检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Osaba, Eneko Villar-Rodriguez, Esther Del Ser, Javier Nebro, Antonio J. Molina, Daniel LaTorre, Antonio Suganthan, Ponnuthurai N. Coello Coello, Carlos A. Herrera, Francisco Derio48160 Spain Bilbao48013 Spain DaSCI Andalusian Institute of Data Science and Computational Intelligence University of Granada Granada18071 Spain Dept. de Lenguajes y Ciencias de la Computación ITIS Software Universidad de Málaga Spain CINVESTAV-IPN DF Mexico07360 Mexico Nanyang Technological University 639798 Singapore Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain

In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments. © 2024, CC BY-NC-ND.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Single-Loop Deterministic and Stochastic Interior-Point algorithms for Nonlinearly Constrained optimization

arXiv

引用

arXiv 2024年

作者： Curtis, Frank E. Jiang, Xin Wang, Qi Department of Industrial and Systems Engineering Lehigh University United States

An interior-point algorithm framework is proposed, analyzed, and tested for solving nonlinearly constrained continuous optimization problems. The main setting of interest is when the objective and constraint functions may be nonlinear and/or nonconvex, and when constraint values and derivatives are tractable to compute, but objective function values and derivatives can only be estimated. The algorithm is intended primarily for a setting that is similar for stochastic-gradient methods for unconstrained optimization, namely, the setting when stochastic-gradient estimates are available and employed in place of gradients of the objective, and when no objective function values (nor estimates of them) are employed. This is achieved by the interior-point framework having a single-loop structure rather than the nested-loop structure that is typical of contemporary interior-point methods. For completeness, convergence guarantees for the framework are provided both for deterministic and stochastic settings. Numerical experiments show that the algorithm yields good performance on a large set of test problems. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

On-Line optimization of Switched-Mode Systems: algorithms and Convergence Properties

On-Line Optimization of Switched-Mode Systems: Algorithms an...

引用

Proceedings of the 46th IEEE Conference on Decision and Control, Pages 1-459

作者： Y. Wardi X.C. Ding M. Egerstedt S. Azuma School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Department of Systems Science Graduate School of Informatics Kyoto University Kyoto Japan

ISBN: (纸本)9781424414970;1424414970

This paper concerns the problem of on-line (real-time) computation of solutions to the optimal switching time problem in hybrid systems. The systems under consideration are autonomous, and the performance measure to be optimized has the form of a cost functional defined on the state trajectory. The state variable cannot, however, be measured directly and it has to be estimated by a suitable observer. In this paper, we propose an on-line optimization algorithm based on the state observer, and derive bounds on its convergence rate.

关键词： State variable switching pattern Observers IN-LINE optimization algorithms hybrid systems Converge real-time computation state trajectory

来源：评论

学校读者我要写书评

暂无评论

Optimal and Efficient algorithms for Decentralized Online Convex optimization

arXiv

引用

arXiv 2024年

作者： Wan, Yuanyu Wei, Tong Xue, Bo Song, Mingli Zhang, Lijun School of Software Technology Zhejiang University Ningbo China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering Southeast University Nanjing China Department of Computer Science City University of Hong Kong Hong Kong National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Artificial Intelligence Nanjing University Nanjing China

We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using only local computations and communications. Previous studies have established O(n5/4ρ−1/2√T) and O(n3/2ρ−1 log T) regret bounds for convex and strongly convex functions respectively, where n is the number of local learners, ρ √T) for convex functions and Ω(n) for strongly convex functions. To fill these gaps, in this paper, we first develop a novel D-OCO algorithm that can respectively reduce the regret bounds for convex and strongly convex functions to Õ(nρ−1/4√T) and Õ(nρ−1/2 log T). The primary technique is to design an online accelerated gossip strategy that enjoys a faster average consensus among local learners. Furthermore, by carefully exploiting spectral properties of a specific network topology, we enhance the lower bounds for convex and strongly convex functions to Ω(nρ−1/4√T) and Ω(nρ−1/2 log T), respectively. These results suggest that the regret of our algorithm is nearly optimal in terms of T, n, and ρ for both convex and strongly convex functions. Finally, we propose a projection-free variant of our algorithm to efficiently handle practical applications with complex constraints. Our analysis reveals that the projection-free variant can achieve O(nT 3/4) and O(nT 2/3(log_ T)1/3) regret bounds for convex and strongly convex functions with nearly optimal Õ(ρ−1/2√T) and Õ(ρ−1/2T 1/3(log T)2/3) communication rounds, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Efficient and provably convergent randomized greedy algorithms for neural network optimization

