in this paper‎, ‎following a very recent‎ ‎and new approach‎, ‎we further generalize recently introduced‎ ‎summability methods‎, ‎namely‎, ‎$i$-statistical convergence and‎ ‎$i$-lacunary statistical convergence (which extend the important‎ ‎summability methods‎, ‎statistical convergence and lacunary‎ ‎statistical convergence using ideals of $mathbb{n}$) and‎ ‎introduce the notions of $i$-statis...

We focus on a class of non-standard problems involving non-parametric estimation of a monotone function that is characterized by n rate of convergence of the maximum likelihood estimator, non-Gaussian limit distributions and the non-existence of ffiffiffi n p -regular estimators. We have shown elsewhere that under a null hypothesis of the type w(z0) 1⁄4 h0 (w being the monotone function of inte...

Stochastic gradient descent (SGD) is a simple and very popular iterative method to solve stochastic optimization problems which arise in machine learning. A common practice is to return the average of the SGD iterates. While the utility of this is well-understood for general convex problems, the situation is much less clear for strongly convex problems (such as solving SVM). Although the standa...

We introduce a “Coulombian renormalized energy” W which is a logarithmic type of interaction between points in the plane, computed by a “renormalization.” We prove various of its properties, such as the existence of minimizers, and show in particular, using results from number theory, that among lattice configurations the triangular lattice is the unique minimizer. Its minimization in general r...

In the typical type of ANP with a matrix U evaluating alternatives by criteria and a matrix W evaluating criteria by alternatives in the so-called supermatrix S, W is often said to be unstable. Here, we propose a method to revise W into a stable Ŵ and to calculate the weights of criteria and alternatives at the same time under the revised supermatrix Ŝ. Our method is formulated as an optimizati...

Godunov type schemes form a special class of transport projection methods for the approximate solution of nonlinear hyperbolic conservation laws. We study the convergence rate of such schemes in the context of scalar conservation laws. We show how the question of consistency for Godunov type schemes can be answered solely in terms of the behavior of the associated projection operator. Namely, w...

Recently, J. D. Lawson encouraged the domain theory community to consider the scientific program of developing domain theory in the wider context of T0 spaces instead of restricting to posets. In this paper, we respond to this calling by proving a topological parallel of a 2005 result due to B. Zhao and D. Zhao, i.e., an order-theoretic characterisation of those posets for which the lim-inf con...

1. The purpose of this note is to describe the extension of the almost everywhere convergence of Poisson integrals to the case of the bounded symmetric domains of Cartan. It is useful to realize such a bounded symmetric domain D as a generalized upper half-plane (i.e., a Siegel domain of type II), and this is done as follows. Let V1 and V2 be two finite-dimensional vector spaces over C. Assume ...

Word W is said to encounter word V provided there is a homomorphism φ mapping letters to nonempty words so that φ(V) is a substring of W. For example, taking φ such that φ(h) = c and φ(u) = ien, we see that " science " encounters " huh " since cienc = φ(huh). The density of V in W , δ(V, W), is the proportion of substrings of W that are homomorphic images of V. So the density of " huh " in " sc...

Iterative Proportional Fitting (IPF) generates from an input matrix W a sequence of matrices that converges, under certain conditions, to a specific limit matrix Ŵ . This limit is the relative-entropy nearest solution to W among all matrices of prescribed row marginals r and column marginals c. We prove this known fact by a novel strategy that contributes a pure algorithmic intuition. Then we f...