Name
Title | ||
|---|---|---|
Self-concordance and Decomposition-based Interior Point Methods for the Two-stage Stochastic Convex Optimization Problem |
Abstract | ||
|---|---|---|
We study the two-stage stochastic convex optimization problem whose first- and second-stage feasible regions admit a self-concordant barrier. We show that the barrier recourse functions and the composite barrier functions for this problem form self-concordant families. These results are used to develop prototype primal interior point decomposition algorithms that are more suitable for a heterogeneous distributed computing environment. We show that the worst case iteration complexity of the proposed algorithms is the same as that for the short- and long-step primal interior algorithms applied to the extensive formulation of this problem. The generality of our results allows the possibility of using barriers other than the standard log-barrier in an algorithmic framework. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1137/080742026 | SIAM Journal on Optimization |
Keywords | DocType | Volume |
extensive formulation,prototype primal interior point,two-stage stochastic convex optimization,long-step primal interior,decomposition algorithm,barrier recourse function,problem form self-concordant family,self-concordant barrier,decomposition-based interior point methods,proposed algorithm,composite barrier function,algorithmic framework,interior point methods,benders decomposition,stochastic programming,convex programming | Journal | 21 |
Issue | ISSN | Citations |
4 | 1052-6234 | 1 |
PageRank | References | Authors |
0.37 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Chen | 1 | 2 | 0.72 |
| Sanjay Mehrotra | 2 | 521 | 77.18 |