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3D semi-Analytical and Numerical Upper-Bound Homogenisation Approaches to the Out-of-Plane Strength Domain of a Running-Bond Masonry Wall

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INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS 2024年第18期48卷 4469-4490页

作者： Donval, Elodie Hassen, Ghazi Pham, Duc Toan de Buhan, Patrick Vigroux, Martin Univ Gustave Eiffel Ecole Ponts Lab Navier CNRS Marne La Vallee France Ctr Sci & Tech Batiment CSTB Marne la Vallee France Ctr Tech Mat Nat Construct Paris France

The present contribution proposes a new semi-analytical homogenisation approach to determine a running-bond masonry wall's in- and out-of-plane strength domain based on the yield design framework. The main novelty of such an approach is that it does not rely on simplifying assumptions such as infinitely thin joints or plane stress state within the blocks, by making use of 3D virtual failure mechanisms in the kinematic approach. The new semi-analytical approach is then compared to a state-of-the-art numerical implementation of the kinematic approach of yield design, relying on semi-definite programming. Several comparisons show a good agreement between the semi-analytical and the numerical approaches and outline the computational efficiency of the semi-analytical approach as well as the fact that it is very well suited for engineering design purposes. Both proposed approaches are then compared to existing approaches based on the limit analysis or yield design framework.

关键词： homogenisation masonry out-of-plane strength domain semi-definite programming yield design

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Efficient Low-Rank semidefinite programming With Robust Loss Functions

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第10期44卷 6153-6168页

作者： Yao, Quanming Yang, Hansi Hu, En-Liang Kwok, James T. 4Paradigm Inc Beijing 100089 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Yunnan Normal Univ Dept Math Kunming 650092 Yunnan Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Kowloon Hong Kong Peoples R China

In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness of a large class of learning algorithms that are formulated as low-rank semi-definite programming (SDP) problems. Traditional formulations use the square loss, which is notorious for being sensitive to outliers. We propose to replace this with more robust noise models, including the l(1)-loss and other nonconvex losses. However, the resultant optimization problem becomes difficult as the objective is no longer convex or smooth. To alleviate this problem, we design an efficient algorithm based on majorization-minimization. The crux is on constructing a good optimization surrogate, and we show that this surrogate can be efficiently obtained by the alternating direction method of multipliers (ADMM). By properly monitoring ADMM's convergence, the proposed algorithm is empirically efficient and also theoretically guaranteed to converge to a critical point. Extensive experiments are performed on four machine learning applications using both synthetic and real-world data sets. Results show that the proposed algorithm is not only fast but also has better performance than the state-of-the-arts.

关键词： semi-definite programming robustness majorization-minimization alternating direction method of multipliers

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Global solution of quadratic problems using interval methods and convex relaxations

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JOURNAL OF GLOBAL OPTIMIZATION 2025年第2期91卷 331-353页

作者： Elloumi, Sourour Lambert, Amelie Neveu, Bertrand Trombettoni, Gilles Inst Polytech Paris ENSTA Paris UMA F-91120 Palaiseau France CEDRIC Cnam F-75003 Paris France Univ Gustave Eiffel Ecole Ponts ParisTech LIGM CNRS Marne La Vallee France Univ Montpellier LIRMM CNRS Montpellier France

Interval branch-and-bound solvers provide reliable algorithms for handling non-convex optimization problems by ensuring the feasibility and the optimality of the computed solutions, i.e. independently from the floating-point rounding errors. Moreover, these solvers deal with a wide variety of mathematical operators. However, these solvers are not dedicated to quadratic optimization and do not exploit nonlinear convex relaxations in their framework. We present an interval branch-and-bound method that can efficiently solve quadratic optimization problems. At each node explored by the algorithm, our solver uses a quadratic convex relaxation which is as strong as a semi-definite programming relaxation, and a variable selection strategy dedicated to quadratic problems. The interval features can then propagate efficiently this information for contracting all variable domains. We also propose to make our algorithm rigorous by certifying firstly the convexity of the objective function of our relaxation, and secondly the validity of the lower bound calculated at each node. In the non-rigorous case, our experiments show significant speedups on general integer quadratic instances, and when reliability is required, our first results show that we are able to handle medium-sized instances in a reasonable running time.

