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Probability-One Homotopy Maps for Mixed Complementarity Problems

Probability-one homotopy algorithms have strong convergence characteristics under mild assumptions. Such algorithms for mixed complementarity problems (MCPs) have potentially wide impact because MCPs are pervasive in science and engineering. A probability-one homotopy algorithm for MCPs was developed earlier by Billups and Watson based on the default homotopy mapping. This algorithm had guaranteed global convergence under some mild conditions, and was able to solve most of the MCPs from the MCPLIB test library. This thesis extends that work by presenting some other homotopy mappings, enabling the solution of all the remaining problems from MCPLIB. The homotopy maps employed are the Newton homotopy and homotopy parameter embeddings. / Master of Science

Identiferoai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/31539
Date10 April 2007
CreatorsAhuja, Kapil
ContributorsComputer Science, Watson, Layne T., Ribbens, Calvin J., Sachs, Ekkehard W.
PublisherVirginia Tech
Source SetsVirginia Tech Theses and Dissertation
Detected LanguageEnglish
TypeThesis
Formatapplication/pdf, application/octet-stream
RightsIn Copyright, http://rightsstatements.org/vocab/InC/1.0/
Relationthesis.pdf, models.zip

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