Approximating Concavely Parameterized Optimization Problems

NeurIPS 2012 Joachim GiesenJens MuellerSoeren LaueSascha Swiercy

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 $\varepsilon >0$ by a set of size $O(1/\sqrt{\varepsilon})$. A lower bound of size $\Omega (1/\sqrt{\varepsilon})$ shows that the upper bound is tight up to a constant factor... (read more)

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