关键词： MINLP Quadratic programming Interval methods semi-definite programming Reliable solver Experiments

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ADMM-RMCBF-Net: A neural network decision for distributed robust multi-cell beamforming

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PHYSICAL COMMUNICATION 2025年 70卷

作者： Xu, Jing Wang, Qiaozhi Xu, Chongbin Fan, Wujie Zhang, Yizhai Xi An Jiao Tong Univ Sch Informat & Commun Engn Shaanxi Key Lab Deep Space Explorat Intelligent In Xian 710049 Peoples R China ZTE Corp State Key Lab Mobile Network & Mobile Multimedia T Shenzhen 518057 Peoples R China Fudan Univ Dept Commun Sci & Engn Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200433 Peoples R China Northwestern Polytech Univ Res Ctr Intelligent Robot Sch Astronaut Xian 710072 Peoples R China

In this paper, to construct a promising deep-learning architecture for the distributed robust multi-cell beam- forming (RMCBF) decision, we reconsider the existing typical power-minimization problem. By thoroughly unfolding the conventional algorithm of the Alternating Direction Method of Multipliers (ADMM), we establish a novel neural network for fast distributed RMCBF design, namely ADMM-RMCBF-Net. The most important step in unfolding lies in that we explicitly solve the key semi-definite programming sub-problems by resorting to the primal-dual inter-point method instead of the encapsulated convex solvers. It is worth stressing that all parameters of the ADMM algorithm can be learned from end-to-end data-driven training in the proposed ADMM-RMCBF-Net, rather than being predetermined empirically in the conventional ADMM method. Simulation results confirm the advantages of the proposed deep-learning approach over its conventional optimization-based counterpart in terms of these distributed RMCBF decisions' performance. Specifically, in addition to accelerating the convergence of the distributed RMCBF design, more importantly, the proposed ADMM-RMCBF-Net could adapt to the practical propagation environment quickly through small-sample learning.

关键词： Neural network Primal-dual interior point method Alternating direction method of multipliers (ADMM) Robust multi-cell beamforming (RMCBF) semi-definite programming

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Optimal Stochastic Injection Attack Strategy Against Discrete Time-Varying System via Moment Matrices

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11026-11037页

作者： Gao, Sheng Gramlich, Dennis Zhang, Hao Yan, Huaicheng Ebenbauer, Christian Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Shanghai 200092 Peoples R China Rhein Westfal TH Aachen Chair Intelligent Control Syst D-52074 Aachen Germany Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200092 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

This paper investigates injection attacks incorporating stochastic noise against linear discrete time-varying system, which is more general but also more challenging to defend than deterministic injection attacks. Based on the optimization theory and novel key defining matrices, an optimal stochastic injection attack strategy is proposed. Unlike existing strategies, this strategy is stochastic, making it harder for defenders to predict. Therefore, the newly designed attack is expected to be widely used to disrupt system performance. By leveraging estimated data from the observer and the attack input, a virtual residual system is established, which more accurately reflects changes in the system error before and after the attack than the traditional error system. Using the state and output residuals along with the stochastic attack input, two performances are defined to ensure the stealthiness and effectiveness of the attack, respectively. Subsequently, an optimal attack problem with non-convex objective function and constraint is formulated. The key to acquiring the designed optimal stochastic injection attack strategy is to apply a semi-definite relaxation involving moment matrices for transforming this non-convex optimization problem into a convex optimization problem and solving it. Finally, the effectiveness of the proposed attack strategy is validated through numerical simulation of a networked mass-spring-damper system and a V-formation experiment involving three quadrotors. Note to Practitioners-The primary objective of this paper is to focus on the cyber security of discrete time-varying systems from the perspective of the attacker, which provides insight into the way of generating attack strategies under the stochastic case. The majority of existing injection attack strategies against intelligent systems are deterministic, and this can make the attacks less effective as the attacked system reconstructs and compensates for the attack signals, and the attack s

