A Global Optimization Algorithm for Generalized Quadratic Programming | Zendy

Hongwei Jiao | Zendy; Yongqiang Chen | Zendy

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A Global Optimization Algorithm for Generalized Quadratic Programming

Author(s) -

Hongwei Jiao,

Yongqiang Chen

Publication year - 2013

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2013/215312

Subject(s) - quadratic programming , robustness (evolution) , mathematical optimization , linear programming , criss cross algorithm , relaxation (psychology) , mathematics , quadratically constrained quadratic program , global optimization , linear fractional programming , second order cone programming , sequential quadratic programming , algorithm , linear programming relaxation , quadratic equation , active set method , computer science , nonlinear programming , convex optimization , regular polygon , nonlinear system , psychology , social psychology , biochemistry , chemistry , physics , geometry , quantum mechanics , gene

We present a global optimization algorithm for solving generalized quadratic programming (GQP), that is, nonconvex quadratic programming with nonconvex quadratic constraints. By utilizing a new linearizing technique, the initial nonconvex programming problem (GQP) is reduced to a sequence of relaxation linear programming problems. To improve the computational efficiency of the algorithm, a range reduction technique is employed in the branch and bound procedure. The proposed algorithm is convergent to the global minimum of the (GQP) by means of the subsequent solutions of a series of relaxation linear programming problems. Finally, numerical results show the robustness and effectiveness of the proposed algorithm

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