Discretization and localization in successive convex relaxation methods for nonconvex quadratic optimization

@article{Kojima2000DiscretizationAL,
  title={Discretization and localization in successive convex relaxation methods for nonconvex quadratic optimization},
  author={Masakazu Kojima and Levent Tunçel},
  journal={Math. Program.},
  year={2000},
  volume={89},
  pages={79-111}
}
Based on the authors' previous work which established theoretical foundations of two, conceptual, successive convex relaxation methods, i.e., the SSDP (Successive Semide nite Programming) Relaxation Method and the SSILP (Successive Semi-In nite Linear Programming) Relaxation Method, this paper proposes their implementable variants for general quadratic optimization problems. These problems have a linear objective function cTx to be maximized over a nonconvex compact feasible region F described… CONTINUE READING
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