Showing papers on "Linear programming published in 1996"

Linear Programming: Foundations and Extensions

Abstract: Preface. Part 1: Basic Theory - The Simplex Method and Duality. 1. Introduction. 2. The Simplex Method. 3. Degeneracy. 4. Efficiency of the Simplex Method. 5. Duality Theory. 6. The Simplex Method in Matrix Notation. 7. Sensitivity and Parametric Analyses. 8. Implementation Issues. 9. Problems in General Form. 10. Convex Analysis. 11. Game Theory. 12. Regression. Part 2: Network-Type Problems. 13. Network Flow Problems. 14. Applications. 15. Structural Optimization. Part 3: Interior-Point Methods. 16. The Central Path. 17. A Path-Following Method. 18. The KKT System. 19. Implementation Issues. 20. The Affine-Scaling Method. 21. The Homogeneous Self-Dual Method. Part 4: Extensions. 22. Integer Programming. 23. Quadratic Programming. 24. Convex Programming. Appendix A: Source Listings. Answers to Selected Exercises. Bibliography. Index.

Competitive Markov decision processes

Abstract: 1 Introduction.- 1.0 Background.- 1.1 Raison d'Etre and Limitations.- 1.2 A Menu of Courses and Prerequisites.- 1.3 For the Cognoscenti.- 1.4 Style and Nomenclature.- I Mathematical Programming Perspective.- 2 Markov Decision Processes: The Noncompetitive Case.- 2.0 Introduction.- 2.1 The Summable Markov Decision Processes.- 2.2 The Finite Horizon Markov Decision Process.- 2.3 Linear Programming and the Summable Markov Decision Models.- 2.4 The Irreducible Limiting Average Process.- 2.5 Application: The Hamiltonian Cycle Problem.- 2.6 Behavior and Markov Strategies.- 2.7 Policy Improvement and Newton's Method in Summable MDPs.- 2.8 Connection Between the Discounted and the Limiting Average Models.- 2.9 Linear Programming and the Multichain Limiting Average Process.- 2.10 Bibliographic Notes.- 2.11 Problems.- 3 Stochastic Games via Mathematical Programming.- 3.0 Introduction.- 3.1 The Discounted Stochastic Games.- 3.2 Linear Programming and the Discounted Stochastic Games.- 3.3 Modified Newton's Method and the Discounted Stochastic Games.- 3.4 Limiting Average Stochastic Games: The Issues.- 3.5 Zero-Sum Single-Controller Limiting Average Game.- 3.6 Application: The Travelling Inspector Model.- 3.7 Nonlinear Programming and Zero-Sum Stochastic Games.- 3.8 Nonlinear Programming and General-Sum Stochastic Games.- 3.9 Shapley's Theorem via Mathematical Programming.- 3.10 Bibliographic Notes.- 3.11 Problems.- II Existence, Structure and Applications.- 4 Summable Stochastic Games.- 4.0 Introduction.- 4.1 The Stochastic Game Model.- 4.2 Transient Stochastic Games.- 4.2.1 Stationary Strategies.- 4.2.2 Extension to Nonstationary Strategies.- 4.3 Discounted Stochastic Games.- 4.3.1 Introduction.- 4.3.2 Solutions of Discounted Stochastic Games.- 4.3.3 Structural Properties.- 4.3.4 The Limit Discount Equation.- 4.4 Positive Stochastic Games.- 4.5 Total Reward Stochastic Games.- 4.6 Nonzero-Sum Discounted Stochastic Games.- 4.6.1 Existence of Equilibrium Points.- 4.6.2 A Nonlinear Compementarity Problem.- 4.6.3 Perfect Equilibrium Points.- 4.7 Bibliographic Notes.- 4.8 Problems.- 5 Average Reward Stochastic Games.- 5.0 Introduction.- 5.1 Irreducible Stochastic Games.- 5.2 Existence of the Value.- 5.3 Stationary Strategies.- 5.4 Equilibrium Points.- 5.5 Bibliographic Notes.- 5.6 Problems.- 6 Applications and Special Classes of Stochastic Games.- 6.0 Introduction.- 6.1 Economic Competition and Stochastic Games.- 6.2 Inspection Problems and Single-Control Games.- 6.3 The Presidency Game and Switching-Control Games.- 6.4 Fishery Games and AR-AT Games.- 6.5 Applications of SER-SIT Games.- 6.6 Advertisement Models and Myopic Strategies.- 6.7 Spend and Save Games and the Weighted Reward Criterion.- 6.8 Bibliographic Notes.- 6.9 Problems.- Appendix G Matrix and Bimatrix Games and Mathematical Programming.- G.1 Introduction.- G.2 Matrix Game.- G.3 Linear Programming.- G.4 Bimatrix Games.- G.5 Mangasarian-Stone Algorithm for Bimatrix Games.- G.6 Bibliographic Notes.- Appendix H A Theorem of Hardy and Littlewood.- H.1 Introduction.- H.2 Preliminaries, Results and Examples.- H.3 Proof of the Hardy-Littlewood Theorem.- Appendix M Markov Chains.- M.1 Introduction.- M.2 Stochastic Matrix.- M.3 Invariant Distribution.- M.4 Limit Discounting.- M.5 The Fundamental Matrix.- M.6 Bibliographic Notes.- Appendix P Complex Varieties and the Limit Discount Equation.- P.1 Background.- P.2 Limit Discount Equation as a Set of Simultaneous Polynomials.- P.3 Algebraic and Analytic Varieties.- P.4 Solution of the Limit Discount Equation via Analytic Varieties.- References.

