The design, analysis, and application of a new recurrent neural network for quadratic programming, called simplified dual neural network, are discussed. The analysis mainly concentrates on the convergence property and...
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The design, analysis, and application of a new recurrent neural network for quadratic programming, called simplified dual neural network, are discussed. The analysis mainly concentrates on the convergence property and the computational complexity of the neural network. The simplified dual neural network is shown to be globally convergent to the exact optimal solution. The complexity of the neural network architecture is reduced with the number of neurons equal to the number of inequality constraints. Its application to k-winners-take-all (KWTA) operation is discussed to demonstrate how to solve problems with this neural network.
A nonlinear optimization algorithm is applied to the design of air-cooled hear exchangers. In such equipment. the cold fluid (air) is impelled across hanks of finned tubes by means of fans in forced or induced draft. ...
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A nonlinear optimization algorithm is applied to the design of air-cooled hear exchangers. In such equipment. the cold fluid (air) is impelled across hanks of finned tubes by means of fans in forced or induced draft. The hot stream flows inside the tubes in one or more passes, and the process that rakes place may be cooling of either a gas or a liquid. or condensation of either a pure vapor or a mixture. The objective function is the minimum cost of the unit (investment and operation), subject to certain geometric and thermohydraulic constraints. The optimization algorithm used is that developed by Biegler and Cuthrell [1], and programmed by them in the OPT package. The problem posed in this case is made of 10 optimization variables, subject to five constraints related to geometric and operational parameters of the hear exchanger
In this article, we solve the problem of monotone control theoretic splines. Monotone control theoretic splines have been solved only when the target system is the second-order integrator 1/s2, but not for other cases...
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In this paper, a discrete-time recurrent neural network with global exponential stability is proposed for solving linear constrained quadratic programming problems. Compared with the existing neural networks for quadr...
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In this paper, a discrete-time recurrent neural network with global exponential stability is proposed for solving linear constrained quadratic programming problems. Compared with the existing neural networks for quadratic programming, the proposed neural network in this paper has lower model complexity with only one-layer structure. Moreover, the global exponential stability of the neural network can be guaranteed under some mild conditions. Simulation results with some applications show the performance and characteristic of the proposed neural network. (C) 2011 Elsevier B.V. All rights reserved.
In this paper, we represent a method which is based on the violated constraints by the unconstrained minimum of the objective function of the quadratic programming problem for exploring, locating and computing the opt...
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In this paper, we represent a method which is based on the violated constraints by the unconstrained minimum of the objective function of the quadratic programming problem for exploring, locating and computing the optimal solution of the problem without using additional information as have been done in most of the favourite established methods. (C) 2000 Published by Elsevier Science Inc. All rights reserved.
In this paper, we propose a new range-free localization algorithm called optimal proximity distance map using quadratic programming (OPDMQP). First, the relationship between geographical distances and proximity among ...
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In this paper, we propose a new range-free localization algorithm called optimal proximity distance map using quadratic programming (OPDMQP). First, the relationship between geographical distances and proximity among sensor nodes in the given wireless sensor network is mathematically built. Then, the characteristics of the given network is represented as a set of constraints on the given network topology and the localization problem is formulated into a quadratic programming problem. Finally, the proposed method is applied to two anisotropic networks the topologies of which are very similar to those of the real-world applications. Unlike the most of previous localization methods which work well in the isotropic networks but not in the anisotropic networks, it is shown that the proposed method exhibits excellent and robust performances not only in the isotropic networks but also in the anisotropic networks. (C) 2010 Elsevier B.V. All rights reserved.
This paper considers the problem of approximating a given crystallite orientation distribution function (codf) by a set of texture components. Problems of this type arise for example if the codf has to be reconstructe...
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This paper considers the problem of approximating a given crystallite orientation distribution function (codf) by a set of texture components. Problems of this type arise for example if the codf has to be reconstructed from discrete orientations or if one looks for a physical interpretation of the codf. The same problem is encountered if crystallographic texture based constitutive models have to be specified. The equivalence of these tasks to a mixed integer quadratic programming problem (MIQP) - a standard but challenging problem in optimization theory - is shown. Special emphasis is given to the generation of a class of approximations with an increasing number of texture components. Furthermore, the constraints resulting from the non-negativity, the normalization, and the symmetry of the codf are analyzed. Finally, a set of approximations of three different experimental textures determined with this solution scheme is presented and discussed. Based on these hierarchical solutions, the engineer can decide in what detail the microstructure is considered. (c) 2005 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
The reactive-power dispatch is formulated as the minimization of real-power losses in the system, utilizing a full set of control variables: generator voltages, switchable shunt susceptances, and transformer taps. The...
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The reactive-power dispatch is formulated as the minimization of real-power losses in the system, utilizing a full set of control variables: generator voltages, switchable shunt susceptances, and transformer taps. The solution of the loss problem is obtained by successively solving quadratic programming problems. First- and second-order loss sensitivity coefficients are derived for the quadratic problem formulation. The derivations are based on the Jacobian method for sensitivity calculations. Sensitivity relations for the dependent constraints are based on the complete reactive-power model of the fast decoupled load flow method. The active-set projection method for quadratic programming is described and utilized as the solution algorithm for the quadratic reactive-power dispatch problems. Tests are conducted on the IEEE 30-bus and Mexican 253-bus systems. The computer results are discussed.< >
In this article we present the first effective method based on global optimization for the reconstruction of image puzzles comprising rectangle pieces-Puzzle Solving by quadratic programming (PSQP). The proposed novel...
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In this article we present the first effective method based on global optimization for the reconstruction of image puzzles comprising rectangle pieces-Puzzle Solving by quadratic programming (PSQP). The proposed novel mathematical formulation reduces the problem to the maximization of a constrained quadratic function, which is solved via a gradient ascent approach. The proposed method is deterministic and can deal with arbitrary identical rectangular pieces. We provide experimental results showing its effectiveness when compared to state-of-the-art approaches. Although the method was developed to solve image puzzles, we also show how to apply it to the reconstruction of simulated strip-shredded documents, broadening its applicability.
Although quadratic programming problems are a special class of nonlinear programming, they can also be seen as general linear programming problems. These quadratic problems are of the utmost importance in an increasin...
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Although quadratic programming problems are a special class of nonlinear programming, they can also be seen as general linear programming problems. These quadratic problems are of the utmost importance in an increasing variety of practical fields. As, in addition, ambiguity and vagueness are natural and ever-present in real-life situations requiring operative solutions, it makes perfect sense to address them using fuzzy concepts formulated as quadratic programming problems with uncertainty, i.e., as Fuzzy quadratic programming problems. This work proposes two novel fuzzy-sets-based methods to solve a particular class of Fuzzy quadratic programming problems which have vagueness coefficients in the objective function. Moreover, two other linear approaches are extended to solve the quadratic case. Finally, it is shown that the solutions reached from the extended approaches may be obtained from two proposed parametric multiobjective approaches.
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