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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2022年第2期6卷 1186-1197页

作者： Nguyen, Thuy Dung Nguyen, Tuyen Nguyen, Thanh Hung Nguyen, Kien Le Nguyen, Phi Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi 100000 Vietnam Chiba Univ Grad Sch Engn Chiba 2638522 Japan

Wireless rechargeable sensor networks (WRSNs) have emerged as a potential solution to solve the challenge of prolonging battery-powered sensor networks' lifetime. In a WRSN, a mobile charger moves around and charges the rechargeable sensors when stopping at charging spots. This paper newly considers a joint optimization of the charging location and the charging time to avoid node failure in WRSNs. We formulate the optimization and propose a solution for that. The proposal includes an algorithm to find a list of potential charging locations and their associated optimal charging time. Moreover, the Q-learning technique is adopted to determine the optimal next charging location among the candidates. We implement the algorithm and conduct experiments to compare it to the related works. The results show that the proposed algorithm outperforms the others in terms of network lifetime. Specifically, the highest performance gap of our proposal to the best algorithm among the others is 8.29 times.

关键词： Optimization Heuristic algorithms Wireless sensor networks approximation algorithms Proposals Partitioning algorithms Mathematical models WRSN Q-learning on-demand charging algorithm target coverage connectivity

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A (3/2+ε)-approximation algorithm for scheduling on two parallel machines with job delivery coordination

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2021年第9期72卷 1929-1942页

作者： Chen, Yong Zhang, An Tan, Zhiyi Xue, Ying Chen, Guangting Hangzhou Dianzi Univ Dept Math Hangzhou Zhejiang Peoples R China Zhejiang Univ Dept Math Hangzhou 310027 Zhejiang Peoples R China Taizhou Univ Taizhou Peoples R China

Integration of production and distribution operations at the scheduling level is one of the most important issues concerned in recent years in supply chain management. We investigate the scheduling problem with job delivery coordination. Concretely, we are given a set of jobs with non-identical processing times and non-identical sizes to be first processed by two parallel-identical machines and then delivered to a common customer by a single vehicle, which has a limited capacity that can carry up to a fixed total size of jobs in one batch. The objective is to minimize the makespan, ie, the time when all the jobs have reached the customer and the delivery vehicle has returned to the machine. The problem does not admit a polynomial time approximation algorithm with worst-case ratio smaller than unless P = NP. By introducing some novel ideas and new methods, including algorithms solving multiple knapsack problem as subprocedures and the rediscovery of the profound nature of the bin-packing algorithm, we propose a polynomial time approximation algorithm with worst-case ratio arbitrary close to . The algorithm, acquired after long-term research containing many earlier related works, is the first algorithm for this kind of problems and its theoretical performance is nearly best.

关键词： Scheduling job delivery approximation algorithms worst-case ratio

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Joint Device Assignment and Power Allocation in Multihoming Heterogeneous Multicarrier NOMA Networks

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IEEE SYSTEMS JOURNAL 2022年第1期16卷 671-682页

作者： Yin, Weixin Xu, Lei Wang, Ping Wang, Yulin Yang, Yuwang Chai, Tianyou Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

To improve the radio throughput, novel radio access techniques need to be designed. With exploiting the multiuser diversity at power domain, nonorthogonal multiple access (NOMA) is one promising candidate technique for sixth-generation (6G). In this article, a downlink joint device assignment and power allocation problem is investigated in multihoming heterogeneous multicarrier NOMA networks. The joint device assignment and power allocation problem maximizes the network throughput with total power constraint at each base station, required minimum transmission rate for each device, and maximum number of accessed devices at each subchannel. Since the joint device assignment and power allocation problem is a mixed-integer nonlinear problem, the original optimization problem is divided into a device assignment subproblem and a power allocation subproblem. Then, the greedy method is applied to propose a device assignment algorithm, and successive convex approximation theory is adopted to design a power allocation algorithm. Finally, an iterative joint device assignment and power allocation algorithm is proposed. Numerical simulation results demonstrate that the proposed algorithm can improve the throughput and the average satisfactory of devices compared with other algorithms significantly.

关键词： NOMA Resource management Wireless networks Macrocell networks Throughput approximation algorithms Scheduling Heterogeneous multicarrier nonorthogonal multiple access (NOMA) networks joint device assignment and power allocation multihoming successive convex approximation (SCA)

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An Improved Spatial Branch-and-Bound Algorithm for Non-Convex Optimal Electricity-Gas Flow

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第2期37卷 1326-1339页

作者： Liu, Pengxiang Wu, Zhi Gu, Wei Lu, Yuping Southeast Univ Sch Elect Engn Nanjing 210000 Peoples R China

Addressing non-convexity plays a fundamental role in solving the optimal electricity-gas flow models. In this paper, an improved spatial branch-and-bound algorithm is proposed to solve the non-convex problem, which is formulated as a mixed-integer bilinear programming, for its exact solution. The core of the algorithm is to divide the non-convex model into convex and small sub-models by branching on specific continuous variables, so that the non-convex problem can be equivalent to a rooted tree for exploration. The exactness of the algorithm is guaranteed by the same criterion as the classical branch-and-bound algorithm. To alleviate the computational burden, a novel two-stage spatial branching strategy is developed to improve the effectiveness and efficiency of the branching operations. The performance of the proposed algorithm is verified on two integrated electricity-gas systems with different sizes. Numerical results demonstrate that our method achieves a balance among feasibility, optimality, and efficiency. The comparison with another 6 convexification-based methods, 3 state-of-the-art non-convex optimization solvers, and 2 spatial branch-and-bound algorithms with classical branching rules further shows the superiority of our algorithm.

关键词： Manganese Pipelines Mathematical model Programming Optimization Compressors approximation algorithms Optimal electricity-gas flow branching strategy spatial branch-and-bound mixed-integer bilinear programming non-convex optimization

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Reconstruction of Optical Coherence Tomography Images Using Mixed Low Rank approximation and Second Order Tensor Based Total Variation Method

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2021年第3期40卷 865-878页

作者： Daneshmand, Parisa Ghaderi Mehridehnavi, Alireza Rabbani, Hossein Isfahan Univ Med Sci Med Image & Signal Proc Res Ctr Sch Adv Technol Med Esfahan *** Iran

This paper proposes a mixed low-rank approximation and second-order tensor-based total variation (LRSOTTV) approach for the super-resolution and denoising of retinal optical coherence tomography (OCT) images through effective utilization of nonlocal spatial correlations and local smoothness properties. OCT imaging relies on interferometry, which explains why OCT images suffer from a high level of noise. In addition, data subsampling is conducted during OCT A-scan and B-scan acquisition. Therefore, using effective super-resolution algorithms is necessary for reconstructing high-resolution clean OCT images. In this paper, a low-rank regularization approach is proposed for exploiting nonlocal self-similarity prior to OCT image reconstruction. To benefit from the advantages of the redundancy of multi-slice OCT data, we construct a third-order tensor by extracting the nonlocal similar three-dimensional blocks and grouping them by applying the k-nearest-neighbor method. Next, the nuclear norm is used as a regularization term to shrink the singular values of the constructed tensor in the non-local correlation direction. Further, the regularization approaches of the first-order tensor-based total variation (FOTTV) and SOTTV are proposed for better preservation of retinal layers and suppression of artifacts in OCT images. The alternative direction method of multipliers (ADMM) technique is then used to solve the resulting optimization problem. Our experiments show that integrating SOTTV instead of FOTTV into a low-rank approximation model can achieve noticeably improved results. Our experimental results on the denoising and super-resolution of OCT images demonstrate that the proposed model can provide images whose numerical and visual qualities are higher than those obtained by using state-of-the-art methods.

关键词： Tensors Image reconstruction Noise reduction Correlation Retina Redundancy approximation algorithms 1st 2nd-order tensor-based total variation (TTV) low-rank approximation optical coherent tomography (OCT) super-resolution nuclear norm

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Distributed Attack-Robust Submodular Maximization for Multirobot Planning

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IEEE TRANSACTIONS ON ROBOTICS 2022年第5期38卷 3097-3112页

作者： Zhou, Lifeng Tzoumas, Vasileios Pappas, George J. Tokekar, Pratap Virginia Tech Dept Elect & Comp Engn Blacksburg VA 24061 USA Univ Penn GRASP Lab Philadelphia PA 19104 USA Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA

In this article, we design algorithms to protect swarm-robotics applications against sensor denial-of-service attacks on robots. We focus on applications requiring the robots to jointly select actions, e.g., which trajectory to follow, among a set of available actions. Such applications are central in large-scale robotic applications, such as multirobot motion planning for target tracking. But the current attack-robust algorithms are centralized. In this article, we propose a general-purpose distributed algorithm toward robust optimization at scale, with local communications only. We name it distributed robust maximization (DRM). DRM proposes a divide-and-conquer approach that distributively partitions the problem among cliques of robots. Then, the cliques optimize in parallel, independently of each other. We prove DRM achieves a close-to-optimal performance. We demonstrate DRM's performance in Gazebo and MATLAB simulations, in scenarios of active target tracking with swarms of robots. In the simulations, DRM achieves computational speed-ups, being 1 to 2 orders faster than the centralized algorithms. Yet, it nearly matches the tracking performance of the centralized counterparts. Since, DRM overestimates the number of attacks in each clique, in this article, we also introduce an improved distributed robust maximization (IDRM) algorithm. IDRM infers the number of attacks in each clique less conservatively than DRM by leveraging three-hop neighboring communications. We verify IDRM improves DRM's performance in simulations.

关键词： Robot sensing systems Robot kinematics Planning approximation algorithms Multi-robot systems Task analysis Target tracking Adversarial attacks approximation algorithm distributed optimization multirobot planning robust optimization submodular optimization target tracking

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Space Complexity of Minimum Cut Problems in Single-Pass Streams 16

Space Complexity of Minimum Cut Problems in Single-Pass Stre...

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16th Innovations in Theoretical Computer Science Conference, ITCS 2025

作者： Ding, Matthew Garces, Alexandro Li, Jason Lin, Honghao Nelson, Jelani Shah, Vihan Woodruff, David P. University of California BerkeleyCA United States Massachusetts Institute of Technology CambridgeMA United States Carnegie Mellon University PittsburghPA United States University of Waterloo Canada

ISBN: (纸本)9783959773614

We consider the problem of finding a minimum cut of a weighted graph presented as a single-pass stream. While graph sparsification in streams has been intensively studied, the specific application of finding minimum cuts in streams is less well-studied. To this end, we show upper and lower bounds on minimum cut problems in insertion-only streams for a variety of settings, including for both randomized and deterministic algorithms, for both arbitrary and random order streams, and for both approximate and exact algorithms. One of our main results is an Oe(n/Ε) space algorithm with fast update time for approximating a spectral cut query with high probability on a stream given in an arbitrary order. Our result breaks the Ω(n/Ε2) space lower bound required of a sparsifier that approximates all cuts simultaneously. Using this result, we provide streaming algorithms with near optimal space of Oe(n/Ε) for minimum cut and approximate all-pairs effective resistances, with matching space lower-bounds. The amortized update time of our algorithms is Oe(1), provided that the number of edges in the input graph is at least (n/Ε2)1+o(1). We also give a generic way of incorporating sketching into a recursive contraction algorithm to improve the post-processing time of our algorithms. In addition to these results, we give a random-order streaming algorithm that computes the exact minimum cut on a simple, unweighted graph using Oe(n) space. Finally, we give an Ω(n/Ε2) space lower bound for deterministic minimum cut algorithms which matches the best-known upper bound up to polylogarithmic factors. © Matthew Ding, Alexandro Garces, Jason Li, Honghao Lin, Jelani Nelson, Vihan Shah, and David P. Woodruff.

