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.