Achieving 100% throughput in an input-queued switch

Abstract: It is well known that head-of-line (HOL) blocking limits the throughput of an input-queued switch with FIFO queues. Under certain conditions, the throughput can be shown to be limited to approximately 58%. It is also known that if non-FIFO queueing policies are used, the throughput can be increased. However it has not been previously shown that if a suitable queueing policy and scheduling algorithm are used then it is possible to achieve 100% throughput for all independent arrival processes. In this paper we prove this to be the case using a simple linear programming argument and quadratic Lyapunov function. In particular we assume that each input maintains a separate FIFO queue for each output and that the switch is scheduled using a maximum weight bipartite matching algorithm.

Hub Location and the p-Hub Median Problem

Abstract: Hub facilities serve as switching and transshipment points in transportation and communication networks. Hub networks concentrate flows on the hub-to-hub links and benefit from economies of scale in interhub transportation. Most hub location research has focused on problems where each origin/destination is allocated to a single hub. However, multiple allocation to more than one hub is necessary to minimize total transportation costs. This paper defines a p-hub median, analogous to a p-median, and presents integer programming formulations for the multiple and single allocation p-hub median problems. Two new heuristics for the single allocation p-hub median problem are evaluated. These heuristics derive a solution to the single allocation p-hub median problem from the solution to the multiple allocation p-hub median problem. Computational results are presented for problems with 10-40 origins/destinations and up to eight hubs. The new heuristics generally perform well in comparison with other heuristics.

The primal-dual method for approximation algorithms and its application to network design problems

Abstract: In the last four decades, combinatorial optimization has been strongly influenced by linear programming. With the mathematical and algorithmic understanding of linear programs came a whole host of ideas and tools that were then applied to combinatorial optimization. Many of these ideas and tools are still in use today, and form the bedrock of our understanding of combinatorial optimization. One of these tools is the primal-dual method. It was proposed by Dantzig, Ford, and Fulkerson [DFF56] as another means of solving linear programs. Ironically, their inspiration came from combinatorial optimization. In the early 1930s, Egervary [Ege31] proved

On the Formulation and Theory of the Newton Interior-Point Method for Nonlinear Programming 1

Abstract: In this work, we first study in detail the formulation of the primal-dual interior-point method for linear programming. We show that, contrary to popular belief, it cannot be viewed.as a damped Newton method applied to the Karush-Kuhn-Tucker conditions for the loga- rithmic barrier function problem. Next, we extend the formulation to general nonlinear programming, and then validate this extension by demonstrating that this algorithm can be implemented so that it is locally and Q-quadratically convergent under only the standard Newton method assumptions. We also establish a global convergence theory for this algorithm and include promising numerical experimentation.

On the formulation and theory of the Newton interior-point method for nonlinear programming

Abstract: In this work, we first study in detail the formulation of the primal-dual interior-point method for linear programming. We show that, contrary to popular belief, it cannot be viewed as a damped Newton method applied to the Karush-Kuhn-Tucker conditions for the logarithmic barrier function problem. Next, we extend the formulation to general nonlinear programming, and then validate this extension by demonstrating that this algorithm can be implemented so that it is locally and Q-quadratically convergent under only the standard Newton method assumptions. We also establish a global convergence theory for this algorithm and include promising numerical experimentation.

An on-line relay coordination algorithm for adaptive protection using linear programming technique

Abstract: An adaptive system for protecting a distribution network should determine and implement relay settings that are most appropriate for the prevailing state of the power system. This paper presents a technique for determining coordinated relay settings. The technique uses the Simplex two-phase method; Phase I determines whether the constraints selected for illustrating the conditionality between primary and back up relays are feasible, and Phase II finds the optimal relay settings. A looped distribution system, protected by directional overcurrent relays, was used for testing the technique. The tests were conducted in a laboratory environment; some results from those tests are reported in the paper.

