Detecting Embedded Networks in LP Using GUB Structures and Independent Set Algorithms

In this paper, we present an alternative multi-stage generalized upper bounds (GUB) based approach for detecting an embedded pure network structure in an LP problem. In order to identify a GUB structure, we use two different approaches; the first is based on the notion of Markowitz merit count and the second exploits independent sets in the corresponding graphs. Our computational experiments show that the multi-stage GUB algorithm based on these approaches performs favourably when compared with other well known algorithms.     

Detecting embedded pure network structures in LP problems

Recently, linear programming problems with special structures have assumed growing importance in mathematical programming. It is well known that exploiting network structures within linear programs can lead to considerable improvement of the computational solution of large-scale linear programming problems. A linear program is said to contain an embedded network structure provided that some subset of its constraints can be interpreted as specifying conservation of flow. If a column of the constraint matrix has at most two non-zeros, then it leads to embedded generalized network structure and if these non-zeros are unit elements and of opposite signs, then it leads to embedded pure network structure. In this paper, we are concerned with algorithms for detecting embedded pure network structures within linear programs. The network extraction methods are presented in two groups. The first group covers deletion and addition based algorithms and the second group covers GUB based algorithms. We have extended the GUB based algorithm appearing in the second group by introducing Markowitz merit count approach for exploiting matrix non zeros. A set of well known test problems has been used to carry out computational experiments which show that our extensions to the GUB based algorithms give better results than the algorithms reported earlier.     

A o(n logn) algorithm for LP knapsacks with GUB constraints

A specialization of the dual simplex method is developed for solving the linear programming (LP) knapsack problem subject to generalized upper bound (GUB) constraints. The LP/GUB knapsack problem is of interest both for solving more general LP problems by the dual simplex method, and for applying surrogate constraint strategies to the solution of 0–1 Multiple Choice integer programming problems. We provide computational bounds for our method of o(n logn), wheren is the total number of problem variables. These bounds reduce the previous best estimate of the order of complexity of the LP/GUB knapsack problem (due to Witzgall) and provide connections to computational bounds for the ordinary knapsack problem.We further provide a variant of our method which has only slightly inferior worst case bounds, yet which is ideally suited to solving integer multiple choice problems due to its ability to post-optimize while retaining variables otherwise weeded out by convex dominance criteria.     

Dual simplex method for GUB problems

The dual simplex method for generalized upper bound (GUB) problems is presented. One of the major operations in the dual simplex method is to update the elements of therth row, wherer is the index for the leaving basic variable. Those updated elements are used for the ratio test to determine the entering basic variabble. A very simple formula for therth row update for the dual simplex method for a GUB problem is derived, which is similar to the formula for the standard linear program. This derivation is based on the change key operation, which is to exchange the key column and its counterpart in the nonkey section. The change key operation is possible because of a theorem that guarantees the existence of such a counterpart.     

Implicit representation of generalized variable upper bounds in linear programming

In certain linear programs, especially those derived from integer programs, large numbers of constraints may have very simple form. Examples are:x _ij 1 (simple upper bounds [SUB]), _i x _ij = 1 (generalized upper bounds [GUB]) andx _ij y _i (variable upper bounds [VUB]). A class of constraints called generalized VUB [GVUB] is introduced which includes GUB and VUB as special cases. Also introduced is a method for representing GVUB constraints implicitly within the mechanics of the simplex method.Research supported in part by the Mobil Oil Corporation.     

Linear programming for weighted deviation problems using compact basis techniques

Weighted deviation problems are linear programs in which weights (or penalties) are attached to deviations from upper and lower bounds on particular linear expressions. In turn the deviations may be bracketed by secondary bounds. These problems include statistical problems of minimizing weighted sums of absolute deviations, standard and extended “goal programming” problems, problems with upper bounds on absolute values of linear affine functions, problems with arbitrarily bounded variables, and combinations of these.Previous specialized linear programming methods for related problems have been restricted to specialized cases that involve only a single basis configuration, or else, by means of “extended GUB” techniques, accommodate a diverse variety of basis structures at the cost of substantially increased computation. We show that, of the several basis configurations that can arise for this problem, precisely three are essential. Special rules are identified to allow transitions between these three structures, to yield valid compact versions of both the primal and the dual simplex methods. Finally, we show how these results lead to improved efficiency as well as reduced problem size.     

