Galois averages

In this paper, we introduce a notion of “Galois average” which allows us to give a suitable answer to the question: how can one extend a finite Galois extension E/F by a prime degree extension N/E to get a Galois extension N/F? Here, N/E is not necessarily a Kummer extension.     

Global solutions of the non‐linear problem describing Joule's heating in three space dimensions

The existence of global‐in‐time weak solutions to the Joule problem modelling heating or cooling in a current and heat conductive medium is proved via the Faedo–Galerkin method. The existence proof entails some a priori estimates that together with the monotonicity and compactness methods make up a main tool to prove the desired result. Under appropriate hypotheses on the data, it will be shown the boundedness in L_∞(Q_T) of the absolute temperature of the medium and of the t‐derivative of this temperature, which is achieved by means of the Gagliardo–Nirenberg theorem, the Sobolev embedding theorem and the method of Stampacchia. The paper is some extension of our investigation initiated in (Math. Meth. Appl. Sci. 1998; 23 :1275–1291). This extension includes relaxing some assumptions in (Math. Meth. Appl. Sci. 1998; 23 :1275–1291) and employing some new methods to establish the result. Copyright © 2005 John Wiley & Sons, Ltd.     

Embedding of analytic function spaces with given mean growth of the derivative

If ? is a positive function defined in [0, 1) and 0 < p < ∞, we consider the space ??(p, ?) which consists of all functions f analytic in the unit disc ?? for which the integral means of the derivative M _p (r, f ′) = 0 < r < 1, satisfy M _p (r, f ′) = O(? (r)), as r → 1. In this paper, for any given p ∈ (0, 1), we characterize the functions ?, among a certain class of weight functions, to be able to embedd ??(p, ?) into classical function spaces. These results complement other previously obtained by the authors for p ≥ 1. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Addendum to “A definable nonstandard enlargement”

?o?'s theorem for bounded D ‐ultrapowers, D being the ultrafilter introduced by Kanovei and Shelah [4], is established. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Sparse geometric graphs with small dilation

Given a set S of n points in , and an integer k such that 0k<n, we show that a geometric graph with vertex set S, at most n−1+k edges, maximum degree five, and dilation O(n/(k+1)) can be computed in time O(nlogn). For any k, we also construct planar n-point sets for which any geometric graph with n−1+k edges has dilation Ω(n/(k+1)); a slightly weaker statement holds if the points of S are required to be in convex position.     

Adaptive sampling for geometric problems over data streams

Geometric coordinates are an integral part of many data streams. Examples include sensor locations in environmental monitoring, vehicle locations in traffic monitoring or battlefield simulations, scientific measurements of earth or atmospheric phenomena, etc. This paper focuses on the problem of summarizing such geometric data streams using limited storage so that many natural geometric queries can be answered faithfully. Some examples of such queries are: report the smallest convex region in which a chemical leak has been sensed, or track the diameter of the dataset, or track the extent of the dataset in any given direction. One can also pose queries over multiple streams: for instance, track the minimum distance between the convex hulls of two data streams, report when datasets A and B are no longer linearly separable, or report when points of data stream A become completely surrounded by points of data stream B, etc. These queries are easily extended to more than two streams.

In this paper, we propose an adaptive sampling scheme that gives provably optimal error bounds for extremal problems of this nature. All our results follow from a single technique for computing the approximate convex hull of a point stream in a single pass. Our main result is this: given a stream of two-dimensional points and an integer r, we can maintain an adaptive sample of at most 2r+1 points such that the distance between the true convex hull and the convex hull of the sample points is O(D/r²), where D is the diameter of the sample set. The amortized time for processing each point in the stream is O(logr). Using the sample convex hull, all the queries mentioned above can be answered approximately in either O(logr) or O(r) time.     

Entropy in linear programs

This paper treats entropy constrained linear programs from modelling as well as computational aspects. The optimal solutions to linear programs with one additional entropy constraint are expressed in terms of Lagrange-multipliers. Conditions for uniqueness are given. Sensitivity and duality are studied. The Newton—Kantorovich method is used to obtain a locally convergent iterative procedure. Related problems based on maximum entropy or minimum information are discussed.     

Quadratic geometric programming with application to machining economics

Geometric Programming is extended to include convex quadratic functions. Generalized Geometric Programming is applied to this class of programs to obtain a convex dual program. Machining economics problems fall into this class. Such problems are studied by applying this duality to a nested set of three problems. One problem is zero degree of difficulty and the solution is obtained by solving a simple system of equations. The inclusion of a constraint restricting the force on the tool to be less than or equal to the breaking force provides a more realistic solution. This model is solved as a program with one degree of difficulty. Finally the behavior of the machining cost per part is studied parametrically as a function of axial depth. This research was supported by the Air Force Office of Scientific Research Grant AFOSR-83-0234     

Comparison of a special-purpose algorithm with general-purpose algorithms for solving geometric programming problems

We study the performance of four general-purpose nonlinear programming algorithms and one special-purpose geometric programming algorithm when used to solve geometric programming problems. Experiments are reported which show that the special-purpose algorithm GGP often finds approximate solutions more quickly than the general-purpose algorithm GRG2, but is usually not significantly more efficient than GRG2 when greater accuracy is required. However, for some of the most difficult test problems attempted, GGP was dramatically superior to all of the other algorithms. The other algorithms are usually not as efficient as GGP or GRG2. The ellipsoid algorithm is most robust.This work was supported in part by the National Science Foundation, Grant No. MCS-81-02141.