Upgrading edges in the maximal covering location problem

We study the upgrading version of the maximal covering location problem with edge length modifications on networks. This problem aims at locating p facilities on the vertices (of the network) so as to maximise coverage, considering that the length of the edges can be reduced at a cost, subject to a given budget. Hence, we have to decide on: the optimal location of p facilities and the optimal edge length reductions. This problem is NP-hard on general graphs. To solve it, we propose three different mixed-integer formulations and a preprocessing phase for fixing variables and removing some of the constraints. Moreover, we strengthen the proposed formulations including valid inequalities. Finally, we compare the three formulations and their corresponding improvements by testing their performance over different datasets. © 2022 The Author(s

Correlations, Risk and Crisis: From Physiology to Finance

We study the dynamics of correlation and variance in systems under the load of environmental factors. A universal effect in ensembles of similar systems under the load of similar factors is described: in crisis, typically, even before obvious symptoms of crisis appear, correlation increases, and, at the same time, variance (and volatility) increases too. This effect is supported by many experiments and observations of groups of humans, mice, trees, grassy plants, and on financial time series. A general approach to the explanation of the effect through dynamics of individual adaptation of similar non-interactive individuals to a similar system of external factors is developed. Qualitatively, this approach follows Selye's idea about adaptation energy.Comment: 42 pages, 15 figures, misprints corrections, a proof is added, improved journal versio

High performance algorithms for large scale placement problem

Placement is one of the most important problems in electronic design automation (EDA). An inferior placement solution will not only affect the chip’s performance but might also make it nonmanufacturable by producing excessive wirelength, which is beyond available routing resources. Although placement has been extensively investigated for several decades, it is still a very challenging problem mainly due to that design scale has been dramatically increased by order of magnitudes and the increasing trend seems unstoppable. In modern design, chips commonly integrate millions of gates that require over tens of metal routing layers. Besides, new manufacturing techniques bring out new requests leading to that multi-objectives should be optimized simultaneously during placement. Our research provides high performance algorithms for placement problem. We propose (i) a high performance global placement core engine POLAR; (ii) an efficient routability-driven placer POLAR 2.0, which is an extension of POLAR to deal with routing congestion; (iii) an ultrafast global placer POLAR 3.0, which explore parallelism on POLAR and can make full use of multi-core system; (iv) some efficient triple patterning lithography (TPL) aware detailed placement algorithms

Projection pursuit methods for exploratory supervised classification

In high-dimensional data, one often seeks a few interesting low-dimensional projections which reveal important aspects of the data. Projection pursuit is a procedure for searching high-dimensional data for interesting low-dimensional projections via the optimization of a criterion function called the projection pursuit index. Very few projection pursuit indices incorporate class or group information in the calculation, and hence can be adequately applied to supervised classification problems. We introduce new indices derived from linear discriminant analysis that can be used for exploratory supervised classification.;When we have the small number of observations relative to the number of variables, the class structure of optimal projection can be biased too much. In this situation, most of classical multivariate analysis methods also be problematic, too. We discuss how the sample size and dimensionality are related, and we propose a new projection pursuit index that considers the penalty for the projection coefficients and overcomes the small number of observation problem

Investigating Polyhedra by Oracles and Analyzing Simple Extensions of Polytopes

Otto-von-Guericke-Universität Magdeburg, Fakultät für Mathematik, Dissertation, 2016von Dipl.-Comp.-Math. Matthias WalterLiteraturverzeichnis: Seite 167-17

Proceedings of the 8th Cologne-Twente Workshop on Graphs and Combinatorial Optimization

International audienceThe Cologne-Twente Workshop (CTW) on Graphs and Combinatorial Optimization started off as a series of workshops organized bi-annually by either Köln University or Twente University. As its importance grew over time, it re-centered its geographical focus by including northern Italy (CTW04 in Menaggio, on the lake Como and CTW08 in Gargnano, on the Garda lake). This year, CTW (in its eighth edition) will be staged in France for the first time: more precisely in the heart of Paris, at the Conservatoire National d’Arts et Métiers (CNAM), between 2nd and 4th June 2009, by a mixed organizing committee with members from LIX, Ecole Polytechnique and CEDRIC, CNAM