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23
Selfish Unsplittable Flows
 Theoretical Computer Science
, 2004
"... What is the price of anarchy when unsplittable demands are routed selfishly in general networks with loaddependent edge delays? Motivated by this question we generalize the model of [14] to the case of weighted congestion games. We show that varying demands of users crucially affect the nature o ..."
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Cited by 77 (9 self)
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What is the price of anarchy when unsplittable demands are routed selfishly in general networks with loaddependent edge delays? Motivated by this question we generalize the model of [14] to the case of weighted congestion games. We show that varying demands of users crucially affect the nature of these games, which are no longer isomorphic to exact potential games, even for very simple instances. Indeed we construct examples where even a singlecommodity (weighted) network congestion game may have no pure Nash equilibrium.
Pseudonormality and a Lagrange Multiplier Theory for Constrained Optimization
, 2000
"... We consider optimization problems with equality, inequality, and abstract set constraints, and we explore various characteristics of the constraint set that imply the existence of Lagrange multipliers. We prove a generalized version of the FritzJohn theorem, and we introduce new and general conditi ..."
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Cited by 12 (2 self)
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We consider optimization problems with equality, inequality, and abstract set constraints, and we explore various characteristics of the constraint set that imply the existence of Lagrange multipliers. We prove a generalized version of the FritzJohn theorem, and we introduce new and general conditions that extend and unify the major constraint qualifications. Among these conditions, two new properties, pseudonormality and quasinormality, emerge as central within the taxonomy of interesting constraint characteristics. In the case where there is no abstract set constraint, these properties provide the connecting link between the classical constraint qualifications and two distinct pathways to the existence of Lagrange multipliers: one involving the notion of quasiregularity and Farkas' Lemma, and the other involving the use of exact penalty functions. The second pathway also applies in the general case where there is an abstract set constraint.
Input selection for radial basis function networks by constrained optimization
 Proceedings of the 17th International Conference on Artificial Neural Networks (ICANN 2007
"... Abstract. Input selection in the nonlinear function approximation is important and difficult problem. Neural networks provide good generalization in many cases, but their interpretability is usually limited. However, the contributions of input variables in the prediction of output would be valuabl ..."
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Cited by 4 (3 self)
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Abstract. Input selection in the nonlinear function approximation is important and difficult problem. Neural networks provide good generalization in many cases, but their interpretability is usually limited. However, the contributions of input variables in the prediction of output would be valuable information in many real world applications. In this work, an input selection algorithm for Radial basis function networks is proposed. The selection of input variables is achieved using a constrained cost function, in which each input dimension is weighted. The constraints are imposed on the values of weights. The proposed algorithm solves a logbarrier reformulation of the original optimization problem. The input selection algorithm was applied to both simulated and benchmark data and obtained results were compelling. 1
On the Chromatic Number of Graphs
 Journal of Optimization Theory and Applications
, 2001
"... Computing the chromatic number of a graph is an NPhard problem. For random graphs and some other classes of graphs, estimators of the expected chromatic number have been well studied. In this paper, a new 01 integer programming formulation for the graph coloring problem is presented. The prop ..."
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Cited by 1 (1 self)
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Computing the chromatic number of a graph is an NPhard problem. For random graphs and some other classes of graphs, estimators of the expected chromatic number have been well studied. In this paper, a new 01 integer programming formulation for the graph coloring problem is presented. The proposed new formulation is used to develop a method that generates graphs of known chromatic number by using the KKT optimality conditions of a related continuous nonlinear program.
An augmented Lagrangean scheme for capacitated traffic assignment problems
 In
, 1994
"... The inclusion of explicit bounds on the link ows in tra c assignment models has been proposed as a means to obtain a more accurate description of the tra c behaviour than that given by the basic, uncapacitated, model. Although the capacitated problem has the advantage of being numerically more tract ..."
