Level bundle methods for constrained convex optimization with various oracles

摘要: We propose restricted memory level bundle methods for minimizing constrained convex nonsmooth optimization problems whose objective and constraint functions are known through oracles (black-boxes) that might provide inexact information. Our approach is general covers many instances of oracles, such as upper, lower on-demand accuracy oracles. show the proposed convergent long to at least four well chosen linearizations: two linearizations function, constraints. The particularly suitable both joint chance-constrained two-stage stochastic programs with risk measure assessed on realistic energy problems, arising when dealing robust cascaded-reservoir management.