Entropy Minimization Problems with Linear Constraints, Schrödinger Bridge and a Conditional Sanov Theorem

1994 ◽  
pp. 21-39
Author(s):  
Austin Blaquière ◽  
M. Sigal-Pauchard
Keyword(s):  
Linear Constraints ◽  
Minimization Problems ◽  
Entropy Minimization ◽  
Schrödinger Bridge ◽  
Sanov Theorem

scholarly journals Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems

2008 ◽  
Vol 2008 ◽  
pp. 1-13 ◽  
Author(s):  
Rafal Zdunek ◽  
Andrzej Cichocki
Keyword(s):  
Matrix Factorization ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
High Efficiency ◽  
Nonnegative Matrix ◽  
Linear Constraints ◽  
Projected Gradient ◽  
Minimization Problems ◽  
Subspace Optimization ◽  
Factorization Algorithms

Recently, a considerable growth of interest in projected gradient (PG) methods has been observed due to their high efficiency in solving large-scale convex minimization problems subject to linear constraints. Since the minimization problems underlying nonnegative matrix factorization (NMF) of large matrices well matches this class of minimization problems, we investigate and test some recent PG methods in the context of their applicability to NMF. In particular, the paper focuses on the following modified methods: projected Landweber, Barzilai-Borwein gradient projection, projected sequential subspace optimization (PSESOP), interior-point Newton (IPN), and sequential coordinate-wise. The proposed and implemented NMF PG algorithms are compared with respect to their performance in terms of signal-to-interference ratio (SIR) and elapsed time, using a simple benchmark of mixed partially dependent nonnegative signals.

Sampling from Gaussian Markov random fields conditioned on linear constraints

2008 ◽  
Vol 48 ◽  
pp. 1041 ◽  
Author(s):  
Daniel Peter Simpson ◽  
Ian W. Turner ◽  
A. N. Pettitt
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Linear Constraints ◽  
Gaussian Markov Random Fields ◽  
Markov Random
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