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POWhat are the differences between the various boost ublas sparse vectors?
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copied!<p>In <code>boost::numeric::ublas</code>, there are <a href="http://www.boost.org/doc/libs/1_43_0/libs/numeric/ublas/doc/vector_sparse.htm" rel="noreferrer">three sparse vector types</a>.</p> <p>I can see that the <code>mapped_vector</code> is essentially an <code>stl::map</code> from index to value, which considers all not-found values to be 0 (or whatever is the common value).</p> <p>But the documentation is sparse (ha ha) on information about <code>compressed_vector</code> and <code>coordinate_vector</code>.</p> <p>Is anyone able to clarify? I'm trying to figure out the algorithmic complexity of adding items to the various vectors, and also of dot products between two such vectors.</p> <p>A very helpful answer offered that compressed_vector is very similar to <a href="http://www.guwi17.de/ublas/matrix_sparse_usage.html" rel="noreferrer">compressed_matrix</a>. But it seems that, for example, <a href="http://www.netlib.org/linalg/html_templates/node91.html#SECTION00931100000000000000" rel="noreferrer">compressed row storage</a> is only for storing matrices -- not just vectors.</p> <p>I see that <code>unbounded_array</code> is the storage type, but I'm not quite sure what the specification is for that, either. If I create a compressed_vector with size 200,000,000, but with only 5 non-zero locations, is this less efficient in any way than creating a compressed_vector with size 10 and 5 non-zero locations?</p> <p>Many thanks!</p>

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