This paper presents an analyze of numeric conditioning of the Hessian matrix of Lagrangian of modified barrier function Lagrangian method (MBFL) and primal-dual logarithmic barrier method (PDLB), which are obtained in...
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ISBN:
(纸本)9781424421893
This paper presents an analyze of numeric conditioning of the Hessian matrix of Lagrangian of modified barrier function Lagrangian method (MBFL) and primal-dual logarithmic barrier method (PDLB), which are obtained in the process of solution of an optimal power flow problem (OPF). This analyze is done by a comparative study through the singular values decomposition (SVD) of those matrixes. In the MBLF method the inequality constraints are treated by the modified barrier and PDLB methods. The inequality constraints are transformed into equalities by introducing positive auxiliary variables and are perturbed by the barrier parameter. The first-order necessary conditions of the Lagrangian function are solved by Newton's method. The perturbation of the auxiliary variables results in an expansion of the feasible set of the original problem, allowing the limits of the inequality constraints to be reached. The electric systems IEEE 14, 162 and 300 buses were used in the comparative analysis.
Many real world decision processes require to solve optimization problems. In this paper, an integrated Multiagent-Genetic Algorithm (MA-GA) is considered to solve constrained optimization problems. The applied approa...
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ISBN:
(纸本)9780769528410
Many real world decision processes require to solve optimization problems. In this paper, an integrated Multiagent-Genetic Algorithm (MA-GA) is considered to solve constrained optimization problems. The applied approach is new in the literature for solving constrained optimization problems. Ten benchmark problems are used to test the performance of the approach and the results show impressive performance.
We propose a new method called ElectroMagnetic BRain Imaging by Optimization in Spectral Space (EMBRIOSS) for electromagnetic source imaging of the human brain. The method incorporates the physiological knowledge that...
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ISBN:
(纸本)9781424406715
We propose a new method called ElectroMagnetic BRain Imaging by Optimization in Spectral Space (EMBRIOSS) for electromagnetic source imaging of the human brain. The method incorporates the physiological knowledge that electrical activities in the brain are spatially coherent and often sparse. For spatial coherency, we confine the solution to a subspace spanned by the low-frequency eigenvectors of a Laplacian of the cortical surface mesh. For sparseness, we apply a p-norm regularization with p<2. The resulting nonlinear regularization problem is solved efficiently using half-quadratic programming. Through realistic simulations, we have compared our method with existing approaches. The results show that our method performs better.
Installing Distribution Generation (DG) in the distribution level has positive impacts on the system voltage profile as well on the substations' capacity. However the extent of such benefits depends greatly of the...
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ISBN:
(纸本)9781424415823
Installing Distribution Generation (DG) in the distribution level has positive impacts on the system voltage profile as well on the substations' capacity. However the extent of such benefits depends greatly of the DG size and location. Heavily loaded systems need more than one DG to rectify the voltage profile and to achieve other DG promised benefits. In this paper the number of Distribution Generators (DGs) and their sizes are investigated thoroughly for installing single and multiple DGs. The optimal DG number and sizing are formulated as nonlinear programming (NLP) problem subject to boundary restriction and nonlinear equality and inequality constraints imposed on the system. In this paper, a radial distribution case study comprises of 33-Bus is tested. A comparative study is performed to evaluate three DG situations. The original system with no DG added is evaluated first, then single and multiple DG installations are assessed later in this research.
Multifingered robot hands are necessary for robots in order to realize many tasks. Many researches on decision of contact points of multi fingered robot hands have been performed in order to minimize fingertip force f...
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ISBN:
(纸本)9781424411832
Multifingered robot hands are necessary for robots in order to realize many tasks. Many researches on decision of contact points of multi fingered robot hands have been performed in order to minimize fingertip force for a given task. Some of these methods, however, take much time until decision of fingertip position because nonlinear programming problem including friction condition is solved for all combinations of candidates of fingertip positions. This paper proposes a new algorism using the optimization indexes of fingertip force derived from norm of manipulating force and angle limits of grasping force vector derived from condition of static equilibrium. By using the indexes, unnecessary candidates are deleted in advance to realize fast decision of fingertip position. Lastly, numerical verification for the proposed method is performed.
This paper considers the optimization of analog filters which are not covered by classical approaches like Butterworth, Chebyshev, or Cauer approximations. Therefore, a novel and highly efficient method for analog fil...
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ISBN:
(纸本)9781424414482
This paper considers the optimization of analog filters which are not covered by classical approaches like Butterworth, Chebyshev, or Cauer approximations. Therefore, a novel and highly efficient method for analog filter design using nonlinear optimization is presented. This approach enables high flexibility and easy handling as compared to existing approximation techniques. By describing particular design specifications as constraints, a solution can be obtained by using suitable optimization software. Apart from the straightforward approximation of filter transfer functions, this approach is extensible to consider coefficient uncertainties of component variations. Furthermore, it can be used to synthesize filters comprised of only real poles. Examples are also provided to validate the benefits of the resulting design method.
The problems concerned in this paper are a class of constrained min-max problems. By introducing the Lagrange multipliers to the linear constraints, such problems can be solved by some projection type prediction-corre...
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We consider the task of tuning hyperparameters in SVM models based on minimizing a smooth performance validation function, e.g., smoothed k-fold crossvalidation error, using non-linear optimization techniques. The key...
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ISBN:
(纸本)9780262195683
We consider the task of tuning hyperparameters in SVM models based on minimizing a smooth performance validation function, e.g., smoothed k-fold crossvalidation error, using non-linear optimization techniques. The key computation in this approach is that of the gradient of the validation function with respect to hyperparameters. We show that for large-scale problems involving a wide choice of kernel-based models and validation functions, this computation can be very efficiently done;often within just a fraction of the training time. Empirical results show that a near-optimal set of hyperparameters can be identified by our approach with very few training rounds and gradient computations.
Sensor network coverage refers to the quality of service provided by a sensor network surveilling a region of interest. So far, coverage problems have been formulated to address area coverage or to maintain line-of-si...
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Sensor network coverage refers to the quality of service provided by a sensor network surveilling a region of interest. So far, coverage problems have been formulated to address area coverage or to maintain line-of-sight visibility in the presence of obstacles (i.e., art-gallery problems). Although very useful in many sensor applications, none of the existing formulations address coverage as it pertains to target tracking by means of multiple sensors, nor do they provide a closed-form function that can be applied to the problem of allocating sensors for the surveilling objective of maximizing target detection while minimizing false alarms. This dissertation presents a new coverage formulation addressing the quality of service of sensor networks that cooperatively detect targets traversing a region of interest, and is readily applicable to the current sensor network coverage formulations. The problem of track coverage consists of finding the positions of n sensors such that the amount of tracks detected by at least k sensors is optimized. This dissertation studies the geometric properties of the network, addressing a deterministic track-coverage formulation and binary sensor models. It is shown that the tracks detected by a network of heterogeneous omnidirectional sensors are the geometric transversals of non-translates families of disks. A novel methodology based on cones and convex analysis is presented for representing and measuring sets of transversals as closed-form functions of the sensors positions and ranges. As a result, the problem of optimally deploying a sensor network with the aforementioned objectives can be formulated as an optimization problem subject to mission dynamics and constraints. The sensor placement problem, in which the sensors are placed such that track coverage is maximized for a fixed sensor network, is formulated as a nonlinear program and solved using sequential quadratic programming. The sensor deployment, involving a dynamic sensor ne
A kind of new nonlinear programming is put forward, which is called nondecomposable minimax optimization. The continuity and differentiability of the objective function f(x) are studied in detail. In particular, some ...
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