On the Lipschitz constant of the RSK correspondence

We view the RSK correspondence as associating to each permutation π∈Sn a Young diagram λ=λ(π), i.e. a partition of n. Suppose now that π is left-multiplied by t transpositions, what is the largest number of cells in λ that can change as a result? It is natural refer to this question as the search for the Lipschitz constant of the RSK correspondence.We show upper bounds on this Lipschitz constant as a function of t. For t=1, we give a construction of permutations that achieve this bound exactly. For larger t we construct permutations which come close to matching the upper bound that we prove.     

Semigroups of mappings with rigid Lipschitz constant

It is shown that two well-known uniformly fixed point free lipschitzian semigroups of mappings have minimal Lipschitz constant on the positive part of the unit ball of . This implies that a question raised by T. Kuczumow has a negative answer.

     

A Lipschitz constant associated with lemke's closest-point principle

We give the very simple proof of the Lipschitzian behavior of the solution of a steepestascent problem.     

关于紧集上局部Lipschitz条件的讨论

许多常微分方程教材关于解的整体连续依赖性的讨论都用到了一个"紧性"事实:欧氏空间中的紧集上一个局部Lipschitz函数一定在该紧集上是全局Lipschitz的.然而这一事实在教学中并非显然,不少学生在试图给出证明时都走入了一个误区.本文对这一问题从正反两方面进行了讨论.     

Geometric conditions of differentiability for a regular locally Lipschitz function

The differentiability of a norm of a Banach space may be characterized by its unit sphere. This paper generalizes these geometric conditions of norm's differentiability to the case of a regular locally Lipschitz function. Supported by the National Natural Science Foundation of China     

A univariate global search working with a set of Lipschitz constants for the first derivative

In the paper, a global optimization problem is considered where the objective function f (x) is univariate, black-box, and its first derivative f ′(x) satisfies the Lipschitz condition with an unknown Lipschitz constant K. In the literature, there exist methods solving this problem by using an a priori given estimate of K, its adaptive estimates, and adaptive estimates of local Lipschitz constants. Algorithms working with a number of Lipschitz constants for f ′(x) chosen from a set of possible values are not known in spite of the fact that a method working in this way with Lipschitz objective functions, DIRECT, has been proposed in 1993. A new geometric method evolving its ideas to the case of the objective function having a Lipschitz derivative is introduced and studied in this paper. Numerical experiments executed on a number of test functions show that the usage of derivatives allows one to obtain, as it is expected, an acceleration in comparison with the DIRECT algorithm. This research was supported by the RFBR grant 07-01-00467-a and the grant 4694.2008.9 for supporting the leading research groups awarded by the President of the Russian Federation.     

Lipschitzian optimization without the Lipschitz constant

We present a new algorithm for finding the global minimum of a multivariate function subject to simple bounds. The algorithm is a modification of the standard Lipschitzian approach that eliminates the need to specify a Lipschitz constant. This is done by carrying out simultaneous searches using all possible constants from zero to infinity. On nine standard test functions, the new algorithm converges in fewer function evaluations than most competing methods.The motivation for the new algorithm stems from a different way of looking at the Lipschitz constant. In particular, the Lipschitz constant is viewed as a weighting parameter that indicates how much emphasis to place on global versus local search. In standard Lipschitzian methods, this constant is usually large because it must equal or exceed the maximum rate of change of the objective function. As a result, these methods place a high emphasis on global search and exhibit slow convergence. In contrast, the new algorithm carries out simultaneous searches using all possible constants, and therefore operates at both the global and local level. Once the global part of the algorithm finds the basin of convergence of the optimum, the local part of the algorithm quickly and automatically exploits it. This accounts for the fast convergence of the new algorithm on the test functions.     

Embedding with a Lipschitz function

We investigate a new notion of embedding of subsets of {?1,1}ⁿ in a given normed space, in a way which preserves the structure of the given set as a class of functions on {1, …, n}. This notion is an extension of the margin parameter often used in Nonparametric Statistics. Our main result is that even when considering “small” subsets of {?1, 1}ⁿ, the vast majority of such sets do not embed in a better way than the entire cube in any normed space that satisfies a minor structural assumption. © 2005 Wiley Periodicals, Inc. Random Struct. Alg., 2005     

On using estimates of Lipschitz constants in global optimization

Several authors have proposed estimating Lipschitz constants in global optimization by a multiple of the largest slope (in absolute value) between successive evaluation points. A class of univariate functions is exhibited for which the global optimum will be missed when using such a procedure, even if the multiple is arbitrarily large.Research of the first and third authors was supported by AFOSR Grants 0271 and 0066 to Rutgers University. Research of the second author was supported by NSERC Grant GP0036426 and FCAR Grant 90NC0305.This research was done while the first author was Professor and the third author was Graduate Student at RUTCOR, Rutgers University.     

