Operator K-theory and its applications

A survey of basic technical constructions associated with the K-bifunctor is given along with main results obtained through it, statements of unsolved problems are given, some hypotheses are stated.Translated from Itogi Nauki i Tekhniki, Seriya Sovremennye Problemy Matematiki (Noveishie Dostizheniya), Vol. 27, pp. 3–31, 1985.     

Quasi-likelihood analysis and its applications

The Ibragimov–Khasminskii theory established a scheme that gives asymptotic properties of the likelihood estimators through the convergence of the likelihood ratio random field. This scheme is extending to various nonlinear stochastic processes, combined with a polynomial type large deviation inequality proved for a general locally asymptotically quadratic quasi-likelihood random field. We give an overview of the quasi-likelihood analysis and its applications to ergodic/non-ergodic statistics for stochastic processes.

     

Sequence factorial and its applications

In this note, we introduce sequence factorial and use this to study generalized M-bonomial coefficients. For the sequence of natural numbers, the twin concepts of sequence factorial and generalized M-bonomial coefficients, respectively, extend the corresponding concepts of factorial of an integer and binomial coefficients. Some latent properties of generalized M-bonomial coefficients by which a vast majority of practical problems involving generalized M-bonomial coefficients can be solved are derived.     

Dual ultracontractivity and its applications

The ultracontractivity is well studied and several equivalent conditions are known. In this paper, we introduce the dual notion of the ultracontractivity, which we call the dual ultracontractivity. We give necessary and sufficient conditions for the dual ultracontractivity. As an application, we discuss one-dimensional diffusion processes. We can write the conditions for the dual ultracontractivity in terms of speed measures.     

Convex compactness and its applications

The concept of convex compactness, weaker than the classical notion of compactness, is introduced and discussed. It is shown that a large class of convex subsets of topological vector spaces shares this property and that is can be used in lieu of compactness in a variety of cases. Specifically, we establish convex compactness for certain familiar classes of subsets of the set of positive random variables under the topology induced by convergence in probability. Two applications in infinite-dimensional optimization—attainment of infima and a version of the Minimax theorem—are given. Moreover, a new fixed-point theorem of the Knaster-Kuratowski-Mazurkiewicz-type is derived and used to prove a general version of the Walrasian excess-demand theorem.     

Optimum quantization and its applications

Minimum sums of moments or, equivalently, distortion of optimum quantizers play an important role in several branches of mathematics. Fejes Tóth's inequality for sums of moments in the plane and Zador's asymptotic formula for minimum distortion in Euclidean d-space are the first precise pertinent results in dimension d?2. In this article these results are generalized in the form of asymptotic formulae for minimum sums of moments, resp. distortion of optimum quantizers on Riemannian d-manifolds and normed d-spaces. In addition, we provide geometric and analytic information on the structure of optimum configurations. Our results are then used to obtain information on

(i)

the minimum distortion of high-resolution vector quantization and optimum quantizers,

(ii)

the error of best approximation of probability measures by discrete measures and support sets of best approximating discrete measures,

(iii)

the minimum error of numerical integration formulae for classes of Hölder continuous functions and optimum sets of nodes,

(iv)

best volume approximation of convex bodies by circumscribed convex polytopes and the form of best approximating polytopes, and

(v)

the minimum isoperimetric quotient of convex polytopes in Minkowski spaces and the form of the minimizing polytopes.

     

Multiplicative convexity and its applications

This paper is concerned with some sequences based on multiplicative convexity. Our results yield a class of new inequalities between ratios and differences of means, some of which extend the known ones.     

Close-to-normal structure and its applications

Let K be a bounded subset of a metric space (B, d). Let W(K) be the supremum of the cardinals of all subsets H of K such that the distance between any two distinct points in H is equal to the diameter of K. This function W on the family of all bounded subsets of B is used to prove the following result. Let K be a weakly compact convex subset of a Banach space B. Then K has a close-to-normal structure if B satisfies any of the following conditions: (a) B is strictly convex; (b) B is separable; (c) B has the property A: For any sequence {x_n} in B, {x_n} converges to a point x in B if it converges weakly to x and {∥x_n∥} converges to ∥x∥. Applications of this result to the fixed point theory are given.     

Multiplicative calculus and its applications

Two operations, differentiation and integration, are basic in calculus and analysis. In fact, they are the infinitesimal versions of the subtraction and addition operations on numbers, respectively. In the period from 1967 till 1970 Michael Grossman and Robert Katz gave definitions of a new kind of derivative and integral, moving the roles of subtraction and addition to division and multiplication, and thus established a new calculus, called multiplicative calculus. In the present paper our aim is to bring up this calculus to the attention of researchers and demonstrate its usefulness.     

Distance majorization and its applications

The problem of minimizing a continuously differentiable convex function over an intersection of closed convex sets is ubiquitous in applied mathematics. It is particularly interesting when it is easy to project onto each separate set, but nontrivial to project onto their intersection. Algorithms based on Newton’s method such as the interior point method are viable for small to medium-scale problems. However, modern applications in statistics, engineering, and machine learning are posing problems with potentially tens of thousands of parameters or more. We revisit this convex programming problem and propose an algorithm that scales well with dimensionality. Our proposal is an instance of a sequential unconstrained minimization technique and revolves around three ideas: the majorization-minimization principle, the classical penalty method for constrained optimization, and quasi-Newton acceleration of fixed-point algorithms. The performance of our distance majorization algorithms is illustrated in several applications.     

Hermitian spectral pseudoinversion and its applications

The present paper is devoted to the Hermitian spectral pseudoinversion and its applications to analysis, the solution and reduction of Hermitian differential-algebraic systems. New explicit formulas for the solutions of such systems and the solutions of related generalized Lyapunov equations are proposed. Attainable upper bounds for the norms of the solutions are obtained. A realization of the balanced truncation method not requiring computations involving projections onto deflating subspaces is proposed.     

A Hardy-type inequality and its applications

We prove a Hardy-type inequality that provides a lower bound for the integral ∫₀^∞|f(r)| ^p r ^p−1 dr, p > 1. In the scale of classical Hardy inequalities, this integral corresponds to the value of the exponential parameter for which neither direct nor inverse Hardy inequalities hold. However, the problem of estimating this integral and its multidimensional generalization from below arises in some practical questions. These are, for example, the question of solvability of elliptic equations in the scale of Sobolev spaces in the whole Euclidean space ℝ ⁿ , some questions in the theory of Sobolev spaces, hydrodynamic problems, etc. These questions are studied in the present paper.