SDPT3 version 4.0 (beta) -- a MATLAB software for
semidefinite-quadratic-linear programming
The software was last updated in 12 July 2006.
It implemented an infeasible path-following algorithm (sqlp.m)
for solving SQLP -- conic optimization problems involving
semidefinite, second-order and linear cone constraints.
Note: though this software is fairly well tested, it is still
being refined.
New features that SDPT3 can now handle:
** free variables;
** determinant maximization problems;
** SDP with complex data;
** Matlab 7.3 on 64-bit machine;
** 3-parameter homogeneous self-dual model of SQLP (in HSDsqlp.m);
- Copyright:
This version of SDPT3 is distributed under the GNU General Public License 2.0.
For commercial applications that may be incompatible with this license,
please contact the authors to discuss alternatives.
- SDPT3-4.0-beta.zip
Please read.
Welcome to SDPT3-4.0-beta!
The software requires a few Mex files for execution.
You can generate these executable files
as follows:
- Firstly, unpack the software:
unzip SDPT3-4.0-beta.zip;
- Run Matlab in the directory SDPT3-4.0-beta
- In Matlab command window, type:
>> Installmex
- After that, to see whether you have installed SDPT3 correctly,
type:
>> startup
>> sqlpdemo
- By now, SDPT3 is ready for you to use.
-
The following example shows how SDPT3 call a data file that is
stored in SDPA format:
>> [blk,At,C,b] = read_sdpa('./sdplib/theta3.dat-s');
>> [obj,X,y,Z] = sqlp(blk,At,C,b);
The following example shows how SDPT3 call a data file that is
stored in SeDuMi format:
>> [blk,At,C,b] = read_sedumi(AA,bb,cc,K); or
[blk,At,C,b] = read_sedumi('./dimacs/hamming_7_5_6.mat');
>> [obj,X,y,Z] = sqlp(blk,At,C,b);
- User's guide (ps)
(pdf) (Draft)
- Simple examples to illustrate
the usage of the software
- Citation:
- K.C. Toh, M.J. Todd, and R.H. Tutuncu,
SDPT3 --- a Matlab software package for semidefinite programming,
Optimization Methods and Software, 11 (1999), pp. 545--581.
- R.H Tutuncu, K.C. Toh, and M.J. Todd,
Solving semidefinite-quadratic-linear programs using SDPT3,
Mathematical Programming Ser. B, 95 (2003), pp. 189--217.
- Special thanks go to
Hans Mittelmann
for his effort in benchmarking several SDP software packages on the following
test problems:
benchmark
on SDPLIB problem set;
benchmark
on DIMACS problem set;
benchmark
on some large sparse SDPs;
benchmark
on SDPs with free variables;
benchmark
on SOCP problems generated with the Matlab FIR toolbox.
Acknowledgements:
We thank those who had made suggestions and
reported bugs to make SDPT3 better.
In particular, we thank Hans
Mittelmann for bugs reported while benchmarking SDPT3.
We also thank Johan Lofberg and Didier Henrion for
bug reports while incorporating SDPT3 into YALMIP and
GloptiPoly, respectively. Thanks also go to Michael Grant for bug
reports while testing SDPT3 as an engine for CVX.
Ealier versions
SDPT3-3.02.tar.gz(for unix),
SDPT3-3.02.zip(for Windows)
This research is supported in part by
National University of Singapore, Singapore-MIT Alliance,
NSF through grant DMS-9805602 and ONR through grant N00014-96-1-0050,
and NSF through grant CCR-9875559.
The software was lasted updated in December 2002.
It implemented an infeasible path-following algorithm
for solving conic optimization problems involving
semidefinite, second-order and linear cone constraints.
Note: problem data must be real.