19,340 research outputs found
Optimization Strategies in Complex Systems
We consider a class of combinatorial optimization problems that emerge in a
variety of domains among which: condensed matter physics, theory of financial
risks, error correcting codes in information transmissions, molecular and
protein conformation, image restoration. We show the performances of two
algorithms, the``greedy'' (quick decrease along the gradient) and
the``reluctant'' (slow decrease close to the level curves) as well as those of
a``stochastic convex interpolation''of the two. Concepts like the average
relaxation time and the wideness of the attraction basin are analyzed and their
system size dependence illustrated.Comment: 8 pages, 3 figure
Throughput optimization strategies for large-scale wireless LANs
Thanks to the active development of IEEE 802.11, the performance of wireless local area networks (WLANs) is improving by every new edition of the standard facilitating large enterprises to rely on Wi-Fi for more demanding applications. The limited number of channels in the unlicensed industrial scientific medical frequency band however is one of the key bottlenecks of Wi-Fi when scalability and robustness are points of concern. In this paper we propose two strategies for the optimization of throughput in wireless LANs: a heuristic derived from a theoretical model and a surrogate model based decision engine
Global Optimization strategies for two-mode clustering
Two-mode clustering is a relatively new form of clustering that clusters both rows and columns of a data matrix. To do so, a criterion similar to k-means is optimized. However, it is still unclear which optimization method should be used to perform two-mode clustering, as various methods may lead to non-global optima. This paper reviews and compares several optimization methods for two-mode clustering. Several known algorithms are discussed and a new, fuzzy algorithm is introduced. The meta-heuristics Multistart, Simulated Annealing, and Tabu Search are used in combination with these algorithms. The new, fuzzy algorithm is based on the fuzzy c-means algorithm of Bezdek (1981) and the Fuzzy Steps approach to avoid local minima of Heiser and Groenen (1997) and Groenen and Jajuga (2001). The performance of all methods is compared in a large simulation study. It is found that using a Multistart meta-heuristic in combination with a two-mode k-means algorithm or the fuzzy algorithm often gives the best results. Finally, an empirical data set is used to give a practical example of two-mode clustering.algorithms;fuzzy clustering;multistart;simulated annealing;simulation;tabu search;two-mode clustering
Optimization of a Neutrino Factory: Discovery Machine versus Precision Instrument
We discuss the optimization of a neutrino factory experiment for the purpose
of sin^2(2theta_13), mass hierarchy, and CP violation discoveries. This
includes a review of possible optimization strategies, as well as an
application of these to different sin^2(2theta_13) regions.Comment: 5 pages, 4 figures. Talk given at the NuFact 07 conference, Aug 6-11,
2007, Okayama, Japa
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