检索结果-内蒙古大学图书馆

Optimisation of the duplex D2D network: a deep learning approach

IET NETWORKS 2020年第3期9卷 139-144页

作者： Liu, Xian Univ Arkansas Little Rock Dept Syst Engn Little Rock AR 72204 USA

For wireless networks, it is highly desirable to design a quality-of-service-promotion strategy to allocate the transmission power. In this study, the authors apply the deep learning methodology to solve such a problem in a duplex device-to-device network. The model is formulated as a constrained non-linear programming problem. The training data are generated by a standard optimisation tool. Then, an artificial neural network is designed to learn the system from the training data. Simulation results show the effectiveness of deep leaning.

关键词： learning (artificial intelligence) neural nets optimisation nonlinear programming linear programming cellular radio duplex D2D network deep learning approach wireless networks quality-of-service-promotion strategy transmission power deep learning methodology device-to-device network nonlinear programming problem training data standard optimisation tool artificial neural network deep leaning

来源：评论

学校读者我要写书评

暂无评论

Performance enhancement of Gauss-Newton trust-region solver for distributed Gauss-Newton optimization method

引用

COMPUTATIONAL GEOSCIENCES 2020年第2期24卷 837-852页

作者： Gao, Guohua Jiang, Hao Vink, Jeroen C. van Hagen, Paul P. H. Wells, Terence J. Shell Global Solut US Inc 3333 HWY 6 S Houston TX 77082 USA Shell Global Solut Int BV Kessler Pk 1 NL-2288 GS Rijswijk Netherlands

Distributed Gauss-Newton (DGN) optimization method has been proved very efficient and robust for history matching and uncertainty quantification (HM&UQ). The major bottleneck for performance enhancement is the expensive computational cost of solving hundreds of Gauss-Newton trust-region (GNTR) subproblems in each iteration. The original GNTR solver applies the less efficient iterative Newton-Raphson (NR) method using a derivative which requires solving a large-scale linear system twice in each NR iteration. Instead of using a less accurate linear proxy as in the iterative NR method, the nonlinear GNTR equation is first approximated with an inverse-quadratic (IQ) or a cubic-spline (CS) model, by fitting points generated in previous iterations without using any derivative. Then, the analytical (or numerical) solution of the IQ (or CS) model is used as the new proposal for the next iteration. The performances of the two new GNTR solvers are benchmarked against different methods on different sets of test problems with different numbers of uncertain parameters (ranging from 500 to 100,000) and different numbers of observed data (ranging from 100 to 100,000). In terms of efficiency and robustness, the two new GNTR solvers have comparable performance, and they outperform other methods we tested, including the well-known direct and iterative trust-region solvers of the GALAHAD optimization library. Finally, the proposed GNTR solvers have been implemented in our in-house distributed HM&UQ system. Our numerical experiments indicate that the DGN optimizer using the newly proposed GNTR solver performs quite stable and is effective when applied to real-field history matching problems.

关键词： History matching Distributed Gauss-Newton method Trust-region subproblem solver Cubic spline Inverse quadratic model nonlinear programming

来源：评论

学校读者我要写书评

暂无评论

An Inverse Optimal Control Approach for Trajectory Prediction of Autonomous Race Cars

arXiv

引用

arXiv 2022年

作者： Reiter, Rudolf Messerer, Florian Schratter, Markus Watzenig, Daniel Diehl, Moritz University Freiburg Freiburg79110 Germany Virtual Vehicle Research Center Inffeldgasse 21a Graz8010 Austria Institute of Automation and Control Graz University of Technology Inffeldgasse 21b Graz Austria Department of Mathematics University Freiburg Freiburg79110 Germany

This paper proposes an optimization-based approach to predict trajectories of autonomous race cars. We assume that the observed trajectory is the result of an optimization problem that trades off path progress against acceleration and jerk smoothness, and which is restricted by constraints. The algorithm predicts a trajectory by solving a parameterized nonlinear program (NLP) which contains path progress and smoothness in cost terms. By observing the actual motion of a vehicle, the parameters of prediction are updated by means of solving an inverse optimal control problem that contains the parameters of the predicting NLP as optimization variables. The algorithm therefore learns to predict the observed vehicle trajectory in a least-squares relation to measurement data and to the presumed structure of the predicting NLP. This work contributes with an algorithm that allows for accurate and interpretable predictions with sparse data. The algorithm is implemented on embedded hardware in an autonomous real-world race car that is competing in the challenge Roborace and analyzed with respect to recorded data. Copyright © 2022, The Authors. All rights reserved.

