拟微分映射的Demyanov和的上半连续性的一个注记

本文给出了一类拟可微函数的拟微分映射的Demyanov和的上半连续性的结果,并利用Demyanov和,对具有不等式约束的拟可微函数的极小问题,给出了一个可行的下降方向．     

A Solution Method for a Special Class of Nondifferentiable Unconstrained Optimization Problems

We consider quasidifferentiable functions in the sense of Demyanov and Rubinov, i. e. functions, which are directionally differentiable and whose directional derivative can be expressed as a difference of two sublinear functions, so that its subdifferential, called the quasidifferential, consists of a pair of sets. For these functions a generalized gradient algorithm is proposed. Its behaviour is studied in detail for the special class of continuously subdifferentiable functions. Numerical test results are given. Finally, the general quasidifferentiable case is simulated by means of perturbed subdifferentials, where we make use of the non-uniqueness in the quasidifferential representation.     

Difference of two sets and estimation of clarke generalized Jacobian via quasidifferential

The notion of difference for two convex compact sets inR ⁿ , proposed by Rubinovet al, is generalized toR ^m×n . A formula of the difference for the two sets, which are convex hulls of a finite number of points, is developed. In the light of this difference, the relation between Clarke generalized Jacobian and quasidifferential, in the sense of Demyanov and Rubinov, for a nonsnooth function, is established. Based on the relation, the method of estimating Clarke generalized Jacobian via quasidifferential for a certain class of functions, is presented.     

Demyanov Difference of Two Sets and Optimality Conditions of Lagrange Multiplier Type for Constrained Quasidifferentiable Optimization

In the first part of this paper, the Demyanov difference of two sets is considered. An expression for the Demyanov difference of two sets, which are the convex hulls of a finite number of points, is presented. In the second part, first-order necessary optimality conditions of the Lagrange multiplier type, for quasidifferentiable optimization with equality and inequality constraints, are given by means of the Demyanov difference of subdifferential and negative superdifferential.     

Convex Difference Criteria for the Quantitative Stability of Parametric Quasidifferentiable Systems

In this paper solvability and Lipschitzian stability properties for a special class of nonsmooth parametric generalized systems defined in Banach are studied via a variational analysis approach. Verifiable sufficient conditions for such properties to hold under scalar quasidifferentiability assumptions are formulated by combining *-difference and Demyanov difference of convex compact subsets of the dual space with classic quasidifferential calculus constructions. Applications to the formulation of sufficient conditions for metric regularity/open covering of nonsmooth maps, along with their employment in deriving optimality conditions for quasidifferentiable extremum problems, as well as an application to the study of semicontinuity of the optimal value function in parametric optimization are discussed. In memory of Aleksandr Moiseevich Rubinov (1940–2006).     

两种微分形式下最优性条件的关系

本文考虑具有不等式约束条件不可微优化问题，假定目标函数和约束函数既是Lipschitz的也是拟可微的．证明了该问题拟微分形式下的FritzJohn点必是Clarke广义梯度形式下的FritzJohn点．另外，还给出了拟微分和Clarke广义梯度之间的关系．     

A Farkas lemma for difference sublinear systems and quasidifferentiable programming

A new generalized Farkas theorem of the alternative is presented for systems involving functions which can be expressed as the difference of sublinear functions. Various other forms of theorems of the alternative are also given using quasidifferential calculus. Comprehensive optimality conditions are then developed for broad classes of infinite dimensional quasidifferentiable programming problems. Applications to difference convex programming and infinitely constrained concave minimization problems are also discussed.     

C1,1半定规划的二阶最优性条件

研究了C1,1函数类的半定规划问题,其中的目标函数是连续可微的,其梯度是拟可微的.利用拟可微分析分别给出了二阶必要条件和二阶充分条件.     

Relation between Demyanov difference and Minkowski difference of convex compact subsets inR 2

A necessary and sufficient condition for Demyanov difference and Minkowski difference of compact convex subsets inR ² being equal is given in this paper. Several examples are computed by Matlab to test our result. The necessary and sufficient condition makes us to compute Clarke subdifferential by quasidifferential for a special of Lipschitz functions.     

