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.
The website for this package is http://www.is.titech.ac.jp/~kojima/SparseCoLO,
where the package SparseCoLO and this manual can be downloaded.