DSDP

The DSDP software package is an implementation of the dual-scaling algorithm for semidefinite programming. This interior-point algorithm has a convergence proof and worst-case polynomial complexity under mild assumptions on the data. It provides feasible primal and dual solutions, exploits low-rank structure and sparsity in the data, and has low memory requirements compared to other interior-point methods.

This implementation of the algorithm can be used as a set of subroutines, through Matlab, or by reading and writing to data files. Furthermore, it offers scalable parallel performance for large problems and a well documented interface.

Some of the most popular applications of semidefinite programming are model control, truss topology design, and relaxations of combinatorial and global optimization problems.

Source Code, binaries, and documentation are available from the developers Steve Benson, and Yinyu Ye.

DSDP using NEOS

Submit a model in SDPA format to solve a semidefinite programming problem. Examples of models in sparse SDPA format can be found in the SDPLIB library.
SDPA file (local file):

-gaptol <1e-6>    stop when relative duality gap less than
-r0 <-1>          if nonnegative, initialize S by adding this multiple of the identity matrix
-penalty <1e8>    penalize dual infeasibility
-boundy <1e7>     bound for variables y
-maxit <200>      set maximum iterates
-zbar <1e10>      Upper bound for dual solution
-mu0 <-1>         if positive, set initial barrier parameter
-rho <3>          Potential parameter as multiple of dimension
-drho <1>         Use dynamic rho strategy
-pnormtol <1e30>  stop only if pnorm less than
-reuse < >        Reuse the Schur Matrix this many times >=0
-bigM <0>         if positive, keep dual infeasiblility positive
-dloginfo <0>     print more information for higher numbers
-print <10>       print status at every nth iteration
-help             for this help message