We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum

-Fletcher-Goldfarb-Shanno memoryless quasi-Newton algorithm (BFGS) [1]. SCG yields a speed-up of at least an order of magnitude relative to BP. The speed-up depends on the convergence criterion, i.e., the bigger demand for reduction in error the bigger the speed-up. SCG is fully automated including no user dependent parameters

) algorithm, it does not require careful initialization. The proposed method also avoids another drawback of EM for mixture fitting: the possibility of convergence toward a singular estimate at the boundary of the parameter space. The novelty of our approach is that we do not use a model selection criterion

process requiring O(N log N) operations per iteration. Thus, it is the rst image restoration algorithm that optimizes a wavelet-based penalized likelihood criterion and has computational complexity comparable to that of standard wavelet denoising or frequency domain deconvolution methods. The convergence

is developed to evaluate the optimal transmit spectrum under the maximum sum data rate criterion. The numerical algorithm has an iterative water-filling int#j pret#4968 . It converges from any starting point and it reaches with in s per output dimension per transmission from the K-user multiple access sum

This paper investigates how monetary policy should be conducted in a two-region, general equilibrium model with monopolistic competition and price stickiness. This framework delivers a simple welfare criterion based on the utility of the consumers that has the usual tradeoff between stabilizing

acceptance criterion and a non-monotone pathsearch are then used to choose the next iterate. The algorithm is shown to be globally convergent under assumptions which generalize those required to obtain similar results in the smooth case. Several implementation issues are discussed, and extensive

. The obtained sample average optimization problem is solved, and the procedure is repeated several times until a stopping criterion is satisfied. We discuss convergence rates and stopping rules of this procedure and present a numerical example of the stochastic knapsack problem. Key words. Stochastic

information is stored in a common memory vector. The theory applies to both recognition and recall and covers both accuracy and latency. Noise in the decision stage neces-sitates a two-criterion decision system, and over time the criteria converge until a decision is reached. Performance is predicted from

classifications. 65F15, 65-04. Introduction. Simultaneous diagonalization of several commuting matrices has been recently considered in [1], mainly motivated by stability and convergence concerns. Exact or approximate simultaneous diagonalization was also independently introduced as a solution to a statistical