Mathematical Programming: Series A and B
Proximity control in bundle methods for convex
Mathematical Programming: Series A and B
Experiments in quadratic 0-1 programming
Mathematical Programming: Series A and B
.879-approximation algorithms for MAX CUT and MAX 2SAT
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Laplacian eigenvalues and the maximum cut problem
Mathematical Programming: Series A and B
New variants of bundle methods
Mathematical Programming: Series A and B
Connections between semidefinite relaxations of the max-cut and stable set problems
Mathematical Programming: Series A and B
Some optimal inapproximability results
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Semidefinite programming in combinatorial optimization
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Semidefinite programming and combinatorial optimization
HPOPT '96 Proceedings of the Stieltjes workshop on High performance optimization techniques
Solving Large-Scale Sparse Semidefinite Programs for Combinatorial Optimization
SIAM Journal on Optimization
A Spectral Bundle Method for Semidefinite Programming
SIAM Journal on Optimization
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Solving the well known relaxations for large scale combinatorial optimization problems directly is out of reach. We use Lagrangian relaxations and solve it with the bundle method. The cutting plane model at each iteration which approximates the original problem can be kept moderately small and we can solve it very quickly. We report successful numerical results for approximating maximum cut.