### Katya Scheinberg (Lehigh University, USA)

#### Accelerating first order methods for convex composite optimization problems

*Tuesday 26 June 2012 at 14.30, JCMB 6206*

##### Abstract

First-order methods with favorable convergence rates have recently become a
focal point of much research in the field of convex optimization. These
methods have low per-iteration complexity and hence are applicable to very
large scale model, such as the ones arising in signal processing, statistics
and machine learning. We will first show how these convergence properties
extend to a certain class of alternating direction methods - also recently
popular for large scale convex problems. All the methods in question employ
prox term parameter which is often assumed to be fixed. We will discuss
theoretical and practical implications of various strategies for choosing the
prox parameter in prox gradient methods and related alternating direction
methods. We will show extension of existing convergence rates for both
accelerated and classical first-order methods. Practical comparison based on a
testing environment for L1 optimization will be presented.

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