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Optimization on directionally convex sets
Central European Journal of Operations Research, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Optimizations of Rough Set Model
Fundamenta Informaticae, 1998Rough set methodology is based on concept (set) approximations constructed from available background knowledge represented in information systems [14]. In many applications only partial knowledge about approximated concepts is given. Hence quite often first a parametrized family of concept approximations is built and next, by parameters tuning the best,
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Optimal Sampling Sets in Cographs
2019 IEEE Data Science Workshop (DSW), 2019In this paper, we calculate the optimal sampling sets for bandlimited signals on cographs. We take into account the tree structure of the cograph to derive closed form results for the uniqueness sets of signals with a given bandwidth. These results do not require expensive spectral decompositions and represent a promising tool for the analysis of ...
Dominique Guillot +3 more
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Lifts of convex sets in optimization
Mathematical Programming, 2015This special issue is dedicated to the geometry and complexity of lifts or extended formulations of convex sets which has been an active area of research in recent years. This developing field lies at the intersection of several areas such as convex geometry, polyhedral theory, real algebraic geometry, combinatorics, optimization, and computer science,
Volker Kaibel, Rekha R. Thomas
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Working sets and near-optimality
ACM SIGOPS Operating Systems Review, 1983In [1] Denning concludes from numerous observations of program behaviour that the WS algorithm with a single window size 8 is likely to "deliver throughput typically no worse than 10 percent from optimum". The authors of [2] report about observations of a set of programs, which requires several different window sizes for a i0 percent detuned WS ...
Hermann Schmutz, P. Silberbusch
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On Set-Based Multiobjective Optimization
IEEE Transactions on Evolutionary Computation, 2010Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: 1) how to formalize what type of Pareto set approximation is sought; 2) how to use this information within an algorithm to efficiently search for a good Pareto set approximation; and 3) how to ...
Eckart Zitzler +2 more
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Improvement sets and vector optimization
European Journal of Operational Research, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
César Gutiérrez +2 more
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Optimal Approximations with Rough Sets
2013When arbitrary sets are approximated by more structured sets, it may not be possible to obtain an exact approximation that is equivalent to a given set. A proposal is presented for a 'metric' approach to Rough Sets. This includes a definition of the 'optimal' or best approximation with respect to a measure of similarity, and an algorithm to find it ...
Ryszard Janicki, Adam Lenarcic
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Aggregates in Answer Set Optimization
2011Answer set optimization (ASO) is a flexible framework for qualitative optimization in answer set programming (ASP). The approach uses a generating program to construct the space of problem solutions, and a preference program to assess the quality of solutions.
Emad Saad, Gerhard Brewka
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Optimal approximations of complete sets
1986Orponen, Russo, and Sch6ning [0RS85] investigated the notion of polynomially levelable sets and showed that many "natural" intractable sets are polynomially levelable. For example, they show that i f a set is not in P and is "paddable" or "self-reducible" then i t is also polynomially levelable. Their results are suff ic ient ly powerful to show that i
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