检索结果-内蒙古大学图书馆

A unified framework for the application and evaluation of different methods for neural parameter optimization

A unified framework for the application and evaluation of di...

International Joint conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Mohacsi, Mate Torok, Mark Patrik Saray, Sara Kali, Szabolcs Pazmany Peter Catholic Univ Inst Expt Med Fac Informat Technol & Bion Budapest Hungary Inst Expt Med Budapest Hungary

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169262

Automated parameter search has become a standard method in the modeling of neural systems. These studies could potentially take advantage of recent developments in nonlinear optimization, and the availability of software packages containing high-quality implementations of algorithms that proved useful in other domains. However, a systematic comparison of the available algorithms for problems that are typical in neuroscience has not been performed. We developed a software tool for fitting the parameters of neural models, which provides intuitive, uniform access to a variety of state-of-the-art optimization algorithms implemented by four different Python packages. We also established a set of benchmark problems of different complexity that involve a variety of widely used neuronal models. We then used our optimization tool to systematically evaluate the performance of the algorithms on our set of benchmark problems. We found that several evolutionary and related algorithms consistently provided good solutions for all of our benchmarks. However, the relative performance of the different methods, both in terms of the quality of the final result and in terms of convergence speed, depended substantially on the nature of the problem. We hope that our software tool and benchmarking results will facilitate the choice and application of the best parameter-fitting methods in neuroscientific research.

关键词： neuronal modeling Python software simulation model fitting parameter optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Energy Management in Plug-In Hybrid Electric Vehicles: Convex optimization algorithms for Model Predictive Control

引用

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2020年第6期28卷 2191-2203页

作者： East, Sebastian Cannon, Mark Univ Oxford Dept Engn Sci Oxford OX1 3PJ England

This article details an investigation into the computational performance of algorithms used for solving a convex formulation of the optimization problem associated with model predictive control for energy management in hybrid electric vehicles with nonlinear losses. A projected interior-point method is proposed, where the size and complexity of the Newton step matrix inversion is reduced by applying inequality constraints on the control input as a projection, and its properties are demonstrated through simulation in comparison with an alternating direction method of multipliers (ADMM) algorithm and a general purpose convex optimization software CVX. It is found that the ADMM algorithm has favorable properties when a solution with modest accuracy is required, whereas the projected interior-point method is favorable when high accuracy is required, and that both are significantly faster than CVX.

关键词： Engines Energy management optimization Heuristic algorithms Batteries Convex functions Mechanical power transmission Alternating direction method of multipliers (ADMM) energy management interior-point method model predictive control (MPC) plug-in hybrid electric vehicles (PHEVs)

来源：评论

学校读者我要写书评

暂无评论

Optimized start-up control of an industrial-scale evaporation system with hybrid dynamics

引用

CONTROL ENGINEERING PRACTICE 2008年第8期16卷 976-990页

作者： Sonntag, Christian Su, Wanjing Stursberg, Olaf Engell, Sebastian Univ Dortmund BCI AST Proc Control Lab D-44221 Dortmund Germany Tech Univ Munich EI LSR Inst Automat Control Engn D-80290 Munich Germany

In this paper, the task to start the operation of an evaporation system with hybrid dynamics is considered. The evaporator system was provided as a benchmark for hybrid control by a major chemical company. Rigorous modeling gives rise to a hybrid automaton with high-dimensional nonlinear DAE dynamics that describe the continuous evolution in different discrete modes of operation. The problem of optimized start-up is solved by a branch-and-bound algorithm with embedded nonlinear dynamic optimization over a finite look-ahead horizon. The nonlinear optimization problems are solved by nonlinear programming and by evolutionary algorithms. Important elements of this formulation of the optimization problems are the introduction of a dynamic choice of the time intervals over which the zero-order hold controls are constant and the utilization of tailored penalty functions in order to obtain solutions which are close to the bounds of the feasible state regions. The two approaches are compared with respect to their performance for the evaporation system. 2007 Elsevier Ltd. All rights reserved.

