424 search results - page 23 / 85 » Boosted sampling: approximation algorithms for stochastic op... |
140
Voted
ICML
2009
IEEE
16 years 4 months ago
2009
IEEE
Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics...
148
Voted
SODA
2010
ACM
16 years 24 days ago
2010
ACM
We consider a robust model proposed by Scarf, 1958, for stochastic optimization when only the marginal probabilities of (binary) random variables are given, and the correlation be...
248
Voted
NLP
2000
15 years 7 months ago
2000
Abstract. The purpose of this paper is (1) to provide a theoretical justification for the use of Monte-Carlo sampling for approximate resolution of NP-hard maximization problems in...
131
Voted
CCE
2004
15 years 3 months ago
2004
A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncer...
151
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SAGA
2007
Springer
15 years 9 months ago
2007
Springer
Abstract. Motivated by an application in project portfolio analysis under uncertainty, we develop an algorithm S-VNS for solving stochastic combinatorial optimization (SCO) problem...