Foundations of Software Technology and Theoretical Computer by Vijaya Ramachandran (auth.), S. Ramesh, G Sivakumar (eds.)

By Vijaya Ramachandran (auth.), S. Ramesh, G Sivakumar (eds.)

This e-book constitutes the refereed complaints of the seventeenth overseas convention on Foundations of software program know-how and Theoretical desktop technology, FSTTCS'97. The 18 revised complete papers provided have been chosen from a complete of sixty eight submissions. additionally incorporated are 5 invited papers through Ed Clarke, Deepak Kapur, Madhu Sudan, Vijaya Ramachandran, and Moshe Vardi. one of the issues addressed are concurrency, Petri nets, graph computations, application verification, version checking, recursion conception, rewriting, and error-correcting codes.

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Additional resources for Foundations of Software Technology and Theoretical Computer Science: 17th Conference Kharagpur, India, December 18–20, 1997 Proceedings

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In order to achieve final error ed < e, we choose ~ = e/n. Computing D from D. At each stage of the algorithm the partial distribution D constructed will be uniform on its support. If Isupp(b) is less than E0, then D = D, nothing needs to be done. Otherwise, D is obtained f r o m / ) as follows. esupp(fi) = (6) EoPrfi{w} = for each Mi, s = s~,, and t 6 Nij, (7) (s) ~esupp(/~) These equations define a latt. app. problem, whose solution is the desired probability space D: The support of the space D will be precisely the support of this lattice vector, and the elements in the support will be assigned probability 1/supp(D).

It a t t e m p t s to capture the idea of a good sample from a set. The simplest example, the set discrepancy problem considers a set system (X, 8), where X is a ground set and 5 C_ 2x is a family of subsets of X. Here one is interested in a subset R C_ X such that for each S 6 8 the difference IR n S - IR N sll, called the discrepancy, is small. Using Chernoff-Hoeffding bounds 11,16,30,29,6,31, it is found t h a t a random sample R C_ X with each 9 6 X taken into R independently with probability 1/2, results with nonzero probability in a low discrepancy set: for each S E S, IIR n s l - IRn Sll = o(~/ISlloglSl).

Motivated by a physical mapping scenario in Computational Biology, we consider graph editing to the class of bipartite interval graphs (BIGs). We prove asymptotic and exact bounds on the minimum number of editions needed to convert a graph into a BIG. 1 Introduction Graph editing problems deal with the complexity of transforming a given input graph G from class ~ to any graph H in the target class 7/using editing operations, namely, adding, deleting edges or doing a combination of both; we denote this process by ~ -+ 7/.

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