A new convex relaxation for quadratically constrained quadratic programming | Zendy

Duzhi Wu | Zendy; Ai-Ping Hu | Zendy; Jie Zhou | Zendy; Songlin Wu | Zendy

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A new convex relaxation for quadratically constrained quadratic programming

Author(s) -

Duzhi Wu,

Ai-Ping Hu,

Jie Zhou,

Songlin Wu

Publication year - 2013

Publication title -

filomat

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.449

H-Index - 34

eISSN - 2406-0933

pISSN - 0354-5180

DOI - 10.2298/fil1308511w

Subject(s) - quadratic growth , quadratically constrained quadratic program , mathematics , relaxation (psychology) , mathematical optimization , quadratic programming , semidefinite programming , second order cone programming , quadratic equation , constraint (computer aided design) , regular polygon , convex optimization , algorithm , psychology , social psychology , geometry

A new relaxation strategy is presented in this paper to approximately solve the quadratically and linearly constrained quadratic programming. To improve the conservation of traditional semidefinite relaxation (SDR) strategy, we introduce a new linear constraint, which can be derived from the constraints of original problem, to the SDR problem. Furthermore, a randomization method is provided to extract good feasible solution of original problem from optimal solution of relaxed problem. Some numerical examples show that the proposed method can efficiently improve the performance of the traditional SDR strategy.

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