Analysis of mathematical programming problems prior to applying the simplex algorithm

Large practical linear and integer programming problems are not always presented in a form which is the most compact representation of the problem. Such problems are likely to posses generalized upper bound(GUB) and related structures which may be exploited by algorithms designed to solve them efficiently. The steps of an algorithm which by repeated application reduces the rows, columns, and bounds in a problem matrix and leads to the freeing of some variables are first presented. The ‘unbounded solution’ and ‘no feasible solution’ conditions may also be detected by this. Computational results of applying this algorithm are presented and discussed. An algorithm to detect structure is then described. This algorithm identifies sets of variables and the corresponding constraint relationships so that the total number of GUB-type constraints is maximized. Comparisons of computational results of applying different heuristics in this algorithm are presented and discussed.     

Using an interior point method for the master problem in a decomposition approach

We address some of the issues that arise when an interior point method is used to handle the master problem in a decomposition approach. The main points concem the efficient exploitation of the special structure of the master problem to reduce the cost of a single interior point iteration. The particular structure is the presence of GUB constraints and the natural partitioning of the constraint matrix into blocks built of cuts generated by different subproblems.The method can be used in a fairly general case, i.e., in any decomposition approach whenever the master is solved by an interior point method in which the normal equations are used to compute orthogonal projections.Computational results demonstrate its advantages for one particular decomposition approach: Analytic Center Cutting Plane Method (ACCPM) is applied to solve large scale nonlinear multicommodity network flow problems (up to 5000 arcs and 10000 commodities)     

A Partitioned Inverse in Linear Programming

This note deals with linear programs in which a subset of the constraints have a special structure. This structure allows linear equations involving these constraints only to be solved particularly easily, e.g. GUB rows. A method is described for restricting the gaussian elimination in LU decomposition to the non-special rows.     

Restart procedures for the conjugate gradient method

A compact and flexible updating procedure using matrix augmentation is developed. It is shown that the representation of the updated inverse does not grow monotonically in size, and may actually decrease during certain simplex iterations. Angular structures, such as GUB, are handled naturally within the partitioning framework, and require no modifications of the simplex method.     

New labeling procedures for the basis graph in generalized networks

Labeling procedures for the basis graph of a generalized network are introduced which build on procedures designed for pure networks. Computational results are presented which show that a primal simplex code which uses these procedures is about 60 times faster than a general purpose linear programming code.     

Mean-value analysis for a class of Petri nets and batch-movement queueing networks with product-form equilibrium distributions

A number of recent papers have shown that there are classes of queueing networks, with batches of customers served and routed through the network, which have generalized product form equilibrium distributions. This extends to some Petri nets. In this paper, we indicate how a class of these is amenable to a mean-value analysis similar to that used for single-movement networks. To bring out the simplicity of the underlying ideas, we do this by working a simple example rather than presenting the development in its generality.     

Large-scale linear programming: Geometry,working bases and factorizations

This paper is concerned with linear programming problems in which many of the constraints are handled implicitly by requiring that the vector of decision variables lie in a polyhedronX. It is shown that the simplex method can be implemented using a working basis whose size is the number of explicit constraints as long as the local structure ofX around the current point is known. Various ways of describing this local structure lead to known implementations whenX is defined by generalized or variable upper bounds or flow conservation constraints. In the general case a decomposition principle can be used to generate this local structure. We also show how to update factorizations of the working basis.     

