The role of intrachain dipole interactions in the formation of the supramolecular structure of crystallizing ferroelectric polymers

The role of intrachain dipole interactions in the formation of a lamellar morphology is investigated using a number of statistical vinylidene fluoride copolymers with nonpolar components as an example. It is shown that the large and interlamellar spacings regularly increase as the intrachain dipole interaction weakens.     

Second order algorithms for the posynomial geometric programming dual,part I: Analysis

In this paper we analyse algorithms for the geometric dual of posynomial programming problems, that make explicit use of second order information. Out of two possible approaches to the problem, it is shown that one is almost always superior. Interestingly enough, it is the second, inferior approach that has dominated the geometric programming literature.This work was partially supported by the National Research Council of Canada, Grant A3552 and National Science Foundation Grant ENG78-21615.Earlier versions of this paper were presented at the Optimization Days Conference in Montreal (May 1976) and the TIMS meeting in Athens (July 1977).     

Thin points for Brownian motion

Let Θ(x,r) denote the occupation measure of the ball of radius r centered at x for Brownian motion {W_t}_0≤t≤1 in . We prove that for any analytic set E in [0,1], we have

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, where dim_P(E) is the packing dimension of E. We deduce that for any a≥1, the Hausdorff dimension of the set of “thin points” x for which

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, is almost surely 2−2/a; this is the correct scaling to obtain a nondegenerate “multifractal spectrum” for the “thin” part of Brownian occupation measure. The methods of this paper differ considerably from those of our work on Brownian thick points, due to the high degree of correlation in the present case. To prove our results, we establish general criteria for determining which deterministic sets are hit by random fractals of ‘limsup type' in the presence of long-range correlations. The hitting criteria then yield lower bounds on Hausdorff dimension. This refines previous work of Khoshnevisan, Xiao and the second author, that required decay of correlations.     

Scenario optimization

Uncertainty in the parameters of a mathematical program may present a modeller with considerable difficulties. Most approaches in the stochastic programming literature place an apparent heavy data and computational burden on the user and as such are often intractable. Moreover, the models themselves are difficult to understand. This probably explains why one seldom sees a fundamentally stochastic model being solved using stochastic programming techniques. Instead, it is common practice to solve a deterministic model with different assumed scenarios for the random coefficients. In this paper we present a simple approach to solving a stochastic model, based on a particular method for combining such scenario solutions into a single, feasible policy. The approach is computationally simple and easy to understand. Because of its generality, it can handle multiple competing objectives, complex stochastic constraints and may be applied in contexts other than optimization. To illustrate our model, we consider two distinct, important applications: the optimal management of a hydro-thermal generating system and an application taken from portfolio optimization.     

Dealing with degeneracy in reduced gradient algorithms

Many algorithms for linearly constrained optimization problems proceed by solving a sequence of subproblems. In these subproblems, the number of variables is implicitly reduced by using the linear constraints to express certain ‘basic’ variables in terms of other variables. Difficulties may arise, however, if degeneracy is present; that is, if one or more basic variables are at lower or upper bounds. In this situation, arbitrarily small movements along a feasible search direction in the reduced problem may result in infeasibilities for basic variables in the original problem. For such cases, the search direction is typically discarded, a new reduced problem is formed and a new search direction is computed. Such a process may be extremely costly, particularly in large-scale optimization where degeneracy is likely and good search directions can be expensive to compute. This paper is concerned with a practical method for ensuring that directions that are computed in the reduced space are actually feasible in the original problem. It is based on a generalization of the ‘maximal basis’ result first introduced by Dembo and Klincewicz for large nonlinear network optimization problems. Research supported in part by NSF Grant ECS-8119513 and DOT Research Grant CT-06-0011.     

Current state of the art of algorithms and computer software for geometric programming

This paper attempts to consolidate over 15 years of attempts at designing algorithms for geometric programming (GP) and its extensions. The pitfalls encountered when solving GP problems and some proposed remedies are discussed in detail. A comprehensive summary of published software for the solution of GP problems is included. Also included is a numerical comparison of some of the more promising recently developed computer codes for geometric programming on a specially chosen set of GP test problems. The relative performance of these codes is measured in terms of their robustness as well as speed of computation. The performance of some general nonlinear programming (NLP) codes on the same set of test problems is also given and compared with the results for the GP codes. The paper concludes with some suggestions for future research.An earlier version of this paper was presented at the ORSA/TIMS Conference, Chicago, 1975.This work was supported in part by the National Research Council of Canada, Grant No. A-3552, Canada Council Grant No. S74-0418, and a research grant from the School of Organization and Management, Yale University. The author wishes to thank D. Himmelblau, T. Jefferson, M. Rijckaert, X. M. Martens, A. Templeman, J. J. Dinkel, G. Kochenberger, M. Ratner, L. Lasdon, and A. Jain for their cooperation in making the comparative study possible.     

A set of geometric programming test problems and their solutions

This paper attempts to provide a set of standard test examples for researchers working in the area of geometric programming and general nonlinear, continuous, nonconvex programming algorithms. The examples consist partly of applications of nonlinear programming that have appeared in the literature and partly of original geometric programming applications. Solutions to all the problems are provided as well as the starting points from which these solutions were computed. Other computationally important aspects such as tolerances and degree of accuracy with which these problems were solved, are also included.This work was supported in part by Canada Council Grant #S74-0148 and National Research Council of Canada Grant #084-6319-32.     

Brownian Particles with Rank-Dependent Drifts: Out-of-Equilibrium Behavior

We study the long-range asymptotic behavior for an out-of-equilibrium, countable, one-dimensional system of Brownian particles interacting through their rank-dependent drifts. Focusing on the semi-infinite case, where only the leftmost particle gets a constant drift to the right, we derive and solve the corresponding one-sided Stefan (free-boundary) equations. Via this solution we explicitly determine the limiting particle-density profile as well as the asymptotic trajectory of the leftmost particle. While doing so we further establish stochastic domination and convergence to equilibrium results for the vector of relative spacings among the leading particles. © 2019 Wiley Periodicals, Inc.