American Institute of Mathematical Sciences

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April  2014, 10(2): 543-556. doi: 10.3934/jimo.2014.10.543

Doubly nonnegative relaxation method for solving multiple objective quadratic programming problems

Yanqin Bai 1, and  Chuanhao Guo 1,

1. 

Department of Mathematics, Shanghai University, Shanghai 200444, China, China

Received  October 2012 Revised  August 2013 Published  October 2013

Multicriterion optimization and Pareto optimality are fundamental tools in economics. In this paper we propose a new relaxation method for solving multiple objective quadratic programming problems. Exploiting the technique of the linear weighted sum method, we reformulate the original multiple objective quadratic programming problems into a single objective one. Since such single objective quadratic programming problem is still nonconvex and NP-hard in general. By using the techniques of lifting and doubly nonnegative relaxation, respectively, this single objective quadratic programming problem is transformed to a computable convex doubly nonnegative programming problem. The optimal solutions of this computable convex problem are (weakly) Pareto optimal solutions of the original problem under some mild conditions. Moreover, the proposed method is tested with two examples and a practical portfolio selection example. The test examples are solved by CVX package which is a solver for convex optimization. The numerical results show that the proposed method is effective and promising.
Keywords: linear weighted sum method, Multiple objective programming, quadratic programming, completely positive programming., copositive programming.
Mathematics Subject Classification: Primary: 90C29, 90C26; Secondary: 49M2.
Citation: Yanqin Bai, Chuanhao Guo. Doubly nonnegative relaxation method for solving multiple objective quadratic programming problems. Journal of Industrial & Management Optimization, 2014, 10 (2) : 543-556. doi: 10.3934/jimo.2014.10.543
References:
[1]

E. E. Ammar, On solutions of fuzzy random multiobjective quadratic programming with applications in portfolio problem, Inform. Sci., 178 (2008), 468-484. doi: 10.1016/j.ins.2007.03.029.  Google Scholar

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E. E. Ammar, On fuzzy random multiobjective quadratic programming, European J. Oper. Res., 193 (2009), 329-341. doi: 10.1016/j.ejor.2007.11.031.  Google Scholar

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Y. Q. Bai, and C. H. Guo and L. M. Sun, A new algorithm for solving nonconvex quadratic programming over an ice cream cone, Pac. J. Optim., 8 (2012), 651-665.  Google Scholar

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A. Berman and N. Shaked-Monderer, Completely Positive Matrices, World Scientific Publishing Co., Inc., River Edge, NJ, 2003. doi: 10.1142/9789812795212.  Google Scholar

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H. Bonnel and N. S. Pham, Nonsmooth optimization over the (weakly or properly) Pareto set of a linear-quadratic multi-objective control problem: Explicit optimality conditions, J. Ind. Manag. Optim., 7 (2011), 789-809. doi: 10.3934/jimo.2011.7.789.  Google Scholar

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I. M. Bomze, Copositive optimization-recent developments and applications, European J. Oper. Res., 216 (2012), 509-520. doi: 10.1016/j.ejor.2011.04.026.  Google Scholar

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S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, Cambridge, 2004.  Google Scholar

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S. Burer, On the copositive representation of binary and continuous nonconvex quadratic programs, Math. Program., 210 (2009), 479-495. doi: 10.1007/s10107-008-0223-z.  Google Scholar

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S. Burer, Optimizing a polyhedral-semidefinite relaxation of completely positive programs, Math. Program. Comput., 2 (2010), 1-19. doi: 10.1007/s12532-010-0010-8.  Google Scholar

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O. Dandekar, W. Plishker, S. Bhattacharyya and R. Shekhar, Multi-objective optimization for reconfigurable implementation of medical image registration, International Journal of Reconfigurable Computing, 2008 (2009), 1-17. Google Scholar

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P. H. Diananda, On non-negative forms in real variables some or all of which are non-negative, Proc. Cambridge Philos. Soc., 58 (1962), 17-25. doi: 10.1017/S0305004100036185.  Google Scholar

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P. Dickinson and L. Gijben, On the Computational Complexity of Membership Problems for the Completely Positive Cone and its Dual, Technical Report, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands, 2011. Google Scholar

[13]

M. Grant and S. Boyd, CVX: Matlab Software for Disciplined Convex Programming, version 1.21, April, 2011. Available from: http://cvxr.com/cvx. Google Scholar

[14]

Y. Hu, Efficiency Theory of Multiobjective Programming, Shanghai Scientific and Technical Publishers, 1994. Google Scholar

[15]

P. Korhonen and G. Y. Yu, A reference direction approach to multiple objective quadratic-linear programming, European Journal of Operational Research, 102 (1997), 601-610. doi: 10.1016/S0377-2217(96)00245-7.  Google Scholar

[16]

C. Lu, S.-C. Fang, Q. W. Jin, Qingwei, Z. B. Wang and W. X. Xing, KKT solution and conic relaxation for solving quadratically constrained quadratic programming problems, SIAM J. Optim., 21 (2011), 1475-1490. doi: 10.1137/100793955.  Google Scholar

[17]

