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Balas E.,Carnegie Mellon University | Kis T.,Institute for Computer Science and Control
Discrete Optimization

This note is meant to elucidate the difference between intersection cuts as originally defined, and intersection cuts as defined in the more recent literature. It also states a basic property of intersection cuts under their original definition. © 2015 Elsevier B.V. Source

Balas E.,Carnegie Mellon University | Kis T.,Institute for Computer Science and Control
Mathematical Programming

We examine the connections between the classes of cuts in the title. We show that lift-and-project (L&P) cuts from a given disjunction are equivalent to generalized intersection cuts from the family of polyhedra obtained by taking positive combinations of the complements of the inequalities of each term of the disjunction. While L&P cuts from split disjunctions are known to be equivalent to standard intersection cuts (SICs) from the strip obtained by complementing the terms of the split, we show that L&P cuts from more general disjunctions may not be equivalent to any SIC. In particular, we give easily verifiable necessary and sufficient conditions for a L&P cut from a given disjunction D to be equivalent to a SIC from the polyhedral counterpart of D. Irregular L&P cuts, i.e. those that violate these conditions, have interesting properties. For instance, unlike the regular ones, they may cut off part of the corner polyhedron associated with the LP solution from which they are derived. Furthermore, they are not exceptional: their frequency exceeds that of regular cuts. A numerical example illustrates some of the above properties. © 2016 Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society Source

Bassler K.E.,University of Houston | Bassler K.E.,Max Planck Institute for the Physics of Complex Systems | Genio C.I.D.,University of Warwick | Genio C.I.D.,Max Planck Institute for the Physics of Complex Systems | And 5 more authors.
New Journal of Physics

Many real-world networks exhibit correlations between the node degrees. For instance, in socialnetworks nodes tend to connect to nodes of similar degree and conversely, in biological and technological networks, high-degree nodes tend to be linked with low-degree nodes. Degree correlations also affect the dynamics of processes supported by a network structure, such as the spread of opinions or epidemics. The proper modelling of these systems, i.e., without uncontrolled biases, requires the sampling of networks with a specified set of constraints.Wepresent a solution to the sampling problem when the constraints imposed are the degree correlations. In particular, we develop an exact method to construct and sample graphs with a specified joint-degree matrix, which is a matrix providing the number of edges between all the sets of nodes of a given degree, for all degrees, thus completely specifying all pairwise degree correlations, and additionally, the degree sequence itself. Our algorithm always produces independent samples without backtracking. The complexity of the graph construction algorithmis ⊙(NM)whereNis the number of nodes andMis the number of edges. © 2015 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft. Source

Manno-Kovacs A.,Institute for Computer Science and Control
2014 IEEE International Conference on Image Processing, ICIP 2014

Active contour methods are widely used for efficient contour detection. This paper proposes a novel contribution for the Harris based Vector Field Convolution (HVFC) method, using the orientation information of feature points in the image by analyzing the gradient information in the small neighborhood. Based on the orientation information, relevant edges are emphasized and an improved edge map is used in the iterative process. The main advantage of the introduced Directional HVFC (DHVFC) method is the ability of exploiting orientation information for increased contour detection accuracy even in case of high curvature boundaries and strong background clutter. The quantitative and qualitative evaluation and comparison with other state-of-the-art methods show that the additional directional information increases the detection performance. © 2014 IEEE. Source

Kovacs L.,Institute for Computer Science and Control | Kovacs L.,University of Szeged
Proceedings of the IEEE International Conference on Computer Vision

In this paper we present a method for local processing of photos and associated sensor information on mobile devices. Our goal is to lay the foundations of a collaborative multi-user framework where ad-hoc device groups can share their data around a geographical location to produce more complex composited views of the area, without the need of a centralized server-client - cloud-based - architecture. We focus on processing as much data locally on the devices as possible, and reducing the amount of data that needs to be shared. The main results are the proposal of a lightweight processing and feature extraction framework, based on the analysis of vision graphs, and presenting preliminary composite view generation based on these results. © 2013 IEEE. Source

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