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Annual IEEE Symposium on Foundations of Computer Science

作者： Shiri Chechik Tianyi Zhang Blavatnik School of Computer Science Tel Aviv University Tel Aviv Israel

ISBN: (数字)9781665455190

ISBN: (纸本)9781665455206

In a weighed directed graph $G=(V, E, \omega)$ with m edges and n vertices, we are interested in its basic graph parameters such as diameter, radius and eccentricities, under the nonstandard measure of min-distance which is defined for every pair of vertices $u, v \in V$ as the minimum of the shortest path distances from u to v and from v to u. Similar to standard shortest paths distances, computing graph parameters exactly in terms of min-distances essentially requires $\tilde{\Omega}(m n)$ time under plausible hardness conjectures 1 . Hence, for faster running time complexities we have to tolerate approximations. Abboud, Vassilevska Williams and Wang [SODA 2016] were the first to study min-distance problems, and they obtained constant factor approximation algorithms in acyclic graphs, with running time $\tilde{O}(m)$ and $\tilde{O}(m \sqrt{n})$ for diameter and radius, respectively. The time complexity of radius in acyclic graphs was recently improved to $\tilde{O}(m)$ by Dalirrooyfard and Kaufmann [ICALP 2021], but at the cost of an $O(\log n)$ approximation ratio. For general graphs, the authors of [DWV+, ICALP 2019] gave the first constant factor approximation algorithm for diameter, radius and eccentricities which runs in time $\tilde{O}(m \sqrt{n})$; besides, for the diameter problem, the running time can be improved to $\tilde{O}(m)$ while blowing up the approximation ratio to $O(\log n)$. A natural question is whether constant approximation and near-linear time can be achieved simultaneously for diameter, radius and eccentricities; so far this is only possible for diameter in the restricted setting of acyclic graphs. In this paper, we answer this question in the affirmative by presenting near-linear time algorithms for all three parameters in general graphs. 1 As usual, the $\tilde{O}(\cdot)$ notation hides poly-logarithmic factors in n

关键词： Weight measurement Computer science Costs Directed graphs approximation algorithms Time complexity Standards

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Learning Sparsity and Randomness for Data-driven Low Rank approximation

arXiv

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arXiv 2022年

作者： Chen, Tiejin Tao, Yicheng Department of Statistics University of Michigan United States Department of Computer Science University of Michigan United States

Learning-based low rank approximation algorithms can significantly improve the performance of randomized low rank approximation with sketch matrix. With the learned value and fixed non-zero positions for sketch matrices from learning-based algorithms, these matrices can reduce the test error of low rank approximation significantly. However, there is still no good method to learn non-zero positions as well as overcome the out-of-distribution performance loss. In this work, we introduce two new methods Learning Sparsity and Learning Randomness which try to learn a better sparsity patterns and add randomness to the value of sketch matrix. These two methods can be applied with any learning-based algorithms which use sketch matrix directly. Our experiments show that these two methods can improve the performance of previous learning-based algorithm for both test error and out-of-distribution test error without adding too much complexity. © 2022, CC BY.

关键词： approximation algorithms

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Lyapunov function approach for approximation algorithm design and analysis: with applications in continuous submodular maximization

arXiv

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arXiv 2022年

作者： Du, Donglei Faculty of Management University of New Brunswick FrederictonNBE3B 5A3 Canada

We propose a two-phase systematical framework for approximation algorithm design and analysis via Lyapunov function. The first phase consists of using Lyapunov function as an input and outputs a continuous-time approximation algorithm with a provable approximation ratio. The second phase then converts this continuous-time algorithm to a discrete-time algorithm with almost the same approximation ratio along with provable time complexity. One distinctive feature of our framework is that we only need to know the parametric form of the Lyapunov function whose complete specification will not be decided until the end of the first phase by maximizing the approximation ratio of the continuous-time algorithm. Some immediate benefits of the Lyapunov function approach include: (i) unifying many existing algorithms;(ii) providing a guideline to design and analyze new algorithms;and (iii) offering new perspectives to potentially improve existing algorithms. We use various submodular maximization problems as running examples to illustrate our framework. © 2022, CC BY.

关键词： approximation algorithms

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Quadratic approximation based heuristic for optimization-based coordination of automated vehicles in confined areas

Quadratic approximation based heuristic for optimization-bas...

