Parallel Eigenvalue Algorithms for Large-Scale Control-Optimization Problems

1996 ◽  
Vol 9 (3) ◽  
pp. 70-79 ◽  
Author(s):  
A. Saleh ◽  
H. Adeli
Author(s):  
Meng Wang ◽  
Kihyung Kim ◽  
Michael R. von Spakovsky ◽  
Douglas J. Nelson

As primary tools for the development of energy systems, optimization techniques have been studied for decades. However, for large-scale synthesis/design and operation/control optimization problems, it may turn out that it is impractical to solve the entire problem as a single optimization problem. In this paper, a multi-level optimization strategy, dynamic iterative local-global optimization (DILGO), is utilized for the synthesis/design and operation/control optimization of a 5 kWe PEMFC (Proton Exchange Membrane Fuel Cell) system. The strategy decomposes the system into three subsystems: a stack subsystem (SS), a fuel processing subsystem (FPS), and a work and air recovery subsystem (WRAS) and, thus, into three optimization sub-problems. To validate the decomposition strategy, the results are compared with a single-level dynamic optimization, in which the whole system is optimized together. In addition, for the purpose of comparison between different optimization algorithms, gradient-based optimization results are compared with those for a hybrid heuristic/gradient-based optimization algorithm.


Author(s):  
Angelia Nedić ◽  
Ji Liu

Advances in wired and wireless technology have necessitated the development of theory, models, and tools to cope with the new challenges posed by large-scale control and optimization problems over networks. The classical optimization methodology works under the premise that all problem data are available to a central entity (a computing agent or node). However, this premise does not apply to large networked systems, where each agent (node) in the network typically has access only to its private local information and has only a local view of the network structure. This review surveys the development of such distributed computational models for time-varying networks. To emphasize the role of the network structure in these approaches, we focus on a simple direct primal (sub)gradient method, but we also provide an overview of other distributed methods for optimization in networks. Applications of the distributed optimization framework to the control of power systems, least squares solutions to linear equations, and model predictive control are also presented.


Author(s):  
Paul Cronin ◽  
Harry Woerde ◽  
Rob Vasbinder

1999 ◽  
Vol 9 (3) ◽  
pp. 755-778 ◽  
Author(s):  
Paul T. Boggs ◽  
Anthony J. Kearsley ◽  
Jon W. Tolle

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peiran Zhang ◽  
Joseph Rufo ◽  
Chuyi Chen ◽  
Jianping Xia ◽  
Zhenhua Tian ◽  
...  

AbstractThe ability to precisely manipulate nano-objects on a large scale can enable the fabrication of materials and devices with tunable optical, electromagnetic, and mechanical properties. However, the dynamic, parallel manipulation of nanoscale colloids and materials remains a significant challenge. Here, we demonstrate acoustoelectronic nanotweezers, which combine the precision and robustness afforded by electronic tweezers with versatility and large-field dynamic control granted by acoustic tweezing techniques, to enable the massively parallel manipulation of sub-100 nm objects with excellent versatility and controllability. Using this approach, we demonstrated the complex patterning of various nanoparticles (e.g., DNAs, exosomes, ~3 nm graphene flakes, ~6 nm quantum dots, ~3.5 nm proteins, and ~1.4 nm dextran), fabricated macroscopic materials with nano-textures, and performed high-resolution, single nanoparticle manipulation. Various nanomanipulation functions, including transportation, concentration, orientation, pattern-overlaying, and sorting, have also been achieved using a simple device configuration. Altogether, acoustoelectronic nanotweezers overcome existing limitations in nano-manipulation and hold great potential for a variety of applications in the fields of electronics, optics, condensed matter physics, metamaterials, and biomedicine.


Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.


Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4193-4198 ◽  
Author(s):  
Midya Parto ◽  
William E. Hayenga ◽  
Alireza Marandi ◽  
Demetrios N. Christodoulides ◽  
Mercedeh Khajavikhan

AbstractFinding the solution to a large category of optimization problems, known as the NP-hard class, requires an exponentially increasing solution time using conventional computers. Lately, there has been intense efforts to develop alternative computational methods capable of addressing such tasks. In this regard, spin Hamiltonians, which originally arose in describing exchange interactions in magnetic materials, have recently been pursued as a powerful computational tool. Along these lines, it has been shown that solving NP-hard problems can be effectively mapped into finding the ground state of certain types of classical spin models. Here, we show that arrays of metallic nanolasers provide an ultra-compact, on-chip platform capable of implementing spin models, including the classical Ising and XY Hamiltonians. Various regimes of behavior including ferromagnetic, antiferromagnetic, as well as geometric frustration are observed in these structures. Our work paves the way towards nanoscale spin-emulators that enable efficient modeling of large-scale complex networks.


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