赋Orlicz范数的Musielak-Orlicz序列空间的单调点

给出赋Ｏｒｌｉｃｚ范数的Ｍｕｓｉｅｌａｋ－Ｏｒｌｉｃｚ序列空间中上（下）单调点以及上（下）局部一致单调点的充分必要条件．     

Applications of the method of partial inverses to convex programming: Decomposition

A primal–dual decomposition method is presented to solve the separable convex programming problem. Convergence to a solution and Lagrange multiplier vector occurs from an arbitrary starting point. The method is equivalent to the proximal point algorithm applied to a certain maximal monotone multifunction. In the nonseparable case, it specializes to a known method, the proximal method of multipliers. Conditions are provided which guarantee linear convergence.This research was sponsored, in part, by the Air Force Office of Scientific Research under grant 80-0195.     

A unified theory for coherent systems in reliability part III: Two special cases for multinary systems—binary-type coherence and homogeneous coherence

As shown in a companion-paper,¹ binary and multinary coherent systems can be studied with unified arguments, through monotone binary coherent systems. These are binary coherent systems submitted to some monotone constraint and generalize the classic theory of free binary coherent systems. By considering the unified point of view thus obtained, this paper gives what is perhaps the most suggestive representation for multinary coherent systems, since this extends the definition of binary coherent systems in terms of series-parallel (parallel-series) structures. Then, this paper examines the special case of multinary systems that can be studied directly with the classic theory of free binary coherent systems. It thus enlarges and complements, in a shorter unified manner, the particular cases considered in earlier studies.     

Global linear convergence of a path-following algorithm for some monotone variational inequality problems

We consider a primal-scaling path-following algorithm for solving a certain class of monotone variational inequality problems. Included in this class are the convex separable programs considered by Monteiro and Adler and the monotone linear complementarity problem. This algorithm can start from any interior solution and attain a global linear rate of convergence with a convergence ratio of 1 ?c/√m, wherem denotes the dimension of the problem andc is a certain constant. One can also introduce a line search strategy to accelerate the convergence of this algorithm.     

On a characterization of the exponential distribution by spacings

Summary LetX be a non-negative random variable with probability distribution functionF. SupposeX _i,n (i=1,…,n) is theith smallest order statistics in a random sample of sizen fromF. A necessary and sufficient condition forF to be exponential is given which involves the identical distribution of the random variables (n−i)(X _i+1,n−X_i,n) and (n−j)(X _j+1,n−X_j,n) for somei, j andn, (1≦i<j<n). The work was partly completed when the author was at the Dept. of Statistics, University of Brasilia, Brazil.     

Convexification and Concavification for a General Class of Global Optimization Problems

A kind of general convexification and concavification methods is proposed for solving some classes of global optimization problems with certain monotone properties. It is shown that these minimization problems can be transformed into equivalent concave minimization problem or reverse convex programming problem or canonical D.C. programming problem by using the proposed convexification and concavification schemes. The existing algorithms then can be used to find the global solutions of the transformed problems.     

Polyhedral extensions of some theorems of linear programming

Three theorems of linear programming form our starting point: Tucker's theorem (1956) concerning the existence of optimal solutions satisfying the complementary slackness conditions strictly, and Williams' two theorems (1970) concerning the coordinatewise complementary behavior of feasible and optimal solutions. Here, we establish that the same phenomena hold in another, more versatile framework involving general polyhedral convexity. As one main application, the results are transferred into the context of the monotone complementarity problem. Several other theoretical applications are indicated.Research supported in part by the National Science Foundation under grant number MCS 79-05793 at the University of Illinois at Urbana-Champaign.     

Ergodicity for Nonlinear Stochastic Equations in Variational Formulation

This paper is concerned with nonlinear partial differential equations of the calculus of variation (see [13]) perturbed by noise. Well-posedness of the problem was proved by Pardoux in the seventies (see [14]), using monotonicity methods. The aim of the present work is to investigate the asymptotic behaviour of the corresponding transition semigroup P_t. We show existence and, under suitable assumptions, uniqueness of an ergodic invariant measure ν. Moreover, we solve the Kolmogorov equation and prove the so-called "identite du carre du champs". This will be used to study the Sobolev space W^1,2(H,ν) and to obtain information on the domain of the infinitesimal generator of P_t.