关键词： nonlinear programming

来源：评论

学校读者我要写书评

暂无评论

The Projection Technique for Two Open Problems of Unconstrained Optimization Problems

引用

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2020年第2期186卷 590-619页

作者： Yuan, Gonglin Wang, Xiaoliang Sheng, Zhou Guangxi Univ Coll Math & Informat Sci Nanning 530004 Guangxi Peoples R China Dalian Univ Technol Sch Math Sci Dalian 116024 Liaoning Peoples R China Nanjing Univ Aeronaut & Astronaut Dept Math Nanjing 211106 Jiangsu Peoples R China

There are two problems for nonconvex functions under the weak Wolfe-Powell line search in unconstrained optimization problems. The first one is the global convergence of the Polak-Ribiere-Polyak conjugate gradient algorithm and the second is the global convergence of the Broyden-Fletcher-Goldfarb-Shanno quasi-Newton method. Many scholars have proven that the two problems do not converge, even under an exact line search. Two circle counterexamples were proposed to generate the nonconvergence of the Polak-Ribiere-Polyak algorithm for the nonconvex functions under the exact line search, which inspired us to define a new technique to jump out of the circle point and obtain the global convergence. Thus, a new Polak-Ribiere-Polyak algorithm is designed by the following steps. (i) Given the current point and a parabolic surface is designed;(ii) An assistant point is defined based on the current point;(iii) The assistant point is projected onto the surface to generate the next point;(iv) The presented algorithm has the global convergence for nonconvex functions with the weak Wolfe-Powell line search. A similar technique is used for the quasi-Newton method to get a new quasi-Newton algorithm and to establish its global convergence. Numerical results show that the given algorithms are more competitive than other similar algorithms. Meanwhile, the well-known hydrologic engineering application problem, called parameter estimation problem of nonlinear Muskingum model, is also done by the proposed algorithms.

关键词： nonlinear programming PRP method BFGS method Global convergence Nonconvex functions Muskingum model

来源：评论

学校读者我要写书评

暂无评论

On Approximate Stationary Points of the Regularized Mathematical Program with Complementarity Constraints

引用

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2020年第2期186卷 504-522页

作者： Dussault, Jean-Pierre Haddou, Mounir Kadrani, Abdeslam Migot, Tangi Univ Sherbrooke Dept Informat Sherbrooke PQ Canada Univ Rennes INSA Rennes CNRS IRMARUMR 6625 F-35000 Rennes France INSEA Lab SI2M Rabat Inst Rabat Morocco Univ Guelph Dept Math & Stat Guelph ON Canada

We discuss the convergence of regularization methods for mathematical programs with complementarity constraints with approximate sequence of stationary points. It is now well accepted in the literature that, under some tailored constraint qualification, the genuine necessary optimality condition for this problem is the M-stationarity condition. It has been pointed out, (Kanzow and Schwartz in Math Oper Res 40(2):253-275. 2015), that relaxation methods with approximate stationary points fail to ensure convergence to M-stationary points. We define a new strong approximate stationarity concept, and we prove that a sequence of strong approximate stationary points always converges to an M-stationary solution. We also prove under weak assumptions the existence of strong approximate stationary points in the neighborhood of an M-stationary solution.

关键词： nonlinear programming MPCC Regularization methods Constraint qualification Optimization model with complementarity constraints

来源：评论

学校读者我要写书评

暂无评论

Machine-Learning Techniques for the Optimal Design of Acoustic Metamaterials

引用

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2020年第3期187卷 630-653页

作者： Bacigalupo, Andrea Gnecco, Giorgio Lepidi, Marco Gambarotta, Luigi IMT Sch Adv Studies MUSAM Res Unit Piazza S Francesco 19 I-55100 Lucca Italy IMT Sch Adv Studies AXES Res Unit Piazza S Francesco 19 I-55100 Lucca Italy Univ Genoa Dept Civil Chem & Environm Engn Via Montallegro 1 I-16145 Genoa Italy

