Globally Convergent Variable Metric Method for Nonconvex Nondifferentiable Unconstrained Minimization

A special variable metric method is given for finding the stationary points of locally Lipschitz continuous functions which are not necessarily convex or differentiable. Time consuming quadratic programming subproblems do not need to be solved. Global convergence of the method is established. Some encouraging numerical experience is reported.     

Globally Convergent Variable Metric Method for Convex Nonsmooth Unconstrained Minimization1

A special variable metric method is given for finding minima of convex functions that are not necessarily differentiable. Time-consuming quadratic programming subproblems do not need to be solved. Global convergence of the method is established. Some encouraging numerical experience is reported.     

Convergence of the Gradient Projection Method for Generalized Convex Minimization

This paper develops convergence theory of the gradient projection method by Calamai and Moré (Math. Programming, vol. 39, 93–116, 1987) which, for minimizing a continuously differentiable optimization problem min{f(x) : x } where is a nonempty closed convex set, generates a sequence x_k+1 = P(x_k – _k f(x_k)) where the stepsize _k > 0 is chosen suitably. It is shown that, when f(x) is a pseudo-convex (quasi-convex) function, this method has strong convergence results: either x_k x^* and x^* is a minimizer (stationary point); or x_k arg min{f(x) : x } = , and f(x_k) inf{f(x) : x }.     

Global Minimization via Piecewise-Linear Underestimation

Given a function on Rⁿ with many multiple local minima we approximate it from below, via concave minimization, with a piecewise-linear convex function by using sample points from the given function. The piecewise-linear function is then minimized using a single linear program to obtain an approximation to the global minimum of the original function. Successive shrinking of the original search region to which this procedure is applied leads to fairly accurate estimates, within 0.57%, of the global minima of synthetic nonconvex piecewise-quadratic functions for which the global minima are known exactly.     

Nonconvex Piecewise-Quadratic Underestimation for Global Minimization

Motivated by the fact that important real-life problems, such as the protein docking problem, can be accurately modeled by minimizing a nonconvex piecewise-quadratic function, a nonconvex underestimator is constructed as the minimum of a finite number of strictly convex quadratic functions. The nonconvex underestimator is generated by minimizing a linear function on a reverse convex region and utilizes sample points from a given complex function to be minimized. The global solution of the piecewise-quadratic underestimator is known exactly and gives an approximation to the global minimum of the original function. Successive shrinking of the initial search region to which this procedure is applied leads to fairly accurate estimates, within 0.0060%, of the global minima of synthetic nonconvex functions for which the global minima are known. Furthermore, this process can approximate a nonconvex protein docking function global minimum within four-figure relative accuracy in six refinement steps. This is less than half the number of refinement steps required by previous models such as the convex kernel underestimator (Mangasarian et al., Computational Optimization and Applications, to appear) and produces higher accuracy here.     

A Decomposition Method for Global and Local Quadratic Minimization

We present a decomposition method for indefinite quadratic programming problems having n variables and m linear constraints. The given problem is decomposed into at most m QP subproblems each having m linear constraints and n-1 variables. All global minima, all isolated local minima and some of the non-isolated local minima for the given problem are obtained from those of the lower dimensional subproblems. One way to continue solving the given problem is to apply the decomposition method again to the subproblems and repeatedly doing so until subproblems of dimension 1 are produced and these can be solved directly. A technique to reduce the potentially large number of subproblems is formulated.     

时滞Hopfield神经网络模型的全局吸引性和全局指数稳定性

对具有时滞的Hopfield神经网络模型,在非线性神经元激励函数是Lipschitz连续(而非已有的大部分文献中假设是Sigmoid函数)的条件下,通过构造适当的泛函,给出了这类模型全局吸引和平衡点全局指数稳定的易于验证的充分条件。     

Lienard方程全局稳定性的条件

在本文中，利用Ｆｉｌｉｐｐｏｖ变换，我们给出了判定Ｌｉｅｎａｒｄ系统的原点是全局稳定的两个准则．     

Global Stability for the Cauchy Problem of a Class of Reaction-diffusion Systems

By using invariant region argument together with Liapunov function technique for ODE, we give under certain circumstances a global stability analysis of the solutions for Cauchy problem of reaction-diffusion systems.     

