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The main objective of the paper is to highlight the impact of agricultural performance on working poverty in the EU countries, in the 2008–2013 period, for identifying some measures that need to be ta...
We describe a potential reduction method for convex optimization problems involving matrix inequalities. The method is based on the theory developed by Nesterov and Nemirovsky and generalizes Gonzaga ...
Large scale wind turbines are lightly damped mechanical structures driven by wind that is constantly fluctuating. In this paper, we address the design of a model-based receding horizon control scheme ...
“Knowledge of either analytical or numerical approximations should enable more efficient simulation estimators to be constructed.” This principle seems intuitively plausible and certainly attractive, ...
We discuss methods for solving the key linear equations within primal-dual barrier methods for linear and quadratic programming. Following Freund and Jarre, we explore methods for reducing the Newto...
We address the persistence and stability of three-dimensional vortex configurations in the discrete nonlinear Schrödinger equation and develop a symbolic package based on Wolfram’s MATHEMATICA fo...
The disaster management literature is replete with surveys at the community and household levels. However, few exist at the organizational level. This study attempts to fill this void by examining the...
Most business processes are, by nature, multivariate and autocorrelated. Highdimensionality is rooted in processes where more than one variable is considered simultaneously to provide a more comprehen...
Industrialization of agriculture has a number of comparative advantages however adverse implications such as environmental impacts, health hazards, GHG emissions establish the need to identify pathway...
In recent years, manifold learning has become increasingly popular as a tool for performing non-linear dimensionality reduction. This has led to the development of numerous algorithms of varying degre...
A numerical method is proposed to approximate the inverse of a general bi-Lipschitz nonlinear dimensionality reduction mapping, where the forward and consequently the inverse mappings are only explici...
Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variabilit...
现有基于Lattice Reduction (LR)技术的多输入多输出(MIMO)系统检测算法,虽然可以有效地提高MIMO系统的误比特率(BER)性能,但其检测性能与最优的最大似然(ML)算法相比仍然存在差距。针对这一问题,提出了一种新的基于信道分组的线性Lattice Reduction辅助检测算法。该算法首先将信道分为两组,对通过条件最差子信道的信号采用最优的ML算法检测,然后将其从接收到的信...
In multivariate nonparametric analysis, sparseness of the co- variates also called curse of dimensionality, forces one to use large smoothing parameters. This leads to a biased smoother. Instead of ...
A collaborative convex framework for factoring a data matrix $X$ into a non-negative product $AS$, with a sparse coefficient matrix $S$, is proposed. We restrict the columns of the dictionary matrix $...

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