector.h header.  These are the add_to(), subtract_from(),
and max_index_plus_one() functions.

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which operate on STL containers and matrix objects.

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sparse samples which were of type std::vector<std::pair<> >.  Moreover, some of
the trainers have a dependency on kernel_matrix() so this fix makes those trainers
also work with this kind of sparse sample.

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of the labels output vector before adding the loaded label data.

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rather than resolving it from scratch during each iteration. This
improves the speed and stability of the algorithm.

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assignment problem (in the new max_cost_assignment() routine).

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