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Proceedings of the 46th IEEE Conference on Decision and Control, Pages 5511-5970

作者： Tao Cui Tracey Ho Lijun Chen Division of Engineering and Applied Science California State University Sacramento USA

ISBN: (纸本)9781424414970;1424414970

In this paper, we consider minimum cost lossless source coding for multiple multicast sessions. Each session comprises a set of correlated sources whose information is demanded by a set of sink nodes. We propose a distributed end-to-end algorithm which operates over given multicast trees, and a back-pressure algorithm which optimizes routing and coding over the whole network. Unlike other existing algorithms, the source rates need not be centrally coordinated;the sinks control transmission rates across the sources. With random network coding, the proposed approach yields completely distributed and optimal algorithms for intra-session network coding. We prove the convergence of our proposed algorithms. Some practical considerations are also discussed. Experimental results are provided to complement our theoretical analysis.

关键词： lossless source coding Multicast Network coding optimization algorithms Random Network coding TRANSMISSION RATE multicast communication solidification Distribute algorithms Network routing Theoretical analysis Sinks source rate

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Last Iterate Convergence of Incremental Methods and Applications in Continual Learning

arXiv

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arXiv 2024年

作者： Cai, Xufeng Diakonikolas, Jelena Department of Computer Sciences University of Wisconsin-Madison United States

Incremental gradient and incremental proximal methods are a fundamental class of optimization algorithms used for solving finite sum problems, broadly studied in the literature. Yet, without strong convexity, their convergence guarantees have primarily been established for the ergodic (average) iterate. Motivated by applications in continual learning, we obtain the first convergence guarantees for the last iterate of both incremental gradient and incremental proximal methods, in general convex smooth (for both) and convex Lipschitz (for the proximal variants) settings. Our oracle complexity bounds for the last iterate nearly match (i.e., match up to a square-root-log or a log factor) the best known oracle complexity bounds for the average iterate, for both classes of methods. We further obtain generalizations of our results to weighted averaging of the iterates with increasing weights and for randomly permuted ordering of updates. We study incremental proximal methods as a model of continual learning with generalization and argue that large amount of regularization is crucial to preventing catastrophic forgetting. Our results generalize last iterate guarantees for incremental methods compared to state of the art, as such results were previously known only for overparameterized linear models, which correspond to convex quadratic problems with infinitely many solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Optimal Path Selection of UAV in Daily Maintenance of Expressway Slopes

Optimal Path Selection of UAV in Daily Maintenance of Expres...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Ping Wang Wubei Yuan Yinli Jin Institute for Transportation Systems Engineering Research Chang’an University Xi’an China School of Electronics and Control Engineering Chang'an University Xi’an China

ISBN: (纸本)9781728137933

Unmanned aerial vehicles (UAVs) have gained special attention in recent years, among others in monitoring and inspection applications. We used UAV to analyze the characteristics of the slopes of Hanning Expressway and designed different aerial photography solutions. Compared with manual inspection, the work efficiency increased by 43.5%. Then, we selected the suspicious damage points as the collection points for the second inspection, and introduced the adjacency matrix to describe the situation between the collection points. We used the ant colony algorithm, simulated annealing algorithm and genetic algorithm to solve the shortest path. The results showed that the simulated annealing algorithm was the optimal algorithm.

关键词： Unmanned aerial vehicle Path planning Simulated annealing algorithm Adjacency matrix simulated annealing algorithm Unmanned Aerial Vehicle drone honey bees Freeway Path planning vehicle ant algorithm optimal path optimization algorithms

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Time Aggregation Based Optimal Control and Lebesgue Sampling

Time Aggregation Based Optimal Control and Lebesgue Sampling

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Proceedings of the 46th IEEE Conference on Decision and Control, Pages 1839-2300

作者： Yan-Kai Xu Xi-Ren Cao Center for Intelligent and Networked Systems the Department of Automation Tsinghua University Beijing China The Department of Electrical and Electronic Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

ISBN: (纸本)9781424414970;1424414970

Time aggregation based optimal control model is proposed in the paper, by using Lebesgue sampling technique. Time aggregation approach in MDP (Markov Decision Processes) theory is applied to handle the problem, then policy iteration can be implemented. Both analytical solution and sample path based optimization algorithms are given. Compared with periodic sampling based approach, Lebesgue sampling based approach can be applied to practical control systems to obtain improvement in system performance and reduction in resource utilization.

