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A Partial Exact Penalty Function Approach for Constrained Optimization

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arXiv 2023年

作者： Xiao, Nachuan Liu, Xin Toh, Kim-Chuan The Institute of Operations Research and Analytics National University of Singapore Singapore State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China Department of Mathematics Institute of Operations Research and Analytics National University of Singapore Singapore119076 Singapore

In this paper, we focus on a class of constrained nonlinear optimization problems (NLP), where some of its equality constraints define a closed embedded submanifold M in Rn. Although NLP can be solved directly by various existing approaches for constrained optimization in Euclidean space, these approaches usually fail to recognize the manifold structure of M. To achieve better efficiency by utilizing the manifold structure of M in directly applying these existing optimization approaches, we propose a partial penalty function approach for NLP. In our proposed penalty function approach, we transform NLP into the corresponding constraint dissolving problem (CDP) in the Euclidean space, where the constraints that define M are eliminated through exact penalization. We establish the relationships on the constraint qualifications between NLP and CDP, and prove that NLP and CDP have the same stationary points and KKT points in a neighborhood of the feasible region under mild conditions. Therefore, various existing optimization approaches developed for constrained optimization in the Euclidean space can be directly applied to solve NLP through CDP. Preliminary numerical experiments demonstrate that by dissolving the constraints that define M, CDP gains superior computational efficiency when compared to directly applying existing optimization approaches to solve NLP, especially in high dimensional scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

Augmenting photoluminescence of monolayer MoS_(2) using high order modes in a metal dimer-on-film nanocavity

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Photonics Research 2021年第4期9卷 501-506页

作者： Shiyin Cao Liping Hou Qifa Wang Chenyang Li Weixing Yu Xuetao Gan Kaihui Liu Malin Premaratne Fajun Xiao Jianlin Zhao MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical UniversityXi’an 710129China Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences Xi’an 710119China State Key Laboratory for Mesoscopic Physics Collaborative Innovation Centre of Quantum MatterSchool of PhysicsPeking UniversityBeijing 100871China Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems EngineeringMonash UniversityClaytonVIC 3800Australia

Plasmonic particle-on-film nanocavities,supporting gap modes with ultra-small volume,provide a great solution to boost light–matter interactions at the *** this work,we report on the photoluminescence(PL)enhancement of monolayer MoS_(2) using high order modes of an Au nanosphere dimer-on-film nanocavity(DoFN).The high order plasmon modes,consisting of two bonding quadrupoles in the dimer and their images in the Au film,are revealed by combining the polarization-resolved scattering spectra with the numerical *** integrating the monolayer MoS_(2) into the DoFN,these high order modes are used to enhance PL intensity through simultaneously boosting the absorption and emission processes,producing a 1350-fold enhancement *** opens an avenue to enhance the light–matter interaction with high order plasmon modes and may find applications in future optoelectronics and nanophotonics devices.

关键词： cavity high integrating

Technical Function-effect Based Patent Multi-to-One Negation Game Model 25

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Technical Function-effect Based Patent Multi-to-One Negation...

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Du, Lu Liu, Weidong Xiao, Kaiyang Gao, Shixuan Han, Yong College of Computer Science Inner Mongolia University Hohhot China Institute of Scientific and Technical Information of China Beijing China Natl. and Loc. Jt. Eng. Res. Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China Inner Mongolia Intellectual Property Service Center Hohhot China

ISBN: (纸本)9781665405270

As the number of patent applications increases yearly, the negation relation between patents has become intertwined, which makes it difficult for constructing negation relation in patent examination manually. Therefore, how to automatically construct negation relation between patents has become the hot research. Constructing negation relation is the process of comparing technical features between patents, which depends on the technical features in the patent claims and the technical function-effect. Although there are many researches on patent one-to-one negation, few researches on patent multi-to-one negation. The research on negation relation of multi-to-one patents, has the following challenges: (1) the definition of patent features is ambiguous in comparison of negation relation;(2) the role of patent function-effect is often ignored in judgement of negation relation. To solve the above issues, we propose a technical function-effect based patent multi-to-one negation game model. In the model, we decompose patent technical features into technical utility and technical function-effect. Patent technical utility is mainly realized by using the valence theory in linguistics. After obtaining technical utility, we choose to have the same technical effect, express the technical features of different technical functions, and obtain the patent technical function-effect. Finally, we combine the technical utility and function-effect to construct a technical utility/function-effect game tree to judge the patent negation relation. According to the two-tailed test in the A/B-test, our model outperforms the baseline model in the evaluation measurement. This model can subsequently be used to calculate the portfolio value and the strategic value of the patents. © 2022 IEEE.

关键词： Patents and inventions

Federated Linear Bandit Learning via Over-the-air Computation

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Federated Linear Bandit Learning via Over-the-air Computatio...

IEEE Conference on Global Communications (GLOBECOM)

作者： Jiali Wang Yuning Jiang Xin Liu Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China Automatic Control Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (AirComp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings.

