A Cutting Plane Algorithm for Large Scale Semidefinite Relaxations
نویسندگان
چکیده
The recent spectral bundle method allows to compute, within reasonable time, approximate dual solutions of large scale semidefinite quadratic 0-1 programming relaxations. We show that it also generates a sequence of primal approximations that converge to a primal optimal solution. Separating with respect to these approximations gives rise to a cutting plane algorithm that converges to the optimal solution under reasonable assumptions on the separation oracle and the feasible set. We have implemented a practical variant of the cutting plane algorithm for improving semidefinite relaxations of constrained quadratic 0-1 programming problems by odd-cycle inequalities. We also consider separating oddcycle inequalities with respect to a larger support than given by the cost matrix and present a heuristic for selecting this support. Our preliminary computational results for max-cut instances on toroidal grid graphs and balanced bisection instances indicate that warm start is highly efficient and that enlarging the support may sometimes improve the quality of relaxations considerably M S C 2000: 90C22; 90C25, 90C27, 90C09, 90C20, 90C06
منابع مشابه
Interior Point and Semidefinite Approaches in Combinatorial Optimization
Conic programming, especially semidefinite programming (SDP), has been regarded as linear programming for the 21st century. This tremendous excitement was spurred in part by a variety of applications of SDP in integer programming (IP) and combinatorial optimization, and the development of efficient primal-dual interior-point methods (IPMs) and various first order approaches for the solution of ...
متن کاملLP and SDP branch-and-cut algorithms for the minimum graph bisection problem: a computational comparison
While semidefinite relaxations are known to deliver good approximations for combinatorial optimization problems like graph bisection, their practical scope is mostly associated with small dense instances. For large sparse instances, cutting plane techniques are considered the method of choice. These are also applicable for semidefinite relaxations via the spectral bundle method, which allows to...
متن کاملEfficient Maximum Margin Clustering via Cutting Plane Algorithm
Maximum margin clustering (MMC) is a recently proposed clustering method, which extends the theory of support vector machine to the unsupervised scenario and aims at finding the maximum margin hyperplane which separates the data from different classes. Traditionally, MMC is formulated as a non-convex integer programming problem and is thus difficult to solve. Several methods have been proposed ...
متن کاملCutting Plane Methods and Subgradient Methods
Interior point methods have proven very successful at solving linear programming problems. When an explicit linear programming formulation is either not available or is too large to employ directly, a column generation approach can be used. Examples of column generation approaches include cutting plane methods for integer programming and decomposition methods for many classes of optimization pr...
متن کاملA Linear Programming Approach to Semidefinite Programming Problems
A semidefinite programming problem can be regarded as a convex nonsmooth optimization problem, so it can be represented as a semi-infinite linear programming problem. Thus, in principle, it can be solved using a cutting plane approach; we describe such a method. The cutting plane method uses an interior point algorithm to solve the linear programming relaxations approximately, because this resu...
متن کامل