The geometric codes are the duals of the codes defined by the designs associated with finite geometries. The latter are generalized Reed-Muller codes, but the geometric codes are, in general, not. We obtain values for the minimum weight of these codes in the binary case, using geometric constructions in the associated geometries, and the BCH bound from coding theory. Using Hamada’s formula, we ...

A generalized polynomial is a real-valued function which is obtained from conventional polynomials by the use of the operations of addition, multiplication, and taking the integer part; a generalized polynomial mapping is a vectorvalued mapping whose coordinates are generalized polynomials. We show that any bounded generalized polynomial mapping u:Z −→ R has a representation u(n) = f(φ(n)x), n ...

Generalized polynomials are functions obtained from conventional polynomials by applying the operations of taking the integer part, addition, and multiplication. We construct a system of “basic” generalized polynomials with the property that any bounded generalized polynomial is representable as a piecewise polynomial function of these basic ones. Such a representation is unique up to a functio...

The class of generalized satisfiability problems, introduced in 1978 by Schaefer, presents a uniform way of studying the complexity of satisfiability problems with special conditions. The complexity of each decision and counting problem in this class depends on the involved logical relations. In 1979, Valiant defined the class *P, the class of functions definable as the number of accepting comp...

We introduce a simple methodology, so-called generalized gamma-polynomial approximation, based on moment-matching technique to approximate survival and hazard functions in the context of parametric survival analysis. We use the generalized gamma-polynomial approximation to approximate the density and distribution functions of convolutions and finite mixtures of random variables, from which the ...

In this paper we review some applications of generalized polynomial chaos expansion for uncertainty quantification. The mathematical framework is presented and the convergence of the method is demonstrated for model problems. In particular, we solve the first-order and second-order ordinary differential equations with random parameters, and examine the efficiency of generalized polynomial chaos...