Tensor decomposition and approximation schemes for constraint satisfaction problems

W. Fernandez de la Vega, Marek Karpinski, Ravi Kannan, Santosh Vempala
<span title="">2005</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jlc5kugafjg4dl7ozagimqfcbm" style="color: black;">Proceedings of the thirty-seventh annual ACM symposium on Theory of computing - STOC &#39;05</a> </i> &nbsp;
The only general class of MAX-rCSP problems for which Polynomial Time Approximation Schemes (PTAS) are known are the dense problems. In this paper, we give PTAS's for a much larger class of weighted MAX-rCSP problems which includes as special cases the dense problems and, for r = 2, all metric instances (where the weights satisfy the triangle inequality) and quasimetric instances; for r > 2, our class includes a generalization of metrics. Our algorithms are based on low-rank approximations with
more &raquo; ... two novel features: (1) a method of approximating a tensor by the sum of a small number of "rank-1" tensors, akin to the traditional Singular Value Decomposition (this might be of independent interest) and (2) a simple way of scaling the weights. Besides MAX-rCSP problems, we also give PTAS's for problems with a constant number of global constraints such as maximum weighted graph bisection and some generalizations.
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