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JORS
2011
67views more  JORS 2011»
13 years 3 months ago
Robust parameter design optimization of simulation experiments using stochastic perturbation methods
Stochastic perturbation methods can be applied to problems for which either the objective function is represented analytically, or the objective function is the result of a simula...
A. K. Miranda, E. Del Castillo
TIP
2008
133views more  TIP 2008»
13 years 8 months ago
A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields
This paper presents recursive cavity modeling--a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to su...
Jason K. Johnson, Alan S. Willsky
NIPS
2008
13 years 9 months ago
Adapting to a Market Shock: Optimal Sequential Market-Making
We study the profit-maximization problem of a monopolistic market-maker who sets two-sided prices in an asset market. The sequential decision problem is hard to solve because the ...
Sanmay Das, Malik Magdon-Ismail
PAMI
2006
173views more  PAMI 2006»
13 years 8 months ago
Structure from Motion with Wide Circular Field of View Cameras
This paper presents a method for fully automatic and robust estimation of two-view geometry, autocalibration, and 3D metric reconstruction from point correspondences in images take...
Branislav Micusík, Tomás Pajdla
ICALP
2010
Springer
14 years 24 days ago
Thresholded Covering Algorithms for Robust and Max-min Optimization
The general problem of robust optimization is this: one of several possible scenarios will appear tomorrow, but things are more expensive tomorrow than they are today. What should...
Anupam Gupta, Viswanath Nagarajan, R. Ravi