SparseCoLO (Conversion Methods for SPARSE COnic-form Linear Optimization) 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, second-order cone programs with equality/inequality constraints. Among the four conversion methods, two methods utilize the domain-space sparsity of a semidefinite matrix variable and the other two methods the range-space 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.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Kojima, Masakazu; Yamashita, Makoto: Enclosing ellipsoids and elliptic cylinders of semialgebraic sets and their application to error bounds in polynomial optimization (2013)
- Kim, Sunyoung; Kojima, Masakazu; Mevissen, Martin; Yamashita, Makoto: Exploiting sparsity in linear and nonlinear matrix inequalities via positive semidefinite matrix completion (2011)
- Kojima, Masakazu: Exploiting structured sparsity in large scale semidefinite programming problems (2010)
- Zhu, Zhisu; So, Anthony Man-Cho; Ye, Yinyu: Universal rigidity and edge sparsification for sensor network localization (2010)