The augmented Lagrangian method (ALM) is extended to a broader-than-ever setting of generalized nonlinear programming in convex and nonconvex optimization that is capable of handling many common manifestations of nons...
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The augmented Lagrangian method (ALM) is extended to a broader-than-ever setting of generalized nonlinear programming in convex and nonconvex optimization that is capable of handling many common manifestations of nonsmoothness. With the help of a recently developed sufficient condition for local optimality, it is shown to be derivable from the proximal point algorithm through a kind of local duality corresponding to an optimal solution and accompanying multiplier vector that furnish a local saddle point of the augmented Lagrangian. This approach leads to surprising insights into stepsize choices and new results on linear convergence that draw on recent advances in convergence properties of the proximal point algorithm. Local linear convergence is shown to be assured for a class of model functions that covers more territory than before.
Using the kinematic approach of limit analysis (LA) for a hollow sphere whose solid matrix obeys the von Mises criterion, Gurson (1977) derived a macroscopic criterion for ductile porous media. The relevance of this c...
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Using the kinematic approach of limit analysis (LA) for a hollow sphere whose solid matrix obeys the von Mises criterion, Gurson (1977) derived a macroscopic criterion for ductile porous media. The relevance of this criterion has been widely confirmed in several studies and in particular in Trillat and Pastor (2005) through numerical lower- and upper-bound formulations of LA. In the present paper, these formulations are extended to the case of a pressure dependent matrix obeying the parabolic Mises-Schleicher criterion. This extension has been made possible by the use of a specific component of conic optimization. We first provide the basics of LA for this class of materials and of the required conic optimization;then the LA hollow sphere model and the resulting static and mixed kinematic codes are briefly presented. The numerical bounds obtained prove to be very accurate when compared to available exact solutions in the particular case of isotropic loadings. A second series of tests is devoted to assessing the upper bound and the approximate criterion established by Lee and Oung (2000) as well as the criterion proposed by Durban, Cohen, and Hollander (2010). As a matter of conclusion, these criteria can be considered as admissible only for a slight tension/compression asymmetry ratio for the matrix;in other words, these results show that the determination of the macroscopic criterion of the "porous Mises-Schleicher" material remains an open problem. (C) 2013 Elsevier Ltd. All rights reserved.
Power resource allocation is crucial for localization since it affects not only the conventionally recognized lifetime, throughput, and covertness, but also localization efficiency of the network. In this paper, we pr...
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ISBN:
(纸本)9781467309219;9781467309202
Power resource allocation is crucial for localization since it affects not only the conventionally recognized lifetime, throughput, and covertness, but also localization efficiency of the network. In this paper, we present an optimization framework for range-based localization to improve the power efficiency and localization accuracy. Our framework unifies the analysis for active and passive localization through the examples of wireless network localization (WNL) and multiple radar localization (MRL). In particular, we determine the functional properties of localization accuracy metric, and based on those properties we formulate the power allocation problem as conic programs. Moreover, we propose robust counterparts that retain the conic structures for power allocation in the presence of parameter uncertainty. Our simulation results validate the efficiency and robustness of the proposed methods.
Power resource allocation is crucial for localization since it affects not only the conventionally recognized lifetime, throughput, and covertness, but also localization efficiency of the network. In this paper, we pr...
详细信息
ISBN:
(纸本)9781467309202
Power resource allocation is crucial for localization since it affects not only the conventionally recognized lifetime, throughput, and covertness, but also localization efficiency of the network. In this paper, we present an optimization framework for range-based localization to improve the power efficiency and localization accuracy. Our framework unifies the analysis for active and passive localization through the examples of wireless network localization (WNL) and multiple radar localization (MRL). In particular, we determine the functional properties of localization accuracy metric, and based on those properties we formulate the power allocation problem as conic programs. Moreover, we propose robust counterparts that retain the conic structures for power allocation in the presence of parameter uncertainty. Our simulation results validate the efficiency and robustness of the proposed methods.
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