Book Chapter10.1007/3-540-44436-X_8
Randomized path coloring on binary trees
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TL;DR: The class of greedy algorithms that use randomization is defined and it is proved that, with high probability, randomized greedy algorithms cannot achieve a performance ratio better than 3/2 when applied to binary trees of depth Ω(l), and 1.293 - o(1) when applications tobinary trees of constant depth.
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Abstract: Motivated by the problem of WDM routing in all-optical networks, we study the following NP-hard problem. We are given a directed binary tree T and a set R of directed paths on T. We wish to assign colors to paths in R, in such a way that no two paths that share a directed arc of T are assigned the same color and that the total number of colors used is minimized. Our results are expressed in terms of the depth of the tree and the maximum load l of R, i.e., the maximum number of paths that go through a directed arc of T.
So far, only deterministic greedy algorithms have been presented for the problem. The best known algorithm colors any set R of maximum load l using at most 5l/3 colors. Alternatively, we say that this algorithm has performance ratio 5/3. It is also known that no deterministic greedy algorithm can achieve a performance ratio better than 5/3.
In this paper we define the class of greedy algorithms that use randomization. We study their limitations and prove that, with high probability, randomized greedy algorithms cannot achieve a performance ratio better than 3/2 when applied to binary trees of depth Ω(l), and 1.293 - o(1) when applied to binary trees of constant depth.
Exploiting inherent properties of randomized greedy algorithms, we obtain the first randomized algorithm for the problem that uses at most 7l/5 + o(l) colors for coloring any set of paths of maximum load l on binary trees of depth o(l1/3), with high probability. We also present an existential upper bound of 7l/5 + o(l) that holds on any binary tree.
In the analysis of our bounds we use tail inequalities for random variables following hypergeometrical probability distributions which may be of their own interest.
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Citations
Fractional Path Coloring with Applications to WDM Networks
Ioannis Caragiannis,Afonso Ferreira,Christos Kaklamanis,Stéphane Pérennes,Hervé Rivano +4 more
- 08 Jul 2001
TL;DR: This paper addresses the natural relaxation of the path coloring problem, in which one needs to color directed paths on a symmetric directed graph with a minimum number of colors, in such a way that paths using the same arc of the graph have different colors.
36
Randomized path coloring on binary trees
TL;DR: This paper defines the class of greedy algorithms that use randomization and obtains the first randomized algorithm for the problem that uses at most 7l/5 + o(l) colors for coloring any set of paths of maximum load l on binary trees of depth O(l1/3-e), with high probability.
27
Randomized path coloring on binary trees
TL;DR: The class of greedy algorithms that use randomization is defined and it is proved that, with high probability, randomized greedy algorithms cannot achieve a performance ratio better than 3/2 when applied to binary trees of depth Ω(l), and 1.293 - o(1) when applications tobinary trees of constant depth.
20
•Dissertation
Algorithms for wavelength assignment and call control in optical networks
Zhengbing Bian
- 01 Jan 2008
TL;DR: Better approximation algorithms which use at most 3L (L is the maximum number of paths in a fiber) colors for the minimum path coloring problem in trees of rings and better approximation algorithms for the maximum RPC problem in rings are given.
9
Patent
Method and system for correlating practical constraints in a network
Antonio Nucci,Nina Taft,Christophe Diot,Frederic Giroire +3 more
- 09 Jul 2003
TL;DR: In this article, a system and method for identifying optimal mapping of logical links to the physical topology of a network is provided, where the mapping options are correlated with the maximum time delay.
9
References
Probability Inequalities for sums of Bounded Random Variables
TL;DR: In this article, upper bounds for the probability that the sum S of n independent random variables exceeds its mean ES by a positive number nt are derived for certain sums of dependent random variables such as U statistics.
A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations
TL;DR: In this paper, it was shown that the likelihood ratio test for fixed sample size can be reduced to this form, and that for large samples, a sample of size $n$ with the first test will give about the same probabilities of error as a sample with the second test.
4.1K
•Book
An Introduction to Probability Theory and Its Applications, Volume II
Frank E. Grubbs,William Feller +1 more
- 01 Jan 1971
1.4K
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