关键词： hybrid systems processing systems large-scale systems optimal control nonlinear programming evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

A scalable parallel interior point algorithm for stochastic linear programming and robust optimization

引用

COMPUTATIONAL optimization AND APPLICATIONS 1997年第1期7卷 143-158页

作者： Yang, DF Zenios, SA UNIV CYPRUS DEPT PUBL & BUSINESS ADM NICOSIA CYPRUS

We present a computationally efficient implementation of an interior point algorithm for solving large-scale problems arising in stochastic linear programming and robust optimization. A matrix factorization procedure is employed that exploits the structure of the constraint matrix, and it is implemented on parallel computers. The implementation is perfectly scalable. Extensive computational results are reported for a library of standard test problems from stochastic linear programming, and also for robust optimization formulations. The results show that the codes are efficient and stable for problems with thousands of scenarios. Test problems with 130 thousand scenarios, and a deterministic equivalent linear programming formulation with 2.6 million constraints and 18.2 million variables, are solved successfully.

关键词： planning under uncertainty parallel computing optimization software

来源：评论

学校读者我要写书评

暂无评论

On the number of inner iterations per outer iteration of a globally convergent algorithm for optimization with general nonlinear inequality constraints and simple bounds

引用

COMPUTATIONAL optimization AND APPLICATIONS 1997年第1期7卷 41-69页

作者： Conn, AR Gould, N Toint, PL RUTHERFORD APPLETON LAB CHILTONOXONENGLAND FAC UNIV NOTRE DAME PAIX DEPT MATHNAMURBELGIUM

This paper considers the number of inner iterations required per outer iteration for the algorithm proposed by Conn er al. [9]. We show that asymptotically, under suitable reasonable assumptions, a single inner iterat... 详细信息

关键词： nonlinear optimization inequality constraints barrier methods complexity

来源：评论

学校读者我要写书评

暂无评论

Iterative computation of negative curvature directions in large scale optimization

引用

COMPUTATIONAL optimization AND APPLICATIONS 2007年第1期38卷 81-104页

作者： Fasano, Giovanni Roma, Massimo Univ Roma La Sapienza Dipartimento Informat & Sistemist A Ruberti Rome Italy Ist Nazl & Esperienze Architettura Navale INS Rome Italy

In this paper we deal with the iterative computation of negative curvature directions of an objective function, within large scale optimization frameworks. In particular, suitable directions of negative curvature of the objective function represent an essential tool, to guarantee convergence to second order critical points. However, an '' adequate '' negative curvature direction is often required to have a good resemblance to an eigenvector corresponding to the smallest eigenvalue of the Hessian matrix. Thus, its computation may be a very difficult task on large scale problems. Several strategies proposed in literature compute such a direction relying on matrix factorizations, so that they may be inefficient or even impracticable in a large scale setting. On the other hand, the iterative methods proposed either need to store a large matrix, or they need to rerun the recurrence. On this guideline, in this paper we propose the use of an iterative method, based on a planar Conjugate Gradient scheme. Under mild assumptions, we provide theory for using the latter method to compute adequate negative curvature directions, within optimization frameworks. In our proposal any matrix storage is avoided, along with any additional rerun.

关键词： Conjugate Gradient method large scale optimization negative curvature directions convergence to second order critical points

来源：评论

学校读者我要写书评

暂无评论

Impact of partial separability on large-scale optimization

引用

COMPUTATIONAL optimization AND APPLICATIONS 1997年第1期7卷 27-40页

作者： Bouaricha, A More, JJ Argonne National Laboratory Argonne

ELSO is an environment for the solution of large-scale optimization problems. With ELSO the user is required to provide only code for the evaluation of a partially separable function. ELSO exploits the partial separability structure of the function to compute the gradient efficiently using automatic differentiation. We demonstrate ELSO's efficiency by comparing the various options available in ELSO. Our conclusion is that the hybrid option in ELSO provides performance comparable to the hand-coded option, while having the significant advantage of not requiring a hand-coded gradient or the sparsity pattern of the partially separable function. In our test problems, which have carefully coded gradients, the computing time for the hybrid AD option is within a factor of two of the hand-coded option.

关键词： large-scale optimization partial separability automatic differentiation

来源：评论

学校读者我要写书评

暂无评论

NRAgo: Solving SMT(NRA) Formulas with Gradient-based optimization 38

NRAgo: Solving SMT(NRA) Formulas with Gradient-based Optimiz...

引用

38th IEEE/ACM International conference on Automated software Engineering (ASE)

作者： Liu, Minghao Lv, Kunhang Huang, Pei Han, Rui Jia, Fuqi Zhang, Yu Ma, Feifei Zhang, Jian Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Stanford Univ Stanford CA USA Chinese Acad Sci Inst Software Lab Parallel Software & Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350329964

The satisfiability problem modulo the nonlinear real arithmetic (NRA) theory serves as the foundation for a wide range of important applications, such as model checking, program analysis, and software testing. However, due to the high computational complexity, developing efficient solving algorithms for this problem has consistently presented a substantial challenge. We present a hybrid SMT(NRA) solver, called NRAgo, which combines the efficiency of gradient-based optimization method with the completeness of algebraic solving algorithm. With our approach, the practical performance on many satisfiable instances is substantially improved. The experimental evaluation shows that NRAgo achieves remarkable acceleration effects on a set of challenging SMT(NRA) benchmarks that are hard to solve for state-of-the-art SMT solvers.

关键词： satisfiability modulo theories nonlinear real arithmetic gradient-based optimization

来源：评论

学校读者我要写书评

暂无评论

Some iterative methods for the solution of a symmetric indefinite KKT system

Some iterative methods for the solution of a symmetric indef...

引用

conference on high performance algorithms and software for nonlinear optimization

作者： Bonettini, Silvia Ruggiero, Valeria Univ Modena & Reggio Emilia Dipartimento Matemat Pura & Applicata I-41100 Modena Italy Univ Ferrara Dipartmento Matemat I-44100 Ferrara Italy

This paper is concerned with the numerical solution of a Karush-Kuhn-Tucker system. Such symmetric indefinite system arises when we solve a nonlinear programming problem by an Interior-Point (IP) approach. In this framework, we discuss the effectiveness of two inner iterative solvers: the method of multipliers and the preconditioned conjugate gradient method. We discuss the implementation details of these algorithms in an IP scheme and we report the results of a numerical comparison on a set of large scale test-problems arising from the discretization of elliptic control problems.

关键词： indefinite symmetric KKT system large scale nonlinear programming problems Interior Point method Hestenes' multipliers method preconditioned conjugate gradient method elliptic control problems

来源：评论

学校读者我要写书评

暂无评论

optimization-based design of plant-friendly multisine signals using geometric discrepancy criteria

Optimization-based design of plant-friendly multisine signal...

引用

conference on high performance algorithms and software for nonlinear optimization

作者： Mittelmann, Hans D. Pendse, Gautam Rivera, Daniel E. Lee, Hyunjin Arizona State Univ Dept Math & Stat Tempe AZ 85287 USA Harvard Univ Sch Med McLean Hosp Brain Imaging Ctr Cambridge MA 02138 USA Arizona State Univ Dept Chem Engn Control Syst Engn Lab Tempe AZ 85287 USA Rensselaer Polytech Inst Dept Chem & Biol Engn Troy NY USA

System identification is an important means for obtaining dynamical models for process control applications;experimental testing represents the most time-consuming step in this task. The design of constrained, '' plant-friendly '' multisine input signals that optimize a geometric discrepancy criterion arising from Weyl's Theorem is examined in this paper. Such signals are meaningful for data-centric estimation methods, where uniform coverage of the output state-space is critical. The usefulness of this problem formulation is demonstrated by applying it to a linear problem example and to the nonlinear, highly interactive distillation column model developed by Weischedel and McAvoy. The optimization problem includes a search for both the Fourier coefficients and phases in the multisine signal, resulting in an uniformly distributed output signal displaying a desirable balance between high and low gain directions. The solution involves very little user intervention (which enhances its practical usefulness) and has great benefits compared to multisine signals that minimize crest factor. The constrained nonlinear optimization problems that are solved represent challenges even for high-performance optimization software.

关键词： system identification process control constrained optimization

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：