Mixed-Integer Linear Programming Model for Refinery Short-Term Scheduling of Crude Oil Unloading with Inventory Management

Abstract: This paper addresses the problem of inventory management of a refinery that imports several types of crude oil which are delivered by different vessels. This problem involves optimal operation of crude oil unloading, its transfer from storage tanks to charging tanks, and the charging schedule for each crude oil distillation unit. A mixed-integer optimization model is developed which relies on time discretization. The problem involves bilinear equations due to mixing operations. However, the linearity in the form of a mixed-integer linear program (MILP) is maintained by replacing bilinear terms with individual component flows. The LP-based branch and bound method is applied to solve the model, and several techniques, such as priority branching and bounding, and special ordered sets are implemented to reduce the computation time. This formulation and solution method was applied to an industrial-size problem involving 3 vessels, 6 storage tanks, 4 charging tanks, and 3 crude oil distillation units over 15 time intervals. The MILP model contained 105 binary variables, 991 continuous variables, and 2154 constraints and was effectively solved with the proposed solution approach.

Implementation of Interior Point Methods for Large Scale Linear Programming

Abstract: In this paper we give an overview of the mostimportant characteristics of advanced implementations of interior point methods.

Mixed 0-1 Programming by Lift-and-Project in a Branch-and-Cut Framework

Abstract: We investigate the computational issues that need to be addressed when incorporating general cutting planes for mixed 0-1 programs into a branch-and-cut framework The cuts we use are of the lift-and-project variety Some of the issues addressed have a theoretical answer, but others are of an experimental nature and are settled by comparing alternatives on a set of test problems The resulting code is a robust solver for mixed 0-1 programs We compare it with several existing codes On a wide range of test problems it performs as well as, or better than, some of the best currently available mixed integer programming codes

Multiple centrality corrections in a primal-dual method for linear programming

Abstract: A modification of the (infeasible) primal-dual interior point method is developed. The method uses multiple corrections to improve the centrality of the current iterate. The maximum number of corrections the algorithm is encouraged to make depends on the ratio of the efforts to solve and to factorize the KKT systems. For any LP problem, this ratio is determined right after preprocessing the KKT system and prior to the optimization process. The harder the factorization, the more advantageous the higher-order corrections might prove to be. The computational performance of the method is studied on more difficult Netlib problems as well as on tougher and larger real-life LP models arising from applications. The use of multiple centrality corrections gives on the average a 25% to 40% reduction in the number of iterations compared with the widely used second-order predictor-corrector method. This translates into 20% to 30% savings in CPU time.

Coordination of directional overcurrent relay timing using linear programming

Abstract: A successive linear programming methodology is presented to treat more effectively those applications where a local structure change is performed to a power system already in operation, and where the modification of the settings of already existent relays is not desirable. The dimension of the optimization problems to be solved is substantially reduced, and a sequence of small linear programming problems is stated and solved in terms of the time dial settings, until a feasible solution is reached. With the proposed technique, the number of relays of the original system to be reset is reduced substantially. It is found that there is a trade-off between the number of relays to be reset and the optimality of the settings of the relays.

A new neural network for solving linear and quadratic programming problems

Abstract: A new neural network for solving linear and quadratic programming problems is presented and is shown to be globally convergent. The new neural network improves existing neural networks for solving these problems: it avoids the parameter turning problem, it is capable of achieving the exact solutions, and it uses only simple hardware in which no analog multipliers for variables are required. Furthermore, the network solves both the primal problems and their dual problems simultaneously.

A production planning methodology for semiconductor manufacturing based on iterative simulation and linear programming calculations

Abstract: We introduce a methodology for automated production planning of semiconductor manufacturing based on iterative linear programming (LP) optimization and discrete-event simulation calculations. The LP formulation incorporates epoch dependent parameters for flow times from lot release up to each operation on each manufacturing route. LP-derived release schedules are used as input to the simulation model, from which statistics on flow times are collected and used to reformulate the LP model for a revised planning calculation. Iteration continues until satisfactory agreement between simulation and LP models is obtained. We demonstrate in experiments on an industry data set that a relatively small number of iterations is required to develop a production plan correctly characterizing future flow times as a function of factory load and product mix. The methodology makes possible automated production planning of semiconductor manufacturing on an engineering work station.

Normal-boundary intersection: an alternate method for generating pareto optimal points in multicriteria optimization problems

Abstract: This paper proposes an alternate method for finding several Pareto optimal points for a general nonlinear multicriteria optimization problem, aimed at capturing the tradeoff among the various conflicting objectives. It can be rigorously proved that this method is completely independent of the relative scales of the functions and is quite successful in producing an evenly distributed set of points in the Pareto set given an evenly distributed set of `weights'', a property which the popular method of linear combinations lacks. Further, this method can be easily extended in case of more than two objectives while retaining the computational efficiency of continuation-type algorithms, which is an improvement over homotopy techniques for tracing the tradeoff curve.

