Showing papers on "Discrete optimization published in 1981"
TL;DR: In this article, an interior penalty function is used to convert the original constrained problem into an unconstrained parametric problem, and then the search for the optimal solution to the parametric problems is based on a discrete direction gradient.
Abstract: A new method for solving discrete structural optimization problems is presented. An interior penalty function is used to convert the original constrained problem into an unconstrained parametric problem. Then the search for the optimal solution to the parametric problem is based on a discrete direction gradient. Solving an appropriate sequence of these unconstrained parametric problems is equivalent to solving the original constrained optimization problem. This method is illustrated first on a small reinforced concrete problem, and then to the design of steel building frames which are made up of standard sections. Results for a one-story four-bay unsymmetrical frame and an eight-story three-bay symmetrical frame are described.
66 citations
TL;DR: Inverse optimization refers to the fact that each time a Lagrangean calculation is made for a specific problem with a given resources vector, an optimal solution is obtained for a related problem with suitably adjusted resources vector.
Abstract: Lagrangean techniques have had wide application to the optimization of discrete optimization problems. Inverse optimization refers to the fact that each time a Lagrangean calculation is made for a specific problem with a given resources vector, an optimal solution is obtained for a related problem with a suitably adjusted resources vector. This property is studied in depth for the capacitated plant location problem and new parametric methods for that problem are suggested. Computational experience is reported.
31 citations
TL;DR: Two important features of the classical nonbifurcated flow deviation algorithm, originally proposed by Fratta et al, are improved: the path length metric and the starting flow calculation.
20 citations
TL;DR: A semi-analytical approach to the discrete optimization of large space-truss structures as a cost (weight) minimization subject to constraints both on stresses and displacements.
Abstract: The paper presents a semi-analytical approach to the discrete optimization of large space-truss structures. The optimization problem is stated as a cost (weight) minimization subject to constraints both on stresses and displacements. Some of the variables such as the cross-sectional areas of bars are chosen from a discrete set (catalogue) of available sections. The mined nonlinear programming problem is transformed to discrete nonlinear programming using the Galerkin procedure in solving the partial difference equations which describe displacements of nodes of the structure. Finally, introducing Boolean variables the problem is solved using algorithms of logical programming. Numerical results are presented for a “Unistrut” type space-truss structure.
15 citations
Book•
01 Jan 1981
TL;DR: More information is available at www.agh.edu.pl about the 23rd IFIP TC 7 Conference on extended abstract submission and the deadline for reduced registration fee and short abstract submission of accepted talks.
Abstract: More Information http://ifip2007.agh.edu.pl e-mail: ifip2007@agh.edu.pl Conference Fees Normal rate: 240,EURO per person Reduced rate for graduate students and postdocs up to age 30: 190,EURO per person 23rd IFIP TC 7 Conference on • Extended abstract submission • Notification of acceptance of the presentation • Deadline for reduced registration fee and short abstract submission of accepted talks • Deadline for hotel reservation • Deadline for registration February 12, 2007
10 citations
TL;DR: A method of model reduction, previously given by the authors for continuous-time systems, is extended to the discrete case and a comparison is made with other methods, which are based on the retention of dominant modes.
Abstract: A method of model reduction, previously given by the authors for continuous-time systems, is extended to the discrete case. The results are applied to two practical examples and the effect of varying the sampling period is investigated. A comparison is made with other methods, which are based on the retention of dominant modes.
9 citations
TL;DR: An adaptive optimization scheme for economic machining is developed and tested by a series of experiments and shows that the method is practical, valid and independent of the initially selected cutting conditions.
Abstract: An adaptive optimization scheme for economic machining is developed and tested by a series of experiments. The scheme involves actual shop data and optimization techniques based on computer simulation. The computer simulation model is formulated for stages with one, two and three or more sets of shop data. The results show that the method is practical, valid and independent of the initially selected cutting conditions.
8 citations
DOI•
01 Nov 1981
TL;DR: One of the approaches to the construction of locally optimal optimization algorithms on multiprocessor computers, called frontal, is described, which confirms that the frontal algorithms are highly successful even with single-processor computers.
Abstract: The authors describe one of the approaches to the construction of locally optimal optimization algorithms on multiprocessor computers. Algorithms of this type, called frontal, have been realized previously on single-processor computers, although this configuration does not fully exploit the specific features of their computational scheme. Experience with a number of practical discrete optimization problems confirms that the frontal algorithms are highly successful even with single-processor computers. 9 references.
TL;DR: In this article, the authors present a Rejoinder to Golden and Assad's Review of Computer Optimization Techniques (ROCO), which is a review of computer optimization techniques.
Abstract: (1981). Rejoinder to Golden and Assad's Review of Computer Optimization Techniques. American Journal of Mathematical and Management Sciences: Vol. 1, No. 1, pp. 93-101.
01 Aug 1981
TL;DR: Criteria of optimization, classical methods of optimized, numerical methods, and optimal search procedures are discussed and methods of optimization with special applications to simulation are discussed.
Abstract: : Simulation techniques utilize methods of optimization in several aspects. Validation of simulation models, estimation of models and design of simulation experiments require optimization. Methods of optimization are discussed with special applications to simulation. Criteria of optimization, classical methods of optimization, numerical methods, and optimal search procedures are discussed. (Author)
TL;DR: Results in functional analysis are applied to the computation of the optimal control of a parabolic distributed parameter system, and an a-priori feedback is determined in the case of a constant control on the optimization interval.
01 Jan 1981
TL;DR: The method based on a one-step one-dimensionai Bayesian technique seems to be very efficient, as a neighbourhood of the global optimum is attained in a relatively small number of function evaluations.
Abstract: In this paper a new method for global optimization is presented. The method based on a one-step one-dimensionai Bayesian technique seems to be very efficient, as a neighbourhood of the global optimum is attained in a relatively small number of function evaluations. The method is then extended to higher dimensional cases. A number of numerical examples is presented to illustrate the behaviour of the algorithm.
01 Jan 1981