检索结果-内蒙古大学图书馆

A SOLUTION METHOD FOR ARBITRARY POLYHEDRAL CONVEX SET OPTIMIZATION PROBLEMS

SIAM JOURNAL ON OPTIMIZATION 2025年第1期35卷 330-346页

作者： Loehne, Andreas Friedrich Schiller Univ Jena Fac Math & Comp Sci D-07737 Jena Germany

We provide a solution method for the polyhedral convex set optimization problem. that is, the problem to minimize a set-valued maping F: (RRq)-R-n paired right arrows with polyhedral convex graph with respect to a set ordering relation which is gelerated by a polyhedral convex cone C subset of R-q. The methods proven to be correct and finite without any further assumption to the problem.

关键词： set optimization vector linear programming multiple objective linear program- ming linear programming

来源：评论

学校读者我要写书评

暂无评论

Characterization of Constrained Continuous Multiobjective Optimization Problems: A Performance Space Perspective

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第1期29卷 275-285页

作者： Vodopija, Aljosa Tusar, Tea Filipic, Bogdan Jozef Stefan Inst Dept Intelligent Syst Ljubljana 1000 Slovenia Jozef Stefan Int Postgrad Sch Ljubljana 1000 Slovenia

Constrained multiobjective optimization has gained much interest in the past few years. However, constrained multiobjective optimization problems (CMOPs) are still unsatisfactorily understood. Consequently, the choice of adequate CMOPs for benchmarking is difficult and lacks a formal background. This article takes a step toward addressing this issue by exploring CMOPs from a performance space perspective. First, it presents a novel performance assessment approach designed explicitly for constrained multiobjective optimization. This methodology offers a first attempt at simultaneously measuring the performance in approximating the Pareto front and constraint satisfaction. Second, it proposes an approach to measure the capability of the given optimization problem to differentiate among algorithm performances. Finally, this approach is used to compare eight frequently used artificial test suites of CMOPs. The experimental results reveal which suites are more efficient in discerning between four well-known multiobjective optimization algorithms.

关键词： Optimization Evolutionary computation Benchmark testing linear programming Convergence Behavioral sciences Runtime Benchmarking constrained multiobjective optimization constraint violation hypervolume multiobjective evolutionary algorithm (MOEA) performance measurement test problem

来源：评论

学校读者我要写书评

暂无评论

Multidimensional Scaling Orienting Discriminative Co-Representation Learning

引用

IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2025年第1期55卷 71-82页

作者： Qin, Zhang Zhang, Yinghui Wang, Hongjun Luo, Zhipeng Li, Chongshou Li, Tianrui Southwest Jiaotong Univ Tangshan Res Inst Tangshan 063000 Peoples R China Chengdu Neusoft Univ Chengdu 611844 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Minist Educ Engn Res Ctr Sustainable Urban Intelligent Transpo Chengdu 611756 Peoples R China

Co-representation, which co-represents samples and features, has been widely used in various machine learning tasks, such as document clustering, gene expression analysis, and recommendation systems. It not only reveals the cluster structure of both samples and features, but also reveals the sample-feature correlation. Given a tabular data matrix, co-representation usually exhibits as the co-occurrence structures of rows and columns. However, identifying such structured patterns in complex real-world data can be very challenging. To address this problem, we propose an unsupervised discriminative co-representation learning model based on multidimensional scaling (DCLMDS). The main novelty is that DCLMDS introduces a co-representation learning term to ensure the discriminability between co-occurrence structures. As a result, the co-representation learned by DCLMDS contains richer information of the underlying correlation between samples and features within data. This could subsequently enhance the capacity of machines and systems for processing complex real-world information more proficiently. Furthermore, inspired by the fuzzy set theory, we integrate fuzzy membership degree that can accurately capture the uncertainty within data, thus enabling DCLMDS to learn a more effective co-representation in a soft manner. To evaluate the performance of DCLMDS, we conduct extensive experiments on 18 datasets, and the results demonstrate that DCLMDS can generate both accurate and discriminative co-representation, which well meets our desired outcomes.

