SparseCoLO
(Conversion Methods for SPARSE
COnic-form Linear
Optimization)
K. Fujisawa, S. Kim, M. Kojima, Y. Okamoto and M. Yamashita
February 12, 2008
Revised
April,
2010
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.
Paper: S. Kim, M.
Kojima, M. Mevissen, and M. Yamashita
, "Exploiting sparsity in linear and nonlinear matrix inequalities via positive semidefinite matrix
completion," Research Report B-452, Dept. of Mathematical and Computing Sciences, Tokyo Institute
of Technology, Oh-Okayama, Meguro, Tokyo 152-8552, Japan.
We are collecting
instances of sparse linear optimization problems (LOPs)
to evaluate and improve the performance of SparseCoLO.
Any instances of sparse LOPs that you could send
us to
kojima.m.aa-sparsecolo ''insert at'' m.titech.ac.jp.
would be very much appreciated.
SparseCoLO is now distributed under the GNU GPL (General Public
License) v2.
If you have any question, please send a message to kojima.m.aa-sparsecolo ''insert at'' m.titech.ac.jp.