Results 241 to 250 of about 1,503,716 (266)
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Large-Scale Optimization of Eigenvalues
SIAM Journal on Optimization, 1992The paper is concerned with a large scale optimization problem involving eigenvalues of a symmetric \(n\times n\) matrix \(A(x)\), where \(A(x)\) depends smoothly on a vector of parameters \(x\in\mathbb{R}^ m\). This is a nonsmooth optimization problem due to the nondifferentiability of the eigenvalues of \(A(x)\) at points \(x\) where they coalesce ...
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On large scale nonlinear Network optimization
Mathematical Programming, 1990zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Toint, Ph.L., Tuyttens, D.
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Optimal Decomposition of Large-Scale Networks
IEEE Transactions on Systems, Man, and Cybernetics, 1979Summary: The underlying concept of decomposition here is that a large complex system representing many interacting elements is broken into subsystems of lower dimensionality. These subsystems are then treated independently for whatever the purpose -- optimization, control, design, etc. -- in consideration of interconnections between subsystems.
Lee, Jang G. +2 more
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Large-scale optimization of neuron arbors
Physical Review E, 1999At the global as well as local scales, some of the geometry of types of neuron arbors-both dendrites and axons-appears to be self-organizing: Their morphogenesis behaves like flowing water, that is, fluid dynamically; waterflow in branching networks in turn acts like a tree composed of cords under tension, that is, vector mechanically. Branch diameters
C, Cherniak, M, Changizi, D, Kang
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Large scale unconstrained optimization
Computers & Chemical Engineering, 1983Abstract This paper concerns recent developments in methods for minimizing unconstrained nonlinear functions in many variables. Recent theoretical results and computational experience with truncated Newton methods and Buckley's variable storage preconditioned conjugate gradient method are stressed.
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Robust Optimization of Large-Scale Systems
Operations Research, 1995Mathematical programming models with noisy, erroneous, or incomplete data are common in operations research applications. Difficulties with such data are typically dealt with reactively—through sensitivity analysis—or proactively—through stochastic programming formulations.
Mulvey, John M. +2 more
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A Competitive Swarm Optimizer for Large Scale Optimization
IEEE Transactions on Cybernetics, 2015In this paper, a novel competitive swarm optimizer (CSO) for large scale optimization is proposed. The algorithm is fundamentally inspired by the particle swarm optimization but is conceptually very different. In the proposed CSO, neither the personal best position of each particle nor the global best position (or neighborhood best positions) is ...
Cheng, R, Jin, Y
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Large Scale Unconstrained Optimization
1997Abstract This paper reviews advances in Newton, quasi-Newton and conjugate gradient methods for large scale optimization. It also describes several packages developed during the last ten years, and illustrates their performance on some practical problems.
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Large-Scale Unconstrained Optimization
This work is a survey on the methods for large scale unconstrained optimization. Besides its own theoretical importance, the growing interest in the last years in solving problems with a larger and larger number of variables are arising very frequently from real world as a result of modeling systems with a very complex structure. In this paper the mainopenaire +2 more sources
Large Scale Structural Optimization
1995The purpose here is to describe efficient methods for large scale structural optimization. A brief review of historical developments shows that techniques are now available to make the structural optimization task quite efficient and reliable.
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