The problem of approximating the greatest common divisor(GCD) for multivariate polynomials with inexact coefficients can be formulated as a low rank approximation problem with Sylvester matrix. This paper presents a method based on Structured Total Least Norm(STLN) for constructing the nearest Sylvester matrix of given lower rank. We present algorithms for computing the nearest GCD and a certif...

I show that the cluster variation method, long used as a powerful hierarchy of approximations for discrete (Ising-like) two-dimensional lattice models, yields exact results on the disorder varieties which appear when competitive interactions are put into these models. I consider, as an example, the plaquette approximation of the cluster variation method for the square lattice Ising model with n...

We study determinantal translation-invariant random point processes on the real line. Under some technical assumptions on the correlation kernel, we prove that the smallest nearest spacings in a large interval have Poisson statistics as the length of the interval goes to infinity. 2000 Math. Subj. Class. 60G55, 60G70.

This paper examines the problem of database organization and retrieval based on computing metric pairwise distances. A low-dimensional Euclidean approximation of a high-dimensional metric space is not efficient, while search in a high-dimensional Euclidean space suffers from the “curse of dimensionality”. Thus, techniques designed for searching metric spaces must be used. We evaluate several su...

In this paper we discuss the problem of computing the real radius of controllability of the Single Input Multi Output (SIMO) systems described by univariate polynomial matrices. The problem is equivalent to computing the nearest noncoprime polynomial matrix to the polynomial matrix describing the system in some prescribed norm. A particular case of this problem is to compute approximate GCD of ...

The individual decision in this study is denoted as interval weights of alternatives. Based on the idea that the inconsistency among comparisons stems from the uncertainty of the weights in a decision maker’s mind in giving them, the uncertain weight is assumed as interval in Interval AHP. Then, the group interval weight is obtained as an approximation of the individual interval weights based o...

Most methods for computing offsets and bisectors of parametric curves are based on a local formulation of the distance to a curve. As a consequence, the computed objects may contain spurious parts and components, and have to be trimmed. We approach these problems as global optimization problems, and solve them using interval arithmetic, thus generating robust approximations that need not be tri...

Including the range of a rational function over an interval is an important problem in numerical computation. A direct interval arithmetic evaluation of a formula for the function yields in general a superset with an error linear in the width of the interval. Special formulas like the centered forms yield a better approximation with a quadratic error. Alefeld posed the question whether in gener...

Modal interval theory is an extension of classical interval theory which provides richer interpretations (including in particular inner and outer approximations of the ranges of real functions). In spite of its promising potential, modal interval theory is not widely used today because of its original and complicated construction. The present paper proposes a new formulation of modal interval t...

In accordance with the ideas of Interval Analysis, a notion of interval-valued semimetric space is proposed. Also, the possibility of applying the resulting theory to some chapters of Computer Science is examined.