Description:

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.