This research develops a novel algorithm addressing non-convex QCQP, suggesting faster convergence through continuous functions.
This paper addresses the solution of a non-convex 0-1 QCQP using two faster convergent augmented Lagrangian methods (ALM) based on a class of continuous functions and a parameter free convexification method. The binary constraints are converted into a class of continuous functions and the non-convex constraints are convexified by a parameter free method. The proposed algorithm is then tested on a set of QCQP problems available in the research of Zheng and co-workers. We also present the comparison results among relaxed versions of SDPs and some state-of-the-art ALMs and obtained an advantage over their counterparts.
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Sahoo et al. (2026) studied this question.
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