Showing papers on "Linear programming published in 1999"

Mathematical Methods and Algorithms for Signal Processing

Abstract: I. INTRODUCTION AND FOUNDATIONS. 1. Introduction and Foundations. II. VECTOR SPACES AND LINEAR ALGEBRA. 2. Signal Spaces. 3. Representation and Approximation in Vector Spaces. 4. Linear Operators and Matrix Inverses. 5. Some Important Matrix Factorizations. 6. Eigenvalues and Eigenvectors. 7. The Singular Value Decomposition. 8. Some Special Matrices and Their Applications. 9. Kronecker Products and the Vec Operator. III. DETECTION, ESTIMATION, AND OPTIMAL FILTERING. 10. Introduction to Detection and Estimation, and Mathematical Notation. 11. Detection Theory. 12. Estimation Theory. 13. The Kalman Filter. IV. ITERATIVE AND RECURSIVE METHODS IN SIGNAL PROCESSING. 14. Basic Concepts and Methods of Iterative Algorithms. 15. Iteration by Composition of Mappings. 16. Other Iterative Algorithms. 17. The EM Algorithm in Signal Processing. V. METHODS OF OPTIMIZATION. 18. Theory of Constrained Optimization. 19. Shortest-Path Algorithms and Dynamic Programming. 20. Linear Programming. APPENDIXES. A. Basic Concepts and Definitions. B. Completing the Square. C. Basic Matrix Concepts. D. Random Processes. E. Derivatives and Gradients. F. Conditional Expectations of Multinomial and Poisson r.v.s.

Constrained Markov Decision Processes

Abstract: This report presents a unified approach for the study of constrained Markov decision processes with a countable state space and unbounded costs. We consider a single controller having several objectives; it is desirable to design a controller that minimize one of cost objective, subject to inequality constraints on other cost objectives. The objectives that we study are both the expected average cost, as well as the expected total cost (of which the discounted cost is a special case). We provide two frameworks: the case were costs are bounded below, as well as the contracting framework. We characterize the set of achievable expected occupation measures as well as performance vectors. This allows us to reduce the original control dynamic problem into an infinite Linear Programming. We present a Lagrangian approach that enables us to obtain sensitivity analysis. In particular, we obtain asymptotical results for the constrained control problem: convergence of both the value and the policies in the time horizon and in the discount factor. Finally, we present and several state truncation algorithms that enable to approximate the solution of the original control problem via finite linear programs.

A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods

Abstract: For pt.II see ibid., vol.14, no.1, p.96-104 (1999). This second of a two part paper offers a survey of literature on optimal power flow from 1968-93. This part treats Newton-based, linear programming and interior point methods of solution.

Multicategory Classification by Support Vector Machines

Abstract: We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how two-class discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic programming (QP) approaches based on Vapnik‘s Support Vector Machine (SVM) can be combined to yield two new approaches to the multiclass problem. In LP multiclass discrimination, a single linear program is used to construct a piecewise-linear classification function. In our proposed multiclass SVM method, a single quadratic program is used to construct a piecewise-nonlinear classification function. Each piece of this function can take the form of a polynomial, a radial basis function, or even a neural network. For the k > 2-class problems, the SVM method as originally proposed required the construction of a two-class SVM to separate each class from the remaining classes. Similarily, k two-class linear programs can be used for the multiclass problem. We performed an empirical study of the original LP method, the proposed k LP method, the proposed single QP method and the original k QP methods. We discuss the advantages and disadvantages of each approach.

Dynamic Programming and Strong Bounds for the 0-1 Knapsack Problem

Abstract: Two new algorithms recently proved to outperform all previous methods for the exact solution of the 0-1 Knapsack Problem. This paper presents a combination of such approaches, where, in additi on, valid inequalities are generated and surrogate relaxed, and a new initial core problem is adopted. The algorithm is able to solve all classical test instances, with up to 10,000 variables, in less than 0.2 seconds on a HP9000-735/99 computer. The C language implementation of the algorithm is available on the internet.

A Computational Study of Search Strategies for Mixed Integer Programming

Abstract: The branch-and-bound procedure for solving mixed integer programming (MIP) problems using linear programming relaxations has been used with great success for decades. Over the years, a variety of researchers have studied ways of making the basic algorithm more effective. Breakthroughs in the fields of computer hardware, computer software, and mathematics have led to increasing success at solving larger and larger MIP instances. The goal of this article is to survey many of the results regarding branch-and-bound search strategies and evaluate them again in light of the other advances that have taken place over the years. In addition, novel search strategies are presented and shown to often perform better than those currently used in practice.