关键词： Time-varying systems Optimization Observers Linear programming programming Estimation error Numerical simulation Convex functions Control systems Automation Stochastic injection attack non-convex optimization problem moment matrices semi-definite programming

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Continuous-Variable Source-Independent Quantum Random Number Generator With Phase-Insensitive Detection

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 2025年第5期31卷

作者： Zhou, Hongyi Chinese Acad Sci Inst Comp Technol State Key Lab Processors Beijing 100190 Peoples R China

Quantum random number generators (QRNGs) harness quantum mechanical unpredictability to produce true randomness, which is crucial for cryptography and secure communications. Among various QRNGs, source-independent QRNGs (SI-QRNGs) relax the trust on the quantum source, allowing for flexible use of advanced detectors to achieve high randomness generation rates. Continuous-variable (CV) SI-QRNGs, in particular, hold promise for practical deployment due to their simplicity and randomness generation rates comparable to trusted-device QRNGs. In this work, we propose a novel CV-SI-QRNG scheme based on phase-insensitive detections, and provide security proof based on semi-definite programming (SDP). We introduce a dimension reduction technique, which rigorously reduces an infinite-dimensional SDP problem to a finite-dimensional one, enabling efficient computation while maintaining valid randomness lower bound. We further validate our method through simulations. These results demonstrate the feasibility of our framework, paving the way for practical and simple SI-QRNG implementations.

关键词： Protocols Security Detectors Photonics Quantum state Generators Numerical models Upper bound Phase measurement Optimization Quantum random number generator semi-definite programming infinite dimensional quantum system

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Approximating M-matrix in Learning Directed Acyclic Graphs Using Methods Involve semidefinite Matrix Constraints

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年第2期50卷 1329-1337页

作者： Al-Homidan, Suliman KFUPM Dept Math Dhahran 31261 Saudi Arabia KFUPM Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia

The task of deducing directed acyclic graphs from observational data has gained significant attention recently due to its broad applicability. Consequently, connecting the log-det characterization domain with the set of M-matrices defined over the cone of positive definite matrices has emerged as a crucial approach in this field. However, experimentally collected data often deviates from the expected positive semidefinite structure due to introduced noise, posing a challenge in maintaining its physical structure. In this paper, we address this challenge by proposing four methods to reconstruct the initial matrix while maintaining its physical structure. Leveraging advanced techniques, including sequential quadratic programming (SQP), we minimize the impact of noise, ensuring the recovery of the reconstructed matrix. We provide a rigorous proof of convergence for the SQP method, highlighting its effectiveness in achieving reliable reconstructions. Through comparative numerical analyses, we demonstrate the effectiveness of our methods in preserving the original structure of the initial matrix, even in the presence of noise.

关键词： Alternating projection method Directed acyclic graphs M- matrix Positive semidefinite matrix semi-definite programming Sequential quadratic programming

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Secure Integrated Sensing and Communication Systems Assisted by Active RIS

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 19791-19796页

作者： Yang, Zheng Zhang, Siyu Chen, Gaojie Dong, Zhicheng Wu, Yi da Costa, Daniel Benevides Fujian Normal Univ Coll Photon & Elect Engn Fuzhou 350117 Peoples R China Sun Yat sen Univ Sch Flexible Elect SoFE Shenzhen 518107 Peoples R China Tibet Univ Sch Informat Sci & Technol Lhasa 850011 Peoples R China King Fahd Univ Petr & Minerals KFUPM Interdisciplinary Res Ctr Commun Syst & Sensing IR Dept Elect Engn Dhahran 31261 Saudi Arabia

In this paper, we investigate secure transmission in active reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems. The dual-function base station communicates with the legitimate user and detects the sensing target simultaneously via active RIS, while the communication signal is intercepted by a potential eavesdropper (Eve). To achieve secure transmission, a novel successive interference cancellation scheme is proposed, where the sensing signal can disrupt the potential eavesdropping while keeping the legitimate user link intact. Then, the secure rate maximization problem is proposed, where the active beamforming of the RIS, the legitimate user's transmit beamforming, the Eve's transmit beamforming, and the radar receive beamforming are jointly optimized. However, the secure rate expression is non-convex, an iteration optimization algorithm based on majorization-minimization method and semi-definite programming are proposed to decompose the original problem into three sub-problems. Simulation results illustrate that the secrecy performance of the proposed scheme outperforms the passive RIS-assisted ISAC systems and the traditional cellular networks aided by active RIS.

