Stochastic semidefinite programming: a new paradigm for stochastic optimization
| Autor: | K. A. Ariyawansa, Yuntao Zhu |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Semidefinite programming
Semidefinite embedding Mathematical optimization Quadratically constrained quadratic program Optimization problem Linear programming MathematicsofComputing_GENERAL MathematicsofComputing_NUMERICALANALYSIS Mathematics::Optimization and Control Management Science and Operations Research Stochastic programming Theoretical Computer Science Management Information Systems Computational Theory and Mathematics Computer Science::Programming Languages Second-order cone programming Stochastic optimization Mathematics |
| Zdroj: | 4OR. 4:239-253 |
| ISSN: | 1614-2411 1619-4500 |
| DOI: | 10.1007/s10288-006-0016-2 |
| Popis: | Semidefinite programs are a class of optimization problems that have been studied extensively during the past 15 years. Semidefinite programs are naturally related to linear programs, and both are defined using deterministic data. Stochastic programs were introduced in the 1950s as a paradigm for dealing with uncertainty in data defining linear programs. In this paper, we introduce stochastic semidefinite programs as a paradigm for dealing with uncertainty in data defining semidefinite programs. |
| Databáze: | OpenAIRE |
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