A dyadic decomposition approach to a finitely degenerate hyperbolic problem

Abstract We use the Littlewood-Paley decomposition technique to obtain a C^∞-well-posedness result for a weakly hyperbolic equation with a finite order of degeneration. Keywords: Littlewood-Paley decomposition, Hyperbolic equations, C^∞-well-posedness, Approximate energy method     

A note on the Weber location problem

In this note, we collect some interesting and useful results about the Weber problem. We investigate an accelerated Weiszfeld procedure which increases the step size and find a formula for the step size that empirically produces the fastest convergence rate. We also derive an estimate for the optimal cost of the system.     

A heuristic algorithm for constrained multi-source Weber problem – The variational inequality approach

For solving the well-known multi-source Weber problem (MWP), each iteration of the heuristic alternate location–allocation algorithm consists of a location phase and an allocation phase. The task of the location phase is to solve finitely many single-source Weber problems (SWP), which are reduced by the heuristic of nearest center reclassification for the customers in the previous allocation phase. This paper considers the more general and practical case – the MWP with constraints (CMWP). In particular, a variational inequality approach is contributed to solving the involved constrained SWP (CSWP), and thus a new heuristic algorithm for CMWP is presented. The involved CSWP in the location phases are reformulated into some linear variational inequalities, whose special structures lead to a new projection–contraction (PC) method. Global convergence of the PC method is proved under mild assumptions. The new heuristic algorithm using the PC method in the location phases approaches to the heuristic solution of CMWP efficiently, which is verified by the preliminary numerical results reported in this paper.     

An innovation approach to the smoothing problem from uncertain observations with correlated signal and noise

This paper treats the least mean‐squared error linear fixed‐point and fixed‐lag smoothing problems from uncertain observations, when the variables describing the uncertainty are independent, and the signal and observation white noise are correlated. Using an innovation approach, recursive algorithms are derived for both estimators without requiring the whole knowledge of the state‐space model generating the signal, but only covariance information of the signal and the observation noise, as well as the probability that the signal exists in the observed values. Copyright © 2005 John Wiley & Sons, Ltd.     

A perturbation-based heuristic for the capacitated multisource Weber problem

This paper proposes a perturbation-based heuristic for the capacitated multisource Weber problem. This procedure is based on an effective use of borderline customers. Several implementations are considered and the two most appropriate are then computationally enhanced by using a reduced neighbourhood when solving the transportation problem. Computational results are presented using data sets from the literature, originally used for the uncapacitated case, with encouraging results.     

A constrained optimization approach to finite element mesh smoothing

The quality of a finite element solution has been shown to be affected by the quality of the underlying mesh. A poor mesh may lead to unstable and/or inaccurate finite element approximations. Mesh quality is often characterized by the “smoothness” or “shape” of the elements (triangles in 2-D or tetrahedra in 3-D). Most automatic mesh generators produce an initial mesh where the aspect ratio of the elements are unacceptably high. In this paper, a new approach to produce acceptable quality meshes from a topologically valid initial mesh is presented. Given an initial mesh (nodal coordinates and element connectivity), a “smooth” final mesh is obtained by solving a constrained optimization problem. The variables for the iterative optimization procedure are the nodal coordinates (excluding, the boundary nodes) of the finite element mesh, and appropriate bounds are imposed on these to prevent an unacceptable finite element mesh. Examples are given of the application of the above method for 2- and 3-D meshes generated using automatic mesh generators. Results indicate that the new method not only yields better quality elements when compared with the traditional Laplacian smoothing, but also guarantees a valid mesh unlike the Laplacian method.     

A new approach to the core and Weber set of multichoice games

Multichoice games have been introduced by Hsiao and Raghavan as a generalization of classical cooperative games. An important notion in cooperative game theory is the core of the game, as it contains the rational imputations for players. We propose two definitions for the core of a multichoice game, the first one is called the precore and is a direct generalization of the classical definition. We show that the precore coincides with the definition proposed by Faigle, and that the set of imputations may be unbounded, which makes its application questionable. A second definition is proposed, imposing normalization at each level, causing the core to be a convex compact set. We study its properties, introducing balancedness and marginal worth vectors, and defining the Weber set and the pre-Weber set. We show that the classical properties of inclusion of the (pre)core into the (pre)-Weber set as well as their coincidence in the convex case remain valid. A last section makes a comparison with the core defined by Van den Nouweland et al. A preliminary and short version of this paper has been presented at 4th Logic, Game Theory and Social Choice meeting, Caen, France, June 2005 (Xie and Grabisch 2005).     

A solution to the lower dimensional Busemann-Petty problem in the hyperbolic space

The lower dimensional Busemann-Petty problem asks whether origin symmetric convex bodies in ℝⁿ with smaller volume of all k-dimensional sections necessarily have smaller volume. As proved by Bourgain and Zhang, the answer to this question is negative if k>3. The problem is still open for k = 2, 3. In this article we formulate and completely solve the lower dimensional Busemann-Petty problem in the hyperbolic space ℍⁿ.     