关键词： Correlation linear programming Data models Accuracy Uncertainty Topology Representation learning Prototypes Human-machine systems Gene expression Co-clustering co-representation co-representation learning multidimensional scaling (MDS) representation learning unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Network-Scale Impact of Vegetation Loss on Coverage and Exposure for 5G Networks

引用

IEEE ACCESS 2025年 13卷 23902-23912页

作者： Schampheleer, Jorn Huss, Anke Deruyck, Margot Univ Ghent IMEC Dept Informat Technol B-9052 Ghent Belgium Univ Utrecht Inst Risk Assessment Sci NL-3584 CS Utrecht Netherlands

This study investigates the effects of vegetation on 5G network performance, with a particular focus on coverage, user exposure, and base station deployment strategies in an urban environment (Utrecht, The Netherlands). This is the first study to perform network planning simulations that account for vegetation and building-induced propagation challenges on a city-wide scale, providing understanding of their effects on 5G network performance and exposure. The study also explores the influence of user height, examining how vegetation's blocking and shielding effects vary with user height. By evaluating both sub-6 GHz and mmWave networks under various simulated scenarios, the research qualifies the dual role of vegetation as both a coverage barrier and a mitigator of user exposure. Key findings include a significant 14.71% reduction in coverage for sub-6 GHz networks in the presence of vegetation and a 42.98% decrease in downlink whole-body SAR in mmWave networks due to vegetation's shielding effects. Flexible base station placement is shown to effectively counteract coverage losses while maintaining stable exposure metrics, but mmWave networks remain highly sensitive to environmental obstructions. These findings emphasize the importance of incorporating vegetation and other environmental factors into network planning, especially for high-frequency 5G networks, to ensure optimal performance and limit user exposure.

关键词： Vegetation 5G mobile communication Attenuation Millimeter wave communication Buildings Base stations Vegetation mapping Planning Meteorology linear programming 5G network MaMIMO mmWave network planning vegetation

来源：评论

学校读者我要写书评

暂无评论

Distributed Randomized Gradient-Free Convex Optimization With Set Constraints Over Time-Varying Weight-Unbalanced Digraphs

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第2期12卷 610-622页

作者： Zhu, Yanan Li, Qinghai Li, Tao Wen, Guanghui Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China Southeast Univ Sch Math Dept Syst Sci Nanjing 210096 Peoples R China

This paper explores a class of distributed constrained convex optimization problems where the objective function is a sum of $N$ convex local objective functions. These functions are characterized by local non-smoothness yet adhere to Lipschitz continuity, and the optimization process is further constrained by $N$ distinct closed convex sets. To delineate the structure of information exchange among agents, a series of time-varying weight-unbalance directed graphs are introduced. Furthermore, this study introduces a novel algorithm, distributed randomized gradient-free constrained optimization algorithm. This algorithm marks a significant advancement by substituting the conventional requirement for precise gradient or subgradient information in each iterative update with a random gradient-free oracle, thereby addressing scenarios where accurate gradient information is hard to obtain. A thorough convergence analysis is provided based on the smoothing parameters inherent in the local objective functions, the Lipschitz constants, and a series of standard assumptions. Significantly, the proposed algorithm can converge to an approximate optimal solution within a predetermined error threshold for the consisdered optimization problem, achieving the same convergence rate of ${\mathcal O}(\frac{\ln (k)}{\sqrt{k} })$ as the general randomized gradient-free algorithms when the decay step size is selected appropriately. And when at least one of the local objective functions exhibits strong convexity, the proposed algorithm can achieve a faster convergence rate, ${\mathcal O}(\frac{1}{k})$. Finally, rigorous simulation results verify the correctness of theoretical findings.

