Download PDF by B. Luderer, L. Minchenko, T. Satsura: Multivalued Analysis and Nonlinear Programming Problems with

Download PDF by B. Luderer, L. Minchenko, T. Satsura: Multivalued Analysis and Nonlinear Programming Problems with

By B. Luderer, L. Minchenko, T. Satsura

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"The objective of this ebook is to check countless dimensional areas, multivalued mappings and the linked marginal services … . the fabric is gifted in a transparent, rigorous demeanour. along with the bibliographical reviews … references to the literature are given in the textual content. … the unified method of the directional differentiability of multifunctions and their linked marginal capabilities is a amazing function of the booklet … . the booklet is an invaluable contribution to nonsmooth research and optimization." (Winfried Schirotzek, Zentralblatt MATH, Vol. 1061 (11), 2005)

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Extra resources for Multivalued Analysis and Nonlinear Programming Problems with Perturbations

Example text

Vice versa, for every neighbourhood U = F(xo) + c:B of the set F(xo) there exists an open set G such that F (xo) c G CU. According to condition 2, F- (G) is open and for all x E V (xo) = F- (G) the condition F(x) C F(xo) + c:B is valid. 2. {::> 3. 12. 13 The following statements are equivalent: 1. c. at a point Xo; 2. for every open set G such that F(xo) c G, its strong inverse image F- (G) contains some neighbourhood of the point Xo. COROLLARY An analogous characterization is valid for lower semi continuous mappings.

Then the following functions are also quasidifferentiable at x: LEMMA (i) f = h + 12, where Df(x) = [Qh(x) + Qf2(X), 8h(x) + 8h(x)]; (ii) f = >'h, where Df(x) ={ [>'Qfl (x), >'8h (x)] if >. 0 h (x), >'Qh (x)] if >. < 0; _ ~ 0, (iii) f = . max Ii, where t=l, ... ,k Df(x) = [U (Qfi(X) - iEI(x) L 8 fj (X)) , jEI(x),j::pi L 8fi(X)] iEI(x) with I(x) = {i I fi(x) = f(x)}. For the proof see [63], Section 2. An important generalization of statement (iii) developed in [101] concerns the continual maximum of functions, i.

R} is convex if the functions hi : X x Y -+ R, i = 1, ... , r, are convex. EXAMPLE Now we consider the marginal function cp{x) = inf{f{x, y) lyE F(x)}. 33 Let f : X x Y -+ R be a convex function and F : X -+ 2Y be a convex multivalued mapping. Then the function cp is convex. LEMMA Proof. Let Zl = (Xl, Y1), Z2 = (X2' Y2). 11) = A1CP(X1) + A2CP{X2), i. , for all Xl, x2 E X and Al 2': 0, A2 2': 0, Al + A2 = 1 the inequality cp(A1X1 + A2X2) ::; A1CP(X1) + A2CP(X2) is valid. 33 be satisfied. Then domcp =domF if domf = X x Y.

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