arXiv

引用

arXiv 2024年

作者： Xu, Jinchao Xu, Xiaofeng Thuwal23955 Saudi Arabia

Greedy algorithms have been successfully analyzed and applied in training neural networks for solving variational problems, ensuring guaranteed convergence orders. However, their practical applicability is limited due to the subproblems, which often require an exhaustive search over a discrete dictionary and incur significant computational costs. This limitation becomes critical, especially in high-dimensional problems. In this paper, we propose a more practical approach of randomly discretizing the dictionary at each iteration of the greedy algorithm. We quantify the required size of the randomized discrete dictionary and prove that, with high probability, the proposed algorithm realizes a weak greedy algorithm, achieving optimal convergence orders. Through numerous numerical experiments, we demonstrate the advantage of using randomized discrete dictionaries over a deterministic one by showing orders of magnitude reductions in the size of the discrete dictionary, particularly in higher *** Codes 65D15, 65N12, 65N75, 41A25 Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Distributed algorithms for optimization Problems with Equality Constraints

Distributed Algorithms for Optimization Problems with Equali...

引用

IEEE Annual Conference on Decision and Control

作者： Ion Matei John S. Baras Palo Alto Research Center (PARC) Palo Alto CA Institute for Systems Research at the University of Maryland College Park MD

ISBN: (纸本)9781467357159

In this paper we introduce two discrete-time, distributed optimization algorithms executed by a set of agents whose interactions are subject to a communication graph. The algorithms can be applied to optimization problems where the cost function is expressed as a sum of functions, and where each function is associated to an agent. In addition, the agents can have equality constraints as well. The algorithms are not consensus-based and can be applied to non-convex optimization problems with equality constraints. We demonstrate that the first distributed algorithm results naturally from applying a first order method to solve the first order necessary conditions for a lifted optimization problem with equality constraints;the solution of our original problem is embedded in the solution of this lifted optimization problem. Using an augmented Lagrangian idea, we derive a second distributed algorithm that requires weaker conditions for local convergence compared to the first algorithm. For both algorithms we address the local convergence properties.

关键词： distributed algorithms Equality Agents Cost functions algorithms original problem discrete-time optimization algorithms lift

来源：评论

学校读者我要写书评

暂无评论

Splitting algorithms for Distributionally Robust optimization

arXiv

引用

arXiv 2024年

作者： Briceño-Arias, Luis López-Rivera, Sergio Vilches, Emilio Departamento de Matemática Universidad Tecnica Federico Santa Maria Santiago Chile Departamento de Ingeniería Matemática Universidad de Chile Santiago Chile Instituto de Ciencias de la Ingeniería Universidad de O’Higgins Rancagua Chile

MSC Codes 47H05, 65K05, 90C15, 90C17, 90C25, 90C47In this paper, we provide different splitting methods for solving distributionally robust optimization problems in cases where the uncertainties are described by discrete distributions. The first method involves computing the proximity operator of the supremum function that appears in the optimization problem. The second method solves an equivalent monotone inclusion formulation derived from the first-order optimality conditions, where the resolvents of the monotone operators involved in the inclusion are computable. The proposed methods are applied to solve the Couette inverse problem with uncertainty and the denoising problem with uncertainty. We present numerical results to compare the efficiency of the algorithms. © 2024, CC BY.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Quantum and Classical Correlations in Shrinking algorithms for optimization

arXiv

引用

arXiv 2024年

作者： Fischer, Victor Passek, Maximilian Wagner, Friedrich Finžgar, Jernej Rudi Palackal, Lilly Mendl, Christian B. Infineon Technologies AG Munich Germany BMW Group Munich Germany University of Erlangen-Nuremberg Germany Fraunhofer Institute for Integrated Circuits Erlangen Germany Technical University of Munich Germany Ludwig Maximilian University of Munich Germany

