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» A Survey on Approximation Algorithms for Scheduling with Mac...
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SDM
2012
SIAM
237views Data Mining» more  SDM 2012»
11 years 10 months ago
A Distributed Kernel Summation Framework for General-Dimension Machine Learning
Kernel summations are a ubiquitous key computational bottleneck in many data analysis methods. In this paper, we attempt to marry, for the first time, the best relevant technique...
Dongryeol Lee, Richard W. Vuduc, Alexander G. Gray
CORR
2010
Springer
86views Education» more  CORR 2010»
13 years 7 months ago
Online Scheduling on Identical Machines using SRPT
Due to its optimality on a single machine for the problem of minimizing average flow time, ShortestRemaining-Processing-Time (SRPT) appears to be the most natural algorithm to con...
Kyle Fox, Benjamin Moseley
APPROX
2009
Springer
163views Algorithms» more  APPROX 2009»
14 years 2 months ago
The Power of Preemption on Unrelated Machines and Applications to Scheduling Orders
Abstract. Scheduling jobs on unrelated parallel machines so as to minimize the makespan is one of the basic, well-studied problems in the area of machine scheduling. In the first ...
José R. Correa, Martin Skutella, José...
ML
2002
ACM
154views Machine Learning» more  ML 2002»
13 years 7 months ago
Technical Update: Least-Squares Temporal Difference Learning
TD() is a popular family of algorithms for approximate policy evaluation in large MDPs. TD() works by incrementally updating the value function after each observed transition. It h...
Justin A. Boyan
ESA
2006
Springer
136views Algorithms» more  ESA 2006»
13 years 11 months ago
Approximation in Preemptive Stochastic Online Scheduling
Abstract. We present a first constant performance guarantee for preemptive stochastic scheduling to minimize the sum of weighted completion times. For scheduling jobs with release ...
Nicole Megow, Tjark Vredeveld