Approximating Concavely Parameterized Optimization Problems
published: Jan. 16, 2013, recorded: December 2012, views: 192
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We consider an abstract class of optimization problems that are parameterized concavely in a single parameter, and show that the solution path along the parameter can always be approximated with accuracy ε>0 by a set of size O(1/ε). A lower bound of size Ω(1/ε) shows that the upper bound is tight up to a constant factor. We also devise an algorithm that calls a step-size oracle and computes an approximate path of size O(1/ε). Finally, we provide an implementation of the oracle for soft-margin support vector machines, and a parameterized semi-definite program for matrix completion.
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