检索结果-内蒙古大学图书馆

SMOP: Stochastic trust region method for multi-objective problems

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Krejić, Nataša Jerinkić, Nataša Krklec Rutešić, Luka Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Trg Dositeja Obradovića 4 Novi Sad21000 Serbia

The problem considered is a multi-objective optimization problem, in which the goal is to find an optimal value of a vector function representing various criteria. The aim of this work is to develop an algorithm which utilizes the trust region framework with probabilistic model functions, able to cope with noisy problems, using inaccurate functions and gradients. We prove the almost sure convergence of the proposed algorithm to a Pareto critical point if the model functions are good approximations in probabilistic sense. Numerical results demonstrate effectiveness of the probabilistic trust region by comparing it to competitive stochastic multi-objective solvers. The application in supervised machine learning is showcased by training non discriminatory Logistic Regression models on different size data groups. Additionally, we use several test examples with irregularly shaped fronts to exhibit the efficiency of the algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Subgradient Method for System Identification with Non-Smooth Objectives

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Yalcin, Baturalp Lavaei, Javad University of California Berkeley United States

This paper investigates a subgradient-based algorithm to solve the system identification problem for linear time-invariant systems with non-smooth objectives. This is essential for robust system identification in safety-critical applications. While existing work provides theoretical exact recovery guarantees using optimization solvers, the design of fast learning algorithms with convergence guarantees for practical use remains unexplored. We analyze the subgradient method in this setting where the optimization problems to be solved change over time as new measurements are taken, and we establish linear convergence results for both the best and Polyak step sizes after a burn-in period. Additionally, we characterize the asymptotic convergence of the best average sub-optimality gap under diminishing and constant step sizes. Finally, we compare the time complexity of standard solvers with the subgradient algorithm and support our findings with experimental results. This is the first work to analyze subgradient algorithms for system identification with non-smooth *** Codes 62, 90, 93 © 2025, CC BY-NC-ND.

关键词： optimization algorithms

Cover-Relax-Search: A Primal Heuristic for Binary Quadratic Programs

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Huang, Weimin Isenberg, Natalie M. Drgona, Jan Vrabie, Draguna L. Dilkina, Bistra University of Southern California Los AngelesCA United States Pacific Northwest National Laboratory RichlandWA United States Johns Hopkins University BaltimoreMD United States

Binary Quadratic Programs (BQPs) are a class of NP-hard problems that arise in a wide range of applications, including finance, machine learning, and logistics. These problems are challenging to solve due to the combinatorial search space and nonlinearity. In fact, this class of optimization problems is so challenging that, in many instances, standard algorithms struggle to identify feasible solutions within a reasonable time. Primal heuristic algorithms have been developed to quickly identify feasible solutions to BQPs. In this paper, we propose Cover-Relax-Search, an efficient primal heuristic for BQPs. This approach is inspired by multiple local search algorithms, including Undercover. We evaluate the Cover-Relax-Search algorithm on multiple BQP benchmarks and show that our proposed heuristic identifies high-quality solutions at a faster speed and significantly reduces the primal integral compared to state-of-the-art solvers and other local search baselines. © 2025, CC BY.

关键词： optimization algorithms

Genetic algorithms and Satin Bowerbird optimization for optimal allocation of distributed generators in radial system

学校读者我要写书评

暂无评论

APPLIED SOFT COMPUTING 2021年 111卷 107727-107727页

作者： Hemeida, Ashraf Mohamed Bakry, Omaima M. Mohamed, Al-Attar A. Mahmoud, Eman A. Aswan Univ Fac Energy Engn Elect Engn Dept Aswan 81528 Egypt Aswan Univ Fac Engn Elect Engn Dept Aswan Egypt Aswan Univ Fac Sci Dept Math Aswan 81528 Egypt

In this document, the topic of discussion is the combination of two existing algorithms to generate a new hybrid technique. The two algorithms that are subjected to said amalgamation are Genetic algorithms (GA) and Stain Bowerbird optimization algorithms (SBO). These two methodologies have profound utility themselves and are used in a multitude of scenarios. The easy application and the constructive outcomes manifested by these two algorithms birthed the idea of their combined usage. Following up on this, the hybrid GASBO was created. GASBO was an optimization approach used to detect and categorize the allotted renewable energy assets in a specific energy generation complex. This was done to regulate the energy dispensing systems otherwise known as 'distributing' systems. These renewable resources are reflected by environmental factors and the energy they create is also dependent on their surroundings. Factors like sunlight, rain, waves, and tides etcetera play major roles in determining the outcome of the created energy. Contrary to what it may appear like, the position of the DG sources in the structure affects the outcome a lot. These sources contain fuel cells and photovoltaic cells: in short, devices that can harness energy from a seemingly infinite supply like sunlight. As mentioned before, the GASBO assisted in providing the best location for the system and it also categorized the sources according to their abilities. The potential and position of the sources in the grid are of vast importance. The main purpose of GASBO is to optimize the overall system by improving its efficiency and reducing collateral harm. This shows that GASBO is quite a fundamental tool. It has also been tested on several systems like IEEE 33-bus. The facts in this paper are based on published projects. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Genetic algorithms Stain Bowerbird Algorithm optimization algorithms Security constraints Distributed generation Multi-objective optimization

