Branch-and-bound algorithm for non-convex constrained quadratic programming problems
The article introduces a novel parameterized linear relaxation branch-and-bound algorithm for a class of non-convex constrained quadratic programming problems.This algorithm primarily utilizes linear relaxation techniques to obtain the global optimal lower bounds of the original problem and employs region deletion rules to reduce the infeasible region.The convergence of the algorithm is proven,and numerical experiments demonstrate its accelerated convergence rate,indicating its effectiveness and feasibility.
branch and boundlinear relaxation techniquearea reductionquadratic programming
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