Almost sure convergence of the algorithmDu, QiangEmelianenko, MariaJu, Lili
Least median of squares regression presents a daunting computational problem for moderate to large data sets — the optimum is the Chebyshev regression fit to the correct subset of the cases, and finding it exactly requires an investigation of all subsets of the cases of a certain size. The fea...
Moreover, the convergence of the training was proved based on the differentiability of the error function. SpikeProp algorithm is based on gradient descent method for SNNs. But it is difficult for normal SNNs to prove the convergence. In this paper, a smoothing L1∕2 regularization term is ...
Two convergence aspects of the EM algorithm are studied: (i) does the EM algorithm find a local maximum or a stationary value of the (incomplete-data) likelihood function? (ii) does the sequence of parameter estimates generated by EM converge? Several convergence results are obtained under condi...
The EM algorithm is a popular iterative algorithm for finding maximum likelihood estimates from incomplete data. However, the drawback of the EM algorithm is to converge slowly when the proportion of missing data is large. In order to speed up the convergence of the EM algorithm, we propose th...
Thresholding greedy algorithmBranch-greedyNonlinear approximationMultivariate Haar systemMultivariate Haar waveletWe define a family of weak thresholding greedy algorithms for the multivariate Haar basis for L 1 [0,1] d ( d ≥1). We prove convergence and uniform boundedness of the weak greedy ...
The annealing algorithm is a stochastic optimization method which has attracted attention because of its success with certain difficult problems, including
We prove novel convergence results for a stochastic proximal gradient algorithm suitable for solving a large class of convex optimization problems, where a convex objective function is given by the sum of a smooth and a possibly non-smooth component. We consider the iterates convergence and derive...
Convergence of a Relaxed Inertial Forward–Backward Algorithm for Structured Monotone Inclusions In a Hilbert space H , we study the convergence properties of a class of relaxed inertial forward–backward algorithms. They aim to solve structured monoto... H Attouch,A Cabot - 《Applied Mathematics ...
Filter method was initially introduced by Fletcher and Leyffer in [4] to guarantee the global convergence of the algorithm for solving problem (1). The idea is motivated by the aim of avoiding the difficult decisions in regard to the choice of penalty parameters in the merit functions. Numerica...