Difference of compact sets in the sense of demyanov and its application to non-smooth analysis

Operation of the difference of pairs of convex compacta introduced by Demyanov and the related operation proposed by the authors are investigated. Using these operations the relationship between the Clarke subdifferential and quasidifferential is clarified     

具有等式与不等式约束条件拟可微优化的Lagrange乘子型最优性条件

讨论了不等式约束优化问题中拟微分形式下Ｆｒｉｔｚ Ｊｏｈｎ必要条件与 Ｃｌａｒｋｅ广义梯度形式下Ｆｒｉｔｚ Ｊｏｈｎ必要条件的关系．在较弱条件下给出了具有等式与不等式约束条件的两个Ｌａｇｒａｎｇｅ乘子形式的最优性必要条件,在这两个条件中等式约束函数的拟微分和Ｃｌａｒｋｅ广义梯度分别被使用。     

Generalized derivatives and nonsmooth optimization, a finite dimensional tour

During the early 1960’s there was a growing realization that a large number of optimization problems which appeared in applications involved minimization of non-differentiable functions. One of the important areas where such problems appeared was optimal control. The subject of nonsmooth analysis arose out of the need to develop a theory to deal with the minimization of nonsmooth functions. The first impetus in this direction came with the publication of Rockafellar’s seminal work titledConvex Analysis which was published by the Princeton University Press in 1970. It would be impossible to overstate the impact of this book on the development of the theory and methods of optimization. It is also important to note that a large part of convex analysis was already developed by Werner Fenchel nearly twenty years earlier and was circulated through his mimeographed lecture notes titledConvex Cones, Sets and Functions, Princeton University, 1951. In this article we trace the dramatic development of nonsmooth analysis and its applications to optimization in finite dimensions. Beginning with the fundamentals of convex optimization we quickly move over to the path breaking work of Clarke which extends the domain of nonsmooth analysis from convex to locally Lipschitz functions. Clarke was the second doctoral student of R.T. Rockafellar. We discuss the notions of Clarke directional derivative and the Clarke generalized gradient and also the relevant calculus rules and applications to optimization. While discussing locally Lipschitz optimization we also try to blend in the computational aspects of the theory wherever possible. This is followed by a discussion of the geometry of sets with nonsmooth boundaries. The approach to develop the notion of the normal cone to an arbitrary set is sequential in nature. This approach does not rely on the standard techniques of convex analysis. The move away from convexity was pioneered by Mordukhovich and later culminated in the monographVariational Analysis by Rockafellar and Wets. The approach of Mordukhovich relied on a nonconvex separation principle called theextremal principle while that of Rockafellar and Wets relied on various convergence notions developed to suit the needs of optimization. We then move on to a parallel development in nonsmooth optimization due to Demyanov and Rubinov called Quasidifferentiable optimization. They study the class of directionally differentiable functions whose directional derivatives can be represented as a difference of two sublinear functions. On other hand the directional derivative of a convex function and also the Clarke directional derivatives are sublinear functions of the directions. Thus it was thought that the most useful generalizations of directional derivatives must be a sublinear function of the directions. Thus Demyanov and Rubinov made a major conceptual change in nonsmooth optimization. In this section we define the notion of a quasidifferential which is a pair of convex compact sets. We study some calculus rules and their applications to optimality conditions. We also study the interesting notion of Demyanov difference between two sets and their applications to optimization. In the last section of this paper we study some second-order tools used in nonsmooth analysis and try to see their relevance in optimization. In fact it is important to note that unlike the classical case, the second-order theory of nonsmoothness is quite complicated in the sense that there are many approaches to it. However we have chosen to describe those approaches which can be developed from the first order nonsmooth tools discussed here. We shall present three different approaches, highlight the second order calculus rules and their applications to optimization.     

Newton-Type Methods for Quasidifferentiable Equations

In this paper, we present two Newton-type methods for solving quasidifferentiable equations in the sense of Demyanov and Rubinov (Ref. 1). Method I is well defined and is a natural extension of the classical Newton method, based on a generalized Kakutani fixed-point theorem. Method II is a simplified version and requires less computation than Method I. Under some mild assumptions, we establish a locally quadratic convergent theorem for Method I and prove a semilocal convergence theorem for Method II.     