Recently, an increasing research effort has been dedicated to analyze the transmission and dispersion properties of periodic acoustic metamaterials, characterized by the presence of local resonators. Within this context, particular attention has been paid to the optimization of the amplitudes and center frequencies of selected stop and pass bands inside the Floquet-Bloch spectra of the acoustic metamaterials featured by a chiral or antichiral microstructure. Novel functional applications of such research are expected in the optimal parametric design of smart tunable mechanical filters and directional waveguides. The present paper deals with the maximization of the amplitude of low-frequency band gaps, by proposing suitable numerical techniques to solve the associated optimization problems. Specifically, the feasibility and effectiveness of Radial Basis Function networks and Quasi-Monte Carlo methods for the interpolation of the objective functions of such optimization problems are discussed, and their numerical application to a specific acoustic metamaterial with tetrachiral microstructure is presented. The discussion is motivated theoretically by the high computational effort often needed for an exact evaluation of the objective functions arising in band gap optimization problems, when iterative algorithms are used for their approximate solution. By replacing such functions with suitable surrogate objective functions constructed applying machine-learning techniques, well-performing suboptimal solutions can be obtained with a smaller computational effort. Numerical results demonstrate the effective potential of the proposed approach. Current directions of research involving the use of additional machine-learning techniques are also presented.

关键词： nonlinear programming Surrogate optimization Radial basis function networks Lattice materials Dispersion properties

来源：评论

学校读者我要写书评

暂无评论

An improved algorithm for solving profit-maximizing cattle diet problems

引用

ANIMAL 2020年第Sup2期14卷 S257-S266页

作者： Marques, J. G. O. de O. Silva, R. Barioni, L. G. Hall, J. A. J. Tedeschi, L. O. Moran, D. Univ Edinburgh Global Acad Agr & Food Secur Edinburgh EH25 9RG Midlothian Scotland Embrapa Agr Informat BR-13083886 Campinas Brazil Univ Edinburgh Sch Math Edinburgh EH9 3FD Midlothian Scotland Texas A&M Univ Dept Anim Sci College Stn TX 77843 USA

Feeding cattle with on-pasture supplementation or feedlot diets can increase animal efficiency and system profitability while minimizing environmental impacts. However, cattle system profit margins are relatively small and nutrient supply accounts for most of the costs. This paper introduces a nonlinear profit-maximizing diet formulation problem for beef cattle based on well-established predictive equations. nonlinearity in predictive equations for nutrient requirements poses methodological challenges in the application of optimization techniques. In contrast to other widely used diet formulation methods, we develop a mathematical model that guarantees an exact solution for maximum profit diet formulations. Our method can efficiently solve an often-impractical nonlinear problem by solving a finite number of linear problems, that is, linear time complexity is achieved through parametric linear programming. Results show the impacts of choosing different objective functions (minimizing cost, maximizing profit and maximizing profit per daily weight gain) and how this may lead to different optimal solutions. In targeting improved ration formulation on feedlot systems, this paper demonstrates how profitability and nutritional constraints can be met as an important part of a sustainable intensification production strategy.

关键词： linear programming nonlinear programming ration formulation optimization feedlot

来源：评论

学校读者我要写书评

暂无评论

Optimal sizing and multi-energy management strategy for PV-biofuel-based off-grid systems

引用

IET SMART GRID 2020年第1期3卷 83-97页

作者： Kumar, Arun, V Sharma, Sumedha Verma, Ashu Indian Inst Technol Delhi Ctr Energy Studies New Delhi India

This study proposes a comprehensive framework for developing a multi-energy off-grid microgrid with the decoupled flow of thermal and electrical energies in a rural setting. A carbon-neutral microgrid with a hybrid generation system constituting a photovoltaic unit and a biofuel generator is proposed. In order to enhance the fuel utilisation efficiency, the biofuel generator is operated in combined cooling, heating, and power mode, and the recovered thermal energy forms the heat distribution network in the microgrid. The flexibility of system operation is improved by suitable multi-energy (electrical and thermal) storage. Firstly, an optimal sizing framework has been developed for the system as a mixed integer linear programming model. Secondly, a coordinated multi-energy management system (MEMS) has been developed for combined optimal dispatch of multiple generation and storage resources. The MEMS has been developed as a mixed integer non-linear programming model, which minimises system operational cost while considering minimum battery degradation to prolong its lifetime. Finally, a detailed economic analysis of the proposed system has been presented, highlighting the levellised cost of energy and net present value. Extensive case studies and simulation results depict the effectiveness and suitability of the proposed MEMS for the rural off-grid microgrid.

