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FCS
2006
15 years 8 months ago
Speeeding Up Markov Chain Monte Carlo Algorithms
We prove an upper bound on the convergence rate of Markov Chain Monte Carlo (MCMC) algorithms for the important special case when the state space can be aggregated into a smaller ...
Andras Farago
IPCO
1996
89views Optimization» more  IPCO 1996»
15 years 8 months ago
On Dependent Randomized Rounding Algorithms
In recent years, approximation algorithms based on randomized rounding of fractional optimal solutions have been applied to several classes of discrete optimization problems. In t...
Dimitris Bertsimas, Chung-Piaw Teo, Rakesh Vohra
PAMI
2008
139views more  PAMI 2008»
15 years 6 months ago
A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets
We consider the problem of learning a ranking function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data. Relying on an -accurate approxim...
Vikas C. Raykar, Ramani Duraiswami, Balaji Krishna...
CSDA
2004
105views more  CSDA 2004»
15 years 6 months ago
Computational aspects of algorithms for variable selection in the context of principal components
Variable selection consists in identifying a k-subset of a set of original variables that is optimal for a given criterion of adequate approximation to the whole data set. Several...
Jorge Cadima, J. Orestes Cerdeira, Manuel Minhoto
SIAMJO
2000
88views more  SIAMJO 2000»
15 years 6 months ago
A Feasible BFGS Interior Point Algorithm for Solving Convex Minimization Problems
Abstract. We propose a BFGS primal-dual interior point method for minimizing a convex function on a convex set defined by equality and inequality constraints. The algorithm generat...
Paul Armand, Jean Charles Gilbert, Sophie Jan-J&ea...