Alejandro López-Ortiz

Bio: Alejandro López-Ortiz is an academic researcher from University of Waterloo. The author has contributed to research in topics: Competitive analysis & List update problem. The author has an hindex of 33, co-authored 193 publications receiving 3719 citations. Previous affiliations of Alejandro López-Ortiz include Open Text Corporation & University of New Brunswick.

Efficient demand assignment in multi-connected microgrids with a shared central grid

Abstract: With the proliferation of distributed generation, an electrical load can be satisfied either by a centralized generator or by local/nearby distributed generators. Given a set of resource demands in a collection of geographically co-located microgrids connected to the central grid, each such demand characterized by a power level and a duration. We study algorithms that allocate generation resources to demands by configuring switched paths from sources to loads. We consider the case when each demand can be met by two generators, one of them representing the central grid and thus shared among all demands.

Using Parametric Transformations Toward Polynomial Kernels for Packing Problems Allowing Overlaps

Abstract: We consider the problem of discovering overlapping communities in networks that we model as generalizations of the Set and Graph Packing problems with overlap. As usual for Set Packing problems, we seek a collection S′ s S consisting of at least k sets subject to certain disjointness restrictions. In the r-Set Packing with t-Membership, each element of U belongs to at most t sets of S′, while in r-Set Packing with t-Overlap, each pair of sets in S′ overlaps in at most t elements. For both problems, each set of S has at most r elements. Similarly, both of our Graph Packing problems seek a collection K of at least k subgraphs in a graph G, each isomorphic to a graph H i H. In H-Packing with t-Membership, each vertex of G belongs to at most t subgraphs of K, while in H-Packing with t-Overlap, each pair of subgraphs in K overlaps in at most t vertices. For both problems, each member of H has at most r vertices and m edges, where t, r, and m are constants. Here, we show NP-completeness results for all of our packing problems. Furthermore, we give a dichotomy result for the H-Packing with t-Membership problem analogous to the Kirkpatrick and Hell dichotomy [Kirkpatrick and Hell 1978]. Using polynomial parameter transformations, we reduce the r-Set Packing with t-Membership to a problem kernel with O((r + 1)rkr) elements and the H-Packing with t-Membership and its edge version to problem kernels with O((r + 1)rkr) and O((m + 1)mkm) vertices, respectively. On the other hand, by generalizing [Fellows et al. 2008; Moser 2009], we achieve a kernel with O(rrkr − t − 1) elements for the r-Set Packing with t-Overlap and kernels with O(rrkr − t − 1) and O(mmkm − t − 1) vertices for the H-Packing with t-Overlap and its edge version, respectively. In all cases, k is the input parameter, while t, r, and m are constants.

Drawing k 2 , n : A lower bound.

Abstract: We give a tradeoff theorem between the area and the aspect ratio required by any planar straight-line drawing of K2,n on the integer lattice. In particular we show that if the drawing is contained in a rectangle of area O(n) then the rectangle must have aspect ratio Ω (n), and conversely, if the aspect ratio is O(1) then the area must be Ω (n2/log2/n).

The \({\mathcal{G}}\)-Packing with t-Overlap Problem

Abstract: We introduce the k-\(\mathcal{G}\)-Packing with t-Overlap problem to formalize the problem of finding communities in a network In the k-\(\mathcal{G}\)-Packing with t-Overlap problem, we search for at least k communities with possible overlap In contrast with previous work where communities are disjoint, we regulate the overlap through a parameter t Our focus is the parameterized complexity of the k-\(\mathcal{G}\)-Packing with t-Overlap problem Here, we provide a new technique for this problem generalizing the crown decomposition technique [2] Using our global rule, we achieve a kernel with size bounded by 2(rk − r) for the k-\(\mathcal{G}\)-Packing with t-Overlap problem when t = r − 2 and \(\mathcal{G}\) is a clique of size r

Kernelization Algorithms for Packing Problems Allowing Overlaps

Abstract: We consider the problem of discovering overlapping communities in networks which we model as generalizations of the Set and Graph Packing problems with overlap. As usual for Set Packing problems we seek a collection \(\mathcal {S}' \subseteq \mathcal {S}\) consisting of at least \(k\) sets subject to certain disjointness restrictions. In the \(r\)-Set Packing with \(t\)-Membership, each element of \(\mathcal {U}\) belongs to at most \(t\) sets of \(\mathcal {S'}\) while in \(r\)-Set Packing with \(t\)-Overlap each pair of sets in \(\mathcal {S'}\) overlaps in at most \(t\) elements. For both problems, each set of \(\mathcal {S}\) has at most \(r\) elements.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Mining time-changing data streams

Abstract: Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically. Although a number of algorithms have been proposed for learning time-changing concepts, they generally do not scale well to very large databases. In this paper we propose an efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner. This algorithm, called CVFDT, stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate. CVFDT learns a model which is similar in accuracy to the one that would be learned by reapplying VFDT to a moving window of examples every time a new example arrives, but with O(1) complexity per example, as opposed to O(w), where w is the size of the window. Experiments on a set of large time-changing data streams demonstrate the utility of this approach.

Taming the underlying challenges of reliable multihop routing in sensor networks

Abstract: The dynamic and lossy nature of wireless communication poses major challenges to reliable, self-organizing multihop networks. These non-ideal characteristics are more problematic with the primitive, low-power radio transceivers found in sensor networks, and raise new issues that routing protocols must address. Link connectivity statistics should be captured dynamically through an efficient yet adaptive link estimator and routing decisions should exploit such connectivity statistics to achieve reliability. Link status and routing information must be maintained in a neighborhood table with constant space regardless of cell density. We study and evaluate link estimator, neighborhood table management, and reliable routing protocol techniques. We focus on a many-to-one, periodic data collection workload. We narrow the design space through evaluations on large-scale, high-level simulations to 50-node, in-depth empirical experiments. The most effective solution uses a simple time averaged EWMA estimator, frequency based table management, and cost-based routing.

Data streams: algorithms and applications

Abstract: In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges. This article is an overview and survey of data stream algorithmics and is an updated version of [1].

Handbook of Constraint Programming

Abstract: Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics. The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area. The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas. The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming. - Covers the whole field of constraint programming - Survey-style chapters - Five chapters on applications Table of Contents Foreword (Ugo Montanari) Part I : Foundations Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh) Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth) Chapter 3. Constraint Propagation (Christian Bessiere) Chapter 4. Backtracking Search Algorithms (Peter van Beek) Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang) Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel) Chapter 7. Tractable Structures for CSPs (Rina Dechter) Chapter 8. The Complexity of Constraint Languages (David Cohen, Peter Jeavons) Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex) Chapter 10. Symmetry in Constraint Programming (Ian P. Gent, Karen E. Petrie, Jean-Francois Puget) Chapter 11. Modelling (Barbara M. Smith) Part II : Extensions, Languages, and Applications Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace) Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte) Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson) Chapter 15. Operations Research Methods in Constraint Programming (John Hooker) Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers) Chapter 17. Constraints over Structured Domains (Carmen Gervet) Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh) Chapter 19. Temporal CSPs (Manolis Koubarakis) Chapter 20. Distributed Constraint Programming (Boi Faltings) Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel) Chapter 22. Constraint-Based Scheduling and Planning (Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten) Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw) Chapter 24. Configuration (Ulrich Junker) Chapter 25. Constraint Applications in Networks (Helmut Simonis) Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert)