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New PDF release: Algorithmic Applications in Management: First International

By Ellis L. Johnson (auth.), Nimrod Megiddo, Yinfeng Xu, Binhai Zhu (eds.)

ISBN-10: 3540262245

ISBN-13: 9783540262244

This e-book constitutes the refereed lawsuits of the 1st overseas convention on Algorithmic functions in administration, AAIM 2005, held in Xian, China in June 2005.

The forty six revised complete papers awarded including abstracts of two invited talks have been rigorously reviewed and chosen from one hundred forty submissions. one of the issues addressed are approximation, complexity, automated timetabling, scheduling algorithms, game-theoretic algorithms, financial equilibrium computation, graph computations, community algorithms, computational geometry, combinatorial optimization, sequencing, community administration, facts mining, Knapsack difficulties, and so on.

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Extra resources for Algorithmic Applications in Management: First International Conference, AAIM 2005, Xian, China, June 22-25, 2005. Proceedings

Example text

Moreover, the sequence generated converges Q-superlineary to x∗λ . But, we want to find a critical point of our original problem, that is, 0 ∈ ∂f (x). For this purpose, we will use an extension of Attouch’s theorem. We recall that Attouch [1] showed that for convex functions the epi-convergence is the appropriate concept of convergence when we are interested in convergence of their subdifferentials. He showed that a sequence {fn } of lower semicontinuous proper convex functions epi-converge to f if and only if the sets {gph ∂fn } converge to gph ∂f and there exists {(xn , un )} ∈ gph fn converging to {(x, u)} with u ∈ ∂f (x) and fn (xn ) converging to f (x).

We consider the Moreau envelopes 1 I would like to thank to Prof. A. Jofr´e for advising this work. Partially supported by FONDAP-Matem´ aticas Aplicadas. N. Megiddo, Y. Xu, and B. ): AAIM 2005, LNCS 3521, pp. 26–36, 2005. c Springer-Verlag Berlin Heidelberg 2005 An Algorithm for Nonconvex Lower Semicontinuous Optimization Problems 27 2 eλ f which provide a sort of regularization of f and use the fact that eλ f (·)+r · is a convex function for some r and eλ f is a lower-C 2 function under appropriate mild conditions.

Pi = p for ∀i = 1, 2, . . , n, T −→ ∞ C C 2. np ∈ [p, 1−β − ε] ∪ [ 1−β − ε, ∞) and ε is a small constant. to of An algorithm A that is to buy tickets at the regular price until the total cost up − ε, and then decide to purchase a Bahncard can achieve the lower bound C 1−β (1 − β)2 p (1 − β)2 =2−β− C n∗ C ∗ where n = p . If there is β = 0 such that r∗ = inf (rA ) = 2 − β − r∗ = inf (rA ) = 2 − 1 . n∗ 40 3 L. Ding, C. Xin, and J. Chen Competitive Analysis of the Bahncard Problem with a Risk-Reward Strategy From the above analysis, the competitive algorithm A can overcome the weakness of the worst-case analysis.

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Algorithmic Applications in Management: First International Conference, AAIM 2005, Xian, China, June 22-25, 2005. Proceedings by Ellis L. Johnson (auth.), Nimrod Megiddo, Yinfeng Xu, Binhai Zhu (eds.)

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