Showing papers on "Discrete optimization published in 1977"
TL;DR: This paper presents a new approach to the solution of multi-target tracking problems that is approached as an unsupervised pattern recognition problem and has the computational structure of the set packing and set partitioning problems of 0-1 integer programming.
Abstract: This paper presents a new approach to the solution of multi-target tracking problems. 0-1 integer programming methods are used to alleviate the combinatorial computing difficulties that accompany any but the smallest of such problems. Multitarget tracking is approached as an unsupervised pattern recognition problem. A multiple-hypothesis test is performed to determine which particular combination of the many feasible tracks is most likely to represent actual targets. This multiple hypothesis test is shown to have the computational structure of the set packing and set partitioning problems of 0-1 integer programming. Multitarget tracking problems that are translated into this form can be rapidly solved, using well-known discrete optimization techniques such as implicit enumeration.
311 citations
101 citations
TL;DR: In this paper, the authors consider econometric models involving variables that are defined continuously over time, or more frequently than they are observed, and give justification for the use of standard discrete time models.
44 citations
32 citations
TL;DR: This paper is a state-of-the-art review of software for combined continuous/discrete systems simulation with the following 18 languages or packages discussed.
Abstract: This paper is a state-of-the-art review of software for combined continuous/discrete systems simulation. The following 18 languages or packages are discussed:
28 citations
05 Dec 1977
TL;DR: An optimization module is added to the GASP IV software package to provide automated optimization of a set of user defined decision variables and can be used for decision optimization in discrete, continuous, and combined simulation models.
Abstract: This paper describes an optimization module which can be added to the GASP IV software package to provide automated optimization of a set of user defined decision variables. The optimization procedure is a variation of the Hooke-Jeeves pattern search and can be used for decision optimization in discrete, continuous, and combined simulation models. The application of the optimization module is illustrated with two examples.
16 citations
5 citations
01 May 1977
TL;DR: The proposed algorithm is shown to be fairly fast, while Branch-and-Bound methods are likely to be more time consuming, and at least for the examples provided elsewhere, better than the results of another heuristic method.
Abstract: This paper presents a novel algorithm for the discrete optimization of the coefficients of digital filters implemented as direct, parallel, or cascade structures. This optimization takes into account arbitrary magnitude specifications. The proposed algorithm is based on several aspects of discrete optimization (e.g. one-variable, two-variable and random search) and on the relation between DC gain and coefficients in a digital filter. Several examples are provided and the results obtained for them are compared with those given by four other methods recently published [2, 3, 5, 10] . The effectiveness of the proposed algorithm is subsequently discussed : For small scale examples [2, 3, 10] , results are very similar to those of some more "mathematical" methods (such as Branch-and-Bound) [2, 3] and, at least for the examples provided elsewhere, better than the results of another heuristic method [10]. For a larger scale example (8th order elliptic filter) [5] the proposed algorithm is shown to be fairly fast, while Branch-and-Bound methods are likely to be more time consuming.
4 citations
3 citations
TL;DR: The foundations, applications, and convergence properties of discrete weighted residual methods (DWRM's) are presented in Refs. 1-3 and as mentioned in this paper, which serve to illustrate the applicability of DWRM for solving a sensitive nonlinear discrete boundary-value problem.
Abstract: The foundations, applications, and convergence properties of discrete weighted residual methods (DWRM's) are presented in Refs. 1–3. This paper serves to illustrate DWRM's for solving a sensitive nonlinear discrete boundary-value problem. The results indicate that DWRM's can be applied to provide models of increasing complexity which can then be utilized for the analysis and design of physical systems.
01 Jan 1977
TL;DR: Different approaches to evaluating optimization routines are discussed, and a particular method which uses parameterized test problems is described, which is illustrated through a simple case study of three well-known unconstrained optimization routines applied to three parameterize test problems.
Abstract: Different approaches to evaluating optimization routines are discussed, and a particular method which uses parameterized test problems is described. This approach is illustrated through a simple case study of three well-known unconstrained optimization routines applied to three parameterized test problems. The results are displayed as a set of graphs. 3 figures.
TL;DR: In this paper, the authors discuss the property of reachability and controllability for a linear discrete time system, with the trajectory constrained to satisfy a state variable inequality constraint of the form.
Abstract: In this paper we discuss the property of reachability and controllability for a linear discrete time system, with the trajectory constrained to satisfy a state variable inequality constraint of the form.
TL;DR: In this article, an approach for solving discrete optimization problems by decomposition is illustrated by a linear integer program and two integer problems, and numerical results for the inventory problems are given for both linear integer programs and integer problems.
Abstract: An approach for solving discrete optimization problems by decomposition is illustrated by a linear integer program and two integer problems. Numerical results are given for the inventory problems.
01 Jan 1977
TL;DR: A new approach to the solution of multi- tracks is presented, showing which particular combination of the many feasible targets is most likely to represent actual targets.
Abstract: Abstmci--This paper presents a new approach to the solution of multi- tracks is most likely to represent actual targets. This multiple hypothesis target tracking problems. 0-1 integer programming methods are used to test is shown to have the computational struetore of the set packing and set alleviate the combinatorial computing difficulties that accompany any but partitioning problems of 0-1 integer programming. Multitarget tracking the smallest of such problems. Multitarget tracking is approached as an problems that are translated into this form can be rapidly solved, using onsupervised pattern recognition problem. A multiplehypothesis test is well-known discrete optimization techniques sueh as isplicit enumeration. performed to determine which particular combination of the many feasible