查看更多>>摘要：A concordance measure is often a better way to model dependence than Pearson's correlation coefficient since it is invariant with respect to monotone increasing transformations of the random variables. In this paper, we focus on the relationships between Gini's gamma and Spearman's footrule. We establish the exact region determined by them. We also present copulas where the bounds of the region are attained. We introduce the concordance similarity measure and compute it for all pairs of (weak) concordance measures for which the exact regions determined by them are known. (c) 2022 Elsevier B.V. All rights reserved.
Jothilakshmi, G.Vadivoo, B. S.Almalki, Y.Debbouche, A....
14页
查看更多>>摘要：We study the controllability criteria for a class of fractional integro-differential damped systems with impulsive perturbations. The solution representation is derived for both linear and non-linear damped systems, and the introduced formulations were constructed by employing Laplace transformation with Mittag-Leffler matrix function. We present necessary and sufficient conditions for the indicated systems to be controllable in finite dimensional spaces. Here, controllability conditions are proposed by using Grammian matrix as a powerful tool. Two numerical examples are also given at end to demonstrate the obtained theory. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：In this paper, we establish multiobjective approximate gradient projection (MAGP) and linear multiobjective approximate gradient projection (LMAGP) sequential optimality conditions without constraint qualification for multiobjective constrained optimization problems. Further, we introduce constraint qualifications under which a point that satisfies established optimality conditions also satisfies Karush-Kuhn-Tucker optimality conditions. Such constraint qualifications are called strict constraint qualifications. We discuss the relationship between introduced constraint qualifications and validate it by suitable examples. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：In this paper, the multivariate tail covariance (MTCov) for generalized skew-elliptical distributions is considered. Some special cases for this distribution, such as generalized skew-normal, generalized skew student -t, generalized skew-logistic and generalized skew-Laplace distributions, are also considered. In order to test the theoretical feasibility of our results, the MTCov for skewed and non skewed normal distributions is computed and compared. Finally, we give a special formula of the MTCov for generalized skew-elliptical distributions. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：The Inexact Restoration approach has proved to be an adequate tool for handling the problem of minimizing an expensive function within an arbitrary feasible set by using different degrees of precision. This framework allows one to obtain suitable convergence and complexity results for an approach that rationally combines low-and high-precision evaluations. In this paper we consider the case where the domain of the optimization problem is an abstract metric space. Assumptions about differentiability or even continuity will not be used in the general algorithm based on Inexact Restoration. Although optimization phases that rely on smoothness cannot be used in this case, basic convergence and complexity results are recovered. A new derivative-free optimization phase is defined and the subproblems that arise at this phase are solved using a regularization approach that takes advantage of different notions of stationarity. The new methodology is applied to the problem of reproducing a controlled experiment that mimics the failure of a dam. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：In this paper, we propose a Virtual Element Method (VEM) for the Laplacian eigenvalue problem, which is designed to avoid the requirement of the stabilization terms in standard VEM bilinear forms. In the present method, the constructions of the bilinear forms depend on higher order polynomial projection. To exactly compute the bilinear forms, we need to modify the virtual element space associated to the higher order polynomial projection. Meanwhile, the continuity and coercivity of the discrete VEM bilinear forms depend on the number of vertices of the polygon. By the spectral approximation theory of compact operator and the projection and interpolation error estimates, we prove correct spectral approximation and error estimates for the VEM discrete scheme. Finally, we show numerical examples to verify the theoretical results, including the Laplace eigenvalue problem and the Steklov eigenvalue problem. (c) 2021 Elsevier B.V. All rights reserved.
查看更多>>摘要：The paper develops a first-order random coefficient mixed-thinning integer-valued autoregressive time series model (RCMTINAR(1)) to deal with the data related to the counting of elements of variable character. Moments and autocovariance functions for this model are studied as the distribution of the innovation sequence is unknown. The conditional least squares and modified quasi-likelihood are adopted to estimate the model parameters. Asymptotic properties of the obtained estimators are established. The performances of these estimators are investigated and compared with false mod-ified quasi-likelihood via simulations. Finally, the practical relevance of the model is illustrated by using two applications to a SIMpass data set and a burglary data set with a comparison with relevant models that exist so far in the literature. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：Accurate spatio-temporal traffic data is crucial to intelligent transportation systems. Missing traffic data is an important problem to solve. Low-rank matrix completion provides an effective way to find the missing data. The completion aims to obtain a low-rank matrix that can approximate the known entries as far as possible. Meanwhile, some linear constraint marginal information of the matrix can also be observed in the real application. In this paper, we utilize such marginal information to largely improve the performance of common matrix completion algorithms and propose an alternating direction method of multipliers (ADMM) and conjugate gradient descent method (CGD) based SoftImpute alternative least square (ALS) algorithm. We analyze their convergence rates and prove that the model can always converge to a first-order stationary point. We also utilize ADMM and CGD to largely accelerate the subproblem and make its complexity of each iteration at the same level as the popular SoftImpute-ALS matrix completion algorithm. Furthermore, this algorithm can be used in distributed computation, suitable for large-scale problems. In the numerical experiments, we demonstrate its outstanding matrix completion performance and high speed in several traffic matrix datasets. (c) 2022 Elsevier B.V. All rights reserved.
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