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» A Guided Monte Carlo Approach to Optimization Problems
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STOC
2005
ACM
174views Algorithms» more  STOC 2005»
15 years 10 months ago
On the average case performance of some greedy approximation algorithms for the uncapacitated facility location problem
In combinatorial optimization, a popular approach to NP-hard problems is the design of approximation algorithms. These algorithms typically run in polynomial time and are guarante...
Abraham Flaxman, Alan M. Frieze, Juan Carlos Vera
CORR
2008
Springer
107views Education» more  CORR 2008»
14 years 10 months ago
Estimating Signals with Finite Rate of Innovation from Noisy Samples: A Stochastic Algorithm
As an example of the recently introduced concept of rate of innovation, signals that are linear combinations of a finite number of Diracs per unit time can be acquired by linear fi...
Vincent Yan Fu Tan, Vivek K. Goyal
ICASSP
2011
IEEE
14 years 1 months ago
Bayesian Compressive Sensing for clustered sparse signals
In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. ...
Lei Yu, Hong Sun, Jean-Pierre Barbot, Gang Zheng
ICPR
2008
IEEE
15 years 4 months ago
Monocular 3D tracking of multiple interacting targets
In this paper, we present a new approach based on Markov Chain Monte Carlo(MCMC) for the stable monocular tracking of variable interacting targets in 3D space. The crucial problem...
Tatsuya Osawa, Kyoko Sudo, Hiroyuki Arai, Hideki K...
ICARCV
2006
IEEE
180views Robotics» more  ICARCV 2006»
15 years 4 months ago
Simultaneous Localization and Mapping with Stereo Vision
— In the simultaneous localization and mapping (SLAM) problem, a mobile robot must build a map of its environment while simultaneously determining its location within that map. W...
Matthew N. Dailey, Manukid Parnichkun