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IEEE Conference on Decision and Control

作者： Stefan Kojchev Robert Hult Jonas Fredriksson Volvo Autonomous Solutions and the Mechatronics Group Systems and Control Chalmers University of Technology Volvo Autonomous Solutions Göteborg Sweden Mechatronics Group Systems and Control Chalmers University of Technology Göteborg Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We investigate the problem of coordinating multiple automated vehicles (AVs) in confined areas. This problem can be formulated as an optimal control problem (OCP) where the motion of the AVs is optimized such that collisions are avoided in cross-intersections, merge crossings, and narrow roads. The problem is combinatorial and solving it to optimality is prohibitively difficult for all but trivial instances. For this reason, we propose a heuristic method to obtain approximate solutions. The heuristic comprises two stages: In the first stage, a Mixed Integer Quadratic Program (MIQP), similar in construction to the Quadratic Programming (QP) sub-problems in Sequential Quadratic Programming (SQP), is solved for the combinatorial part of the solution. In the second stage, the combinatorial part of the solution is held fixed, and the optimal state and control trajectories for the vehicles are obtained by solving a Nonlinear Program (NLP). The performance of the algorithm is demonstrated by a simulation of a non-trivial problem instance.

关键词： Roads Optimal control approximation algorithms Trajectory Quadratic programming

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A Two-Stage Wall Parameters Estimation Algorithm for MIMO Through-the-Wall Radar

A Two-Stage Wall Parameters Estimation Algorithm for MIMO Th...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Zhirun Li Shisheng Guo Jiahui Chen Zhihao Zhu Chenqiu Guolong Cui Yutao Xiang University of Electronic Science and Technology of China Chengdu

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

This paper proposes a two-stage wall parameter estimation method based on multi-channel to retrieve the unknown position, thickness, and relative permittivity of wall. In the first stage, it aims to estimate the slope and intercept of the front surface of the wall. This is achieved by formulating a cost-minimization problem, where the position of the front wall is modeled using a linear analytical expression. Subsequently, in the second stage, it focuses on calculating the positions of equivalent arrays that are aligned parallel to the wall for each transmitting antenna, so as to enable the estimation of the wall’s thickness and its relative permittivity. The effectiveness of the algorithm is validated through electromagnetic simulations.

关键词： Parameter estimation Error analysis Array signal processing Simulation Transmitting antennas approximation algorithms Mathematical models Reflection Permittivity Antenna arrays

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Agent-state based policies in POMDPs: Beyond belief-state MDPs

Agent-state based policies in POMDPs: Beyond belief-state MD...

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IEEE Conference on Decision and Control

作者： Amit Sinha Aditya Mahajan Department of Electrical and Computer Engineering McGill University Montreal QC Canada CIM GERAD Mila and ILLS

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The traditional approach to POMDPs is to convert them into fully observed MDPs by considering a belief state as an information state. However, a belief-state based approach requires perfect knowledge of the system dynamics and is therefore not applicable in the learning setting where the system model is unknown. Various approaches to circumvent this limitation have been proposed in the literature. We present a unified treatment of some of these approaches by viewing them as models where the agent maintains a local recursively updateable “agent state” and chooses actions based on the agent state. We highlight the different classes of agent-state based policies and the various approaches that have been proposed in the literature to find good policies within each class. These include the designer’s approach to find optimal non-stationary agent-state based policies, policy search approaches to find a locally optimal stationary agent-state based policies, and the approximate information state to find approximately optimal stationary agent-state based policies. We then present how ideas from the approximate information state approach have been used to improve Q-learning and actor-critic algorithms for learning in POMDPs.

关键词： Surveys Q-learning System dynamics Tutorials Predictive models Transformers Prediction algorithms approximation algorithms Planning

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Online DRL-based Beam Selection for RIS-Aided Physical Layer Security: An Experimental Study

