Multiterm polyhedral relaxations for nonconvex, quadratically constrained quadratic programs
Optimization Methods & Software - GLOBAL OPTIMIZATION
$${{\mathcal {D}(\mathcal {C})}}$$-optimization and robust global optimization
Journal of Global Optimization
Quadratic minimisation problems in statistics
Journal of Multivariate Analysis
Remarks on solutions to a nonconvex quadratic programming test problem
Journal of Global Optimization
Global optimization for the generalized polynomial sum of ratios problem
Journal of Global Optimization
Hyperdisk based large margin classifier
Pattern Recognition
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Most existing methods of quadratically constrained quadratic optimization actually solve a refined linear or convex relaxation of the original problem. It turned out, however, that such an approach may sometimes provide an infeasible solution which cannot be accepted as an approximate optimal solution in any reasonable sense. To overcome these limitations a new approach is proposed that guarantees a more appropriate approximate optimal solution which is also stable under small perturbations of the constraints.