Newton's Method for Large Bound-Constrained Optimization Problems

Abstract: We analyze a trust region version of Newton's method for bound-constrained problems. Our approach relies on the geometry of the feasible set, not on the particular representation in terms of constraints. The convergence theory holds for linearly constrained problems and yields global and superlinear convergence without assuming either strict complementarity or linear independence of the active constraints. We also show that the convergence theory leads to an efficient implementation for large bound-constrained problems.

Optimal control of hybrid systems

Abstract: This paper presents a method for optimal control of hybrid systems. An inequality of Bellman type is considered and every solution to this inequality gives a lower bound on the optimal value function. A discretization of this "hybrid Bellman inequality" leads to a convex optimization problem in terms of finite-dimensional linear programming. From the solution of the discretized problem, a value function that preserves the lower bound property can be constructed. An approximation of the optimal feedback control law is given and tried on some examples.

An Introductory Tutorial on Stochastic Linear Programming Models

Abstract: Linear programming is a fundamental planning tool. It is often difficult to precisely estimate or forecast certain critical data elements of the linear program. In such cases, it is necessary to address the impact of uncertainty during the planning process. We discuss a variety of LP-based models that can be used for planning under uncertainty. In all cases, we begin with a deterministic LP model and show how it can be adapted to include the impact of uncertainty. We present models that range from simple recourse policies to more general two-stage and multistage SLP formulations. We also include a discussion of probabilistic constraints. We illustrate the various models using examples taken from the literature. The examples involve models developed for airline yield management, telecommunications, flood control, and production planning.

Random Yield and Random Demand in a Production System with Downward Substitution

Abstract: in this paper, we present and solve a single-period, multiproduct, downward substitution model. Our model has one raw material as the production input and produces N different products as outputs. The demands and yields for the products are random. We determine the optimal production input and allocation of the N products to satisfy demands. The problem is modeled as a two-stage stochastic program, which we show can be decomposed into a parameterized network flow problem. We present and compare three different solution methods: a stochastic linear program, a decomposition resulting in a series of network flow subproblems, and a decomposition where the same network flow subproblems are solved by a new greedy algorithm.

Algorithmic Aspects of the Core of Combinatorial Optimization Games

Abstract: We discuss an integer programming formulation for a class of cooperative games. We focus on algorithmic aspects of the core, one of the most important solution concepts in cooperative game theory. Central to our study is a simple but very useful observation that the core for this class is nonempty if and only if an associated linear program has an integer optimal solution. Based on this, we study the computational complexity and algorithms to answer important questions about the cores of various games on graphs, such as maximum flow, connectivity, maximum matching, minimum vertex cover, minimum edge cover, maximum independent set, and minimum coloring.

Optimal capacitor placement using deterministic and genetic algorithms

Abstract: A procedure for solving the power capacitor placement problem is presented. The objective is to determine the minimum investment required to satisfy suitable reactive constraints. Due to the discrete nature of reactive compensation devices, optimal capacitor placement leads to a nonlinear programming problem with mixed (discrete and continuous) variables. It is solved with an iterative algorithm based on successive linearizations of the original nonlinear model. The mixed integer linear programming problem to be solved at each iteration of the procedure is tackled by applying both a deterministic method (branch and bound) and genetic algorithm techniques. A hybrid procedure, aiming to exploit the best features of both algorithms is also considered. The proposed procedures are tested and compared with reference to a small CIGRE system and two actual networks derived from the Italian transmission and distribution system.

Fast and Robust Smallest Enclosing Balls

Abstract: I describe a C++ program for computing the smallest enclosing ball of a point set in d-dimensional space, using floating-point arithmetic only. The program is very fast for d ? 20, robust and simple (about 300 lines of code, excluding prototype definitions). Its new features are a pivoting approach resembling the simplex method for linear programming, and a robust update scheme for intermediate solutions. The program with complete documentation following the literate programming paradigm [3] is available on the Web.

