DSDP is a free open source implementation of an interior-point method for
semidefinite programming. It provides primal and dual solutions, exploits
low-rank structure and sparsity in the data, and has relatively low memory
requirements for an interior-point method. It allows feasible and infeasible
starting points and provides approximate certificates of infeasibility when no
feasible solution exists. The dual-scaling algorithm implemented in this
package has a convergence proof and worst-case polynomial complexity under
mild assumptions on the data. The software can be used as a set of
subroutines, through Matlab, or by reading and writing to data files.
Furthermore, the solver offers scalable parallel performance for large
problems and a well documented interface. Some of the most popular
applications of semidefinite programming and linear matrix inequalities (LMI)
are model control, truss topology design, and semidefinite relaxations of
combinatorial and global optimization problems.
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