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K. Fujisawa(fujisawachuo.ac.jp) Abstract: SparseCoLO is a Matlab package for implementing the four conversion methods, proposed by Kim, Kojima, Mevissen, and Yamashita, via positive semidefinite matrix completion for an optimization problem with matrix inequalities satisfying a sparse chordal graph structure. It is based on quite a general description of optimization problem including both primal and dual form of linear, semidefinite, secondorder cone programs with equality/inequality constraints. Among the four conversion methods, two methods utilize the domainspace sparsity of a semidefinite matrix variable and the other two methods the rangespace sparsity of a linear matrix inequality (LMI) constraint of the given problem. SparseCoLO can be used as a preprocessor to reduce the size of the given problem before applying semidefinite programming solvers. The website for this package is http://www.is.titech.ac.jp/~kojima/SparseCoLO where the package SparseCoLO and this manual can be downloaded. Keywords: Semidefinite program, Sparsity exploitation, Positive semidefinite matrix completion, Category 1: Linear, Cone and Semidefinite Programming Category 2: Optimization Software and Modeling Systems Citation: Research report B453, Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, 2121 OhOkayama, Meguroku, Tokyo 1528552 Japan. Download: [PDF] Entry Submitted: 02/16/2009 Modify/Update this entry  
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