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• W3100992968 abstract "Quadratically constrained quadratic programs (QCQPs) have a wide range of applications in signal processing and wireless communications. Non-convex QCQPs are NP-hard in general. Existing approaches relax the non-convexity using semi-definite relaxation (SDR) or linearize the non-convex part and solve the resulting convex problem. However, these techniques are seldom successful in even obtaining a feasible solution when the QCQP matrices are indefinite. In this letter, a new feasible point pursuit successive convex approximation (FPP-SCA) algorithm is proposed for non-convex QCQPs. FPP-SCA linearizes the non-convex parts of the problem as conventional SCA does, but adds slack variables to sustain feasibility, and a penalty to ensure slacks are sparingly used. When FPP-SCA is successful in identifying a feasible point of the non-convex QCQP, convergence to a Karush-Kuhn-Tucker (KKT) point is thereafter ensured. Simulations show the effectiveness of our proposed algorithm in obtaining feasible and near-optimal solutions, significantly outperforming existing approaches." @default.
• W3100992968 created "2020-11-23" @default.
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• W3100992968 date "2015-07-01" @default.
• W3100992968 modified "2023-10-14" @default.
• W3100992968 title "Feasible Point Pursuit and Successive Approximation of Non-Convex QCQPs" @default.
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• W3100992968 doi "https://doi.org/10.1109/lsp.2014.2370033" @default.
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