Skip to Main content Skip to Navigation
Book sections

First Order Methods for Nonsmooth Convex Large-Scale Optimization, I: General Purpose Methods

Abstract : We discuss several state-of-the-art computationally cheap, as opposed to the polynomial time Interior Point algorithms, rst order methods for minimizing convex objectives over "simple" large-scale feasible sets. Our emphasis is on the general situation of a nonsmooth convex objective represented by deterministic/stochastic First Order oracle and on the methods which, under favorable circumstances, exhibit (nearly) dimension-independent convergence rate.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00981863
Contributor : Anatoli Juditsky <>
Submitted on : Wednesday, April 23, 2014 - 8:34:16 AM
Last modification on : Friday, July 3, 2020 - 4:51:15 PM

Identifiers

  • HAL Id : hal-00981863, version 1

Collections

Citation

Anatoli Juditsky, Arkadii S. Nemirovski. First Order Methods for Nonsmooth Convex Large-Scale Optimization, I: General Purpose Methods. Suvrit Sra, Sebastian Nowozin, Stephen J. Wright. Optimization for Machine Learning, MIT Press, pp.1-28, 2010. ⟨hal-00981863⟩

Share

Metrics

Record views

468