On the 1.1 edge-coloring of multigraphs
SIAM Journal on Discrete Mathematics
The primal-dual method for approximation algorithms and its application to network design problems
Approximation algorithms for NP-hard problems
Combinatorial optimization
On chromatic sums and distributed resource allocation
Information and Computation
New and improved algorithms for minsum shop scheduling
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
On algorithms for efficient data migration
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Using homogeneous weights for approximating the partial cover problem
Journal of Algorithms
A unified approach to approximating resource allocation and scheduling
Journal of the ACM (JACM)
Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP
Journal of the ACM (JACM)
Algorithms for Data Migration with Cloning
SIAM Journal on Computing
Approximation algorithms for partial covering problems
Journal of Algorithms
Local ratio: A unified framework for approximation algorithms. In Memoriam: Shimon Even 1935-2004
ACM Computing Surveys (CSUR)
Data migration to minimize the total completion time
Journal of Algorithms
On the Equivalence between the Primal-Dual Schema and the Local Ratio Technique
SIAM Journal on Discrete Mathematics
Improved results for data migration and open shop scheduling
ACM Transactions on Algorithms (TALG)
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
Improved bounds for sum multicoloring and scheduling dependent jobs with minsum criteria
WAOA'04 Proceedings of the Second international conference on Approximation and Online Algorithms
An improved analysis for a greedy remote-clique algorithm using factor-revealing LPs
APPROX'06/RANDOM'06 Proceedings of the 9th international conference on Approximation Algorithms for Combinatorial Optimization Problems, and 10th international conference on Randomization and Computation
Combinatorial algorithms for data migration to minimize average completion time
APPROX'06/RANDOM'06 Proceedings of the 9th international conference on Approximation Algorithms for Combinatorial Optimization Problems, and 10th international conference on Randomization and Computation
Improved algorithms for data migration
APPROX'06/RANDOM'06 Proceedings of the 9th international conference on Approximation Algorithms for Combinatorial Optimization Problems, and 10th international conference on Randomization and Computation
Sum edge coloring of multigraphs via configuration LP
ACM Transactions on Algorithms (TALG)
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Local Ratio is a well-known paradigm for designing approximation algorithms for combinatorial optimization problems. At a very high level, a local ratio algorithm first decomposes the input weight function w into a positive linear combination of simpler weight functions or models. Guided by this process a solution S is constructed such that S is α-approximate with respect to each model used in the decomposition. As a result, S is α-approximate under w as well. These models usually have a very simple structure that remains "unchanged" throughout the execution of the algorithm. In this work we show that adaptively choosing a model from a richer spectrum of functions can lead to a better local ratio. Indeed, by turning the search for a good model into an optimization problem of its own, we get improved approximations for a data migration problem.