A Euclidean distance-based measure of efficiency in data envelopment analysis

Data envelopment analysis (DEA) has been proven as an excellent data-oriented performance evaluation method when multiple inputs and outputs are present in a set of peer decision-making units (DMUs). Several efficiency measures have been proposed in the DEA literature, see, for instances, radial efficiency measure of Charnes et al. (CCR)(A. Charnes. W.W. Cooper, and E. Rhodes, 1978. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444), Russell graph measure (J.T. Russell, and R. Sirvant. 1999. An enhanced DEA Russell graph efficiency measure. Eur. J. Oper. Res. 115, pp. 596–607) and slack-based measure of Tone (K. Tone, 2001. A slack-based measure of efficiency in DEA. Eur. J. Oper. Res. 130, p. 498–509). In this article, we will propose an Euclidean distance-based measure of efficiency. Then, in order to discriminate the performance of efficient DMUs, an alternative super-efficiency DEA model is proposed. The applicability of the models developed is illustrated in the context of the analysis of gas companies performance.     

Distance Matrix Completion by Numerical Optimization

Consider the problem of determining whether or not a partial dissimilarity matrix can be completed to a Euclidean distance matrix. The dimension of the distance matrix may be restricted and the known dissimilarities may be permitted to vary subject to bound constraints. This problem can be formulated as an optimization problem for which the global minimum is zero if and only if completion is possible. The optimization problem is derived in a very natural way from an embedding theorem in classical distance geometry and from the classical approach to multidimensional scaling. It belongs to a general family of problems studied by Trosset (Technical Report 97-3, Department of Computational & Applied Mathematics—MS 134, Rice University, Houston, TX 77005-1892, 1997) and can be formulated as a nonlinear programming problem with simple bound constraints. Thus, this approach provides a constructive technique for obtaining approximate solutions to a general class of distance matrix completion problems.     

On how to properly calculate the Euclidean distance-based measure in DEA

Recently, in a data envelopment analysis framework, Amirteimoori and Kordrostami [A Euclidean distance-based measure of efficiency in data envelopment analysis, Optimization 59 (2010), pp. 985–996] introduced a Euclidean distance-based measure of efficiency in order to obtain the shortest path to the strongly efficient frontier from an assessed unit. In this article, we show a drawback of the aforementioned approach and two examples of its consequences. In order to overcome this weakness, we slightly modify a model introduced by Aparicio et al. [Closest targets and minimum distance to the Pareto-efficient frontier in DEA, J. Prod. Anal. 28 (2007), pp. 209–218].     

Gaussian Version of a Theorem of Milman and Schechtman

Using Gordon's inequalities, we give a short proof of the existence of an embedding T:l2k to ln such that ||T||||T-1|| c k/ln(1+n/k). In the same way, we give a new proof of a theorem of Milman and Schechtman (1995).     

On the point for which the sum of the distances to <Emphasis Type="Italic">n</Emphasis> given points is minimum

Translation with annotations of E. Weiszfeld, Sur le point pour lequel la somme des distances de n points donnés est minimum, Tôhoku Mathematical Journal (first series), 43 (1937) pp. 355–386.A short introduction about the translation is found in Appendix A. Appendix B lists particular notations used by Weiszfeld and their now more conventional equivalents. Numbered footnotes are those of the original paper of Weiszfeld. Boxed numerals are references to observations about the translation and comments of the translator, all to be found in Appendix C.     

A group majorization ordering for Euclidean distance matrices

This paper investigates some properties of Euclidean distance matrices (EDMs) with focus on their ordering structure. The ordering treated here is the group majorization ordering induced by the group of permutation matrices. By using this notion, we establish two monotonicity results for EDMs: (i) The radius of a spherical Euclidean distance matrix (spherical EDM) is increasing with respect to the group majorization ordering. (ii) The larger an EDM is in terms of the group majorization ordering, the more spread out its eigenvalues are. Minimal elements with respect to this ordering are also described.     

Sharp bounds on distance spectral radius of graphs

Let D(G) denote the distance matrix of a connected graph G. The largest eigenvalue of D(G) is called the distance spectral radius of a graph G, denoted by ?(G). In this article, we give sharp upper and lower bounds for the distance spectral radius and characterize those graphs for which these bounds are best possible.     

Even dimensional higher class groups of orders

Let F be a number field, and let denote the ring of integers in F. Let A be a finite-dimensional central simple F-algebra, and let Λ be an -order in A. In this paper it is shown that the p-torsion of the even dimensional higher class group Cl _2n (Λ) can only occur for primes p, which lie under prime ideals , at which is not maximal, or which divide the dimension of A. Supported by NSFC 10401014, and partially funded by Irish Research Council for Science, Engineering and Technology Basic Research Grant SC/02/265.     

具有最大度距离的单圈图

设U (n)是具有n个顶点的所有单圈图的集合,G(3; n- 3)是由一个三角形C3粘上一条悬挂路P_(n-3)得到的单圈图.本文将证明当n 5时具有最大度距离的单圈图是G(3; n - 3).