The new generation of mass spectrometers produces an astonishing amount of high-quality data in a brief period of time, leading to inevitable data analysis bottlenecks. Automated data analysis algorithms are required ...
详细信息
The new generation of mass spectrometers produces an astonishing amount of high-quality data in a brief period of time, leading to inevitable data analysis bottlenecks. Automated data analysis algorithms are required for rapid and repeatable processing of mass spectra containing hundreds of peaks, the part of the spectra containing information. New data processing algorithms must work with minimal user input, both to save operator time and to eliminate inevitable operator bias. Toward this end an accurate mathematical algorithm is presented that automatically locates and calculates the area beneath peaks. The promising numerical performance of this algorithm applied to raw data is presented. Published by Elsevier Ltd.
In this paper, we introduced a practical version of golden section search algorithm to optimize multi/uni-modal objective functions. Accordingly, this study presented a novel algorithm combining the capabilities of ch...
详细信息
In this paper, we introduced a practical version of golden section search algorithm to optimize multi/uni-modal objective functions. Accordingly, this study presented a novel algorithm combining the capabilities of chaotic maps and the golden section search method in order to solve nonlinear optimization problems. To this end, a bipartite experimental procedure was utilized. (1) Chaotic convertor as a global search: the search space of a problem can be converted to a local search space using the chaotic concept. The chaotic maps can explore a sub-space to satisfy uni-modal condition for the golden section search (GSS) algorithm. (2) GSS as a local search: the n-D GSS applies over the achieved search space to exploit an optimal solution. In order to study the performance of the proposed algorithm, twenty benchmark functions and one real world problem were employed. The experimental results revealed that the proposed algorithm was an effective and efficient optimization algorithm in comparison with some state-of-the-art methods. The proposed algorithm performs effectively for the engineering applications such as the gear train deign problem. (C) 2016 Elsevier Ltd. All rights reserved.
In this paper, we propose a new nonlinearoptimization model to solve semidefinite optimizationproblems (SDPs), providing some properties related to local optimal solutions. The proposed model is based on another non...
详细信息
In this paper, we propose a new nonlinearoptimization model to solve semidefinite optimizationproblems (SDPs), providing some properties related to local optimal solutions. The proposed model is based on another nonlinearoptimization model given by [S. Burer and R. Monteiro, A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization, Math. Program. Ser. B 95 (2003), pp. 329-357], but it has several nice properties not seen in the existing one. Firstly, the decision variable of the proposed model is a triangular low-rank matrix. Secondly, the existence of a strict local optimum of the proposed model is guaranteed under some conditions, whereas the existing model has no strict local optimum. In other words, it is difficult to construct solution methods equipped with fast convergence using the existing model. We also present some numerical results, showing that the use of the proposed model allows to deliver highly accurate solutions.
We investigate description of the tangent cone to the null set of a mapping F at a given point in the case when F is degenerate at . To this aim we introduce the concept of modified 2-regular mappings, which generaliz...
详细信息
We investigate description of the tangent cone to the null set of a mapping F at a given point in the case when F is degenerate at . To this aim we introduce the concept of modified 2-regular mappings, which generalizes the concept of p-regular mappings. Our main result provides the description of the tangent cone to the null set of modified 2-regular mappings. With the help of this result we derive new optimality conditions for a wide class of optimizationproblems with equality constraints.
This paper presents an efficient approach based on recurrent neural network for solving nonlinearoptimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed usin...
详细信息
This paper presents an efficient approach based on recurrent neural network for solving nonlinearoptimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network. (c) 2005 Elsevier Inc. All rights reserved.
This paper proposes a new way of solving geometrical constraints by using the extended Boltzmann machine, which is a kind of artificial neural network. The energy function of the extended Boltzmann machine is defined ...
详细信息
This paper proposes a new way of solving geometrical constraints by using the extended Boltzmann machine, which is a kind of artificial neural network. The energy function of the extended Boltzmann machine is defined to include terms of higher order than quadratic ones with respect to the binary states of units building up the network. Since the extended Boltzmann machine works as a minimizing machine for the higher-order energy function, it can solve nonlinear optimization problems. We show that this machine is a good solver of nonlinear geometrical constraints, and is suitable for drawing pictures such as graphs, trees, and flowcharts that represent the relationships among discrete objects.
In this paper we present a real-time optimal control scheme of a Pendubot based on nonlinear model predictive control (NMPC) combined with nonlinear moving horizon estimation (NMHE). For the control of this fast, unde...
详细信息
ISBN:
(纸本)9781467379397
In this paper we present a real-time optimal control scheme of a Pendubot based on nonlinear model predictive control (NMPC) combined with nonlinear moving horizon estimation (NMHE). For the control of this fast, under-actuated nonlinear mechatronic system we utilize the ACADO Code Generation tool to obtain a highly efficient Gauss-Newton real-time iteration algorithm tailored for solving the underlying nonlinear optimization problems. To further improve the solvers' performance, we aim to parallelize particular algorithmic tasks within the estimation-control scheme. The overall control performance is experimentally verified by steering the Pendubot into its top unstable equilibrium. We also provide a computational efficiency analysis addressing different hardware/software configurations.
This paper presents a hybrid method for the minimization of a nonlinear objective function subject to linear constraints, i.e., a method to determine Kuhn-Tucker points of that problem is proposed. The idea of the use...
详细信息
暂无评论