Studies of lexicography in the generalized network simplex method

This paper introduces an analytical approach for studying lexicography in generalized network problems. The equations obtained can help us to understand and to extend the existing theory. First, it is verified that all nonzero elements have the same sign in each row vector of a basis inverse for a generalized network (GN) problem with positive multipliers. However, this property does not necessarily hold when there exist negative multipliers. Second, we developed a strategy to select the dropping arc in the GN simplex algorithm when addressing GN problems with positive andnegative multipliers. This strategy is also based on lexicography and requires performing some comparisons. However, the values to be compared are already known since they can be obtained as a by-product of the calculations necessary to compute the basis representation of the entering arc. Consequently, the computational effort per pivot step isO(n) in the worst case. This worst case effort is the same as that required by the strongly convergent rules for selecting the dropping arc in the method of strong convergence.     

A partitioning algorithm for the multicommodity network flow problem

A partitioning algorithm for solving the general minimum cost multicommodity flow problem for directed graphs is presented in the framework of a network flow method and the dual simplex method. A working basis which is considerably smaller than the number of capacitated arcs in the given network is employed and a set of simple secondary constraints is periodically examined. Some computational aspects and preliminary experimental results are discussed.     

Implicit representation of generalized variable upper bounds using the elimination form of the inverse on secondary storage

A constraint of a linear program is called a generalized variable upper bound (GVUB) constraint, if the right-hand is nonnegative and each variable with a positive coefficient in the constraint does not have a nonzero coefficient in any other GVUB constraint. Schrage has shown how to handle GVUB constraints implicitly in the simplex-method. It is demonstrated in this paper that the Forrest-Tomlin data structure may be used for the inverse of the working basis, and it is discussed how to update this representation from iteration to iteration.     

Network capacity management under competition

We consider capacity management games between airlines who transport passengers over a joint airline network. Passengers are likely to purchase alternative tickets of the same class from competing airlines if they do not get tickets from their preferred airlines. We propose a Nash and a generalized Nash game model to address the competitive network revenue management problem. These two models are based on well-known deterministic linear programming and probabilistic nonlinear programming approximations for the non-competitive network capacity management problem. We prove the existence of a Nash equilibrium for both games and investigate the uniqueness of a Nash equilibrium for the Nash game. We provide some further uniqueness and comparative statics analysis when the network is reduced to a single-leg flight structure with two products. The comparative statics analysis reveals some useful insights on how Nash equilibrium booking limits change monotonically in the prices of products. Our numerical results indicate that airlines can generate higher and more stable revenues from a booking scheme that is based on the combination of the partitioned booking-limit policy and the generalized Nash game model. The results also show that this booking scheme is robust irrespective of which booking scheme the competitor takes.     

星形热弹性网络系统的稳定性及Riesz基性质

研究了一类星形弹性网络系统在热效应影响以及边界反馈作用下的稳定性问题及系统相应（广义）特征向量的Riesz基性质．基于Green和Naghdi第二类热弹性理论,假设在该热弹性系统中热以有限波速传播,并且在传播过程中无能量耗散．证明了该热弹性网络系统能量渐近衰减到零．并进一步通过系统算子谱分析,讨论得出该系统算子的（广义）特征向量构成状态空间的一组Riesz基．     

变量有广义界线性规划的叠累单纯形法

本文建立变量有广义界线性规划一个新的转轴算法,称之为叠累单纯形算法,新算法其有三个主要特征：１对于检验数为“坏”的非基变量 ｘｓ,进行一轮子转轴运算,使得ｘｓ进基,转轴中具有“好”的检验数的变量始终保持“好”的检验数;２ｘ．进基的子转轴所产生的基既不是原始可行基,也不是对偶可行基,但子转轴结束时产生的基是原始可行的;３目标函数值在整个转抽运算中是单调下降,从而算法可有限步终止．     

两类新的广义Ball曲线曲面的求值算法及其应用

本文研究两类新的广义Ball曲线曲面的求值算法及其应用.其一是把Bezier曲线曲面的求值转换到这两类曲线曲面的求值,大大加快了计算速度.其二是给出Bezier曲线与这两类广义Ball曲线的统一表示,并利用这种表示给出它们之间相互转换的递归算法.