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
Connections between semidefinite relaxations of the max-cut and stable set problems
Mathematical Programming: Series A and B
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
Improved Approximation Algorithms for MAX k-CUT and MAX BISECTION
Proceedings of the 4th International IPCO Conference on Integer Programming and Combinatorial Optimization
Node-and edge-deletion NP-complete problems
STOC '78 Proceedings of the tenth annual ACM symposium on Theory of computing
Congratulations to Naum Shor on his 65th birthday
Journal of Global Optimization
On Solving Nonconvex Optimization Problems by Reducing The Duality Gap
Journal of Global Optimization
Functionally redundant constraints for Boolean quadratic-type optimization problems
Cybernetics and Systems Analysis
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Many polynomial and discrete optimization problems can be reduced to multiextremal quadratic type models of nonlinear programming. For solving these problems one may use Lagrangian bounds in combination with branch and bound techniques. The Lagrangian bounds may be improved for some important examples by adding in a model the so-called superfluous quadratic constraints which modify Lagrangian bounds. Problems of finding Lagrangian bounds as a rule can be reduced to minimization of nonsmooth convex functions and may be successively solved by modern methods of nondifferentiable optimization. This approach is illustrated by examples of solving polynomial-type problems and some discrete optimization problems on graphs.