critical values of the mapping x → f(x), i.e. the set of points {c ∈ C | ∃(xℓ)ℓ∈N ⊂ C n f(xℓ) → c, ||xℓ||||dxℓf| | → 0 when ℓ → ∞}. We prove that the global optimum of f lies in its set of generalized critical values and provide an efficient way of deciding which value is the global optimum.

. Second, interactions between objects, such as occlusions, may cause tracking errors. Third, globally-optimum tracking is hard to achieve since the combinatorial assignment problem is NP-Complete. We present a modified Multiple-Hypothesis Tracking algorithm, MHT, for globally optimum tracking of moving

pseudo-marginals in the associative case we present a polynomial time approximation scheme for global optimization provided the maximum degree is O(log n), anddiscussseveralextensions. I.

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We describe a branch-and-bound search algorithm for finding the exact global optimum gapped sequencestructure alignment ("threading") between a protein sequence and a protein core or structural model, using an arbitrary amino acid pair score function (e.g., contact potentials, knowledge

During the last decade, the unprecedented increase in the affordable computational power has strongly supported the development of optimization techniques for designing antennas. Among these techniques, genetic algorithm [1] and particle swarm optimization [2] could be mentioned. Most of these techniques use physical dimensions of an antenna

The problem of locating the global optimum of functions is studied in a dynamic setting. The dynamics of simple multistable systems under the influence of chaotic forcing is investigated. When the magnitude of the forcing signal decays slowly, it is shown that the system attains an equilibrium

be exploited to compute summary statistics. We present closed-form expressions for the fitness-distance correlation (FDC) based on the elementary landscape decomposition of the problems defined over binary strings in which the objective function has one global optimum. We present some theoretical results

of difference equations, can easily be trapped into one local optimum or another, sensitive to initial conditions, perturbations, and neuron update orders. It doesn’t know how long it will take to converge, as well as if the final solution is a global optimum, or not. In this paper, we present a Hopfield

This talk has two goals: a) A review of the fundamentals of dynamic programming, and an introduction to nonserial dynamic programming; b) An application of the techniques to some of the issues involved in the problem of determining globally optimum storage patterns. Dynamic Programming Dynamic