A covering construction for packing disjoint copies of the Hoffman‐Singleton graph into K50

In this note we show how coverings induced by voltage assignments can be used to produce packings of disjoint copies of the Hoffman‐Singleton graph into K₅₀. © 2003 Wiley Periodicals, Inc. J Combin Designs 11: 408–412, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/jcd.10049     

Capacity Constrained Transit Assignment with Common Lines

This paper proposes the use of absorbing Markov chains to solve the capacity constrained transit network loading problem taking common lines into account. The approach handles congested transit networks, where some passengers will not be able to board because of the absence of sufficient space. The model also handles the common lines problem, where choice of route depends on frequency of arrivals. The mathematical formulation of the problem is presented together with a numerical example. This revised version was published online in July 2006 with corrections to the Cover Date.     

橙皮素A的NMR研究

橙皮素A是从中药北沙参(Glehnia littoralis Fr. Schmidt ex Miquel)的正丁醇萃 取物中分离得到的一种8-O-4′-型异木脂素苷类化合物. 通过化学方法和波谱分析鉴 定了该 化合物的结构. 采用2D NMR技术对其NMR信号进行了全归属. DMSO-d₆代替CD₃OD作为溶剂，并利用重水交换，能准确读出偶合常数J_7,8值.      

Asymptotic Properties of Random Multidimensional Assignment Problems

The multidimensional assignment problem (MAP) is a NP-hard combinatorial optimization problem, occurring in many applications, such as data association. In this paper, we prove two conjectures made in Ref. 1 and based on data from computational experiments on MAPs. We show that the mean optimal objective function cost of random instances of the MAP goes to zero as the problem size increases, when assignment costs are independent exponentially or uniformly distributed random variables. We prove also that the mean optimal solution goes to negative infinity when assignment costs are independent normally distributed random variables.     

Minimum energy configurations on a toric lattice as a quadratic assignment problem

We consider three known bounds for the quadratic assignment problem (QAP): an eigenvalue, a convex quadratic programming (CQP), and a semidefinite programming (SDP) bound. Since the last two bounds were not compared directly before, we prove that the SDP bound is stronger than the CQP bound. We then apply these to improve known bounds on a discrete energy minimization problem, reformulated as a QAP, which aims to minimize the potential energy between repulsive particles on a toric grid. Thus we are able to prove optimality for several configurations of particles and grid sizes, complementing earlier results by Bouman et al. (2013). The semidefinite programs in question are too large to solve without pre-processing, and we use a symmetry reduction method by Permenter and Parrilo (2020) to make computation of the SDP bounds possible.     

The alternating basis algorithm for assignment problems

The purpose of this paper is to present a new primal extreme point algorithm for solving assignment problems which both circumvents and exploits degeneracy. The algorithm is based on the observation that the degeneracy difficulties of the simplex method result from the unnecessary inspection of alternative basis representations of the extreme points. This paper characterizes a subsetQ of all bases that are capable of leading to an optimal solution to the problem if one exists. Using this characterization, an extreme point algorithm is developed which considers only those bases inQ. Computational results disclose that the new algorithm is substantially more efficient than previously developed primal and primal-dual extreme point (simplex) methods for assignment problems.     

4-[(E)-2-(3, 4-二甲氧基苯基)乙烯基]苯氧基乙酸乙酯的合成及其NMR研究

以对硝基甲苯、3, 4-二甲氧基苯甲醛为起始原料,经过缩合,还原,重氮化水解,亲核取代反应,最终合成了新的化合物4-[(E)-2-(3, 4-二甲氧基苯基)乙烯基]苯氧基乙酸乙酯,用¹H 和¹³C NMR及多种二维核磁共振谱确定了该化合物的结构,完成了¹H 和¹³C NMR的归属,给出了分子中各氢,碳原子的准确化学位移.