Online DRL-based Beam Selection for RIS-Aided Physical Layer...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ahmed Nasser Abdulkadir Celik Asmaa Abdallah David Lago Cachón Ruiqi Wang Yiming Yang Atif Shamim Ahmed M. Eltawil Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal KSA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The integration of reconfigurable intelligent surfaces (RIS) and artificial noise (AN) significantly enhances physical layer security (PLS) in wireless networks, provided that RIS’s phase shifts are precisely optimized to prevent security vulnerabilities. This paper introduces a reinforcement learning (RL)based algorithm designed to optimize the phase shifts in RIS-partitioning-aided PLS systems operating in the millimeter wave (mm-Wave), without requiring channel state information (CSI) for any users. The RL algorithm optimizes the phase shifts by efficiently selecting the best beam from a predefined codebook for different partitions, which simultaneously enhances the intended signal for legitimate users and increases the effectiveness of AN on eavesdroppers, thereby maximizing the system’s secrecy capacity (SC) and addressing the inherent non-convex challenges. Additionally, the paper details the development of an experimental testbed that provides essential data to refine the algorithm. The numerical results from the testbed highlight the significant impact of RIS partitioning in PLS, which can enhance the SC by an average of 55% over the full RIS scenario, and confirm the effectiveness of the RL-based algorithm in reducing computational complexity by approximately 80% compared to the exhaustive search algorithm.

关键词： Wireless networks Search methods Reinforcement learning Reconfigurable intelligent surfaces Physical layer security approximation algorithms Partitioning algorithms Security Optimization Signal to noise ratio

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Joint BS and RIS Element Selection with Large Arrays for EE Maximization

Joint BS and RIS Element Selection with Large Arrays for EE ...

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International Symposium on Wireless Communication Systems (ISWCS)

作者： Wilson de Souza Herman Lucas dos Santos Taufik Abrão Electrical Eng. Dept. UEL Londrina Brazil Academic Department of the Electric UTFPR Cornélio Procópio Brazil

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

In this work, we investigate the energy effi-ciency (EE) metric in reconfigurable intelligent surface (RIS)-assisted multi-user (MU) massive multiple-input multiple-output (mMIMO) systems. Our analysis involves the optimization of the number of antennas M and reflective elements (REs) N in the base station (BS) and RIS arrays, respectively, along with the optimization of passive beamforming and power allocation. To address this problem, we introduce a novel joint reinforcement-analytical methodology (JRAM) algorithm, utilizing reinforcement learning (RL) for the elements selection in both arrays and subsequently conventional analytical techniques to optimize passive beamforming and power allocation for the selected elements at the BS and RIS. The numerical results underscore the significance of jointly optimizing the number of operative antennas and REs, leading to a remarkable gain of approximately 40.3% compared to optimizing just the operative elements in the BS and passive beamforming optimization.

关键词： Wireless communication Array signal processing Reinforcement learning Quality of service Reconfigurable intelligent surfaces approximation algorithms Reflector antennas

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Distributed Stochastic Optimisation with Uncertain Coupling Constraints

Distributed Stochastic Optimisation with Uncertain Coupling ...

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IEEE Conference on Decision and Control

作者： Alessandro Del Duca Fredy Ruiz Riccardo Scattolini Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Large-scale multi-agent systems are increasingly relevant in various aspects of society; their operation requires advances in multi-agent distributed optimisation algorithms that can handle uncertain environments. This paper presents a distributed algorithm suitable for solving convex constraint-coupled multi-agent problems with uncertainty directly affecting the coupling constraints. The algorithm exploits the problem structure to solve the large-scale uncertain problem efficiently, leveraging the scenario approach to approximate the coupling chance-constraint. We prove that the number of scenarios required to guarantee a given violation probability level is independent of the agent number, making the solution scalable. We apply the algorithm to a multi-microgrid aggregation problem to provide ancillary services to the Grid, a relevant decarbonisation and energy security topic.

关键词： Couplings Uncertainty approximation algorithms Probabilistic logic Low carbon economy Security Forecasting Distributed algorithms Optimization Multi-agent systems

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Synthesis of Approximate Parametric Circuits for Variational Quantum algorithms

Synthesis of Approximate Parametric Circuits for Variational...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Blake Burgstahler Ellis Wilson Scott Pakin Frank Mueller North Carolina State University Los Alamos National Laboratory

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

This work presents a novel approach to synthesize approximate circuits for the ansatze of variational quantum algorithms (VQA) and demonstrates its effectiveness in the context of solving integer linear programming (ILP) problems. Synthesis is generalized to produce parametric circuits in close approximation of the original circuit and to do so offline . This removes synthesis from the (online) critical path between repeated quantum circuit executions of VQA. We hypothesize that this approach will yield novel high fidelity results beyond those discovered by the baseline without synthesis. Simulation and real device experiments complement the baseline in finding correct results in many cases where the baseline fails to find any and do so with on average 32% fewer CNOTs in circuits.

关键词： Quantum algorithm Program processors Logic gates Integer linear programming approximation algorithms Quantum circuit

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