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Host: DIMACS Center, Rutgers University
Homepage: http://dimacs.rutgers.edu/Workshops/7thchallenge/
Email: challenge@dimacs.rutgers.edu
Organizers: Farid Alizadeh (RUTCOR, Rutgers University), David Johnson (AT&T Labs - Research), Gabor Pataki (Columbia University)
Description:
In conjunction with its special year on large scale discrete optimization problems, the
Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) invites
participation in an implementation challenge on Semidefinite and related optimization
problems. The workshop is to be held on January 24-26, 2000.
The purpose of DIMACS computational challenges has been to encourage the experimental evaluation of algorithms, in particular those with efficient performance from a theoretical point of view. The past Challenges brought together researchers to test time proven, mature, and novel, experimental approaches on a variety of problems in a given subject. As the subject of the last Challenge of this century, one could hardly think of a better choice than Semidefinite Programming (SDP) - one of the most interesting and challenging areas in optimization theory to emerge in the last decade. In the past few years much has been learned on both the kinds of problem classes that SDP can tackle, and the best SDP algorithms for the various classes. In addition a great deal has been learned about the limits of the current approaches to solving SDP's.
In addition to semidefinite programming a closely related problem is that of convex quadratically constrained quadratic programming (QCQP). This problem resides in between linear and semidefinite programming. It also arises in a variety of applications from statistics to engineering; and a number of combinatorial optimization problems, in particular in the Steiner tree problems and plant location problems have found QCQP as a subproblem. Similar to, and indeed by an extension from, semidefinite programming a great deal is known about optimization with convex quadratic constraints as well as limitation of current methods. Finally this knowledge has been extended to problems containing variables and constraints with some or all of linear, convex quadratic or semidefinite constraints.
This Challenge attempts to distill and expand upon this accumulated knowledge.
Date received: October 07, 1999
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