Download e-book for iPad: Advanced Computational Methods for Knowledge Engineering by Nguyen Van Thoai (auth.), Ngoc Thanh Nguyen, Tien van Do,

By Nguyen Van Thoai (auth.), Ngoc Thanh Nguyen, Tien van Do, Hoai An le Thi (eds.)

ISBN-10: 3319002929

ISBN-13: 9783319002927

ISBN-10: 3319002937

ISBN-13: 9783319002934

The publication comprises 29 prolonged chapters that have been chosen and invited from the submissions to the 1st overseas convention on computing device technological know-how, utilized arithmetic and Applications (ICCSAMA 2013) hung on 9-10 may possibly, 2013 in Warsaw, Poland. The ebook is geared up into 5 elements, that are: complicated Optimization tools and Their purposes, Queuing thought and purposes, Computational equipment for wisdom Engineering, wisdom Engineering with Cloud and Grid Computing, and common sense dependent equipment for choice Making and information Mining, respectively. All chapters within the publication speak about theoretical and sensible concerns hooked up with computational equipment and optimization tools for wisdom engineering.

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Example text

Launch DCA from xR0 for solving the corresponding DC approximation problem (Pdc ). Let xR0 be a solution obtained by DCA. Set R0 := {R0 }, η0 := η(R0 ), ω0 := φ(xR0 ). Set x∗ := xR0 . • Iteration k = 0, 1, 2, . . 1 Delete all R ∈ Rk with η(R) ≥ ωk . Let Pk be the set of remaining rectangles. If Pk = ∅ then STOP: x∗ is a global optimal solution. 2 Otherwise, select Rk ∈ Pk such that ηk := η(Rk ) = min{η(R) : R ∈ Pk } and subdivide Rk into Rk1 , Rk2 according to the subdivision process. 3 For each Rkj , j = 1, 2, construct relaxation function φRkj , and solve min{φRkj (x) : x ∈ C ∩ Rkj } (Rkj cp) to obtain xRkj and η(Rkj ).

The step vector w∗ , for all w ∈ Rn can be approximated by wi∗ η(wi ), and then, an approximation of the zero-norm w 0 would be n w η(wi ). 0 i=1 By the way, the l0 -norm in (3) and (4) is approximated by d wi η(wij ). A. C. Nguyen Hence, the resulting optimization problem of (3) takes the form: ⎫ ⎧ Q d ⎬ ⎨ n C ξik + η(wij ) , min ⎭ (w,b,ξ)∈Ω ⎩ i=1 k=yi and that of (4) can be written as: ⎧ ⎨ n min ξik + β C (w,b,ξ)∈Ω ⎩ i=1 k=yi (5) i=1 j=1 Q Q wk k=1 2 2 d + i=1 j=1 ⎫ ⎬ η(wij ) . ⎭ (6) In the sequel we will investigate DC programming and DCA for solving the two problems (5) and (6).

As DCA is a descent and efficient method for nonconvex programming, DCA will be used to improving upper bounds for ω in B&B scheme while lower bounds will be provided by solving relaxation problems constructed over the rectangle n [li , ui ], subsets of R0 , at each iteration. 1 Lower Bounding A lower bound for φ on C ∩ R := n [li , ui ] ⊂ R0 can be determined by the i=1 following way. Let Bi = [li , ui ], i = 1, . . , n. A convex underestimator of the objective function φ over the domain C ∩ R can be chosen as follows (since φ is separable): n φR (x) = φBi (xi ) i=1 where φBi (xi ) is defined by the following way: (15) 38 P.

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Advanced Computational Methods for Knowledge Engineering by Nguyen Van Thoai (auth.), Ngoc Thanh Nguyen, Tien van Do, Hoai An le Thi (eds.)

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