Name of the Package | Author(s) | Purpose |
---|---|---|
CSDP 2.3 | Brian Borchers
New Mexico Tech Mathematics Faculty Department of Mathematics Socorro, NM 87801 Office: Weir Hall 158 Telephone: 505-835-5813 Fax: 505-835-5366 borchers@nmt.edu | CSDP is a library of routines that implements a predictor corrector variant of the semidefinite programming algorithm of
Helmberg, Rendl, Vanderbei, and Wolkowicz.
The main advantages of this code are that it is written to be used as a callable subroutine, it is written in C for efficiency, it makes effective use of sparsity in the constraint matrices, and that it includes support for linear inequality constraints in addition to linear equality constraints. |
CUTSDP | Stefan E. Karisch
Carmen Systems AB, Gothenburg, Sweden. E-mail: Stefan.Karisch@carmen.se | CUTSDP is a package of C programs containing an implementation of a cutting plane approach based on semidefinite
programming. Currently, there are three applications implemented: max-cut, graph bisection, and graph equipartition.
Manual: Stefan E. Karisch. CUTSDP - A Toolbox for a Cutting-Plane Approach Based on Semidefinite Programming. Technical Report IMM-REP-1998, Department of Mathematical Modelling, Technical University of Denmark, June 17, 1998. |
MAXDET | Shao-Po Wu
Information Systems Laboratory, Stanford University, Stanford, CA 94305 E-mail: clive@stanford.edu Lieven Vandenberghe UCLA Electrical Engineering Department 68-119 Engineering IV Los Angeles, CA 90095-1594 E-mail: vandenbe@ee.ucla.edu Stephen P. Boyd Stanford University, Packard 264, Stanford, CA 94305 E-mail: boyd@stanford.edu | Software for determinant maximization problems. Implementation of a long-step path-following method for determinant maximization problems.
Includes full C-source (with calls to LAPACK), which can be used directly or via matlab mex file interfaces, matlab examples, and documentation. Manual of MAXDET: User's Guide, Alpha Version. May 24, 1996. |
SDPA | Katsuki Fujisawa
Masakazu Kojima Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, 2-12-1 Oh-Okayama, Meguro-ku, Tokyo 152-0033, Japan | User's Manual, ps.Z-file (ftp).
See also: K. Fujisawa, M. Fukuda, M. Kojima and K. Nakata. "Numerical Evaluation of SDPA", Research Report B-330, Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Oh-Okayama, Meguro-ku, Tokyo 152, September 1997. ps.Z-file (ftp) or dvi.Z-file (ftp) |
SDPHA | Nathan Brixius
517 South Linn St. #1 Iowa City, IA 52240 E-mail: brixius@cs.uiowa.edu Rongqin Sheng MCS Division, Argonne National Laboratory Florian A. Potra e-mail: potra@math.umbc.edu | SDPHA: A Matlab package for semidefinite programming.
MATLAB code for predictor-corrector algorithms for semidefinite programming using the homogeneous formulation README file for SDPHA v3.0. User guide SDPHA (updated 6/29/99). Technical paper. (updated 6/29/99) |
SDPPACK | Farid Alizadeh,
RUTCOR, Rutgers University, New Brunswick, NJ. E-mail: alizadeh@rutcor.rutgers.edu Jean-Pierre A. Haeberly, Department of Mathematics, Fordham University, Bronx, NY. E-mail: haeberly@murray.fordham.edu. Madhu V. Nayakkankuppam, Courant Institute of Mathematical Sciences, New York University, NY. E-mail: madhu@cs.nyu.edu. Michael L. Overton, Courant Institute of Mathematical Sciences, New York University, NY. E-mail: overton@cs.nyu.edu Stefan Schmieta RUTCOR, Rutgers University, New Brunswick, NJ. E-mail: schmieta@rutcor.rutgers.edu | SDPpack Version 0.9 beta runs under Matlab 5.0. This
version extends the previous release for semidefinite programming (SDP) to
mixed semidefinite-quadratic-linear programs (SQLP), i.e. linear
optimization problems over a product of semidefinite cones, quadratic cones
and the nonnegative orthant. Together, these cones make up all possible
homogeneous self-dual cones over the reals. The main routine implements a
primal-dual Mehrotra predictor-corrector scheme based on the XZ+ZX
search direction.
