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APPROX
2005
Springer
111views Algorithms» more  APPROX 2005»
15 years 5 months ago
Sampling Bounds for Stochastic Optimization
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
Moses Charikar, Chandra Chekuri, Martin Pál
IPSN
2005
Springer
15 years 5 months ago
The sensor selection problem for bounded uncertainty sensing models
We address the problem of selecting sensors so as to minimize the error in estimating the position of a target. We consider a generic sensor model where the measurements can be in...
Volkan Isler, Ruzena Bajcsy
CIMAGING
2009
130views Hardware» more  CIMAGING 2009»
14 years 9 months ago
Quantitative phase and amplitude imaging using Differential-Interference Contrast (DIC) microscopy
We present an extension of the development of an alternating minimization (AM) method1 for the computation of a specimen's complex transmittance function (magnitude and phase...
Chrysanthe Preza, Joseph A. O'Sullivan
ICASSP
2011
IEEE
14 years 3 months ago
Sparse decomposition of transformation-invariant signals with continuous basis pursuit
Consider the decomposition of a signal into features that undergo transformations drawn from a continuous family. Current methods discretely sample the transformations and apply s...
Chaitanya Ekanadham, Daniel Tranchina, Eero P. Sim...
ML
2007
ACM
106views Machine Learning» more  ML 2007»
14 years 11 months ago
Surrogate maximization/minimization algorithms and extensions
Abstract Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. A...
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung