Existence and multiplicity of solutions for an impulsive boundary value problem with a parameter via critical point theory

In this paper, an impulsive boundary value problem with a parameter is considered. By using critical point theory, some criteria are obtained to guarantee that the impulsive problem has at least one solution, two solutions and infinitely many solutions when the parameter lies in different intervals. The results obtained are also valid and new for a problem discussed in the literature.     

Metrical characterization of conformal capacity zero

The introduction of a parameter in the fixed point equation yields a family of operators with identical fixed points. A method is presented for minimization of the Lipschitz constant as a function of this parameter. This yields an operator with an optimal error estimate. The choice of a suitable weighting function is essential. As an example, a nonlinear boundary value problem is treated. A one-sided Lipschitz condition is sufficient.     

Fuzzy criteria for feature selection

The presence of less relevant or highly correlated features often decrease classification accuracy. Feature selection in which most informative variables are selected for model generation is an important step in data-driven modeling. In feature selection, one often tries to satisfy multiple criteria such as feature discriminating power, model performance or subset cardinality. Therefore, a multi-objective formulation of the feature selection problem is more appropriate. In this paper, we propose to use fuzzy criteria in feature selection by using a fuzzy decision making framework. This formulation allows for a more flexible definition of the goals in feature selection, and avoids the problem of weighting different goals is classical multi-objective optimization. The optimization problem is solved using an ant colony optimization algorithm proposed in our previous work. We illustrate the added value of the approach by applying our proposed fuzzy feature selection algorithm to eight benchmark problems.     

An adaptive weighting parameter estimation between local and global intensity fitting energy for image segmentation

Local and global intensity fitting energy are widely used for image segmentation. In order to improve the segmentation quality in the presence of intensity inhomogeneity, in this paper, we propose a new adaptive rule for obtaining weighting parameter estimation between the local and global intensity fitting energy. Following the minimization of the energy functional, the value of the weighting parameter is dynamically updated with the contour evolution, which is effective and accurate for extracting the object.     

Global multi-parametric optimal value bounds and solution estimates for separable parametric programs

In this tutorial, a strategy is described for calculating parametric piecewise-linear optimal value bounds for nonconvex separable programs containing several parameters restricted to a specified convex set. The methodology is based on first fixing the value of the parameters, then constructing sequences of underestimating and overestimating convex programs whose optimal values respectively increase or decrease to the global optimal value of the original problem. Existing procedures are used for calculating parametric lower bounds on the optimal value of each underestimating problem and parametric upper bounds on the optimal value of each overestimating problem in the sequence, over the given set of parameters. Appropriate updating of the bounds leads to a nondecreasing sequence of lower bounds and a nonincreasing sequence of upper bounds, on the optimal value of the original problem, continuing until the bounds satisfy a specified tolerance at the value of the parameter that was fixed at the outset. If the bounds are also sufficiently tight over the entire set of parameters, according to criteria specified by the user, then the calculation is complete. Otherwise, another parameter value is selected and the procedure is repeated, until the specified criteria are satisfied over the entire set of parameters. A parametric piecewise-linear solution vector approximation is also obtained. Results are expected in the theory, computations, and practical applications. The general idea of developing results for general problems that are limits of results that hold for a sequence of well-behaved (e.g., convex) problems should be quite fruitful.     

Oscillation criteria for boundary value problems of high-order partial functional differential equations

In this paper, a class of boundary value problems associated with high-order partial functional differential equations with distributed deviating arguments is investigated. Some oscillation criteria of solutions to the problem are developed. Our approach is to reduce the multi-dimensional oscillation problem to a one-dimensional oscillation one by employing some integral means of solutions and introducing some parameter functions. One illustrative example is considered.     

Asymptotical stability of partial difference equations with variable coefficients

The dynamical systems considered have scalar state, are multivariate, linear, time-discrete, and time-variable and are described by an initial value problem for a class of evolutionary partial difference equations. The time set is the nonnegative part of the integer lattice in several dimensions. Parts of the asymptotical stability set in the parameter space spanned by the time-variable coefficients are explicitly found. To assess the quality of the sufficient stability criteria, a comparison with the exact stability set is made in an example.     

The optimal value and optimal solutions of the proximal average of convex functions

The proximal average of a finite collection of convex functions is a parameterized convex function that provides a continuous transformation between the convex functions in the collection. This paper analyzes the dependence of the optimal value and the minimizers of the proximal average on the weighting parameter. Concavity of the optimal value is established and implies further regularity properties of the optimal value. Boundedness, outer semicontinuity, single-valuedness, continuity, and Lipschitz continuity of the minimizer mapping are concluded under various assumptions. Sharp minimizers are given further attention. Several examples are given to illustrate our results.     

基于FCM-组合赋权的难采储量分类

目前储量的分类标准是通过划分指标值的范围来确定的,这就要求所有指标值恰好符合既定的指标范围,否则难以划分储量类别。为克服这一问题,本文结合模糊c-均值算法和组合赋权法实现难采储量的分类。首先基于效益指标运用模糊c-均值算法自动搜索储量的最佳类别,再利用主客观赋权偏差最小的思想,构建组合赋权模型,确定属性指标的权重,并计算储量效益指标值,结合模糊c-均值结果判别难采储量类别。最后以大庆某油田为实例,对其难采储量进行了分类,有效指导难采储量滚动开发决策。     

A sequential method for a class of box constrained quadratic programming problems

The aim of this paper is to propose an algorithm, based on the optimal level solutions method, which solves a particular class of box constrained quadratic problems. The objective function is given by the sum of a quadratic strictly convex separable function and the square of an affine function multiplied by a real parameter. The convexity and the nonconvexity of the problem can be characterized by means of the value of the real parameter. Within the algorithm, some global optimality conditions are used as stopping criteria, even in the case of a nonconvex objective function. The results of a deep computational test of the algorithm are also provided. This paper has been partially supported by M.I.U.R.     

Existence and multiplicity results for nonlinear periodic boundary value problems

This paper studies the existence of positive solutions for periodic boundary value problems. The criteria for the existence, nonexistence and multiplicity of positive solutions are established by using the Global continuation theorem, fixed point index theory and approximate method. The results obtained herein generalize and complement some previous findings of [J.R. Graef, L. Kong, H. Wang, Existence, multiplicity, and dependence on a parameter for a periodic boundary value problem, J. Differential Equations 245 (2008) 1185–1197] and some other known results.     

On Eigenvalue Intervals and Eigenfunctions of Nonresonance Singular Dirichlet Boundary Value Problems

In this paper we shall consider the nonresonance Dirichlet boundary value problemwhere λ>0 is a parameter, p>0 is a constant. Intervals of A are determined to ensure the existence of a nonnegative solution of the boundary value problem. For λ=1, we shall also offer criteria for the existence of eigenfunctions. The main results include and improve on those of [2,4,6,8].     

Robustness and information levels in case-based multiple criteria sorting

Case-based preference elicitation methods for multiple criteria sorting problems have the advantage of posing rather small cognitive demands on a decision maker, but they may lead to ambiguous results when preference parameters are not uniquely determined. We use a simulation approach to determine the extent of this problem and to study the impact of additional case information on the quality of results. Our experiments compare two decision analysis tools, case-based distance sorting and the simple additive weighting method, in terms of the effects of additional case information on sorting performance, depending on problem dimension – number of groups, number of criteria, etc. Our results confirm the expected benefit of additional case information on the precision of estimates of the decision maker’s preferences. Problem dimension, however, has some unexpected effects.     

Estimating satisfiability

The problem of estimating the proportion of satisfiable instances of a given CSP (constraint satisfaction problem) can be tackled through weighting. It consists in putting onto each solution a non-negative real value based on its neighborhood in a way that the total weight is at least 1 for each satisfiable instance. We define in this paper a general weighting scheme for the estimation of satisfiability of general CSPs. First we give some sufficient conditions for a weighting system to be correct. Then we show that this scheme allows for an improvement on the upper bound on the existence of non-trivial cores in 3-SAT obtained by Maneva and Sinclair (2008) [17] to 4.419. Another more common way of estimating satisfiability is ordering. This consists in putting a total order on the domain, which induces an orientation between neighboring solutions in a way that prevents circuits from appearing, and then counting only minimal elements. We compare ordering and weighting under various conditions.     

New delay‐dependent global robust passivity analysis for stochastic neural networks with Markovian jumping parameters and interval time‐varying delays

The problem of passivity analysis for stochastic neural networks with Markovian jumping parameters and interval time‐varying delays is investigated in this article. By constructing a novel Lyapunov–Krasovskii functional based on the complete delay‐decomposing idea and using improved free‐weighting matrix method, some improved delay‐dependent passivity criteria are established in terms of linear matrix inequalities. Numerical examples are also given to show the effectiveness of the proposed methods. © 2015 Wiley Periodicals, Inc. Complexity 21: 167–179, 2016     

Offline and online weighted least squares estimation of nonstationary power ARCH processes

This paper proposes two estimation methods based on a weighted least squares criterion for non-(strictly) stationary power ARCH models. The weights are the squared volatilities evaluated at a known value in the parameter space. The first method is adapted for fixed sample size data while the second one allows for online data available in real time. It will be shown that these methods provide consistent and asymptotically Gaussian estimates having asymptotic variance equal to that of the quasi-maximum likelihood estimate (QMLE) regardless of the value of the weighting parameter. Finite-sample performances of the proposed WLS estimates are shown via a simulation study for various sub-classes of power ARCH models.     

Improved delay-dependent stability criteria for continuous system with two additive time-varying delay components

This paper investigated the problem of improved delay-dependent stability criteria for continuous system with two additive time-varying delay components. Free weighting matrices and convex combination method are not involved, which achieves much less numbers of linear matrix inequalities (LMIs) and LMIs scalar decision variables. By taking advantage of integral inequality and new Lyapunov–Krasovskii functional, new less conservative delay-dependent stability criterion is derived. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.     

Weighted linear associative memory approach to nonlinear parameter estimation

The method of linear associative memory (LAM), a notion from the field of artificial neural nets, has been applied recently in nonlinear parameter estimation. In the LAM method, a model response, nonlinear with respect to the parameters, is approximated linearly by a matrix, which maps inversely from a response vector to a parameter vector. This matrix is determined from a set of initial training parameter vectors and their response vectors, and can be update recursively and adaptively with a pair of newly generated parameter response vectors. The LAM advantage is that it can yield a good estimation of the true parameters from a given observed response, even if the initial training parameter vectors are far from the true values.In this paper, we present a weighted linear associative memory (WLAM) for nonlinear parameter estimation. WLAM improves LAM by taking into account an observed response vector oriented weighting. The basic idea is to weight each pair of parameter response vectors in the cost function such that, if a response vector is closer to the observed one, then this pair plays a more important role in the cost function. This weighting algorithm improves significantly the accuracy of parameter estimation as compared to a LAM without weighting. In addition, we are able to construct the associative memory matrix recursively, while taking the weighting procedure into account, and simultaneously update the ridge parameter of the cost function further improving the efficiency of the WLAM estimation. These features enable WLAM to be a powerful tool for nonlinear parameter simulation.This work was supported by National Science Foundation, Grants BCS-93-15886 and INT-94-17206. We thank Mr. L. Yobas for fruitful discussions.     

A local search method for optimization problem with d.c. inequality constraints

This paper addresses a nonconvex optimization problem with the cost function and inequality constraints given by d.c. functions. The original problem is reduced to a problem without inequality constraints by the exact penalization procedure. A special local search method for the penalized problem is developed, which is based, first, on the linearization procedure with respect to the basic nonconvexity and, second, on the consecutive solutions of linearized convex problems. Convergence properties of the method are investigated. In particular, it is shown that a limit point of the sequence produced by the method is considerably stronger than the usual KKT-vector.In addition, the relations between an approximate solution of linearized convex problem and the KKT-vector of the original problem are established, and the various stopping criteria are substantiated. Besides, we established the relations among the Lagrange multipliers of the original problem, those ones of the linearized problem, and the value of the penalty parameter. Finally, a preliminary computational testing of the LSM developed has been carried out on several test problems taken from literature.     

一种基于前景理论和改进TOPSIS的模糊随机多准则决策方法及其应用

针对准则值为区间灰数直觉模糊数、准则权系数部分已知以及自然状态出现概率为灰数的多准则决策问题,提出一种结合前景理论和改进TOPSIS的决策方法。该方法首先定义了灰色直觉模糊数的前景价值函数和概率权重函数,并利用前景理论构建出前景决策矩阵;接着从两个方面对传统TOPSIS决策方法进行改进:(1)过定义方案间综合差异的概念,采用离差最大化思想,建立平均综合差异最大化规划模型,给出了一种兼顾主客观权重信息确定准则权系数的新方法;(2)用灰关联替换备选方案与正负理想方案的距离,据此刻画了各方案与正负理想方案的贴近度。进而利用改进TOPSIS决策方法中的综合贴近度对方案进行了排序。最后通过实例验证了该方法的有效性。