Quasi-Multiplier Rules for Quasidifferentiable Extremum Problems

The purpose of the present article is to contribute to clarify the role of the Lagrange multipliers within the theory of the first order necessary optimality conditions for nonsmooth constrained optimization, when the directional derivatives of functions involved in the extremum problems are not sublinear. This task is accomplished in the particular case of quasidifferentiable problems with side constraints. In such setting, making use of the image-space approach, it is possible to establish a generalized (nonlinear) separation result by means of which a new Lagrange principle is obtained. According to this principle, which seems to fit better quasidifferentiable extremum problems than the classic one, the concept of linear multiplier is to be replaced with that of quasi-multiplier, a sublinear and continuous functional whose existence can be guaranteed under mild assumptions, even when classic multipliers fail to exist. Such as extension allows to formulate in terms of Lagrange function the known optimality necessary condition for unconstrained quasidifferentiable optimization expressed in form of quasidifferential inclusion. Along with this, other multiplier rules are established.     

Comparison Between Quasidifferentials and Exhausters

Directional derivatives play one of the major roles in optimization. Optimality conditions can be described in terms of these objects. These conditions, however, are not constructive. To overcome this problem, one has to represent the directional derivative in special forms. Two such forms are quasidifferentials and exhausters proposed by V.F. Demyanov. Quasidifferentials were introduced in 1980s. Optimality conditions in terms of these objects were developed by L.N. Polyakova and V.F. Demyanov. It was described how to find directions of steepest descent and ascent when these conditions are not satisfied. This paved a way for constructing new optimization algorithms. Quasidifferentials allow one to treat a wide class of functions. V.F. Demyanov introduced the notion of exhausters in 2000s to expand the class of functions that can be treated. It should be noted that a great contribution to the emergence of this notion was made by B.N. Pshenichny and A.M. Rubinov. In this work it is shown that exhausters not only allow one to treat a wider class of functions than quasidifferentials (since every quasidifferentiable function has exhausters) but is also preferable even for quasidifferentiable functions when solving nonsmooth optimization problems.     

Some relationships among quasidifferential,weak subdifferential and exhausters

In this study, some relationships between quasidifferentiability and weakly subdifferentiability of a function are investigated, and some results on calculating the quasidifferential in terms of weak exhausters are presented. Moreover, under some assumptions weak subdifferentiability of a quasidifferentiable function is proved and the weak subdifferential is obtained by using the quasidifferential. Under some assumptions, it is proved that a positively homogeneous and lower semicontinuous function is quasidifferentiable. It is also shown that directional differentiability and weak subdifferentiability imply quasidifferentiability. Furthermore, a method to evaluate the quasidifferential is given by using reduced weak lower exhausters.     

Representation of the Clarke Generalized Jacobian via the Quasidifferential

Two differences of convex compact sets in ^{m× n} are proposed. In the light of these differences, representations of the Clarke generalized Jacobian and the B-differential via the quasidifferential are developed for a certain class of functions. These representations can be used to calculate the Clarke generalized Jacobian and the B-differential via the quasidifferential.     

Newton Methods for Quasidifferentiable Equations and Their Convergence

The Newton method and the inexact Newton method for solving quasidifferentiable equations via the quasidifferential are investigated. The notion of Q-semismoothness for a quasidifferentiable function is proposed. The superlinear convergence of the Newton method proposed by Zhang and Xia is proved under the Q-semismooth assumption. An inexact Newton method is developed and its linear convergence is shown.Project sponsored by Shanghai Education Committee Grant 04EA01 and by Shanghai Government Grant T0502.     

Translating Solutions of the Nonparametric Mean Curvature Flow with Nonzero Neumann Boundary Data in Product Manifold Mn × R*

In this paper, the authors can prove the existence of translating solutions to the nonparametric mean curvature flow with nonzero Neumann boundary data in a prescribed product manifold Mⁿ × R, where Mⁿ is an n-dimensional (n ≥ 2) complete Riemannian manifold with nonnegative Ricci curvature, and R is the Euclidean 1-space.     

On the structure of phase spaces in Synergetic Inter-Representation Network

Exchanging ideas and concepts among people can be handled with by so-called Synergetic Inter-Representation Network, which is described by n -dimensional ordinary differential equations with cubic nonlinearity. The novel find is the fact that the basic equation set is transformed to a set of n -dimensional Lotka-Volterra equations. This discovery allows us to scrutinize the existence and stability of the fixed points: (1) The origin is an unstable fixed point; (2) The single representation survival, for example (0, 0, …, λ_j /C, …, 0), is a stable fixed point; (3) The coexistence of several representations exists as a saddle point. If all of these exist, the number of the saddle points is 2ⁿ – n – 1. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)