Preconditioned Kaczmarz-Extended algorithm with relaxation parameters

We analyse in this paper the possibility of using preconditioning techniques as for square non-singular systems, also in the case of inconsistent least-squares problems. We find conditions in which the minimal norm solution of the preconditioned least-squares problem equals that of the original problem. We also find conditions such that the Kaczmarz-Extended algorithm with relaxation parameters (analysed by the author in [4]), can be adapted to the preconditioned least-squares problem. In the last section of the paper we present numerical experiments, with two variants of preconditioning, applied to an inconsistent linear least-squares model problem.     

Extensions of block-projections methods with relaxation parameters to inconsistent and rank-deficient least-squares problems

There exist many classes of block-projections algorithms for approximating solutions of linear least-squares problems. Generally, these methods generate sequences convergent to the minimal norm least-squares solution only for consistent problems. In the inconsistent case, which usually appears in practice because of some approximations or measurements, these sequences do no longer converge to a least-squares solution or they converge to the minimal norm solution of a “perturbed” problem. In the present paper, we overcome this difficulty by constructing extensions for almost all the above classes of block-projections methods. We prove that the sequences generated with these extensions always converge to a least-squares solution and, with a suitable initial approximation, to the minimal norm solution of the problem. Numerical experiments, described in the last section of the paper, confirm the theoretical results obtained.     

Characterization of the solutions set of inconsistent least-squares problems by an extended Kaczmarz algorithm

We give a new characterization of the solutions set of the general (inconsistent) linear least-squares problem using the set of limit-points of an extended version of the classical Kaczmarz’s projections method. We also obtain a “ step error reduction formula” which, in some cases, can give us apriori information about the convergence properties of the algorithm. Some numerical experiments with our algorithm and comparisons between it and others existent in the literature, are made in the last section of the paper.     

On Tutte polynomial expansion formulas in perspectives of matroids and oriented matroids

We introduce the active partition of the ground set of an oriented matroid perspective (or morphism, or quotient, or strong map) on a linearly ordered ground set. The reorientations obtained by arbitrarily reorienting parts of the active partition share the same active partition. This yields an equivalence relation for the set of reorientations of an oriented matroid perspective, whose classes are enumerated by coefficients of the Tutte polynomial, and a remarkable partition of the set of reorientations into Boolean lattices, from which we get a short direct proof of a 4-variable expansion formula for the Tutte polynomial in terms of orientation activities. This formula was given in the last unpublished preprint by Michel Las Vergnas; the above equivalence relation and notion of active partition generalize a former construction in oriented matroids by Michel Las Vergnas and the author; and the possibility of such a proof technique in perspectives was announced in the aforementioned preprint. We also briefly highlight how the 5-variable expansion of the Tutte polynomial in terms of subset activities in matroid perspectives comes in a similar way from the known partition of the power set of the ground set into Boolean lattices related to subset activities (and we complete the proof with a property which was missing in the literature). In particular, the paper applies to matroids and oriented matroids on a linearly ordered ground set, and applies to graph and directed graph homomorphisms on a linearly ordered edge-set.     

Fitting semiparametric clustering models to dissimilarity data

The cluster analysis problem of partitioning a set of objects from dissimilarity data is here handled with the statistical model-based approach of fitting the “closest” classification matrix to the observed dissimilarities. A classification matrix represents a clustering structure expressed in terms of dissimilarities. In cluster analysis there is a lack of methodologies widely used to directly partition a set of objects from dissimilarity data. In real applications, a hierarchical clustering algorithm is applied on dissimilarities and subsequently a partition is chosen by visual inspection of the dendrogram. Alternatively, a “tandem analysis” is used by first applying a Multidimensional Scaling (MDS) algorithm and then by using a partitioning algorithm such as k-means applied on the dimensions specified by the MDS. However, neither the hierarchical clustering algorithms nor the tandem analysis is specifically defined to solve the statistical problem of fitting the closest partition to the observed dissimilarities. This lack of appropriate methodologies motivates this paper, in particular, the introduction and the study of three new object partitioning models for dissimilarity data, their estimation via least-squares and the introduction of three new fast algorithms.     

Experimental analysis of local searches for sparse reflexive generalized inverses

Journal of Global Optimization - The well-known M-P (Moore-Penrose) pseudoinverse is used in several linear-algebra applications; for example, to compute least-squares solutions of inconsistent...     

