Approximating Concavely Parameterized Optimization Problems

no code implementations • NeurIPS 2012 • Joachim Giesen, Jens Mueller, Soeren Laue, Sascha 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 $O(1/\sqrt{\varepsilon})$.

Matrix Completion