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Cited by 1 (1 self)
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The inclusion of explicit bounds on the link ows in tra c assignment models has been proposed as a means to obtain a more accurate description of the tra c behaviour than that given by the basic, uncapacitated, model. Although the capacitated problem has the advantage of being numerically more tractable than models involving travel cost functions which tend to in nity as ows approach the link capacities, it has received very limited attention in the past. The main reason for this is that the explicit capacities make the problem computationally more demanding, since they destroy the Cartesian product structure of the feasible set of the uncapacitated model, which enables the development of highly e cient solution procedures for that problem. The availability of e cient procedures for the basic model motivates the use of dualization approaches for handling the capacity constraints of the more complex model � we propose and evaluate an augmented Lagrangean method in which uncapacitated tra c assignment subproblems are solved with the disaggregate simplicial decomposition algorithm. This algorithm fully exploits the subproblems ' structure and also has very favourable reoptimization facilities. Both these properties are of greatest importance for achieving
Gaussian fitting based FDA for chemometrics
 In Proceedings 9th International WorkConference on Artificial Neural Networks, Sandoval F, Prieto A, Cabestany J, Graña M (eds). IWANN’2007, LNCS
"... Abstract. In Functional Data Analysis (FDA) multivariate data are considered as sampled functions. We propose a nonsupervised method for finding a good function basis that is built on the data set. The basis consists of a set of Gaussian kernels that are optimized for an accurate fitting. The propo ..."
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Abstract. In Functional Data Analysis (FDA) multivariate data are considered as sampled functions. We propose a nonsupervised method for finding a good function basis that is built on the data set. The basis consists of a set of Gaussian kernels that are optimized for an accurate fitting. The proposed methodology is experimented with two spectrometric data sets. The obtained weights are further scaled using a Delta Test (DT) to improve the prediction performance. Least Squares Support Vector Machine (LSSVM) model is used for estimation. 1
Specification and Deployment of Distributed Monitoring and Adaptation Infrastructures
"... Abstract. This paper presents a new domainspecific language that allows to define integrated monitoring and adaptation functionality for controlling heterogeneous systems. We propose a mechanism for optimal deployment of the defined control operators onto available resources. Deployment is based on ..."
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Abstract. This paper presents a new domainspecific language that allows to define integrated monitoring and adaptation functionality for controlling heterogeneous systems. We propose a mechanism for optimal deployment of the defined control operators onto available resources. Deployment is based on solving a quadratic programming problem, and helps to achieve minimized reaction times, low overhead, as well as scalable monitoring and adaptation.
J Math Imaging Vis (2011) 39: 45–61 DOI 10.1007/s1085101002235 Normalized Cuts Revisited: A Reformulation for Segmentation with Linear Grouping Constraints
, 2010
"... Abstract Indisputably Normalized Cuts is one of the most popular segmentation algorithms in pattern recognition and computer vision. It has been applied to a wide range of segmentation tasks with great success. A number of extensions to this approach have also been proposed, including ones that can ..."
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Abstract Indisputably Normalized Cuts is one of the most popular segmentation algorithms in pattern recognition and computer vision. It has been applied to a wide range of segmentation tasks with great success. A number of extensions to this approach have also been proposed, including ones that can deal with multiple classes or that can incorporate a priori information in the form of grouping constraints. However, what is common for all these methods is that they are noticeably limited in the type of constraints that can be incorporated and can only address segmentation problems on a very specific form. In this paper, we present a reformulation of Normalized Cut segmentation that in a unified way can handle linear equality constraints for an arbitrary number of classes. This is done by restating the problem and showing how linear constraints can be enforced exactly in the optimization scheme through duality. This allows us to add group priors, for example, that certain pixels should belong to a given class. In addition, it provides a principled way to perform multiclass segmentation for tasks like interactive segmentation. The method has been tested on real data showing good performance and improvements compared to standard normalized cuts.
valueatrisk via nondifferentiable optimization
, 2007
"... Portfolio optimization by minimizing conditional ..."
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