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A decomposition algorithm for solving large-scale quadratic programming problems. (English) Zbl 1090.65074
Authors’ summary: An algorithm for solving large-scale programming is proposed. We decompose a large-scale quadratic programming into a serial of small-scale ones and then approximate the solution of the large-scale quadratic programming via the solutions of these small-scale ones. It is proved that the accumulation point of the iterates generated by the algorithm is a global minimum point of the quadratic programming. The algorithm has performed very well in numerical testing. It is a new way of solving large-scale quadratic programming problems.

65K05Mathematical programming (numerical methods)
90C20Quadratic programming
90C06Large-scale problems (mathematical programming)
Full Text: DOI
[1] Zhu, Z. B.; Zhang, K. C.: A new SQP method of feasible directions for nonlinear programming. Applied mathematics and computation 148, No. 1, 121-134 (2004) · Zbl 1174.90894
[2] Mangasarian, O. L.: Solution of symmetric linear complementarity problems by iterative methods. Journal of optimization theory and applications 22, No. 4, 465-485 (1977) · Zbl 0341.65049
[3] Zeng, J.: Estimation of convergence rate of multiplicative schawz algorithm for solving complementary problem. Computational mathematics 8, 225-232 (1997) · Zbl 0906.65070
[4] Yuan, Ya-Xiang; Sun, Wen-Yu: Optimization of theory and method. (1997)
[5] Mohd, I. B.; Yosza, D.: Constraint exploration method for quadratic programming problem. Applied mathematics and computation 112, No. 2-3, 161-170 (2000) · Zbl 1049.90526
[6] Boland, N. L.: A dual-active-set algorithm for positive semi-definite quadratic programming. Mathematical programming 78, No. 1, 1-27 (1997) · Zbl 0893.90139