The variable metric algorithm is a frequently used method for calculating the least value of a function of several variables. However it has been proved only that the method is successful if the objective function is quadratic, although in practice it treats many types of objective functions successfully. This paper extends the theory, for it proves that successful convergence is obtained provi...

A modification of the limited-memory variable metric BNS method for large scale unconstrained optimization is proposed, which consist in corrections (derived from the idea of conjugate directions) of the used difference vectors for better satisfaction of previous quasiNewton conditions. In comparison with [16], where a similar approach is used, correction vectors from more previous iterations c...

An important type of methods for learning belief networks from data are those based on the use of a scoring metric, to evaluate the fitness of any given candidate network to the data base, and a search procedure to explore the set of candidate networks. In this paper we propose a new method that carries out the search not in the space of directed acyclic graphs but in the space of the orderings...

Available bandwidth is an important network performance metric, which can benefit a lot of network protocols and applications. Though many works have been done for the bandwidth estimation in wired network, it is still a challenge in wireless networks. In this paper, based on the shared medium nature of wireless networks, the concept of general link is introduced for the analysis and estimation...

In this paper, we propose a novel model-based approach to recover 3D hand pose from 2D images through a compact articulated 3D hand model whose parameters are inferred in a Bayesian manner. To this end, we propose generative models for hand and background pixels leading to a loglikelihood objective function which aims at enclosing hand-like pixels within the silhouette of the projected 3D model...

Variable Metric Methods are "Newton-Raphson-like" algorithms for unconstrained minimization in which the inverse Hessian is replaced by an approximation, inferred from previous gradients and updated at each iteration, During the past decade various approaches have been used to derive general classes of such algorithms having the common properties of being Conjugate Directions methods and having...

in this paper, we propose a new definition of intuitionistic fuzzyquasi-metric and pseudo-metric spaces based on intuitionistic fuzzy points. weprove some properties of intuitionistic fuzzy quasi- metric and pseudo-metricspaces, and show that every intuitionistic fuzzy pseudo-metric space is intuitionisticfuzzy regular and intuitionistic fuzzy completely normal and henceintuitionistic fuzzy nor...

A number of recent works have emphasized the prominent role played by the KurdykaLojasiewicz inequality for proving the convergence of iterative algorithms solving possibly nonsmooth/nonconvex optimization problems. In this work, we consider the minimization of an objective function satisfying this property, which is a sum of a non necessarily convex differentiable function and a non necessaril...

Inspired by the recent paper (L. Ying, Journal of Scientific Computing, 84, 1–14 (2020), we explore relationship between mirror descent and variable metric method. When in decent is induced a convex function, whose Hessian close to objective this method enjoys both robustness from superlinear convergence for Newton type methods. applied linearly constrained minimization problem, prove global lo...

This paper deals with a nonparametric shape respecting estimation method for U-shaped or unimodal functions. A general upper bound for the nonasymptotic L1-risk of the estimator is given. The method is applied to the shape respecting estimation of several classical functions, among them typical intensity functions encountered in the reliability field. In each case, we derive from our upper boun...