Efficiency of Quasi-Newton Methods on Strictly Positive Functions
published: Jan. 13, 2011, recorded: December 2010, views: 384
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In this talk we consider a new class of convex optimization problems, which admit faster black-box optimization schemes. For analyzing their rate of convergence, we introduce a notion of mixed accuracy of an approximate solution, which is a convenient generalization of the absolute and relative accuracies. We show that for our problem class, a natural Quasi-Newton method is always faster than the standard gradient method. At the same time, after an appropriate normalization, our results can be extended onto the general convex unconstrained minimization problems.
Download slides: nipsworkshops2010_nesterov_eqn_01.pdf (1.2 MB)
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