User Guide User Guide PostScript. User Guide DVI. |
SDPSOL | Shao-Po Wu
Information Systems Laboratory, Stanford University, Stanford, CA 94305 E-mail: clive@stanford.edu Stephen P. Boyd Stanford University, Packard 264, Stanford, CA 94305 E-mail: boyd@stanford.edu | SDPSOL is a parser/solver for SDP and MAXDET problems with matrix structure.
MAXDET problems have the form: minimize c^Tx - log det G(x) subject to G(x) > 0, F(x) > 0, where G(x)>0 and F(x)>0 are linear matrix inequality (LMI) constraints. Two important special cases are SDP (when G(x)=1) and analytic centering (when c=0 and F(x)=1). MAXDET (and SDP) problems arise in control, statistics, computational geometry, and information and communication theory. In many cases the optimization variables have matrix structure, which makes it tedius in practice to put the problem in the form above. SDPSOL automates this task by allowing the user to specify (and solve) MAXDET (or SDP) problems in a format close to its natural mathematical description. SDPSOL parses problems expressed in the SDPSOL language, solves them using an interior-point method, and reports the results in a convenient form. User's Guide PostScript. User's Cuide PDF. SDPSOL: a parser/solver for SDP and MAXDET problems with matrix structure. In: Recent Advances in LMI Methods for Control, Edited by L. El Ghaoui and S.-I. Niculescu, SIAM, 1999. |
SDPT3 | Toh Kim Chuan,
Department of Mathematics National University of Singapore 10 Kent Ridge Crescent Singapore 119260 Singapore Email: mattohkc@math.nus.edu.sg Michael J. Todd School of Operations Research and Industrial Engineering 229 Frank H.T. Rhodes Hall Cornell University, Ithaca, NY 14853 Email: miketodd@orie.cornell.edu or miketodd@cs.cornell.edu Reha Tütüncü Department of Mathematical Sciences 6113 Wean Hall Carnegie Mellon University Pittsburgh, PA 15213. | Matlab package for Infeasible path-following and homogeneous self-dual algorithms for solving standard SDP (possibly with complex data).
Sparsity in the data is exploited whenever possible. K.C. Toh, M.J. Todd, and R.H. Tutuncu, SDPT3 --- a Matlab software package for semidefinite programming, version 2.1. (User's Guide PostScript) |
SeDuMi | Jos F. Sturm,
Faculty of Economics and Business Administration, Department of Quantitative Economics, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands. E-mail: j.sturm@ke.unimaas.nl | Matlab toolbox for solving optimization problems over symmetric cones, i.e. it allows not only for linear
constraints, but also quasiconvex-quadratic constraints and positive semi-definiteness constraints. Complex valued
entries are allowed. Both symbolic and numerical reordering schemes, balancing speed/accuracy performance.
Sophisticated dense column handling, using Goldfarb-Scheinberg product form idea.
User's Guide: Using SeDuMi 1.02 a MATLAB toolbox for optimization over symmetric cones. |
SP | Lieven Vandenberghe
UCLA Electrical Engineering Department 68-119 Engineering IV Los Angeles, CA 90095-1594 E-mail: vandenbe@ee.ucla.edu Stephen P. Boyd Stanford University, Packard 264, Stanford, CA 94305 E-mail: boyd@stanford.edu Brien Alkire UCLA Electrical Engineering Department School of Engineering and Applied Science University of California, Los Angeles Los Angeles, CA 90095. Email: brien@alkires.com | Implementation of Nesterov and Todd's primal-dual potential reduction method for semidefinite programming.
The code is written in C/C++ with calls to BLAS and LAPACK. Includes full C-source (with calls to LAPACK), which can be used directly or via matlab mex file interfaces, matlab examples, and documentation. Lieven Vandenberghe and Stephen Boyd, Semidefinite Programming SIAM Review 38 (1996), 49-95. |