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DAGSTUHL
2007
14 years 11 months ago
Sampling-based Approximation Algorithms for Multi-stage Stochastic Optimization
Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
Chaitanya Swamy, David B. Shmoys
CDC
2009
IEEE
137views Control Systems» more  CDC 2009»
15 years 2 months ago
Asynchronous gossip algorithms for stochastic optimization
Abstract— We consider a distributed multi-agent network system where the goal is to minimize an objective function that can be written as the sum of component functions, each of ...
Sundhar Srinivasan Ram, Angelia Nedic, Venugopal V...
SIGCOMM
2010
ACM
14 years 10 months ago
Stochastic approximation algorithm for optimal throughput performance of wireless LANs
In this paper, we consider the problem of throughput maximization in an infrastructure based WLAN. We demonstrate that most of the proposed protocols though perform optimally for ...
Sundaresan Krishnan, Prasanna Chaporkar
STOC
2004
ACM
118views Algorithms» more  STOC 2004»
15 years 10 months ago
Boosted sampling: approximation algorithms for stochastic optimization
Several combinatorial optimization problems choose elements to minimize the total cost of constructing a feasible solution that satisfies requirements of clients. In the STEINER T...
Anupam Gupta, Martin Pál, R. Ravi, Amitabh ...
WSC
2007
15 years 12 days ago
Stochastic trust region gradient-free method (strong): a new response-surface-based algorithm in simulation optimization
Response Surface Methodology (RSM) is a metamodelbased optimization method. Its strategy is to explore small subregions of the parameter space in succession instead of attempting ...
Kuo-Hao Chang, L. Jeff Hong, Hong Wan