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International Conference on Computer as a Tool (EUROCON), IEEE

作者： Garcia-Fernandez, Maria Garcia, Cebrian Alvarez, Yuri Las-Heras, Fernando Univ Oviedo Dept Elect Engn Area Signal Theory & Commun Edificio PolivalenteModulo 8Campus Univ Gijon Gijon 33203 Spain

ISBN: (纸本)9781479985692

The main purpose of this article is to analyse the influence of the smoothness and electric size on the reconstruction of a metallic object contour in a 2D case. A multistage hybrid method based on Evolutionary algorithms (EA) and an interior-point algorithm is used for the profile reconstruction. The purpose of this approach is to reduce the required amount of information as well as improving the convergence capabilities, specially when the object has a large electric size. The inverse problem is recast as a nonlinear optimization problem minimizing the difference between the measured and the estimated scattered field on an observation domain. The reconstruction of scatterers with different shapes and sizes is carried out to verify the accuracy of the method. In addition, two different EA are examined: Genetic algorithm (GA) and Particle Swarm Optimization (PSO). Numerical results show that a high reconstruction accuracy can be achieved, even when the Signal to Noise Ratio (SNR) is low.

关键词： genetic algorithms image reconstruction inverse problems particle swarm optimisation 2D case contour smoothness electric size estimated scattered field evolutionary algorithms genetic algorithm hybrid optimization interior-point algorithm inverse problem metallic object contour reconstruction metallic objects multistage hybrid method nonlinear optimization problem observation domain particle swarm optimization profile reconstruction Convergence Cost function Genetic algorithms Lighting Shape Signal to noise ratio Genetic algorithm (GA) Particle swarm optimization Inverse method signal to noise ratios interior point algorithm evolutionary algorithm Cost functions image reconstruction Lighting Converge Reconstructive Surgical Procedures Smoothness

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The Complexity Analysis of an Efficient interior-point algorithm for Linear Optimization

The Complexity Analysis of an Efficient Interior-Point Algor...

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The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

作者： Liying Liu Liya Fan College of Mathematics Science Liaocheng UniversityLiaocheng252059China

In this paper, an efficient primal-dual interiorpoint algorithm for large-update methods is introduced by means of a new kernel function. We analysis the complexity of the algorithm and conclude that its iteration ... 详细信息

关键词： function interior-point algorithm Iteration bounds Linear Optimization

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Two-layer optimization to cooperative conflict detection and resolution for UAVs

Two-layer optimization to cooperative conflict detection and...

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International IEEE Conference on Intelligent Transportation Systems

作者： Jian Yang Dong Yin Qiao Cheng Xu xie Dept. of Mechatron. & Autom. Nat. Univ. of Defense Technol. Changsha China

ISBN: (纸本)9781467365970

This paper focuses on the solution for conflict detection and resolution (CDR) of unmanned aerial vehicles (UAVs) by heading control. The cooperative method is proposed. First, the relationships between conflicts involved UAVs are described by the geometric method. The practical and potential conflicts are considered. Then, the CDR problem is formalized as a nonlinear optimization problem so as to minimize maneuver costs. Moreover, a two-layer strategy composed of stochastic parallel gradient descent (SPGD) and interior-point algorithm is designed to efficiently solve the non-convex optimization problem. Finally, our approach is demonstrated on several scenarios and the simulation results show that it can achieve high performance in obtaining near optimal maneuvers for UAVs CDR.

关键词： autonomous aerial vehicles concave programming cooperative systems geometry gradient methods mobile robots nonlinear programming stochastic processes CDR SPGD UAV conflict detection and resolution cooperative method geometric method interior-point algorithm nonconvex optimization nonlinear optimization stochastic parallel gradient descent unmanned aerial vehicle Airplanes algorithm design and analysis Fuels Optimization Safety Trajectory Vehicles airspace integration conflict resolution optimization unmanned aerial vehicles Unmanned Aerial Vehicle drone honey bees vehicle interior point algorithm algorithm design and analysis plane cooperative systems gradient methods RECORDABLE COMPACT DISC CONCEPTUAL DESIGN REPORT RUNX1T1 gene AKR1C4 gene Conflict Resolution Nonlinear Programming conflict detection

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A SYMMETRIC REDUCTION OF THE NT DIRECTION

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SIAM JOURNAL ON OPTIMIZATION 2014年第2期24卷 714-732页

作者： Jarre, Florian Hergenroeder, Chantal Univ Dusseldorf Math Inst D-40225 Dusseldorf Germany

A stable symmetrization of the linear systems arising in interior-point methods for solving linear programs is introduced. A comparison of the condition numbers of the resulting interior-point linear systems with other commonly used approaches indicates that the new approach may be best suitable for an iterative solution. It is shown that there is a natural generalization of this symmetrization to the Nesterov-Todd (NT) search direction for solving semidefinite programs. The generalization includes a novel pivoting strategy to minimize the norm of the right and side and heavily relies on the symmetry properties of the NT direction. The search directions generated by iterative solvers typically have fairly low relative accuracy. Nevertheless, in some preliminary numerical examples, a suitably adapted interior-point approach results in a rather small number of outer iterations.

