Solving Semidefinite Programs via Nonlinear Programming, Part II: Interior Point Methods for a Subclass of SDPs

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dc.contributor.authorBurer, Samen_US
dc.contributor.authorMonteiro, Renatoen_US
dc.contributor.authorZhang, Yinen_US
dc.date.accessioned2018-06-18T17:47:34Zen_US
dc.date.available2018-06-18T17:47:34Zen_US
dc.date.issued1999-10en_US
dc.date.noteOctober 1999en_US
dc.description.abstractIn Part I of this series of papers, we have introduced transformations which convert a large class of linear and nonlinear semidefinite programs (SDPs) into nonlinear optimization problems over "orthants" of the form (R^n)++ × R^N, where n is the size of the matrices involved in the problem and N is a nonnegative integer dependent upon the specific problem. In doing so, we have effectively reduced the number of variables and constraints. In this paper, we develop interior point methods for solving a subclass of the transformable linear SDP problems where the diagonal of a matrix variable is given. These new interior point methods have the advantage of working entirely within the space of the transformed problem while still maintaining close ties with the original SDP. Under very mild and reasonable assumptions, global convergence of these methods is proved.en_US
dc.format.extent19 ppen_US
dc.identifier.citationBurer, Sam, Monteiro, Renato and Zhang, Yin. "Solving Semidefinite Programs via Nonlinear Programming, Part II: Interior Point Methods for a Subclass of SDPs." (1999) <a href="https://hdl.handle.net/1911/101928">https://hdl.handle.net/1911/101928</a>.en_US
dc.identifier.digitalTR99-23en_US
dc.identifier.urihttps://hdl.handle.net/1911/101928en_US
dc.language.isoengen_US
dc.titleSolving Semidefinite Programs via Nonlinear Programming, Part II: Interior Point Methods for a Subclass of SDPsen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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