Analytic and Luzin spaces (non-separable case)

We introduce the concept of κ-analytic and κ-Luzin spaces as images of complete metric spaces by (disjoint) upper semi-continuous compact-valued correspondences which “preserve discreteness” in some sence (Definition in Section 3.1). The case κ = ω coincides with (Lindelöf) analytic spaces studied by Choquet, the first author and others. The main results are characterizations of uniform analytic spaces in terms of other parametrizations, complete sequences of covers, and Suslin subsets of some product of a compact and a complete metric space (Theorems in Section 3.2 and in Section 4), and characterizations of topological analytic spaces as Suslin subsets of paracompact ?ech-complete spaces (Theorem in Section 5).     

C-Programming: An outline

This paper presents the essentials of a method designed to solve optimization problems whose objective functions are of the form g(x)+ ψ(u(x)), where ψ is differentiable and either concave or convex. It is shown that solutions to such problems can be obtained through the solutions of the Lagrangian problem whose objective function is of the form g(x)+ λu(x).     

A big-M type method for the computation of projections onto polyhedrons

In a previous work (Ref. 1), we examined some active set methods for the computation of the projection of a point onto a polyhedron when a feasible point is known. In this paper, we assume that such a point is not known and examine a method similar to the big-M method developed for the solution of linear programming problems. Special attention is given to the study of computing error propagation.This research was supported partially by the Progetto Finalizzato Informatica del CNR, Sottoprogetto P1, Sofmat.     

Continuous dependence of the controllability set of a nonlinear control system on a parameter

In this note, we consider the mapping which associates a controllability set to each value of the parameter of a system. Sufficient conditions for the existence of breaks of this mapping are obtained. They show that nearly all such mappings have a finite number of breaks.This note was written during the author's stay at the University of Hamburg. He expresses his gratitude to the Institut für Angewandte Mathematik for its warm hospitality.     

Augmented Lagrangian algorithms for linear programming

We introduce new augmented Lagrangian algorithms for linear programming which provide faster global convergence rates than the augmented algorithm of Polyak and Treti'akov. Our algorithm shares the same properties as the Polyak-Treti'akov algorithm in that it terminates in finitely many iterations and obtains both primal and dual optimal solutions. We present an implementable version of the algorithm which requires only approximate minimization at each iteration. We provide a global convergence rate for this version of the algorithm and show that the primal and dual points generated by the algorithm converge to the primal and dual optimal set, respectively.     

Decaying states of perturbed wave equations

We study the solutions of perturbed wave equations that represent free wave motion outside some ball. When there are no trapped rays, it is shown that every solution whose total energy decays to zero must be smooth. This extends results of Rauch to the even-dimensional case and to systems having more than one sound speed. In these results, obstacles are not considered. We show that, even allowing obstacles, waves with compact spatial support cannot decay, assuming a unique continuation hypothesis. An example with obstacle is given where nonsmooth, compactly supported, decaying waves exist.     

Strong convergence of projection-like methods in Hilbert spaces

Many mathematical and applied problems can be reduced to finding a common point of a system of convex sets. The aim of this paper is twofold: first, to present a unified framework for the study of all the projection-like methods, both parallel and serial (chaotic, mostremote set, cyclic order, barycentric, extrapolated, etc.); second, to establish strong convergence results for quite general sets of constraints (generalized Slater, generalized uniformly convex, made of affine varieties, complementary, etc.). This is done by introducing the concept of regular family. We proceed as follows: first, we present definitions, assumptions, theorems, and conclusions; thereafter, we prove them.     

Efficiency and Cramér-Rao type lnequalities for convex loss functions

A general method for obtaining inequalities of Cramér-Rao type for convex loss functions is presented. It is shown under rather weak assumptions that there are at least as many such inequalities as best unbiased estimators. More precisely, it is shown that an estimator is efficient with respect to an inequality of Cramér-Rao type if and only if it is the best in the class of unbiased estimators. Moreover, theorems of Blyth and Roberts (“Proceedings Sixth Berkeley Symposium on Math. Statist. Prob.,”) and of Blyth (Ann. Statist.2, 464–473) are extended. We make an use of methods of convex analysis and properties of convex integral functionals on Orlicz spaces.     

Submodular linear programs on forests

A general linear programming model for an order-theoretic analysis of both Edmonds' greedy algorithm for matroids and the NW-corner rule for transportation problems with Monge costs is introduced. This approach includes the model of Queyranne, Spieksma and Tardella (1993) as a special case. We solve the problem by optimal greedy algorithms for rooted forests as underlying structures. Other solvable cases are also discussed.