生态扩散系统全局渐近稳定的条件

本文研究一类带扩散的非自治捕食系统,该系统由ｎ个斑块组成,食饵种群可以在ｎ个斑块之间扩散,而捕食者种群限定在一个斑块不能扩散．得到系统持续生存和全局渐近稳定的条件．     

时滞差分方程零解的全局渐近稳定性

The difference equation△x_n p_nx_(n-k)=f(n,x_(n-11),…,x_(n-1m),n= 0,1,2,…is considered,where{Pn}is a sequence of nonnegative real numbers,m∈{1,2,,…),k,l_1,…,l_m∈{0,1,2,,…}.Some sufficient conditions for the global asymp- totic stability of zero solution of the equation are obtained.     

变时滞Hopfield神经网络的全局吸引性和全局指数稳定性

对一类具时滞的Hopfeild型神经网络模型,在非线性神经元激励函数只要求满足Lipschitz连续的条件下,利用推广的Halanay时延微分析不等式、Dini导数以及泛函微分析技术,给出了这类模型的平衡点全局指数稳定性和全局吸引性的充分条件,这些条件易于检验,且改进和推广了前人的结论.此外,此文给出了研究神经网络模型的全局吸引性的微分不等式比较方法.     

具有扩散影响的Hopfield型神经网络的全局渐近稳定性

对具有扩散影响的Hopfield型神经网络平衡点的存在唯一性和全局渐近稳定性进行了研究．在激活函数单调非减、可微且关联矩阵和Liapunov对角稳定矩阵有关时,利用拓扑度理论得到了系统平衡点存在的充分条件．通过构造适当的平均Liapunov函数,分析了系统平衡点的全局渐近稳定性．所得结论表明系统的平衡点(如果存在)是全局渐近稳定的而且也蕴含着系统的平衡点的唯一性．     

一类新的求全局优化问题的填充函数

填充函数方法是一种寻找全局极小解的有效方法.本文首先对现有的填充函数进行研究分析,然后构造出一类新的填充函数,设计算法,并通过数值试验验证了该函数和算法的有效性.     

Global Asymptotic Stability for a Class of Nonlinear Populations Model

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二阶非线性微分方程零解的全局渐近稳定性

本文研究了具有四个非线性项零解的全局渐近稳定性。     

广义的Lotka—Volterra方程的全局渐近稳定性

通过构造李亚普诺夫函数的方法,研究了广义的Lotka—Volterra时滞模型方程,而且给出了正平衡点的全局渐近稳定性的充分必要条件,同时对前人的结果进行了改进和推广．     

对延迟细胞神经网络全局渐近稳定性一个结果的改进

给出了延迟细胞神经网络全局渐近稳定性的一个新的充分条件,该条件改进并推广了已有文献中的结论.     

高等数学的阅读能力培养初探

认识高等数学阅读能力的重要性、激发大学生对高数学阅读的兴趣、提供数学阅读的具体方法是培养高等数学阅读能力的措施.其中,数学阅读的具体方法有:带有问题阅读、督促学生作读书笔记、个别指导、组织课堂讨论和经验交流、及时小结、合理评价等.高等数学教师应该努力探寻提高大学生数学阅读能力的有效方法.     

切换系统的全局指数稳定性

提出了一种确定切换系统稳定性分析的方法.引入了两个相关的实例(非完整系统和约束摆)进行说明.用有限个模型的集合组成非线性模型,且切换序列可以是任意的.假定在切换瞬间状态不出现跳跃,并且不出现Zeno现象,即在每个有界时间段上,切换次数是有限的.在对所确定切换系统的分析中,应用了多次Liapunov函数,并证明了全局指数稳定性.系统的指数稳定性平衡关系到实际应用,因为这样的系统有着更强健的抗干扰能力.