Software for Semidefinite Programming

v. 5.8

The DSDP software 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.

DSDP5 was released January, 2005. The interface of the most recent version is compatible to DSDP versions 5.0 and later, but it includes new features and improvements in performance.

The package has been used in many applications and tested for efficiency, robustness, and ease of use. We welcome and encourage further use under the terms of the license included in the distribution.



Source code, precompiled binaries, and Matlab utilities can be downloaded free of charge.

Try DSDP without downloading it using NEOS.

DSDP was developed by Steve Benson, Yinyu Ye, and Xiong Zhang.


A complete description of the algorithm and a proof of convergence can be found in Solving Large-Scale Sparse Semidefinite Programs for Combinatorial Optimization, SIAM Journal on Optimization, 10(2), 2000, pp. 443-461. BibTEX.

Last Updated: January 23, 2006.