关键词： Optimization Stochastic processes Convergence Prediction algorithms linear programming Convex functions Vectors Smoothing methods Directed graphs Closed box Convergence rate distributed multi-agent optimization randomized gradient-free time-varying weight-unbalanced digraphs

来源：评论

学校读者我要写书评

暂无评论

Interpreting and Improving Optimal Control Problems With Directional Corrections

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 4986-4993页

作者： Barron, Trevor Zhang, Xiaojing Apple Inc Cupertino CA 95014 USA

Many robotics tasks, such as path planning or trajectory optimization, are formulated as optimal control problems (OCPs). The key to obtaining high performance lies in the design of the OCP's objective function. In practice, the objective function consists of a set of individual components that must be carefully modeled and traded off such that the OCP has the desired solution. It is often challenging to balance multiple components to achieve the desired solution and to understand, when the solution is undesired, the impact of individual cost components. In this paper, we present a framework addressing these challenges based on the concept of directional corrections. Specifically, given the solution to an OCP that is deemed undesirable, and access to an expert providing the direction of change that would increase the desirability of the solution, our method analyzes the individual cost components for their "consistency" with the provided directional correction. This information can be used to improve the OCP formulation, e.g., by increasing the weight of consistent cost components, or reducing the weight of - or even redesigning - inconsistent cost components. We also show that our framework can automatically tune parameters of the OCP to achieve consistency with a set of corrections.

关键词： Costs Robots linear programming Optimal control Gold Cost function Trajectory System dynamics linear systems Training Optimization and optimal control motion and path planning machine learning for robot control autonomous agents

来源：评论

学校读者我要写书评

暂无评论

A Novel Completion Method for Sparse Traffic Data Imputation

引用

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 2025年第3期17卷 96-108页

作者： Ouyang, Renqiu Hu, Yikun Wang, Haotian Hu, Rong Yang, Wangdong Li, Kenli Hunan Univ Changsha 410082 Peoples R China Hunan Univ Comp Sci & Technol Changsha 410082 Peoples R China

Traffic data imputation is essential in smart cities and the Internet of Things (IoT). Tensor completion is an efficient method for traffic data imputation. However, these methods overlook the integration of contextual and spatial information, which are important for traffic data imputation. Hence, this study proposes a novel tensor completion method leveraging contextual and spatial information for sparse traffic data imputation (STDI). Initially, we develop a model for STDI, treating traffic data as tensors and applying tensor completion for imputing missing values. Then, to account for contextual information, we compute the contextual scores of roads and reorganize the road indices according to the scores. Additionally, we utilize the Laplacian matrix to reveal spatial information and optimize the objective function to enhance imputation accuracy. Finally, we design a parallel algorithm for STDI on GPU for efficient computation. Extensive experiments demonstrate that the proposed method is superior to existing methods.

关键词： Tensors Roads Laplace equations Imputation Trajectory Data models Sparse matrices Parallel algorithms linear programming Internet of Things

来源：评论

学校读者我要写书评

暂无评论

POSES: Patch Optimization Strategies for Efficiency and Stealthiness Using eXplainable AI

引用

IEEE ACCESS 2025年 13卷 57166-57176页

作者： Lee, Han-Ju Kim, Jin-Seoung Lee, Han-Jin Choi, Seok-Hwan Yonsei Univ Div Software Wonju Si 26493 Gangwon Do South Korea

Adversarial examples, which are carefully crafted inputs designed to deceive deep learning models, create significant challenges in Artificial Intelligence. While adversarial examples have primarily focused on digital-world attacks, recent research has proposed adversarial patches as the focus expands to physical-world attacks. Unlike traditional adversarial examples that use small perturbations, adversarial patches employ large perturbations to bypass existing defense mechanisms against adversarial attacks. Adversarial patches have been shown to be highly effective in causing deep learning models to misclassify. However, existing adversarial patches are often limited by their noticeable appearance and the high computational cost of generating them. To solve these problems, we propose a new adversarial patch generation method called Patch Optimization Strategies for Efficiency and Stealthiness (POSES). POSES uses a two-step optimization architecture that employs an eXplainable AI-based method to optimize the location and size of adversarial patches. Experimental results on benchmark datasets demonstrate that POSES enhances the stealthiness of adversarial patches while maintaining a high attack success rate. We also show that POSES improves attack efficiency by reducing the number of iterations required.

