Showing papers on "Discrete optimization published in 1974"

Optimization of recursive digital filters with finite word lengths

Abstract: The application of the "branch and bound" technique for nonlinear discrete optimization, due to Dakin, to the problem of finding the coefficients of a recursive digital filter with prescribed number of bits, to meet arbitrary response specifications of the magnitude characteristic, is investigated. Due to the fact that the objective function is nonlinear and the stability constraints are linear with respect to the parameter, the recent algorithm for nonlinear programming due to Best and Ritter is used. Based on the ideas presented, a general computer program has been developed. Numerical experience with the present approach is also presented.

Cost Minimizing Single Sampling Plans with AIQL and AOQL Constraints

Abstract: This paper investigates and formulates the relationships among average incoming quality limit (AIQL), production process quality level and average fraction defective after production process for a single-stage manufacturing connected-unit situation. Cost components of the system are identified. In order to derive minimum cost single sampling plans where inspection is by attribute, a mathematical model is developed and plans are obtained using discrete optimization for the total expected loss function subject to AIQL and AOQL equality constraints.

The optimization problem

Abstract: The requirement for methods of optimization arises from the mathematical complexity necessary to describe the theory of systems, processes, equipment, and devices which occur in practice. Even quite simple systems must sometimes be represented by theory which may contain approximations, by parameters which change with time, or by parameters that vary in a random manner. For many reasons the theory is imperfect, yet it must be used to predict the optimum operating conditions of a system such that some performance criterion is satisfied. At best such theory can predict only that the system is near to the desired optimum. Optimization methods are then used to explore the local region of operation and predict the way that the system parameters should be adjusted to bring the system to optimum.

An Adaptive Search Optimization Algorithm

Abstract: A very fast nongradient procedure for function optimization is described. The procedure is based on the ideas of Rosenbrock [1] and Swann [2]. These were modified and refined to obtain an algorithm which provides an optimum with a very small number of function evaluations. This algorithm, compared with recently reported algorithms by Lawrence and Steglitz (L-S) [3], and Beltrami and Indusi (B-I) [4], appears to be very robust and reliable. Constrained optimization problems can be handled and a special method for handling optimization with linear constraints is presented.

Optimal sample-data control of prt vehicles

Abstract: In the study of longitudinal control of automated vehicles, two control schemes have evolved. One, the car-following model formulates the control problem in terms of differential position and velocities between each vehicle in a string of n vehicles. The other control scheme, the point-following model, focuses on the design of longitudinal controllers which function in a synchronous or quasi- synchronous manner. This paper develops and analyzes the performance of a deterministic sample-data discrete optimization formulation of the point-follower control scheme.

Optimizing the metal cutting of variable shapes subject to noise

Abstract: A discrete optimization method is described that accounts for the effects of noise. Estimates of the standard deviation of the noise are used to control the adjustments of parameters during optimization. Changes in shape are readily detected and constraint violations are corrected almost immediately.

Comparison of various methods for estimating the parameters characterizing noise in discrete time dynamical systems

Abstract: In this paper we consider various methods for estimating the parameters characterizing process noise in a discrete time dynamical system.The comparison is based on the following factors—storage requirements, numerical computation and statistical efficiency of the estimates. Simulation results are given for a simple model.