Tien van Do, Hoai An Le Thi, Ngoc Thanh Nguyen (eds.)'s Advanced Computational Methods for Knowledge Engineering: PDF

By Tien van Do, Hoai An Le Thi, Ngoc Thanh Nguyen (eds.)

ISBN-10: 3319065688

ISBN-13: 9783319065687

ISBN-10: 3319065696

ISBN-13: 9783319065694

The lawsuits comprises 30 papers that have been chosen and invited from the submissions to the 2nd overseas convention on desktop technological know-how, utilized arithmetic and Applications (ICCSAMA 2014) hung on 8-9 may well, 2014 in Budapest, Hungary. The convention is prepared into 7 periods: complicated Optimization equipment and Their purposes, Queueing types and function evaluate, software program improvement and checking out, Computational equipment for cellular and instant Networks, Computational equipment for wisdom Engineering, common sense established equipment for choice Making and knowledge Mining and Nonlinear structures and purposes, respectively. All chapters within the ebook talk about theoretical and useful concerns hooked up with computational equipment and optimization tools for wisdom engineering. The editors desire that this quantity could be worthy for graduate and Ph.D. scholars and researchers in desktop technological know-how and utilized arithmetic. it's the wish of the editors that readers of this quantity can locate many inspiring rules and use them to their study. Many such demanding situations are urged through specific methods and types awarded in person chapters of this book.

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Extra info for Advanced Computational Methods for Knowledge Engineering: Proceedings of the 2nd International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA 2014)

Example text

Mii := diag(ei ), where ei ∈ RN is the i-th canonical basis vector of RN ). Note that all matrices Fi ’s and Fij ’s DCA for BMI/QMI Feasibility Problems 43 are sparses (only at most 2 nonzero elements for each). The sparse structure of LMI (12) can be handled more efficiently than the dense structure in numerical computation of SDP. Therefore, the two LMIs in (7) are rewritten as one single LMI as N N ˆi + xi Q ˆ W ) := Q ˆ0 + Q(x, i=1 ˆ ij wij Q 0 (13) i,j=1 where ˆ 0 := Qi 0 , (i = 0, . . , N ); Q ˆ ij := Qij 0 (i, j = 1, .

SIAM J. Optim. 16, 250–262 (2005) 73. : The concave-convex procedure. fr Abstract. We propose some new DC (difference of convex functions) programming approaches for solving the Bilinear Matrix Inequality (BMI) Feasibility Problems and the Quadratic Matrix Inequality (QMI) Feasibility Problems. They are both important NP-hard problems in the field of robust control and system theory. The inherent difficulty lies in the nonconvex set of feasible solutions. In this paper, we will firstly reformulate these problems as a DC program (minimization of a concave function over a convex set).

E. Mii := diag(ei ), where ei ∈ RN is the i-th canonical basis vector of RN ). Note that all matrices Fi ’s and Fij ’s DCA for BMI/QMI Feasibility Problems 43 are sparses (only at most 2 nonzero elements for each). The sparse structure of LMI (12) can be handled more efficiently than the dense structure in numerical computation of SDP. Therefore, the two LMIs in (7) are rewritten as one single LMI as N N ˆi + xi Q ˆ W ) := Q ˆ0 + Q(x, i=1 ˆ ij wij Q 0 (13) i,j=1 where ˆ 0 := Qi 0 , (i = 0, . . , N ); Q ˆ ij := Qij 0 (i, j = 1, .

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Advanced Computational Methods for Knowledge Engineering: Proceedings of the 2nd International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA 2014) by Tien van Do, Hoai An Le Thi, Ngoc Thanh Nguyen (eds.)


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