Second Order Behavior of Pattern Search Algorithms

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dc.contributor.authorAbramson, Mark A.
dc.date.accessioned2018-06-18T17:52:01Z
dc.date.available2018-06-18T17:52:01Z
dc.date.issued2004-01
dc.date.noteJanuary 2004
dc.description.abstractPrevious analyses of pattern search algorithms for unconstrained and linearly constrained minimization have focused on proving convergence of a subsequence of iterates to a limit point satisfying either directional or first-order necessary conditions for optimality, depending on the smoothness of the objective function in a neighborhood of the limit point. Even though pattern search methods require no derivative information, we are able to prove some limited directional second-order results. Although not as strong as classical second-order necessary conditions, these results are stronger than the first order conditions that many gradient-based methods satisfy. Under fairly mild conditions, we can eliminate from consideration all strict local maximizers and an entire class of saddle points.
dc.format.extent16 pp
dc.identifier.citationAbramson, Mark A.. "Second Order Behavior of Pattern Search Algorithms." (2004) <a href="https://hdl.handle.net/1911/102016">https://hdl.handle.net/1911/102016</a>.
dc.identifier.digitalTR04-03
dc.identifier.urihttps://hdl.handle.net/1911/102016
dc.language.isoeng
dc.titleSecond Order Behavior of Pattern Search Algorithms
dc.typeTechnical report
dc.type.dcmiText
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