关键词： Perturbation methods Computational modeling Deep learning Explainable AI Data models Optimization methods linear programming Generative adversarial networks Computer vision Benchmark testing Adversarial example adversarial patch eXplainable AI (XAI)

来源：评论

学校读者我要写书评

暂无评论

Fast Data-Driven Greedy Sensor Selection for Ridge Regression

引用

IEEE SENSORS JOURNAL 2025年第6期25卷 10030-10045页

作者： Sasaki, Yasuo Yamada, Keigo Nagata, Takayuki Saito, Yuji Nonomura, Taku Nagoya Univ Dept Aerosp Engn Nagoya 4648603 Japan Tohoku Univ Dept Aerosp Engn Sendai 9808579 Japan Tohoku Univ Frontier Res Inst Interdisciplinary Sci Sendai 9808578 Japan

We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator, which is trained based on the data. The proposed algorithm greedily selects sensors for minimizing the cost function of the estimator. Sensor selection that prevents overfitting of the resulting estimator can be realized by setting a positive regularization parameter. The greedy solution is computed in quite a short time by using some recurrent relations that we derive. The effectiveness of the proposed algorithm is verified for artificial datasets that are generated from linear systems and a real-wold dataset that is aimed for the selection of pressure-sensor locations for estimating the yaw angle of a ground vehicle. The demonstration for the datasets reveals that the proposed algorithm computes a sensor set, resulting in more accurate estimation than existing data-driven selection algorithms in some conditions. Furthermore, it is confirmed that setting a positive regularization parameter in the proposed algorithm leads to accurate estimation when overfitting is problematic.

关键词： Sensors Vectors Greedy algorithms Overfitting Accuracy linear programming Intelligent sensors Computational modeling Training Standards Keywords Greedy algorithm ridge regression sensor selection

来源：评论

学校读者我要写书评

暂无评论

Decentralized and Communication-Based Multi-Agent Traffic Signal Control Model Employing a Graph Representation for the State

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第5期26卷 5947-5960页

作者： Yoon, Jinwon Ahn, Kyuree Lee, Kanghoon Park, Jinkyoo Yeo, Hwasoo Korea Adv Inst Sci & Technol Daejeon 34141 South Korea Omelet Daejeon 34051 South Korea

Reinforcement learning (RL) has emerged as an effective approach for signal control, and several studies attempt to apply RL to network-wide signal control problems. Most existing methods for network-wide signal controls simply apply the pretrained model independently to the network or simply expand the observation range for the neighbors. While several recent studies have proposed cooperative signal control models using multi-agent reinforcement learning (MARL) approaches, they have not considered the transferability of the policy. This issue becomes more critical in treating a multi-agent signal control problem, since there exist innumerable possibilities of data distributions for the demand patterns. Hence, this research aims to develop a transferable and cooperative multi-agent traffic signal control model. As the key idea, we propose a decentralized and communication-based MARL approach that considers the spatial correlation of traffic dynamics in the model design and employs a graph representation for the state. Three experiments in simulated environments of the Gangnam district in Seoul, South Korea are conducted to 1) validate the model, 2) assess the policy's transferability, and 3) evaluate the efficiency of the multi-agent cooperation. The experimental results show that the proposed model obtains a transferable policy so that it adapts to the unexperienced demand scenario. In addition, it is concluded that the joint action of the proposed RL model cooperatively rebalances the traffic demands so that it improves the efficiency of the network-wide signal controls.

关键词： linear programming Training Vehicle dynamics Reinforcement learning Optimization Correlation Adaptation models Turning Throughput Minimization Traffic signal control multi-agent reinforcement learning (MARL) graph neural network transferable policy decentralized control

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：