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Algorithms and Complexity: 4th Italian Conference, CIAC 2000 by Giorgio Ausiello, Stefano Leonardi, Alberto

By Giorgio Ausiello, Stefano Leonardi, Alberto Marchetti-Spaccamela (auth.), Giancarlo Bongiovanni, Rossella Petreschi, Giorgio Gambosi (eds.)

The papers during this quantity have been provided on the Fourth Italian convention on Algorithms and Complexity (CIAC 2000). The convention happened on March 1-3, 2000, in Rome (Italy), on the convention heart of the collage of Rome \La Sapienza". This convention was once born in 1990 as a countrywide assembly to be held each 3 years for Italian researchers in algorithms, information constructions, complexity, and parallel and dispensed computing. because of a signi cant participation of international reaserchers, ranging from the second one convention, CIAC advanced into a world convention. in accordance with the decision for papers for CIAC 2000, there have been forty-one subm- sions, from which this system committee chosen 21 papers for presentation on the convention. every one paper used to be evaluated via at the very least 3 software committee individuals. as well as the chosen papers, the organizing committee invited Giorgio Ausiello, Narsingh Deo, Walter Ruzzo, and Shmuel Zaks to provide plenary lectures on the convention. we want to convey our appreciation to the entire authors of the submitted papers, to this system committee contributors and the referees, to the organizing committee, and to the plenary teachers who authorised our invitation.

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Due to the small scale of chromosomes, it has been difficult to obtain accurate information on their structure. Many techniques relying on statistical inference of indirect data have been applied to deduce this information; see [1] for some examples. More recently, researchers have developed many techniques for estimating of relative positions various genetic features by more direct physical means. We are interested in one called fluorescent in situ hybridization (FISH). In this technique, pairs of fluorescently labeled probes are hybridized (attached) to specific sites on a chromosome.

Section 4 describes the heuristic search algorithm. Section 5 outlines the branch and bound algorithm. We then present the results of simulations of the two algorithms in Section 6. 2 Introduction to the Solution Space Before explaining the development of the algorithms, it is helpful to gain some intuition about the solution space. Given that the data is both noisy and incomplete, the problem can be under-constrained and/or over-constrained. In this domain, a “constraint” refers to a measurement between two probes (since it constrains the placement of the probes).

H Then Lopt (N , H ) = L . Optimal bounds were also derived in [GWZ95, GWZ97] for the average case, using dynamic programming; the results use different recursive constructions, but end up in structures that are symmetric in H and L . These results are stated as follows: Theorem 3. Let n and H be given. Let L be the largest integer such that N ≥ L +H , and let r = N − L +H . Then L L Ltot (N, H ) = H L +H L −1 + r(L + 1). Duality in ATM Layout Problems 49 Theorem 4. Let N and L be given. Let H be the maximal such that N ≥ L +H , and let r = N − L +H .

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