Regularized symmetric indefinite systems in interior point methods for linear and quadratic optimization

Abstract: This paper presents linear algebra techniques used in the implementation of an interior point method for solving linear programs and convex quadratic programs with linear constraints. New regularization techniques for Newton systems applicable to both symmetric positive definite and symmetric indefinite systems are described. They transform the latter to quasidef-inite systems known to be strongly factorizable to a form of Cholesky-like factorization.Two different regularization techniques,primal; and dual, are very well suited to the (infeasible) primal-dual interior point algorithm. This particular algorithm, with an extension of multiple centrality correctors, is implemented in our solver HOPDM. Computational results are given to illustrate the potential advantages of the approach when applied to the solution of very large linear and convex quadratic programs.

Improving an interior-point-based OPF by dynamic adjustments of step sizes and tolerances

Abstract: This paper presents an efficient interior point algorithm for optimal power flow (OPF) problems, in particular, the real power dispatch and the reactive power dispatch problems. The nonlinear OPF problem is solved by a predictor-corrector primal-dual log-barrier (PCPDLB) method as a sequence of linearized sub-problems. Besides discussing the problem formulation, the paper offers a detailed description of the PCPDLB algorithm; it also addresses several implementation issues such as the determination of barrier parameter and the customization of initial points for OPF problems. In addition, practical issues on how to choose linear step sizes and convergence criteria are investigated to evaluate their impact on the performance of the algorithm. Some heuristics of dynamically adjusting step sizes and tolerance are proposed which significantly improve OPF solution speed. Computational results on power systems of 118 and 1062 buses are presented and discussed. Comparisons with other variants of primal-dual log-barrier methods are also provided to demonstrate the superiority of the proposed predictor-corrector interior point algorithm.

A Randomized Linear Programming Method for Computing Network Bid Prices

Abstract: We analyze a randomized version of the deterministic linear programming (DLP) method for computing network bid prices. The method consists of simulating a sequence of realizations of itinerary demand and solving deterministic linear programs to allocate capacity to itineraries for each realization. The dual prices from this sequence are then averaged to form a bid price approximation. This randomized linear programming (RLP) method is only slightly more complicated to implement than the DLP method. We show that the RLP method can be viewed as a procedure for estimating the gradient of the expected perfect information (PI) network revenue. That is, the expected revenue obtained with full information on future demand realizations. The expected PI revenue can, in turn, be viewed as an approximation to the optimal value function. We establish conditions under which the RLP procedure provides an unbiased estimator of the gradient of the expected PI revenue. Computational tests are performed to evaluate the revenue performance of the RLP method compared to the DLP.

Dynamic layout planning using a hybrid incremental solution method

Abstract: Efficiently using site space to accommodate resources throughout the duration of a construction project is a critical problem. It is termed the “dynamic layout planning” problem. Solving it involves creating a sequence of layouts that span the entire project duration, given resources, the timing of their presence on site, their changing demand for space over time, constraints on their location, and costs for their relocation. A dynamic layout construction procedure is presented here. Construction resources, represented as rectangles, are subjected to two-dimensional geometric constraints on relative locations. The objective is to allow site space to all resources so that no spatial conflicts arise, while keeping distance-based adjacency and relocation costs minimal. The solution is constructed stepwise for consecutive time frames. For each resource, selected heuristically one at a time, constraint satisfaction is used to compute sets of feasible positions. Subsequently, a linear program is solved to find ...

Multiclass least squares support vector machines

Abstract: We present an extension of least squares support vector machines (LS-SVMs) to the multiclass case. While standard SVM solutions involve solving quadratic or linear programming problems, the least squares version of SVMs corresponds to solving a set of linear equations, due to equality instead of inequality constraints in the problem formulation. In LS-SVMs the Mercer condition is still applicable. Hence several type of kernels such as polynomial, RBFs and MLPs can be used. The multiclass case that we discuss here is related to classical neural net approaches for classification where multi-classes are encoded by considering multiple outputs for the network. Efficient methods for solving large scale LS-SVMs are available.