Adaptive Algorithm for Constrained Least-Squares Problems

This paper is concerned with the implementation and testing of an algorithm for solving constrained least-squares problems. The algorithm is an adaptation to the least-squares case of sequential quadratic programming (SQP) trust-region methods for solving general constrained optimization problems. At each iteration, our local quadratic subproblem includes the use of the Gauss–Newton approximation but also encompasses a structured secant approximation along with tests of when to use this approximation. This method has been tested on a selection of standard problems. The results indicate that, for least-squares problems, the approach taken here is a viable alternative to standard general optimization methods such as the Byrd–Omojokun trust-region method and the Powell damped BFGS line search method.     

A set of axioms for a value for partition function games

Myerson (1977) derived an efficient value for games in partition function form. In this paper, we present a set of axioms which characterize a different efficient value for such games. This latter value assigns value 0 to dummies and assigns nonnegative values to players in monotone simple games.     

Large-scale geodetic least-squares adjustment by dissection and orthogonal decomposition

Very large-scale matrix problems currently arise in the context of accurately computing the coordinates of points on the surface of the earth. Here geodesists adjust the approximate values of these coordinates by computing least-squares solutions to large sparse systems of equations which result from relating the coordinates to certain observations such as distances or angles between points. The purpose of this paper is to suggest an alternative to the formation and solution of the normal equations for these least-squares adjustment problems. In particular, it is shown how a block-orthogonal decomposition method can be used in conjunction with a nested dissection scheme to produce an algorithm for solving such problems which combines efficient data management with numerical stability. The approach given here parallels somewhat the development of the natural factor formulation, by Argyris et al., for the use of orthogonal decomposition procedures in the finite-element analysis of structures. As an indication of the magnitude that these least-squares adjustment problems can sometimes attain, the forthcoming readjustment of the North American Datum in 1983 by the National Geodetic Survey is discussed. Here it becomes necessary to linearize and solve an overdetermined system of approximately 6,000,000 equations in 400,000 unknowns—a truly large scale matrix problem.     

A fast Kaczmarz-Kovarik algorithm for consistent least-squares problems

In some previous papers the author extended two algorithms proposed by Z. Kovarik for approximate orthogonalization of a finite set of linearly independent vectors from a Hilbert space, to the case when the vectors are rows (not necessary linearly independent) of an arbitrary rectangular matrix. In this paper we describe combinations between these two methods and the classical Kaczmarz’s iteration. We prove that, in the case of a consistent least-squares problem, the new algorithms so obtained converge to any of its solutions (depending on the initial approximation). The numerical experiments described in the last section of the paper on a problem obtained after the discretization of a first kind integral equation ilustrate the fast convergence of the new algorithms.     

Games with externalities: games in coalition configuration function form

In this paper we introduce a model of cooperative game with externalities which generalizes games in partition function form by allowing players to take part in more than one coalition. We provide an extension of the Shapley value (1953) to these games, which is a generalization of the Myerson value (1977) for games in partition function form. This value is derived by considering an adaptation of an axiomatic characterization of the Myerson value (1977).     

On same-realization prediction in an infinite-order autoregressive process

Let observations come from an infinite-order autoregressive (AR) process. For predicting the future of the observed time series (referred to as the same-realization prediction), we use the least-squares predictor obtained by fitting a finite-order AR model. We also allow the order to become infinite as the number of observations does in order to obtain a better approximation. Moment bounds for the inverse sample covariance matrix with an increasing dimension are established under various conditions. We then apply these results to obtain an asymptotic expression for the mean-squared prediction error of the least-squares predictor in same-realization and increasing-order settings. The second-order term of this expression is the sum of two terms which measure both the goodness of fit and model complexity. It forms the foundation for a companion paper by Ing and Wei (Order selection for same-realization predictions in autoregressive processes, Technical report C-00-09, Institute of Statistical Science, Academia Sinica, Taipei, Taiwan, ROC, 2000) which provides the first theoretical verification that AIC is asymptotically efficient for same-realization predictions. Finally, some comparisons between the least-squares predictor and the ridge regression predictor are also given.     

Round robin scheduling – a survey

This paper presents a comprehensive survey on the literature considering round robin tournaments. The terminology used within the area has been modified over time and today it is highly inconsistent. By presenting a coherent explanation of the various notions we hope that this paper will help to obtain a unified terminology. Furthermore, we outline the contributions presented during the last 30 years. The papers are divided into two categories (papers focusing on break minimization and papers focusing on distance minimization) and within each category we discuss the development which has taken place. Finally, we conclude the paper by discussing directions for future research within the area.     