关键词： linear program semidefinite program condition number quasi-minimal residual iteration interior-point algorithm

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A primal-dual interior-point algorithm for second-order cone optimization with full Nesterov-Todd step

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APPLIED MATHEMATICS AND COMPUTATION 2009年第3期215卷 1047-1061页

作者： Wang, G. Q. Bai, Y. Q. Shanghai Univ Engn Sci Coll Adv Vocat Technol Shanghai 200437 Peoples R China Shanghai Univ Dept Math Shanghai 200444 Peoples R China

In this paper we propose a primal-dual path-following interior-point algorithm for second-order cone optimization. The algorithm is based on a new technique for finding the search directions and the strategy of the central path. At each iteration, we use only full Nesterov-Todd step. Moreover, we derive the currently best known iteration bound for the algorithm with small-update method, namely, O(root N log N/epsilon), where N denotes the number of second-order cones in the problem formulation and epsilon the desired accuracy. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Second-order cone optimization interior-point algorithm Small-update method Iteration bound

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Primal-dual interior-point algorithm for convex quadratic semi-definite optimization

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NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS 2009年第7-8期71卷 3389-3402页

作者： Wang, G. Q. Bai, Y. Q. Shanghai Univ Engn Sci Coll Adv Vocat Technol Shanghai 200437 Peoples R China Shanghai Univ Dept Math Shanghai 200444 Peoples R China

In this paper, we present a new primal-dual interior-point algorithm for solving a special case of convex quadratic semi-definite optimization based on a parametric kernel function. The proposed parametric kernel function is used both for determining the search directions and for measuring the distance between the given iterate and the mu-center for the algorithm. These properties enable us to derive the currently best known iteration bounds for the algorithm with large- and small-update methods, namely, O(root n log n log n/epsilon) and O(root n- log n/epsilon), respectively, which reduce the gap between the practical behavior of the algorithm and its theoretical performance results. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Convex quadratic semi-definite optimization interior-point algorithm Large- and small-update methods Iteration bound

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A new primal-dual path-following interior-point algorithm for semidefinite optimization

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JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS 2009年第1期353卷 339-349页

作者： Wang, G. Q. Bai, Y. Q. Shanghai Univ Engn Sci Coll Vocat Technol Shanghai 200437 Peoples R China Shanghai Univ Coll Sci Dept Math Shanghai 200444 Peoples R China

In this paper we present a new primal-dual path-following interior-point algorithm for semidefinite optimization. The algorithm is based on a new technique for finding the search direction and the strategy of the central path. At each iteration, we use only full Nesterov-Todd step. Moreover, we obtain the currently best known iteration bound for the algorithm with small-update method, namely, 0(root n log n/epsilon), which is as good as the linear analogue. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Semidefinite optimization interior-point algorithm Small-update method Iteration bound

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Calculation of Probabilistic Available Transfer Capability in Wind Power Integrated System

Calculation of Probabilistic Available Transfer Capability i...

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International Conference on Renewable Energy and Environmental Technology (REET 2013)

作者： Li, Guoqing Zhang, Fangjing Chen, Houhe Northeast Dianli Univ Jinlin 132012 Peoples R China

ISBN: (纸本)9783037859124

As the number of wind generation facilities in the power system is fast increasing, the research on available transfer capability (ATC) calculation with wind farms has great significance to system operation. With consideration of the uncertainty of the wind power's output, this paper proposes a probability computing method to study the ATC in wind power integrated system. This computing method of ATC is evaluated on non-sequential Monte Carlo simulation, and the ATC of every system state in random sampling is calculated by interior point method. The result shows that the model and algorithm is correct and effective.

关键词： wind farm available transfer capability Monte Carlo simulation interior-point algorithm

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An Infeasible Primal-Dual interior-point algorithm for Linearly Constrained Convex Optimization Based on A Parametric Kernel Function

An Infeasible Primal-Dual Interior-Point Algorithm for Linea...

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2nd International Joint Conference on Computational Sciences and Optimization (CSO)

作者： Wang, Guoqiang Wang, Baocun Fan, Qingduan Shanghai Univ Engn Sci Coll Vocat Technol Shanghai 200437 Peoples R China Shanghai Univ Engn Sci Coll Fundamental Studies Shanghai 200336 Peoples R China

ISBN: (纸本)9780769536057

In this paper we present an infeasible primal-dual interior-point algorithm for linearly constrained convex optimization based on a parametric kernel function, with parameters p is an element of [0, 1] and q >= 1. Numerical test shows that the efficiency of the proposed algorithm and investigates the behavior of the algorithm with different parameters p, q and theta.

关键词： Linearly constrained convex optimization Primal-dual methods interior-point algorithm

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interior-point algorithms for a generalization of linear programming and weighted centring

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OPTIMIZATION METHODS & SOFTWARE 2012年第4-5期27卷 605-612页

作者： Anstreicher, Kurt M. Univ Iowa Dept Management Sci Iowa City IA 52242 USA

In this paper, we consider an extension of ordinary linear programming (LP) that adds weighted logarithmic barrier terms for some variables. The resulting problem generalizes both LP and the problem of finding the weighted analytic centre of a polytope. We show that the problem has a dual of the same form and give complexity results for several different interior-point algorithms. We obtain an improved complexity result for certain cases by utilizing a combination of the volumetric and logarithmic barriers. As an application, we consider the complexity of solving the Eisenberg-Gale formulation of a Fisher equilibrium problem with linear utility functions.

关键词： linear programming weighted analytic centre interior-point algorithm Fisher equilibrium volumetric barrier

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