kernel function

Multivariate regression is an approach for modeling the linear relationship between several variables. This paper proposed a ridge methodology with a kernel-based weighted absolute error target with exact predictors and fuzzy responses. Some standard goodness-of-fit criteria were also used to examine the performance of the proposed method. The effectiveness of the proposed method was then illustrated through two numerical examples including a simulation study. The effectiveness and advantages of the proposed fuzzy multiple linear regression model were also examined and compared with some well-established methods through some common goodness-of-fit criteria. The numerical results indicated that our prediction/estimation gives more accurate results in cases where multicollinearity and/or outliers occur in the data set.

In this paper, we present primal-dual interior-point methods (IPMs) for convex quadratic programming (CQP) based on a new twice parameterized kernel function (KF) with a hyperbolic barrier term.  To our knowledge, this is the first KF with a twice parameterized hyperbolic barrier term. By using some conditions and simple analysis, we derive the currently best-known iteration bounds for large- and small-update methods, namely, $\textbf{O}\big(\sqrt{n}\log n\log\frac{n}{\epsilon}\big)$ and $\textbf{O}\big(\sqrt{n}\log\frac{n}{\epsilon}\big)$, respectively, with  special choices of the parameters. Finally, some numerical results regarding the practical performance of the new proposed KF are reported.

The purpose of this paper is to obtain new complexity results for solving the semidefinite optimization (SDO) problem. We define a new proximity function for the SDO by a new kernel function with an efficient logarithmic barrier term. Furthermore, we formulate an algorithm for the large and small-update primal-dual interior-point method (IPM) for the SDO. It is shown that the best result of iteration bounds for large-update methods and small-update methods can be achieved, namely $\mathcal{O}\left(qn^{\frac{q+1}{2q}}\log \frac{n}{\epsilon }\right) $\ for large-update and $\mathcal{O}(q^{2}\sqrt{n}\log \frac{n}{\epsilon })$ for small-update methods, where $q>1.$ The analysis in this paper is new and different from the one using for LO. Several new tools and techniques are derived in this paper. Furthermore, numerical tests to investigate the behavior of the algorithm so as to be compared with other approaches.

Parametric time series models   typically consists of model identification, parameter estimation, model diagnostic checking, and forecasting. However compared with parametric methods, nonparametric time series models often provide  a very flexible approach to bring out the features of the observed time series. This paper suggested a novel fuzzy nonparametric method in time series models with fuzzy observations. For this purpose, a fuzzy forward fit kernel-based smoothing method was introduced to estimate fuzzy smooth functions corresponding to each observation. A simple optimization algorithm was also suggested to evaluate optimal bandwidths and autoregressive order. Several common goodness-of-fit criteria were also extended to compare the performance of the proposed fuzzy time series method compared to other fuzzy time series model based on fuzzy data. Furthermore, the effectiveness of the proposed method was illustrated through two numerical examples including a simulation study. The results indicate that the proposed model performs better than the previous ones in terms of both scatter plot criteria and goodness-of-fit evaluations.

In this paper, a new approach is presented to fit arobust fuzzy regression model based on some fuzzy quantities. Inthis approach, we first introduce a new distance between two fuzzynumbers using the kernel function, and then, based on the leastsquares method, the parameters of fuzzy regression model isestimated. The proposed approach has a suitable performance topresent the robust fuzzy model in the presence of different typesof outliers. Using some simulated data sets and some real datasets, the application of the proposed approach in modeling somecharacteristics with outliers, is studied.

An infeasible interior-point algorithm for solving the $P_*$-matrix linear complementarity problem based on a kernel function with trigonometric barrier term is analyzed. Each (main) iteration of the algorithm consists of a feasibility step and several centrality steps, whose feasibility step is induced by a trigonometric kernel function. The complexity result coincides with the best result for infeasible interior-point methods for $P_*$-matrix linear complementarity problem.

In this paper, an interior-point algorithm for P∗(κ)-Linear Complementarity Problem (LCP) based on a new parametric trigonometric kernel function is proposed. By applying strictly feasible starting point condition and using some simple analysis tools, we prove that our algorithm has O((1κ)nlog⁡nlog⁡nϵ) iteration bound for large-update methods, which coincides with the best known complexity bound. Moreover, numerical results confirm that our new proposed kernel function is doing well in practice in comparison with some existing kernel functions in the literature.

In this paper, we consider convex quadratic semidefinite optimization problems and provide a primal-dual Interior Point Method (IPM) based on a new kernel function with a trigonometric barrier term. Iteration complexity of the algorithm is analyzed using some easy to check and mild conditions. Although our proposed kernel function is neither a Self-Regular (SR) function nor logarithmic barrier function, the primal-dual IPMs based on this kernel function enjoy the worst case iteration bound $Oleft(sqrt{n}log nlog frac{n}{epsilon}right)$ for the large-update methods with the special choice of its parameters. This bound coincides to the so far best known complexity results obtained from SR kernel functions for linear and semidefinite optimization problems. Finally some numerical issues regarding the practical performance of the new proposed kernel function is reported.