A polynomial-time algorithm for learning noisy linear threshold functions

Abstract: The authors consider the problem of learning a linear threshold function (a halfspace in n dimensions, also called a "perceptron"). Methods for solving this problem generally fall into two categories. In the absence of noise, this problem can be formulated as a linear program and solved in polynomial time with the ellipsoid algorithm (or interior point methods). On the other hand, simple greedy algorithms such as the perceptron algorithm seem to work well in practice and can be made noise tolerant; but, their running time depends on a separation parameter (which quantifies the amount of "wiggle room" available) and can be exponential in the description length of the input. They show how simple greedy methods can be used to find weak hypotheses (hypotheses that classify noticeably more than half of the examples) in polynomial time, without dependence on any separation parameter. This results in a polynomial-time algorithm for learning linear threshold functions in the PAC model in the presence of random classification noise. The algorithm is based on a new method for removing outliers in data. Specifically, for any set S of points in R/sup n/, each given to b bits of precision, they show that one can remove only a small fraction of S so that in the remaining set T, for every vector v, max/sub x/spl epsiv/T/(v/spl middot/x)/sup 2//spl les/poly(n,b)|T|/sup -1//spl Sigma//sub x/spl epsiv/T/(v/spl middot/x)/sup 2/. After removing these outliers, they are able to show that a modified version of the perceptron learning algorithm works in polynomial time, even in the presence of random classification noise.

An improved approximation ratio for the minimum latency problem

Abstract: Given a tour visitingn points in a metric space, thelatency of one of these pointsp is the distance traveled in the tour before reachingp. Theminimum latency problem (MLP) asks for a tour passing throughn given points for which the total latency of then points is minimum; in effect, we are seeking the tour with minimum average “arrival time”. This problem has been studied in the operations research literature, where it has also been termed the “delivery-man problem” and the “traveling repairman problem”. The approximability of the MLP was first considered by Sahni and Gonzalez in 1976; however, unlike the classical traveling salesman problem (TSP), it is not easy to give any constant-factor approximation algorithm for the MLP. Recently, Blum et al. (A. Blum, P. Chalasani, D. Coppersimith, W. Pulleyblank, P. Raghavan, M. Sudan, Proceedings of the 26th ACM Symposium on the Theory of Computing, 1994, pp. 163–171) gave the first such algorithm, obtaining an approximation ratio of 144. In this work, we develop an algorithm which improves this ratio to 21.55; moreover, combining our algorithm with a recent result of Garg (N. Garg, Proceedings of the 37th IEEE Symposium on Foundations of Computer Science, 1996, pp. 302–309) provides an approximation ratio of 10.78. The development of our algorithm involves a number of techniques that seem to be of interest from the perspective of the TSP and its variants more generally. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.

FIR filter design via semidefinite programming and spectral factorization

Abstract: We present a semidefinite programming approach to FIR filter design with arbitrary upper and lower bounds on the frequency response magnitude. It is shown that the constraints can be expressed as linear matrix inequalities (LMIs), and hence they can be easily handled by interior-point methods. Using this LMI formulation, we can cast several interesting filter design problems as convex or quasi-convex optimization problems, e.g. minimizing the length of the FIR filter and computing the Chebychev approximation of a desired power spectrum or a desired frequency response magnitude on a logarithmic scale.

Made-to-measure N-body systems

Abstract: We describe an algorithm for constructing N-body realisations of equilibrium stellar systems. The algorithm complements existing orbit-based modelling techniques using linear programming or other optimization algorithms. The equilibria are constructed by integrating an N-body system while slowly adjusting the masses of the particles until the time-averaged density field and other observables converge to a prescribed value. The procedure can be arranged to maximise a linear combination of the entropy of the system and the $\chi^2$ statistic for the observables. The equilibria so produced may be useful as initial conditions for N-body simulations or for modelling observations of individual galaxies.

Heuristics for the integer one-dimensional cutting stock problem: A computational study

Abstract: In this paper the problem of generating integer solutions to the standard one-dimensional cutting stock problem is treated. In particular, we study a specific class of heuristic approaches that have been proposed in the literature, and some straightforward variants. These methods are compared with respect to solution quality and computing time. Our evaluation is based on having solved 4,000 randomly generated test problems. Not only will it be shown that two methods are clearly superior to the others but also that they solve almost any instance of the standard one-dimensional cutting stock problem to an optimum.