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» Approximating Parameterized Convex Optimization Problems
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NIPS
2007
13 years 10 months ago
Convex Clustering with Exemplar-Based Models
Clustering is often formulated as the maximum likelihood estimation of a mixture model that explains the data. The EM algorithm widely used to solve the resulting optimization pro...
Danial Lashkari, Polina Golland
TSMC
2002
119views more  TSMC 2002»
13 years 8 months ago
Approximation of n-dimensional data using spherical and ellipsoidal primitives
This paper discusses the problem of approximating data points in -dimensional Euclidean space using spherical and ellipsoidal surfaces. A closed form solution is provided for spher...
Giuseppe Carlo Calafiore
ICASSP
2008
IEEE
14 years 3 months ago
Blind channel estimation in MIMO-OFDM systems using semi-definite relaxation
A new blind channel estimation technique for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems is proposed. It estimates the channel pa...
Nima Sarmadi, Alex B. Gershman, Shahram Shahbazpan...
CORR
2011
Springer
176views Education» more  CORR 2011»
13 years 3 months ago
Inner approximations for polynomial matrix inequalities and robust stability regions
Following a polynomial approach, many robust fixed-order controller design problems can be formulated as optimization problems whose set of feasible solutions is modelled by para...
Didier Henrion, Jean B. Lasserre
APPROX
2010
Springer
188views Algorithms» more  APPROX 2010»
13 years 10 months ago
Approximation Algorithms for Reliable Stochastic Combinatorial Optimization
We consider optimization problems that can be formulated as minimizing the cost of a feasible solution wT x over an arbitrary combinatorial feasible set F {0, 1}n . For these pro...
Evdokia Nikolova