Cost-Based Domain Filtering

Abstract: Constraint propagation is aimed at removing from variable domains combinations of values which cannot appear in any consistent solution. Pruning derives from feasibility reasoning. When coping with optimization problems, pruning can be performed also on the basis of costs, i.e., optimality reasoning. Propagation can be aimed at removing combination of values which cannot lead to solutions whose cost is better then the best one found so far. For this purpose, we embed in global constraints optimization components representing suitable relaxations of the constraint itself. These components provide efficient Operations Research algorithms computing the optimal solution of the relaxed problem and a gradient function representing the estimated cost of each variable-value assignment. We exploit these pieces of information for pruning and for guiding the search. We have applied these techniques to a couple of ILOG Solver global constraints (a constraint of difference and a path constraint) and tested the approach on a variety of combinatorial optimization problems such as Timetabling, Travelling Salesman Problems and Scheduling Problems with setup. Comparisons with pure Constraint Programming approaches and related literature clearly show the benefits of the proposed approach. By using cost-based filtering in global constraints, we can optimally solve problems that are one order of magnitude greater than those solved by pure CP approaches, and we outperform other hybrid approaches integrating OR techniques in Constraint Programming.

Improved Approximation Guarantees for Packing and Covering Integer Programs

Abstract: Several important NP-hard combinatorial optimization problems can be posed as packing/covering integer programs; the randomized rounding technique of Raghavan and Thompson is a powerful tool with which to approximate them well. We present one elementary unifying property of all these integer linear programs and use the FKG correlation inequality to derive an improved analysis of randomized rounding on them. This yields a pessimistic estimator, thus presenting deterministic polynomial-time algorithms for them with approximation guarantees that are significantly better than those known.

Simulation vector generation from HDL descriptions for observability-enhanced statement coverage

Abstract: Validation of RTL circuits remains the primary bottleneck in improving design turnaround time, and simulation remains the primary methodology for validation. Simulation-based validation has suffered from a disconnect between the metrics used to measure the error coverage of a set of simulation vectors, and the vector generation process. This disconnect has resulted in the simulation of virtually endless streams of vectors which achieve enhanced error coverage only infrequently. Another drawback has been that most error coverage metrics proposed have either been too simplistic or too inefficient to compute. Recently, an effective observability-based statement coverage metric was proposed along with a fast companion procedure for evaluating it. The contribution of our work is the development of a vector generation procedure targeting the observability-based statement coverage metric. Our method uses repeated coverage computation to minimize the number of vectors generated. For vector generation, we propose a novel technique to set up constraints based on the chosen coverage metric. Once the system of interacting arithmetic and Boolean constraints has been set up, it can be solved using hybrid linear programming and Boolean satisfiability methods. We present heuristics to control the size of the constraint system that needs to be solved. We present experimental results which show the viability of automatically generating vectors using our approach for industrial RTL circuits. We envision our system being used during the design process, as well as during post-design debugging.

Parallel Machine Scheduling by Column Generation

Abstract: Parallel machine scheduling problems concern the scheduling of n jobs on m machines to minimize some function of the job completion times. If preemption is not allowed, then most problems are not only NP-hard, but also very hard from a practical point of view. In this paper, we show that strong and fast linear programming lower bounds can be computed for an important class of machine scheduling problems with additive objective functions. Characteristic of these problems is that on each machine the order of the jobs in the relevant part of the schedule is obtained through some priority rule. To that end, we formulate these parallel machine scheduling problems as a set covering problem with an exponential number of binary variables, n covering constraints, and a single side constraint. We show that the linear programming relaxation can be solved efficiently by column generation because the pricing problem is solvable in pseudo-polynomial time. We display this approach on the problem of minimizing total weighted completion time on m identical machines. Our computational results show that the lower bound is singularly strong and that the outcome of the linear program is often integral. Moreover, they show that our branch-and-bound algorithm that uses the linear programming lower bound outperforms the previously best algorithm.

Ergodic, primal convergence in dual subgradient schemes for convex programming

Abstract: Lagrangean dualization and subgradient optimization techniques are frequently used within the field of computational optimization for finding approximate solutions to large, structured optimization problems. The dual subgradient scheme does not automatically produce primal feasible solutions; there is an abundance of techniques for computing such solutions (via penalty functions, tangential approximation schemes, or the solution of auxiliary primal programs), all of which require a fair amount of computational effort. We consider a subgradient optimization scheme applied to a Lagrangean dual formulation of a convex program, and construct, at minor cost, an ergodic sequence of subproblem solutions which converges to the primal solution set. Numerical experiments performed on a traffic equilibrium assignment problem under road pricing show that the computation of the ergodic sequence results in a considerable improvement in the quality of the primal solutions obtained, compared to those generated in the basic subgradient scheme.