Degeneracy in the multi-source Weber problem

Received June 10, 1996 / Revised version received May 20, 1997 Published online October 21, 1998     

A projected Weiszfeld algorithm for the box-constrained Weber location problem

The Weber problem consists of finding a point in Rⁿ that minimizes the weighted sum of distances from m points in Rⁿ that are not collinear. An application that motivated this problem is the optimal location of facilities in the 2-dimensional case. A classical method to solve the Weber problem, proposed by Weiszfeld in 1937, is based on a fixed-point iteration.In this work we generalize the Weber location problem considering box constraints. We propose a fixed-point iteration with projections on the constraints and demonstrate descending properties. It is also proved that the limit of the sequence generated by the method is a feasible point and satisfies the KKT optimality conditions. Numerical experiments are presented to validate the theoretical results.     

A fuzzy approach to the transportation problem

The transportation problem with fuzzy supply values of the deliverers and with fuzzy demand values of the receivers is analysed. For the solution of the problem the technique of parametric programming is used. This makes it possible to obtain not only the maximizing solution (according to the Bellman-Zadeh criterion) but also other alternatives close to the optimal solution.     

Multisource Bayesian sequential binary hypothesis testing problem

We consider the problem of testing two simple hypotheses about unknown local characteristics of several independent Brownian motions and compound Poisson processes. All of the processes may be observed simultaneously as long as desired before a final choice between hypotheses is made. The objective is to find a decision rule that identifies the correct hypothesis and strikes the optimal balance between the expected costs of sampling and choosing the wrong hypothesis. Previous work on Bayesian sequential hypothesis testing in continuous time provides a solution when the characteristics of these processes are tested separately. However, the decision of an observer can improve greatly if multiple information sources are available both in the form of continuously changing signals (Brownian motions) and marked count data (compound Poisson processes). In this paper, we combine and extend those previous efforts by considering the problem in its multisource setting. We identify a Bayes optimal rule by solving an optimal stopping problem for the likelihood-ratio process. Here, the likelihood-ratio process is a jump-diffusion, and the solution of the optimal stopping problem admits a two-sided stopping region. Therefore, instead of using the variational arguments (and smooth-fit principles) directly, we solve the problem by patching the solutions of a sequence of optimal stopping problems for the pure diffusion part of the likelihood-ratio process. We also provide a numerical algorithm and illustrate it on several examples.     

A combinatorial approach to the classification problem

We study the two-group classification problem which involves classifying an observation into one of two groups based on its attributes. The classification rule is a hyperplane which misclassifies the fewest number of observations in the training sample. Exact and heuristic algorithms for solving the problem are presented. Computational results confirm the efficiency of this approach.     

A viability approach to the skorohod problem

The theory of stochastic differential equations with reflecting boundary conditions leads to the "Skorohod" problem. Thispaper proposes a solution to this problem using techniques from viability theory and non-smooth analysis, allowing very general situations to occur.     

An incremental clustering algorithm based on hyperbolic smoothing

Clustering is an important problem in data mining. It can be formulated as a nonsmooth, nonconvex optimization problem. For the most global optimization techniques this problem is challenging even in medium size data sets. In this paper, we propose an approach that allows one to apply local methods of smooth optimization to solve the clustering problems. We apply an incremental approach to generate starting points for cluster centers which enables us to deal with nonconvexity of the problem. The hyperbolic smoothing technique is applied to handle nonsmoothness of the clustering problems and to make it possible application of smooth optimization algorithms to solve them. Results of numerical experiments with eleven real-world data sets and the comparison with state-of-the-art incremental clustering algorithms demonstrate that the smooth optimization algorithms in combination with the incremental approach are powerful alternative to existing clustering algorithms.     

The multi-facility min-max Weber problem

An algorithm is presented to solve the problem of the locating a given number of facilities on the plane amongst given customers so that the maximum weighted distance from any facility to the customers it services is minimised. The algorithm successfully overcomes the allocation aspects of this problem by generating partitions of customers using a method originally designed for graph colouring embedded within a modified bisection search. Problems of 50 customers and three facilities can be solved in entirely acceptable computer times.     

A nonlocal problem for degenerate hyperbolic equation

We consider a nonlocal problem for a degenerate equation in a domain bounded by characteristics of this equation. The boundary-value conditions of the problem include linear combination of operators of fractional integro-differentiation in the Riemann–Liouville sense. The uniqueness of solution of the problem under consideration is proved by means of the modified Tricomi method, and existence is reduced to solvability of either singular integral equation with the Cauchy kernel or Fredholm integral equation of second kind.     

A smoothing heuristic for a bilevel pricing problem

In this paper, we provide a heuristic procedure, that performs well from a global optimality point of view, for an important and difficult class of bilevel programs. The algorithm relies on an interior point approach that can be interpreted as a combination of smoothing and implicit programming techniques. Although the algorithm cannot guarantee global optimality, very good solutions can be obtained through the use of a suitable set of parameters. The algorithm has been tested on large-scale instances of a network pricing problem, an application that fits our modeling framework. Preliminary results show that on hard instances, our approach constitutes an alternative to solvers based on mixed 0–1 programming formulations.