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)
An Experimental Study of Data Migration Algorithms
WAE '01 Proceedings of the 5th International Workshop on Algorithm Engineering
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)
SIAM Journal on Computing
Improved bounds for scheduling conflicting jobs with minsum criteria
ACM Transactions on Algorithms (TALG)
Complexity results for minimum sum edge coloring
Discrete Applied Mathematics
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
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
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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 $\alpha$-approximate with respect to each model used in the decomposition. As a result, $S$ is $\alpha$-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.