Numerical Analysis of a High-Order Scheme for Nonlinear Fractional Differential Equations with Uniform Accuracy

We introduce a high-order numerical scheme for fractional ordinary differential equations with the Caputo derivative. The method is developed by dividing the domain into a number of subintervals, and applying the quadratic interpolation on each subinterval. The method is shown to be unconditionally stable, and for general nonlinear equations, the uniform sharp numerical order 3 − $ν$ can be rigorously proven for sufficiently smooth solutions at all time steps. The proof provides a general guide for proving the sharp order for higher-order schemes in the nonlinear case. Some numerical examples are given to validate our theoretical results.     

k-均值问题的差分隐私算法综述

$k$-均值问题是机器学习和组合优化领域十分重要的问题。它是经典的NP-难问题, 被广泛的应用于数据挖掘、企业生产决策、图像处理、生物医疗科技等领域。随着时代的发展, 人们越来越注重于个人的隐私保护:在决策通常由人工智能算法做出的情况下, 如何保证尽可能多地从数据中挖掘更多信息,同时不泄露个人隐私。近十年来不断有专家学者研究探索带隐私保护的$k$-均值问题, 得到了许多具有理论指导意义和实际应用价值的结果, 本文主要介绍关于$k$-均值问题的差分隐私算法供读者参考。     

随机Bregman ADMM及其在训练具有离散结构的支持向量机中的应用

针对具有多块可分结构的非凸优化问题提出了一类新的随机Bregman交替方向乘子法,在周期更新规则下, 证明了该算法的渐进收敛性; 在随机更新的规则下, 几乎确定的渐进收敛性得以证明。数值实验结果表明, 该算法可有效训练具有离散结构的支持向量机。     

Convergence,boundedness, and ergodicity of regime-switching diusion processes with infinite memory

We study a class of diffusion processes, which are determined by solutions X(t) to stochastic functional differential equation with infinite memory and random switching represented by Markov chain Λ(t): Under suitable conditions, we investigate convergence and boundedness of both the solutions X(t) and the functional solutions X_t: We show that two solutions (resp., functional solutions) from different initial data living in the same initial switching regime will be close with high probability as time variable tends to infinity, and that the solutions (resp., functional solutions) are uniformly bounded in the mean square sense. Moreover, we prove existence and uniqueness of the invariant probability measure of two-component Markov-Feller process (X_t,Λ(t)); and establish exponential bounds on the rate of convergence to the invariant probability measure under Wasserstein distance. Finally, we provide a concrete example to illustrate our main results.     

微重力环境下无限长柱体内角毛细流动解析近似解研究

应用同伦分析法研究无限长柱体内角毛细流动解析近似解问题,给出了级数解的递推公式.不同于其他解析近似方法,该方法从根本上克服了摄动理论对小参数的过分依赖,其有效性与所研究的非线性问题是否含有小参数无关,适用范围广.同伦分析法提供了选取基函数的自由,可以选取较好的基函数,更有效地逼近问题的解,通过引入辅助参数和辅助函数来调节和控制级数解的收敛区域和收敛速度,同伦分析法为内角毛细流动问题的解析近似求解开辟了一个全新的途径.通过具体算例,将同伦分析法与四阶龙格库塔方法数值解做了比较,结果表明,该方法具有很高的计算精度.     

Lower and upper orientable strong radius and strong diameter of complete k-partite graphs

For two vertices u and v in a strong digraph D, the strong distance sd(u,v) between u and v is the minimum size (the number of arcs) of a strong sub-digraph of D containing u and v. For a vertex v of D, the strong eccentricity se(v) is the strong distance between v and a vertex farthest from v. The strong radius srad(D) (resp. strong diameter sdiam(D)) is the minimum (resp. maximum) strong eccentricity among the vertices of D. The lower (resp. upper) orientable strong radius srad(G) (resp. SRAD(G)) of a graph G is the minimum (resp. maximum) strong radius over all strong orientations of G. The lower (resp. upper) orientable strong diameter sdiam(G) (resp. SDIAM(G)) of a graph G is the minimum (resp. maximum) strong diameter over all strong orientations of G. In this paper, we determine the lower orientable strong radius and diameter of complete k-partite graphs, and give the upper orientable strong diameter and the bounds on the upper orientable strong radius of complete k-partite graphs. We also find an error about the lower orientable strong diameter of complete bipartite graph K_m,n given in [Y.-L. Lai, F.-H. Chiang, C.-H. Lin, T.-C. Yu, Strong distance of complete bipartite graphs, The 19th Workshop on Combinatorial Mathematics and Computation Theory, 2002, pp. 12-16], and give a rigorous proof of a revised conclusion about sdiam(K_m,n).     

On the Convergence of Sampling-Based Decomposition Algorithms for Multistage Stochastic Programs

The paper presents a convergence proof for a broad class of sampling algorithms for multistage stochastic linear programs in which the uncertain parameters occur only in the constraint right-hand sides. This class includes SDDP, AND, ReSa, and CUPPS. We show that, under some independence assumptions on the sampling procedure, the algorithms converge with probability 1.The first author acknowledges support by the Swiss National Science Foundation. The second author acknowledges support by NZPGST Grant UOAX0203. The authors are grateful to the anonymous referees for comments improving the exposition of this paper.     

复有理型插值一致逼近函数及其导数

本文研究在单位圆周{|z| =1}上一致逼近函数f(z)及其导数,利用Hermite插值中的基函数建立复有理型插值,并证明它们在{|z| =1}上分别一致收敛于f(z)或f′(z) ,给出了收敛速度.     

Convergence of Galerkin approximations for the Kuramoto‐Tsuzuki equation

Standard Galerkin approximations, using smooth splines to solutions of the Kuramoto‐Tsuzuki equation are analyzed. The existence, uniqueness, and convergence of the fully discrete Crank‐Nicolson scheme are discussed. Furthermore, a second‐order convergent linearized Galerkin approximation are derived. © 2005 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 21, 2005     

一阶捕食-被捕食系统的单调方法

本文利用单调逼近方法,研究带大小结构的捕食-被捕食系统的初边值问题,得到了系统解的存在唯一性,并且证明了序列是线性收敛的.