Optimum parameter for the SOR-like method for augmented systems

Recently, several proposals for the generalization of Young's SOR method to the saddle point problem or the augmented system has been presented. One of the most practical versions is the SOR-like method given by Golub et al., [(2001). SOR-like methods for augmented systems. BIT, 41, 71–85.], where the convergence and the determination of its optimum parameters were given. In this article, a full characterization of the spectral radius of the SOR-like iteration matrix is given, and an explicit expression for the optimum parameter is given in each case. The new results also lead to different results to that of Golub et al. Besides, it is shown that by the choices of the preconditioning matrix, the optimum SOR-like iteration matrix has no complex eigenvalues, therefore, it can be accelerated by semi-iterative methods.     

A numerical method for solving singularly perturbed systems of nonlinear two-point boundary-value problems

In this paper, a numerical method is proposed to solve singularly perturbed systems of nonlinear two-point boundary-value problems. First, Newton's iteration is used to linearize such problems, reducing these to a sequence of linear singularly perturbed two-point boundary-value problems. Then,a difference scheme is applied to solve the linear systems. The difference scheme is accurate up to O(h ²). Test examples are included to demonstrate the efficiency of the method.     

A new approach to rectangle intersections part I

Rectangle intersections involving rectilinearly-oriented (hyper-) rectangles in d-dimensional real space are examined from two points of view. First, a data structure is developed which is efficient in time and space and allows us to report all d-dimensional rectangles stored which intersect a d-dimensional query rectangle. Second, in Part II, a slightly modified version of this new data structure is applied to report all intersecting pairs of rectangles of a given set. This approach yields a solution which is optimal in time and space for planar rectangles and reasonable in higher dimensions.     

Optimality of an error estimate of a one-step numerical integration method for certain initial value problems

In a recent paper, an error estimate of a one-step numerical method, originated from the Lanczos tau method, for initial value problems for first order linear ordinary differential equations with polynomial coefficients, was obtained, based on the error of the Lanczos econo-mization process. Numerical results then revealed that the estimate gives, correctly, the order of the tau approximant being sought. In the present paper we further establish that the error estimate is optimum with respect to the integration of the error equation. Numerical examples are included for completeness.     

Parameterized eigensolution technique for solving constrained least squares problems ∗

This paper aims to introduce an algorithm for solving large scale least squares problems subject to quadratic inequality constraints. The algorithm recasts the least squares problem in terms of a parameterized eigenproblem. A variant of k-step Arnoldi method is determined to be well suited for computing the parameterized eigenpair. A two-point interpolating scheme is developed for updating the parameter. A local convergence theory for this algorithm is presented. It is shown that this algorithm is superlinearly convergent.     

The integration of ergonomics into design—A review

Abstract

This paper examines the role of ergonomics in product and systems design. Market requirements as well as legislation have increased the use of ergonomics in design. This has increased the demands upon both ergonomists and designers. The ergonomist must learn to participate in the product development team. In return the design team must find the time and resources necessary for the inclusion of ergonomics in the development programme. This paper discusses the different roles played by the ergonomist in each stage of product and systems design. The need to give designers, engineers and management a fundamental education in ergonomics is highlighted as is the need for the ergonomist to consider the financial consequences of his work.     

An aggregation heuristic for large scale p-median problem

The p-median problem (PMP) consists of locating p facilities (medians) in order to minimize the sum of distances from each client to the nearest facility. The interest in the large-scale PMP arises from applications in cluster analysis, where a set of patterns has to be partitioned into subsets (clusters) on the base of similarity.In this paper we introduce a new heuristic for large-scale PMP instances, based on Lagrangean relaxation. It consists of three main components: subgradient column generation, combining subgradient optimization with column generation; a “core” heuristic, which computes an upper bound by solving a reduced problem defined by a subset of the original variables chosen on a base of Lagrangean reduced costs; and an aggregation procedure that defines reduced size instances by aggregating together clients with the facilities. Computational results show that the proposed heuristic is able to compute good quality lower and upper bounds for instances up to 90,000 clients and potential facilities.