Large linear systems of saddle-point type have arisen in a wide variety of applications throughout computational science and engineering. The discretizations of distributedcontrol problems have a saddle-point structu...
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Large linear systems of saddle-point type have arisen in a wide variety of applications throughout computational science and engineering. The discretizations of distributedcontrol problems have a saddle-point structure. The numerical solution of saddle-point problems has attracted considerable interest in recent years. In this work, we propose a novel Braess-Sarazin multigrid relaxation scheme for finite element discretizations of the distributedcontrol problems, where we use the stiffness matrix obtained from the five-point finite difference method for the Laplacian to approximate the inverse of the mass matrix arising in the saddle-point system. We apply local Fourier analysis to examine the smoothing properties of the Braess-Sarazin multigrid relaxation. From our analysis, the optimal smoothing factor for Braess-Sarazin relaxation is derived. Numerical experiments validate our theoretical results. The relaxation scheme considered here shows its high efficiency and robustness with respect to the regularization parameter and grid size. (C) 2022 Elsevier B.V. All rights reserved.
The computation and implementation of distributed optimal control of large scale systems is discussed in this article. A parallelization strategy enabling the parallel and distributed processing of hierarchical contro...
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The computation and implementation of distributed optimal control of large scale systems is discussed in this article. A parallelization strategy enabling the parallel and distributed processing of hierarchical control via the algorithm of Tamura, based on its spatial and temporal partition, is proposed. Two distributed computation environments: one using a CONIC like toolkit and another one using a multitransputer network are studied and utilized for the realization of the above proposal for distributed Computer control Systems. An application to a Power System is discussed with emphasis on parallel and distributed computation issues.
The Escalator Boxcar Train method is used to solve the distributed optimal control problems of forest management numerically. It takes into account intraspecific competition for scarce resources such as light, space, ...
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The Escalator Boxcar Train method is used to solve the distributed optimal control problems of forest management numerically. It takes into account intraspecific competition for scarce resources such as light, space, and nutrients during reproduction, growth, and mortality. It provides an alternative to gradient projection methods and Markov processes. It is implemented with standard software. The application is on the optimal forest management regime in the presence of intraspecific competition.
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