关键词： nonlinear programming energy management systems cogeneration biofuel hybrid power systems linear programming integer programming photovoltaic power systems distributed power generation power generation economics PV-biofuel-based off-grid systems decoupled flow rural setting carbon-neutral microgrid hybrid generation system photovoltaic unit biofuel generator fuel utilisation efficiency recovered thermal energy forms heat distribution network system operation optimal sizing framework mixed integer linear programming model multienergy management system MEMS combined optimal dispatch storage resources mixed integer nonlinear programming model system operational cost combined cooling heating and power mode multi-energy off-grid microgrid levelised cost of energy net present value B0260 Optimisation techniques B8110B Power system management, operation and economics B8210 Energy resources B8230 Thermal power stations and plants B8250 Solar power stations and photovoltaic power systems B8120K Distributed power generation

来源：评论

学校读者我要写书评

暂无评论

A GLOBALLY CONVERGENT PRIMAL-DUAL INTERIOR-POINT RELAXATION METHOD FOR nonlinear PROGRAMS

引用

MATHEMATICS OF COMPUTATION 2020年第323期89卷 1301-1329页

作者： Liu, Xin-Wei Dai, Yu-Hong Hebei Univ Technol Inst Math Tianjin 300401 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

We prove that the classic logarithmic barrier problem is equivalent to a particular logarithmic barrier positive relaxation problem with barrier and scaling parameters. Based on the equivalence, a line-search primal-dual interior-point relaxation method for nonlinear programs is presented. Our method does not require any primal or dual iterates to be interior-points, which has similarity to some warmstarting interior-point methods and is different from most of the globally convergent interior-point methods in the literature. A new logarithmic barrier penalty function dependent on both primal and dual variables is used to prompt the global convergence of the method, where the penalty parameter is adaptively updated. Without assuming any regularity condition, it is proved that our method will either terminate at an approximate KKT point of the original problem, an approximate infeasible stationary point, or an approximate singular stationary point of the original problem. Some preliminary numerical results are reported, including the results for a well-posed problem for which many line-search interior-point methods were demonstrated not to be globally convergent, a feasible problem for which the LICQ and the MFCQ fail to hold at the solution and an infeasible problem, and for some standard test problems of the CUTE collection. Correspondingly, for comparison we also report the numerical results obtained by the interior-point solver IPOPT. These results show that our algorithm is not only efficient for well-posed feasible problems, but is also applicable for some feasible problems without LICQ or MFCQ and some infeasible problems.

关键词： nonlinear programming constrained optimization interior-point method logarithmic barrier problem global convergence

来源：评论

学校读者我要写书评

暂无评论

Allocating resources for land protection using continuous optimization: biodiversity conservation in the United States

引用

ECOLOGICAL APPLICATIONS 2020年第6期30卷 1-13页

作者： Armsworth, Paul R. Benefield, Amy E. Dilkina, Bistra Fovargue, Rachel Jackson, Heather B. Le Bouille, Diane Nolte, Christoph Univ Tennessee Dept Ecol & Evolutionary Biol 569 Dabney Hall1416 Circle Dr Knoxville TN 37996 USA Univ Tennessee Natl Inst Math & Biol Synth 569 Dabney Hall1416 Circle Dr Knoxville TN 37996 USA Univ Southern Calif Dept Comp Sci 941 BloomWalk Los Angeles CA 90089 USA Boston Univ Dept Earth & Environm 685 Commonwealth Ave Boston MA 02215 USA Univ Colorado Dept Ecol & Evolutionary Biol 1900 Pleasant St Boulder CO 80302 USA Univ Oklahoma Dept Geog & Environm Sustainabil 100 East Boyd St Norman OK 73019 USA

Spatial optimization approaches that were originally developed to help conservation organizations determine protection decisions over small spatial scales are now used to inform global or continental scale priority setting. However, the different decision contexts involved in large-scale resource allocation need to be considered. We present a continuous optimization approach in which a decision-maker allocates funding to regional offices. Local decision-makers then use these funds to implement habitat protection efforts with varying effectiveness when evaluated in terms of the funder's goals. We illustrate this continuous formulation by examining the relative priority that should be given to different counties in the coterminous United States when acquiring land to establish new protected areas. If weighting all species equally, counties in the southwest United States, where large areas can be bought cheaply, are priorities for protection. If focusing only on species of conservation concern, priorities shift to locations rich in such species, particularly near expanding exurban areas facing high rates of future habitat conversion (e.g., south-central Texas). Priorities for protection are sensitive to what is assumed about local ecological and decision-making processes. For example, decision-makers who doubt the efficacy of local land protection efforts should focus on a few key areas, while optimistic decision-makers should disperse funding more widely. Efforts to inform large-scale conservation priorities should reflect better the types of choice that decision-makers actually face when working over these scales. They also need to report the sensitivity of recommended priorities to what are often unstated assumptions about local processes affecting conservation outcomes.

关键词： conservation finance conservation organization CONUS cost gap analysis institution nature reserves nonlinear programming protected areas return on investment systematic conservation planning threat

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：