Pandora with Inaccurate Priors

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Banihashem, Kiarash Chen, Xiang Hajiaghayi, MohammadTaghi Kim, Sungchul Mahadik, Kanak Rossi, Ryan Yu, Tong University of Maryland United States Adobe Research United States Adobe Systems United States

We investigate the role of inaccurate priors for the classical Pandora’s box problem. In the classical Pandora’s box problem we are given a set of boxes each with a known cost and an unknown value sampled from a known distribution. We investigate how inaccuracies in the beliefs can affect existing algorithms. Specifically, we assume that the knowledge of the underlying distribution has a small error in the Kolmogorov distance, and study how this affects the utility obtained by the optimal algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Discovering new robust local search algorithms with neuro-evolution

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Sakhri, Mohamed Salim Amri Goëffon, Adrien Goudet, Olivier Saubion, Frédéric Touhami, Chaïmaâ LERIA Université d’Angers 2 Boulevard Lavoisier Angers49045 France

This paper explores a novel approach aimed at overcoming existing challenges in the realm of local search algorithms. Our aim is to improve the decision process that takes place within a local search algorithm so as to make the best possible transitions in the neighborhood at each iteration. To improve this process, we propose to use a neural network that has the same input information as conventional local search algorithms. In this paper, which is an extension of the work presented at EvoCOP2024, we investigate different ways of representing this information so as to make the algorithm as efficient as possible but also robust to monotonic transformations of the problem objective function. To assess the efficiency of this approach, we develop an experimental setup centered around NK landscape problems, offering the flexibility to adjust problem size and ruggedness. This approach offers a promising avenue for the emergence of new local search algorithms and the improvement of their problem-solving capabilities for black-box problems. The last version of this article is published in the journal SN Computer Science (Springer). © 2025, CC BY.

关键词： optimization algorithms

How to optimize K-means?

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Li, Qi School of Information and Technology Beijing Forestry university Beijing100083 China

Center-based clustering algorithms (e.g., K-means) are popular for clustering tasks, but they usually struggle to achieve high accuracy on complex datasets. We believe the main reason is that traditional center-based clustering algorithms identify only one clustering center in each cluster. Once the distribution of the dataset is complex, a single clustering center cannot strongly represent distant objects within the cluster. How to optimize the existing center-based clustering algorithms will be valuable research. In this paper, we propose a general optimization method called ECAC, and it can optimize different center-based clustering algorithms. ECAC is independent of the clustering principle and is embedded as a component between the center process and the category assignment process of center-based clustering algorithms. Specifically, ECAC identifies several extended-centers for each clustering center. The extended-centers will act as relays to expand the representative capability of the clustering center in the complex cluster, thus improving the accuracy of center-based clustering algorithms. We conducted numerous experiments to verify the robustness and effectiveness of ECAC. ECAC is robust to diverse datasets and diverse clustering centers. After ECAC optimization, the accuracy (NMI as well as RI) of center-based clustering algorithms improves by an average of 33.4% and 64.1%, respectively, and even K-means accurately identifies complex-shaped clusters. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Global and Robust Optimisation for Non-Convex Quadratic Programs

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Marousi, Asimina Charitopoulos, Vassilis M. Department of Chemical Engineering The Sargent Centre for Process Systems Engineering University College London Torrington Place LondonWC1E 7JE United Kingdom

This paper presents a novel algorithm integrating global and robust optimisation methods to solve continuous non-convex quadratic problems under convex uncertainty sets. The proposed Robust spatial branch-and-bound (RsBB) algorithm combines the principles of spatial branch-and-bound (sBB) with robust cutting planes. We apply the RsBB algorithm to quadratically constrained quadratic programming (QCQP) pooling problems, utilising McCormick envelopes to obtain convex lower bounds. The performance of the RsBB algorithm is compared with state-of-the-art methods that rely on global solvers. As computational test bed for our proposed approach we focus on pooling problems under different types and sizes of uncertainty sets. The findings of our work highlight the efficiency of the RsBB algorithm in terms of computational time and optimality convergence and provide insights to the advantages of combining robustness and optimality search. © 2025, CC BY.

关键词： optimization algorithms

Turing in the shadows of Nobel and Abel: an algorithmic story behind two recent prizes

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Gamarnik, David Massachusetts Institute of Technology United States

The 2021 Nobel Prize in physics was awarded to Giorgio Parisi "for the discovery of the interplay of disorder and fluctuations in physical systems from atomic to planetary scales," and the 2024 Abel Prize in mathematics was awarded to Michel Talagrand "for his groundbreaking contributions to probability theory and functional analysis, with outstanding applications in mathematical physics and statistics." What remains largely absent in the popular descriptions of these prizes, however, is the profound contributions the works of both individuals have had to the field of algorithms and computation. The ideas first developed by Parisi and his collaborators relying on remarkably precise physics intuition, and later confirmed by Talagrand and others by no less remarkable mathematical techniques, have revolutionized the way we think algorithmically about optimization problems involving randomness. This is true both in terms of the existence of fast algorithms for some optimization problems, but also in terms of our persistent failures of finding such algorithms for some other optimization problems. © 2025, CC BY.

关键词： optimization algorithms