Name
Abstract | ||
|---|---|---|
In this paper we propose new methods for solving huge-scale optimization problems. For problems of this size, even the simplest full-dimensional vector operations are very expensive. Hence, we propose to apply an optimization technique based on random partial update of decision variables. For these methods, we prove the global estimates for the rate of convergence. Surprisingly, for certain classes of objective functions, our results are better than the standard worst-case bounds for deterministic algorithms. We present constrained and unconstrained versions of the method and its accelerated variant. Our numerical test confirms a high efficiency of this technique on problems of very big size. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1137/100802001 | SIAM JOURNAL ON OPTIMIZATION |
Keywords | Field | DocType |
convex optimization,coordinate relaxation,worst-case efficiency estimates,fast gradient schemes,Google problem | Decision variables,Numerical tests,Stochastic optimization,Mathematical optimization,Random coordinate descent,Rate of convergence,Coordinate descent,Convex optimization,Optimization problem,Mathematics | Journal |
Volume | Issue | ISSN |
22 | 2 | 1052-6234 |
Citations | PageRank | References |
355 | 16.40 | 1 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yurii Nesterov | 1 | 1800 | 168.77 |