Optimality, identifiability, and sensitivity

• 成果类型: Article
• 署名作者: Drusvyatskiy, D.; Lewis, A. S.
• 署名单位: Cornell University
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-013-0730-4
• 发表日期: 2014
• 页码: 467-498
• 关键词: nonconvex functions; active constraints; finite convergence; identification; algorithm; points

摘要

Around a solution of an optimization problem, an identifiable subset of the feasible region is one containing all nearby solutions after small perturbations to the problem. A quest for only the most essential ingredients of sensitivity analysis leads us to consider identifiable sets that are minimal. This new notion lays a broad and intuitive variational-analytic foundation for optimality conditions, sensitivity, and active set methods.