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Preserving earlier knowledge in continual learning with the help of all previous feature extractors

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arXiv 2021年

作者： Li, Zhuoyun Zhong, Changhong Liu, Sijia Wang, Ruixuan Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou510000 China

Continual learning of new knowledge over time is one desirable capability for intelligent systems to recognize more and more classes of objects. Without or with very limited amount of old data stored, an intelligent system often catastrophically forgets previously learned old knowledge when learning new knowledge. Recently, various approaches have been proposed to alleviate the catastrophic forgetting issue. However, old knowledge learned earlier is commonly less preserved than that learned more recently. In order to reduce the forgetting of particularly earlier learned old knowledge and improve the overall continual learning performance, we propose a simple yet effective fusion mechanism by including all the previously learned feature extractors into the intelligent model. In addition, a new feature extractor is included to the model when learning a new set of classes each time, and a feature extractor pruning is also applied to prevent the whole model size from growing rapidly. Experiments on multiple classification tasks show that the proposed approach can effectively reduce the forgetting of old knowledge, achieving state-of-the-art continual learning performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Intelligent systems

EFFECT OF RANDOM NOISES ON PATHWISE SOLUTIONS TO THE HIGH-DIMENSIONAL MODIFIED EULER-POINCARÉ SYSTEM

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arXiv 2021年

作者： Zhang, Lei School of Mathematics and Statistics Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan430074 China

In this paper, we study the Cauchy problem for the stochastically perturbed high-dimensional modified Euler-Poincaré system (MEP2) on the torus Td, d ≥ 1. We first establish a local well-posedness framework in the sense of Hadamard for the MEP2 driven by general nonlinear multiplicative noises. Then two kinds of global existence and uniqueness results are demonstrated: One indicates that the MEP2 perturbed by nonlocal-type random noises with proper intensity admits a unique large global strong solution;The other one infers that, if the initial data is sufficiently small, then the MEP2 perturbed by linear multiplicative noise has a unique global solution with high probability. In the case of one dimension, we find that the stochastic MEP2 will break down in finite time when the initial data meets appropriate shape condition. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

Linear uncertainty quantification of graphical model inference 24

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Linear uncertainty quantification of graphical model inferen...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Chenghua Guo Han Yu Jiaxin Liu Chao Chen Qi Li Sihong Xie Xi Zhang Key Laboratory of Trustworthy Distributed Computing and Service (MoE) Beijing University of Posts and Telecommunications China College of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Computer Science and Engineering Lehigh University School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) China Department of Computer Science Iowa State University AI Thrust The Hong Kong University of Science and Technology (Guangzhou) China

ISBN: (纸本)9798331314385

Uncertainty Quantification (UQ) is vital for decision makers as it offers insights into the potential reliability of data and model, enabling more informed and risk-aware decision-making. Graphical models, capable of representing data with complex dependencies, are widely used across domains. Existing sampling-based UQ methods are unbiased but cannot guarantee convergence and are time-consuming on large-scale graphs. There are fast UQ methods for graphical models with closed-form solutions and convergence guarantee but with uncertainty underestimation. We propose LinUProp, a UQ method that utilizes a novel linear propagation of uncertainty to model uncertainty among related nodes additively instead of multiplicatively, to offer linear scalability, guaranteed convergence, and closed-form solutions without underestimating uncertainty. Theoretically, we decompose the expected prediction error of the graphical model and prove that the uncertainty computed by LinUProp is the generalized variance component of the decomposition. Experimentally, we demonstrate that LinUProp is consistent with the sampling-based method but with linear scalability and fast convergence. Moreover, LinUProp outperforms competitors in uncertainty-based active learning on four real-world graph datasets, achieving higher accuracy with a lower labeling budget.

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Decentralized Optimization Over the Stiefel Manifold by an Approximate Augmented Lagrangian Function

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arXiv 2021年

作者： Wang, Lei Liu, Xin State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China

In this paper, we focus on the decentralized optimization problem over the Stiefel manifold, which is defined on a connected network of d agents. The objective is an average of d local functions, and each function is privately held by an agent and encodes its data. The agents can only communicate with their neighbors in a collaborative effort to solve this problem. In existing methods, multiple rounds of communications are required to guarantee the convergence, giving rise to high communication costs. In contrast, this paper proposes a decentralized algorithm, called DESTINY, which only invokes a single round of communications per iteration. DESTINY combines gradient tracking techniques with a novel approximate augmented Lagrangian function. The global convergence to stationary points is rigorously established. Comprehensive numerical experiments demonstrate that DESTINY has a strong potential to deliver a cutting-edge performance in solving a variety of testing problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

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arXiv 2023年

作者： Qian, Junkai Jiang, Yuning Liu, Xin Wang, Qiong Wang, Ting Shi, Yuanming Chen, Wei The MoE Engineering Research Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University China The Automatic Control Laboratory EPFL Switzerland The School of Information Science and Technology ShanghaiTech University China The State Grid Beijing Electric Power Company China The Department of Electronic Engineering Tsinghua University China

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov Decision Process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. Comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning