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dc.contributor.authorZhou, J.L.en_US
dc.contributor.authorTits, A.L.en_US
dc.date.accessioned2007-05-23T09:54:41Z
dc.date.available2007-05-23T09:54:41Z
dc.date.issued1993en_US
dc.identifier.urihttp://hdl.handle.net/1903/5427
dc.description.abstractA common strategy for achieving global convergence in the solution of semi-infinite programming (SIP) problems, and in particular of continuous minimax problems, is to (approximately) solve a sequence of discretized problems, with a progressively finer discretization mesh. Finely discretized minimax and SIP problems, as well as other problems with many more objectives/constraints than variables, call for algorithms in which successive search directions are computed based on a small but significant subset of the objectives/constraints, with ensuing reduced computing cost per iteration and decreased risk of numerical difficulties. In this paper, an SQP-type algorithm is proposed that incorporates this idea in the particular case of minimax problems. The general case will be considered in a separate paper. The quadratic programming subproblem that yields the search direction involves only a small subset of the objectives functions. This subset is updated at each iteration in such a way that global convergence is insured. Heuristics are suggested that take advantage of a possible close relationship between ﲡdjacent objective functions. Numerical results demonstrate the efficiency of the proposed algorithm.en_US
dc.format.extent1326367 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1993-83en_US
dc.subjectcontinuous miminaxen_US
dc.subjectsemi-infinite programmingen_US
dc.subjectmany constraintsen_US
dc.subjectsequential quadratic programmingen_US
dc.subjectdiscretizationen_US
dc.subjectglobal convergence.en_US
dc.subjectIntelligent Servomechanismsen_US
dc.titleAn SQP Algorithm for Finely Discretized Continuous Minimax Problems and Other Minimax Problems with Many Objective Functionsen_US
dc.typeTechnical Reporten_US
dc.contributor.departmentISRen_US


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