直径为5的图的2-导出匹配划分和2-导出匹配覆盖问题的NP-完全性

给定一个简单图G和正整数κ,具有完美匹配的图G的κ-导出匹配划分是对顶点集V(C)的一个κ-划分(V1,V2,...,Vκ),其中对每一个i(1≤i≤κ),由Vi导出的G的子图G[Vi]是1-正则的.κ-导出匹配划分问题是指对给定的图G,判定G是否存在一个κ-导出匹配划分.令M1,M2…,Mκ为图G的κ个导出匹配,如果V(M1)UV(M2)∪...∪V(Mκ)=V(G),则我们称{M1,M2,...,Mκ}是G的κ-导出匹配覆盖.κ-导出匹配覆盖问题是指对给定的图G,判定G是否存在κ-导出匹配覆盖.本文给出了Yang,Yuan和Dong所提出问题的解,证明了直径为5的图的导出匹配2一划分问题和导出匹配2-覆盖问题都是NP-完全的.     

A barrier method for dynamic Leontief-type linear programs

In this paper, we specialize Gill et al.'s projected Newton barrier method to solve a large-scale linear program of dynamic (i.e. multistage) Leontief-type constraints. We propose an efficient and stable method for solving the least-squares subproblems, the crucial part of the barrier method. The key step is to exploit a special structure of the constraint matrix and reduce the matrix of the normal equation for the least-squares problem to a banded matrix. By comparing the average-case operations count of this specialized barrier method with that of the sparse simplex method, we show that this method performs at least O(T) faster than the simplex method for such stype of linear programs, where T is the number of time periods (i.e. stages).     

Neural network for the exact RLS adaptive algorithm

This paper proposes a neural network approach to the implementation of the exact recursive least-squares (RLS) algorithm. We show that the proposed neural network is guaranteed to be stable and to provide the results arbitrarily close to the accurate optimal solution of the RLS algorithm within an elapsed time of only a few characteristic time constants of the network. The parameters of the network (such as interconnections strengths and bias currents) can be obtained from the available data with a few computations, which are much fewer than the computations required in the exact RLS algorithm; as a result, this proposed scheme is very suitable for real time applications of the exact RLS algorithm.     

Solving the segmentation problem for the 2010 Argentine census with integer programming

One of the most challenging tasks within the planning of a demographic census is to partition each census track into sets of homes such that each census taker visits exactly one set from this partition. In this work we introduce the home segmentation problem, which consists in designing such a partition subject to specific constraints. We present an integer programming-based algorithm for this problem, and we report the application of this algorithm for the 2010 census in the main province in Argentina.     

A note on assymmetric graphs

We describe a partition of the points of a graph which is related to its automorphism group. We then prove that the group of a tree is trivial if and only if this partition is the trivial one, and we formulate an algorithm which produces such a partition. Some application to graphs in general are also considered. Work supported in part by NSF Grant GP 11618.     

完全多部图的树划分数的直观证明

r-边染色图G的树划分数tr(G)定义为最小的正整数k,使得只要用r种颜色对图G进行边染色,则存在至多k个顶点不交的单色树覆盖图G的所有顶点.K aneko等确定了t2(K(n1,n2,…,nk))的精确表达式.本文给出了该表达式的一个直观证明.     

An adaptive spectral least-squares scheme for the Burgers equation

A least-squares spectral collocation method for the one-dimensional inviscid Burgers equation is proposed. This model problem shows the stability and high accuracy of these schemes for nonlinear hyperbolic scalar equations. Here we make use of a least-squares spectral approach which was already used in an earlier paper for discontinuous and singular perturbation problems (Heinrichs, J. Comput. Appl. Math. 157:329–345, 2003). The domain is decomposed in subintervals where continuity is enforced at the interfaces. Equal order polynomials are used on all subdomains. For the spectral collocation scheme Chebyshev polynomials are employed which allow the efficient implementation with Fast Fourier Transforms (FFTs). The collocation conditions and the interface conditions lead to an overdetermined system which can be efficiently solved by least-squares. The solution technique will only involve symmetric positive definite linear systems. The scheme exhibits exponential convergence where the exact solution is smooth. In parts of the domain where the solution contains discontinuities (shocks) the spectral solution displays a Gibbs-like behavior. Here this is overcome by some suitable exponential filtering at each time level. Here we observe that by over-collocation the results remain stable also for increasing filter parameters and also without filtering. Furthermore by an adaptive grid refinement we were able to locate the precise position of the discontinuity. Numerical simulations confirm the high accuracy of our spectral least-squares scheme.