On Timonov's algorithm for global optimization of univariate Lipschitz functions

Timonov proposes an algorithm for global maximization of univariate Lipschitz functions in which successive evaluation points are chosen in order to ensure at each iteration a maximal expected reduction of the region of indeterminacy, which contains all globally optimal points. It is shown that such an algorithm does not necessarily converge to a global optimum.     

Lipschitz Kernel of a Closed Set-Valued Map

This paper deals with Lipschitz selections of set-valued maps with closed graphs. First, we characterize Lipschitzianity of a closed set-valued map in the differential games framework in terms of a discriminating property of its graph. This allows us to consider the -Lipschitz kernel of a given set-valued map as the largest -Lipschitz closed set-valued map contained in the initial one, to derive an algorithm to compute the collection of Lipschitz selections, and to extend the Pasch–Hausdorff envelope to set-valued maps.     

An algorithm for global optimization of Lipschitz continuous functions

An algorithm is presented which locates the global minimum or maximum of a function satisfying a Lipschitz condition. The algorithm uses lower bound functions defined on a partitioned domain to generate a sequence of lower bounds for the global minimum. Convergence is proved, and some numerical results are presented.     

满足开集条件的自相似集的Lipschitz等价

固定r∈(0,1)及整数N≥2,设E和E'为由N个形如S(x)=±rx+b的压缩映射所生成的自相似集.设开集条件对于E及E'成立,并且所对应的开集为开区间,证明了E和E'Lipschitz等价.     

扰动系统的Lipschitz稳定性和指数渐近稳定性

本文给出若干扰动微分系统的零解是一致Ｌｉｐｓｃｈｉｔｚ稳定和指数渐近稳定，以及第五个解渐近地趋于零的一些充分条件。     

Proximal subgradient and a characterization of Lipschitz function on Riemannian manifolds

A characterization of Lipschitz behavior of functions defined on Riemannian manifolds is given in this paper. First, it is extended the concept of proximal subgradient and some results of proximal analysis from Hilbert space to Riemannian manifold setting. A technique introduced by Clarke, Stern and Wolenski [F.H. Clarke, R.J. Stern, P.R. Wolenski, Subgradient criteria for monotonicity, the Lipschitz condition, and convexity, Canad. J. Math. 45 (1993) 1167-1183], for generating proximal subgradients of functions defined on a Hilbert spaces, is also extended to Riemannian manifolds in order to provide that characterization. A number of examples of Lipschitz functions are presented so as to show that the Lipschitz behavior of functions defined on Riemannian manifolds depends on the Riemannian metric.     

Uniformly Lipschitz feedback optimal controls in a linear-quadratic framework

A uniformly k-Lipschitz feedback optimal control problem is considered in a linear quadratic framework. The value function is derived by a comparison-based reasoning, via which a necessary condition to the existence of optimal solutions is obtained: optimal feedback controls must be linear.     

Lipschitz properties of the scalarization function and applications

The scalarization functions were used in vector optimization for a long period. Similar functions were introduced and used in economics under the name of shortage function or in mathematical finance under the name of (convex or coherent) measures of risk. The main aim of this article is to study Lipschitz continuity properties of such functions and to give some applications for deriving necessary optimality conditions for vector optimization problems using the Mordukhovich subdifferential.     

The Fourier Transform of Functions Satisfying the Lipschitz Condition on Rank 1 Symmetric Spaces

We prove an analog of the classical Titchmarsh theorem on the image under the Fourier transform of a set of functions satisfying the Lipschitz condition in L² for functions on noncompact rank 1 Riemannian symmetric spaces.     

On the asymptotical behavior of the constant in the Berry-Esseen inequality

LetF be the distribution function of a sumS _n ofn independent centered random variables, denote the standard normal distribution function and its density. It follows from our results that

一类二元相关威布尔分布的可靠性问题

本文考虑生存函数为${\ol{F}(x_{1},x_{2})}=\exp\{-[(x_{1}^{1/\alpha}/\theta_{1})^{1/\delta}+(x_{2}^{1/\alpha}/\theta_{2})^{1/\delta}]^{\delta}\},\;x_{i}>0,\;\alpha>0$, $1\geq\delta>0,\;\theta_{i}>0\;(i=1,2)$的二元威布尔分布的两种可靠性问题, 提出可靠度$\pr$的估计并讨论了它们的渐近性, 最后还作了模拟计算.