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NIPS
2008
15 years 7 months ago
An interior-point stochastic approximation method and an L1-regularized delta rule
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
Peter Carbonetto, Mark Schmidt, Nando de Freitas
IPCO
2001
166views Optimization» more  IPCO 2001»
15 years 7 months ago
Approximate k-MSTs and k-Steiner Trees via the Primal-Dual Method and Lagrangean Relaxation
Garg [10] gives two approximation algorithms for the minimum-cost tree spanning k vertices in an undirected graph. Recently Jain and Vazirani [16] discovered primal-dual approxima...
Fabián A. Chudak, Tim Roughgarden, David P....
SODA
1997
ACM
114views Algorithms» more  SODA 1997»
15 years 7 months ago
Better Approximation Guarantees for Job-shop Scheduling
Job-shop scheduling is a classical NP-hard problem. Shmoys, Stein, and Wein presented the first polynomial-time approximation algorithm for this problem that has a good (polylogar...
Leslie Ann Goldberg, Mike Paterson, Aravind Sriniv...
IPPS
2003
IEEE
15 years 11 months ago
Allocating Servers in Infostations for On-Demand Communications
Given a set of service requests, each char acterize d by a temporal interval and a category, an integer k, and an integer hc for each categoryc, the Server A llocation with Bounde...
Alan A. Bertossi, Maria Cristina Pinotti, Romeo Ri...
ATAL
2007
Springer
15 years 10 months ago
Modeling plan coordination in multiagent decision processes
In multiagent planning, it is often convenient to view a problem as two subproblems: agent local planning and coordination. Thus, we can classify agent activities into two categor...
Ping Xuan