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Process-Parameter Optimization
1998The abrasive water jet-cutting process is characterized by a large number of process parameters that determine efficiency, economy, and quality of the whole process. Therefore, optimization of the process is a primary requirement for a successful application.
Andreas W. Momber, Radovan Kovacevic
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2001
In this chapter we study several topics related to global optimality. First we show that one can characterize the parameters that globally optimize the optimal value function on a particular subset of the feasible set called the “region of cooperation”. The characterization is simplified if the so-called “sandwich condition” holds.
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In this chapter we study several topics related to global optimality. First we show that one can characterize the parameters that globally optimize the optimal value function on a particular subset of the feasible set called the “region of cooperation”. The characterization is simplified if the so-called “sandwich condition” holds.
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Discrete Parameter Optimization
2013In this chapter, we consider the case when optimization has to be performed over a parameter set that is discrete valued and has a finite number of points. We present adaptations of the SPSA and SF algorithms discussed previously using certain projection mappings. We consider here the case of a long-run average cost objective.
S. Bhatnagar, H. Prasad, L. Prashanth
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Parameter Optimization with ACSL Models
SIMULATION, 1987A method whereby a parameter optimization program, written in FORTRAN, can be used in conjunction with ACSL (Advanced Continuous Simulation Language) models of dynamic systems is described. The optimization of a projectile's trajectory is used as an example.
Joseph R. Amyot, Gerard van Blokland
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Optimizing Filtration Parameters
Journal of the Air Pollution Control Association, 1974The fundamental operating principle of the pulse-jet collector is explained with a series of performance curves. The effects of variables on the efficiency of the filters are also examined.
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Optimization Problems with Large Parameters
SIAM Journal on Applied Mathematics, 1973The asymptotic behavior of the solution to a constrained minimization problem, when one or more coefficients of linear parts of the objective function becomes infinite, is studied. It is shown that, under certain circumstances the asymptotic behavior is calculable by solving another extremum problem with additional constraints.
Bigelow, J. H., Shapiro, N. Z.
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Optimal Parameters for Multidimensional Integration
SIAM Journal on Numerical Analysis, 1973A method is given for generating parameters for multidimensional integration which are optimal in the sense of Korobov. It is an extension of Korobov’s method but decreases the time required to compute parameters. In higher dimensions Korobov optimal parameters can be produced by this method more inexpensively than by the original method.
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Data-Driven Alarm Parameter Optimization
Computers & Chemical EngineeringMost manufacturing sector businesses utilize advanced control mechanisms to sustain their ongoing operations. An alarm management system is one of these control mechanisms that works as a safety barrier, and it contains alarm messages indicating abnormal situations to operators.
Tayfun Eylen +2 more
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1967
It is generally agreed that the alternating-direction implicit iterative methods for the solution of the finite-difference approximations to partial differential equations, in particular those of elliptic type, are the best, even with a geometric distribution of the parameters.
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It is generally agreed that the alternating-direction implicit iterative methods for the solution of the finite-difference approximations to partial differential equations, in particular those of elliptic type, are the best, even with a geometric distribution of the parameters.
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Parameter Optimization Algorithms
1995Abstract In previous chapters, the problem of learning in neural networks has been formulated in terms of the minimization of an error function E. This error is a function of the adaptive parameters (weights and biases) in the network, which we can conveniently group together into a single W-dimensional weight vector w with components ...
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