关键词： Signal to noise ratio Array signal processing Integrated sensing and communication Decoding Vectors Interference cancellation Radar cross-sections Active reconfigurable intelligent surface (RIS) integrated sensing and communication (ISAC) secure transmission semi-definite programming

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Transparent RIS: Wireless Coverage Enhancement via Region-Oriented Passive Beamforming

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 13796-13811页

作者： Wang, Jue Hu, Yingdong Xu, Yicheng Li, Ye Gao, Ruifeng Zhang, Jun Han, Yu Jin, Shi Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Nantong Res Inst Adv Commun Technol Nantong 226019 Peoples R China Nantong Univ Sch Transportat & Civil Engn Nantong 226019 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Wireless Commun Nanjing 210003 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

We investigate a new deployment form of reflective intelligent surface (RIS), which aims at enhancing the quality of service of a main communication system in a target region, while without the need of changing its transmission protocol and scheme (i.e., the RIS is "transparent" to the main system). To this end, we mathematically formulate a coverage enhancement problem, where a RIS is used transparently in the sense that the BS can be unaware of its existence, while the minimum channel link strength, measured from every BS antenna to any point in the target region, can be maximized. The formulated problem is non-convex with mixed discrete-continuous variables. To tackle this challenge, we recast it into a convex feasibility problem via spatial sampling and semi-definite relaxation. Based on a derived analytical upper bound on the link strength difference between any two location points, we further characterize the coverage-similarity region of a given location, and accordingly propose an improved spatial sampling scheme for efficient implementation. Simulation results show that the proposed transparent RIS design achieves better coverage performance than benchmark schemes. More importantly, it can effectively improve the communication performance without affecting the transmission scheme originally adopted by the main communication system.

关键词： Wireless communication Microprocessors Computer architecture Antenna measurements Array signal processing Antenna arrays Signal to noise ratio Reflective intelligent surface coverage enhancement spatial sampling semi-definite programming

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Quantum Speedup for Multiuser Detection With Optimized Parameters in Grover Adaptive Search

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IEEE ACCESS 2024年 12卷 83810-83821页

作者： Norimoto, Masaya Mikuriya, Taku Ishikawa, Naoki Yokohama Natl Univ Fac Engn Yokohama 2408501 Japan

Maximum-likelihood multiuser detection incurs a large computational complexity, and its low-complexity detection scheme suffers from a performance loss, where this tradeoff is inevitable and inherent in a classical computer. In this paper, we use the Grover adaptive search (GAS) to break the tradeoff, which is a quantum exhaustive search algorithm guaranteed to obtain the optimal solution, achieving a quadratic speedup. Specifically, we design two specific parameters of GAS to achieve the optimal performance with a reduced complexity: the initial threshold and the number of Grover rotations. The initial threshold of GAS can be optimized using a solution of semi-definite programming, and it is possible to calculate the distribution of the number of solutions smaller than the initial threshold in advance, which depends on instantaneous channel coefficients. In addition, we analyze the number of quantum gates required for GAS and show that the gate count can be reduced by bypassing the higher-order terms in the objective function, leading to a reduced circuit runtime. Our analysis and simulation results demonstrate that the proposed approach achieves the same performance as the optimal maximum-likelihood detection while reducing the query complexity of GAS, implying that the large constant overhead of quadratic speedup can be further reduced.

关键词： Complexity theory Quantum computing Logic gates Linear programming Vectors Multiuser detection Symbols Grover adaptive search multiple-input multiple-output multiuser detection quadratic speedup semi-definite programming

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