Understanding the benefits of quantum computing for solving combinatorial optimization problems (COPs) remains an open research question. In this work, we extend and analyze algorithms that solve COPs by recursively shrinking them. The algorithms leverage correlations between variables extracted from quantum or classical subroutines to recursively simplify the problem. We compare the performance of the algorithms equipped with correlations from the quantum approximate optimization algorithm (QAOA) as well as the classical linear programming (LP) and semi-definite programming (SDP) relaxations. This allows us to benchmark the utility of QAOA correlations against established classical relaxation algorithms. We apply the recursive algorithm to MAXCUT problem instances with up to a hundred vertices at different graph densities. Our results indicate that LP outperforms all other approaches for low-density instances, while SDP excels for high-density problems. Moreover, the shrinking algorithm proves to be a viable alternative to established methods of rounding LP and SDP relaxations. In addition, the recursive shrinking algorithm outperforms its bare counterparts for all three types of correlations, i.e., LP with spanning tree rounding, the Goemans-Williamson algorithm, and conventional QAOA. While the lowest depth QAOA consistently yields worse results than the SDP, our tensor network experiments show that the performance increases significantly for deeper QAOA circuits. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Provably Faster algorithms for Bilevel optimization via Without-Replacement Sampling

arXiv

引用

arXiv 2024年

作者： Li, Junyi Huang, Heng Department of Computer Science Institute of Health Computing University of Maryland College Park College ParkMD20742 United States

Bilevel optimization has experienced significant advancements recently with the introduction of new efficient algorithms. Mirroring the success in single-level optimization, stochastic gradient-based algorithms are widely used in bilevel optimization. However, a common limitation in these algorithms is the presumption of independent sampling, which can lead to increased computational costs due to the complicated hyper-gradient formulation of bilevel problems. To address this challenge, we study the example-selection strategy for bilevel optimization in this work. More specifically, we introduce a without-replacement sampling based algorithm which achieves a faster convergence rate compared to its counterparts that rely on independent sampling. Beyond the standard bilevel optimization formulation, we extend our discussion to conditional bilevel optimization and also two special cases: minimax and compositional optimization. Finally, we validate our algorithms over both synthetic and real-world applications. Numerical results clearly showcase the superiority of our algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Accelerated algorithms for Constrained Nonconvex-Nonconcave Min-Max optimization and Comonotone Inclusion

arXiv

引用

arXiv 2022年

作者： Cai, Yang Oikonomou, Argyris Zheng, Weiqiang Department of Computer Science Yale University New Haven United States

We study constrained comonotone min-max optimization, a structured class of nonconvex-nonconcave min-max optimization problems, and their generalization to comonotone inclusion. In our first contribution, we extend the Extra Anchored Gradient (EAG) algorithm, originally proposed by Yoon & Ryu (2021) for unconstrained min-max optimization, to constrained comonotone min-max optimization and comonotone inclusion, achieving an optimal convergence rate of O T1 among all first-order methods. Additionally, we prove that the algorithm’s iterations converge to a point in the solution set. In our second contribution, we extend the Fast Extra Gradient (FEG) algorithm, as developed by Lee & Kim (2021), to constrained comonotone min-max optimization and comonotone inclusion, achieving the same O T1 convergence rate. This rate is applicable to the broadest set of comonotone inclusion problems yet studied in the literature. Our analyses are based on simple potential function arguments, which might be useful for analyzing other accelerated algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： optimization algorithms

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：