Efficient methods for selfish network design

Fotakis, D; Kaporis, AC; Spirakis, PG

dc.contributor.author	Fotakis, D	en
dc.contributor.author	Kaporis, AC	en
dc.contributor.author	Spirakis, PG	en
dc.date.accessioned	2014-03-01T02:46:07Z
dc.date.available	2014-03-01T02:46:07Z
dc.date.issued	2009	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32564
dc.subject	Computational Complexity	en
dc.subject	Efficient Algorithm	en
dc.subject	Emerging Technology	en
dc.subject	Network Design	en
dc.subject	np-hard problem	en
dc.subject	Polynomial Time	en
dc.subject	Polynomial Time Algorithm	en
dc.subject	Probabilistic Method	en
dc.subject	Quadratic Program	en
dc.subject	Weed Management	en
dc.subject	Linear Program	en
dc.subject	Nash Equilibrium	en
dc.subject.other	Braess's Paradox	en
dc.subject.other	Efficient algorithm	en
dc.subject.other	Efficient method	en
dc.subject.other	Equilibrium flow	en
dc.subject.other	Feasible solution	en
dc.subject.other	Linear programs	en
dc.subject.other	Nash Equilibrium	en
dc.subject.other	Network design	en
dc.subject.other	NP-HARD problem	en
dc.subject.other	Optimal flows	en
dc.subject.other	Optimal traffic allocation	en
dc.subject.other	Polylogarithmic	en
dc.subject.other	Polynomial-time	en
dc.subject.other	Polynomial-time algorithms	en
dc.subject.other	Probabilistic methods	en
dc.subject.other	Quadratic programs	en
dc.subject.other	Real-world networks	en
dc.subject.other	Sub-network	en
dc.subject.other	Approximation algorithms	en
dc.subject.other	Computational complexity	en
dc.subject.other	Linguistics	en
dc.subject.other	Optimization	en
dc.subject.other	Polynomial approximation	en
dc.subject.other	Query languages	en
dc.subject.other	Spacecraft	en
dc.subject.other	Translation (languages)	en
dc.subject.other	Computational efficiency	en
dc.title	Efficient methods for selfish network design	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-02930-1_38	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-02930-1_38	en
heal.publicationDate	2009	en
heal.abstract	Intuitively, Braess's paradox states that destroying a part of a network may improve the common latency of selfish flows at Nash equilibrium. Such a paradox is a pervasive phenomenon in real-world networks. Any administrator, who wants to improve equilibrium delays in selfish networks, is facing some basic questions: (i) Is the network paradox-ridden? (ii) How can we delete some edges to optimize equilibrium flow delays? (iii) How can we modify edge latencies to optimize equilibrium flow delays? Unfortunately, such questions lead to NP-hard problems in general. In this work, we impose some natural restrictions on our networks, e.g. we assume strictly increasing linear latencies. Our target is to formulate efficient algorithms for the three questions above. We manage to provide: A polynomial-time algorithm that decides if a network is paradox-ridden, when latencies are linear and strictly increasing. A reduction of the problem of deciding if a network with arbitrary linear latencies is paradox-ridden to the problem of generating all optimal basic feasible solutions of a Linear Program that describes the optimal traffic allocations to the edges with constant latency. An algorithm for finding a subnetwork that is almost optimal wrt equilibrium latency. Our algorithm is subexponential when the number of paths is polynomial and each path is of polylogarithmic length. A polynomial-time algorithm for the problem of finding the best subnetwork, which outperforms any known approximation algorithm for the case of strictly increasing linear latencies. A polynomial-time method that turns the optimal flow into a Nash flow by deleting the edges not used by the optimal flow, and performing minimal modifications to the latencies of the remaining ones. Our results provide a deeper understanding of the computational complexity of recognizing the Braess's paradox most severe manifestations, and our techniques show novel ways of using the probabilistic method and of exploiting convex separable quadratic programs. © 2009 Springer Berlin Heidelberg.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-02930-1_38	en
dc.identifier.volume	5556 LNCS	en
dc.identifier.issue	PART 2	en
dc.identifier.spage	459	en
dc.identifier.epage	471	en