关键词： approximation algorithms

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Jamming-Assisted Legitimate Eavesdropping and Secure Communication in Multicarrier Interference Networks

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IEEE SYSTEMS JOURNAL 2022年第1期16卷 954-965页

作者： Xu, Ding Zhu, Hongbo Nanjing Univ Posts & Telecommun Jiangsu Key Lab Wireless Commun Nanjing 210003 Peoples R China

This article investigates legitimate eavesdropping and secure communication in a multicarrier interference network, where a suspicious link coexists with an unsuspicious link, in the presence of a full-duplex monitor. It is assumed that the unsuspicious users (UUs) are under illegal eavesdropping threat from the suspicious users (SUs), and the monitor can send jamming signals to interfere with the SUs. The UUs are required to cooperate with the monitor for reducing the secrecy outage probability of the UUs to be lower than that without the monitor's jamming. Under this secrecy outage constraint along with the transmit power constraints, the problem for maximizing the successful eavesdropping probability of the monitor by jointly allocating the transmit powers of the monitor and the UUs is investigated. The problem is solved with the Lagrange duality method by proving the time-sharing property holds, where two algorithms are proposed for obtaining the dual function based on the block coordinate descent method and the successive convex approximation method. Simulation results verify that the proposed algorithms are superior to the noncooperative strategy and the passive eavesdropping strategy, and achieve high successful eavesdropping probability and low secrecy outage probability simultaneously.

关键词： Monitoring Eavesdropping Jamming Interference approximation algorithms Quality of service Fading channels Legitimate eavesdropping physical layer security secure communication suspicious communication unsuspicious communication

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High-precision Q-value measurement and nuclear matrix element calculations for the double-β decay of ⁹⁸Mo

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EUROPEAN PHYSICAL JOURNAL A 2022年第3期58卷 1-10页

作者： Nesterenko, D. A. Jokiniemi, L. Kotila, J. Kankainen, A. Ge, Z. Eronen, T. Rinta-Antila, S. Suhonen, J. Univ Jyvaskyla Dept Phys Accelerator Lab POB 35 FI-40014 Jyvaskyla Finland Univ Barcelona Dept Quantum Phys & Astrophys Barcelona 08028 Spain Univ Barcelona Inst Cosmos Sci Barcelona 08028 Spain Yale Univ Ctr Theoret Phys Sloane Phys Lab New Haven CT 06520 USA

The Mo-98 double-beta decay Q-value has been measured, and the corresponding nuclear matrix elements of neutrinoless double-beta (0 nu beta beta) decay and the standard two-neutrino double-beta (2 nu beta beta) decay have been provided by nuclear theory. The double-beta decay Q-value has been determined as Q(beta beta) = 113.668(68) keV using the JYFLTRAP Penning trap mass spectrometer. It is in agreement with the literature value, Q(beta beta) = 109(6) keV, but almost 90 times more precise. Based on the measured Q-value, precise phase-space factors for 2 nu beta beta decay and 0 nu beta beta decay, needed in the half-life predictions, have been calculated. Furthermore, the involved nuclear matrix elements have been computed in the proton-neutron quasiparticle random-phase approximation (pnQRPA) and the microscopic interacting boson model (IBM-2) frameworks. Finally, predictions for the 2 nu beta beta decay are given, suggesting a much longer half-life than for the currently observed cases.

关键词： approximation algorithms

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A General Framework for Approximating Min Sum Ordering Problems

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INFORMS JOURNAL ON COMPUTING 2022年第3期34卷 1437-1452页

作者： Happach, Felix Hellerstein, Lisa Lidbetter, Thomas Tech Univ Munich Dept Math D-80333 Munich Germany Tech Univ Munich TUM Sch Management D-80333 Munich Germany NYU Dept Comp Sci & Engn Tandon Sch Engn New York NY 11201 USA Rutgers Business Sch Dept Management Sci & Informat Syst Newark NJ 07102 USA

We consider a large family of problems in which an ordering (or, more precisely, a chain of subsets) of a finite set must be chosen to minimize some weighted sum of costs. This family includes variations of min sum set cover, several scheduling and search problems, and problems in Boolean function evaluation. We define a new problem, called the min sum ordering problem (MSOP), which generalizes all these problems using a cost and a weight function defined on subsets of a finite set. Assuming a polynomial time a-approximation algorithm for the problem of finding a subset whose ratio of weight to cost is maximal, we show that under very minimal assumptions, there is a polynomial time 4 alpha-approximation algorithm for MSOP. This approximation result generalizes a proof technique used for several distinct problems in the literature. We apply this to obtain a number of new approximation results.

关键词： scheduling search theory Boolean function evaluation min sum set cover approximation algorithms

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