R. T. Marler and J. S. Arora, The weighted sum method for multi-objective optimization: New insights, Struct. Multidiscip. Optim., 41 (2010), 853-862. doi: 10.1007/s00158-009-0460-7.  Google Scholar

[18]

J. P. Xu and J. Li, A class of stochastic optimization problems with one quadratic & several linear objective functions and extended portfolio selection model, J. Comput. Appl. Math., 146 (2002), 99-113. doi: 10.1016/S0377-0427(02)00421-1.  Google Scholar

[19]

J. P. Xu and J. Li, Multiple Objective Decision Making Theory and Methods, Tsinghua University Press, 2005. Google Scholar

[20]

B. R. Ye and L. H. Yu, Generating noninferior set of a multi-objective quadratic programming and application, Water Resources and Power, 9 (1991), 102-110. Google Scholar

show all references

References:
[1]

E. E. Ammar, On solutions of fuzzy random multiobjective quadratic programming with applications in portfolio problem, Inform. Sci., 178 (2008), 468-484. doi: 10.1016/j.ins.2007.03.029.  Google Scholar

[2]

E. E. Ammar, On fuzzy random multiobjective quadratic programming, European J. Oper. Res., 193 (2009), 329-341. doi: 10.1016/j.ejor.2007.11.031.  Google Scholar

[3]

Y. Q. Bai, and C. H. Guo and L. M. Sun, A new algorithm for solving nonconvex quadratic programming over an ice cream cone, Pac. J. Optim., 8 (2012), 651-665.  Google Scholar

[4]

A. Berman and N. Shaked-Monderer, Completely Positive Matrices, World Scientific Publishing Co., Inc., River Edge, NJ, 2003. doi: 10.1142/9789812795212.  Google Scholar

[5]

H. Bonnel and N. S. Pham, Nonsmooth optimization over the (weakly or properly) Pareto set of a linear-quadratic multi-objective control problem: Explicit optimality conditions, J. Ind. Manag. Optim., 7 (2011), 789-809. doi: 10.3934/jimo.2011.7.789.  Google Scholar

[6]

I. M. Bomze, Copositive optimization-recent developments and applications, European J. Oper. Res., 216 (2012), 509-520. doi: 10.1016/j.ejor.2011.04.026.  Google Scholar

[7]

S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, Cambridge, 2004.  Google Scholar

[8]

S. Burer, On the copositive representation of binary and continuous nonconvex quadratic programs, Math. Program., 210 (2009), 479-495. doi: 10.1007/s10107-008-0223-z.  Google Scholar

[9]

S. Burer, Optimizing a polyhedral-semidefinite relaxation of completely positive programs, Math. Program. Comput., 2 (2010), 1-19. doi: 10.1007/s12532-010-0010-8.  Google Scholar

[10]

O. Dandekar, W. Plishker, S. Bhattacharyya and R. Shekhar, Multi-objective optimization for reconfigurable implementation of medical image registration, International Journal of Reconfigurable Computing, 2008 (2009), 1-17. Google Scholar

[11]

P. H. Diananda, On non-negative forms in real variables some or all of which are non-negative, Proc. Cambridge Philos. Soc., 58 (1962), 17-25. doi: 10.1017/S0305004100036185.  Google Scholar

[12]

P. Dickinson and L. Gijben, On the Computational Complexity of Membership Problems for the Completely Positive Cone and its Dual, Technical Report, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands, 2011. Google Scholar

[13]

M. Grant and S. Boyd, CVX: Matlab Software for Disciplined Convex Programming, version 1.21, April, 2011. Available from: http://cvxr.com/cvx. Google Scholar

[14]

Y. Hu, Efficiency Theory of Multiobjective Programming, Shanghai Scientific and Technical Publishers, 1994. Google Scholar

[15]

P. Korhonen and G. Y. Yu, A reference direction approach to multiple objective quadratic-linear programming, European Journal of Operational Research, 102 (1997), 601-610. doi: 10.1016/S0377-2217(96)00245-7.  Google Scholar

[16]

C. Lu, S.-C. Fang, Q. W. Jin, Qingwei, Z. B. Wang and W. X. Xing, KKT solution and conic relaxation for solving quadratically constrained quadratic programming problems, SIAM J. Optim., 21 (2011), 1475-1490. doi: 10.1137/100793955.  Google Scholar

[17]

R. T. Marler and J. S. Arora, The weighted sum method for multi-objective optimization: New insights, Struct. Multidiscip. Optim., 41 (2010), 853-862. doi: 10.1007/s00158-009-0460-7.  Google Scholar

[18]

J. P. Xu and J. Li, A class of stochastic optimization problems with one quadratic & several linear objective functions and extended portfolio selection model, J. Comput. Appl. Math., 146 (2002), 99-113. doi: 10.1016/S0377-0427(02)00421-1.  Google Scholar

[19]

J. P. Xu and J. Li, Multiple Objective Decision Making Theory and Methods, Tsinghua University Press, 2005. Google Scholar

[20]

B. R. Ye and L. H. Yu, Generating noninferior set of a multi-objective quadratic programming and application, Water Resources and Power, 9 (1991), 102-110. Google Scholar

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