关键词： System performance Optimal control Lebesgue optimal control model based optimization algorithms Resource utilization control systems Markov chain Sampling agglomeration

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The Computation of Full-complexity Polytopic Robust Control Invariant Sets

The Computation of Full-complexity Polytopic Robust Control ...

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IEEE Annual Conference on Decision and Control

作者： Chengyuan Liu Imad M. Jaimoukha Department of Electrical and Electronic Engineering Imperial College London SW7 2AZ UK

ISBN: (纸本)9781479978878

This paper considers the problem of evaluating robust control invariant (RCI) sets for linear discrete-time systems subject to state and input constraints as well as additive disturbances. An RCI set has the property that if the system state is inside the set at any one time, then it is guaranteed to remain in the set for all future times using a predefined state feedback control law. This problem is important in many control applications. We present a numerically efficient algorithm for the computation of full-complexity polytopic RCI sets. Farkas' Theorem is first used to derive necessary and sufficient conditions for the existence of an admissible polytopic RCI set in the form of nonlinear matrix inequalities. An Elimination Lemma is then used to derive sufficient conditions, in the form of linear matrix inequalities, for the existence of the solution. An optimization algorithm to approximate maximal RCI sets is also proposed. Numerical examples are given to illustrate the effectiveness of the proposed algorithm.

关键词： SETTING Sufficient conditions Robust control state feedback control Invariant linear matrix inequalities optimization algorithms

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Novel Spiral Antennas Design Using Swarm Intelligence for Passive UHF RFID Tags

Novel Spiral Antennas Design Using Swarm Intelligence for Pa...

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European Conference on Antennas and Propagation

作者： Sotirios K. Goudos Katherine Siakavara John N. Sahalos Radiocommunications Laboratory: Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece Radio & Telecommunications Lab: Department of Electrical and Computer Engineering University of Nicosia Cyprus

ISBN: (纸本)9781467321877

In this paper, new planar spiral antennas for passive RFID tag application at UHF band are designed and optimized using the Artificial Bee Colony (ABC) algorithm. The optimization goals are antenna size minimization, gain maximization and conjugate matching. The antenna dimensions were optimized and evaluated using ABC in conjunction with commercial EM software. Furthermore, a theoretical analysis of the spiral antennas was performed. The input impedance of the printed spiral, was calculated approximately via the Transmission Line Model and the electromotive force induced along the spirals. The theoretical results seem to be in good agreement with the EM solver results. The optimization results produced show that ABC is a powerful optimization algorithm that can be efficiently applied to tag antenna design problems. RFID tags with dimensions less than 4cm, gain that reaches the value of 2 dBi and read distance about 11.7m were among those obtained by the algorithm.

关键词： RFID tags Helical antennas Particle swarm optimization Passive RFID tags TRANSMISSION LINE MODELLING impedance optimization algorithms UHF band Spiral

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A Nonlinear Estimator Concept for Active Vehicle Suspension Control

A Nonlinear Estimator Concept for Active Vehicle Suspension ...

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American Control Conference

作者： Guido Koch Tobias Kloiber Enrico Pellegrini Boris Lohmann Institute of Automatic Control Technische Universitat Munchen 86748 Garching Germany

ISBN: (纸本)9781424474264

A new Kalman filter based signal estimation concept for active vehicle suspension control is presented in this paper considering the nonlinear damper characteristic of a vehicle suspension setup. The application of a multi-objective genetic optimization algorithm for the tuning of the estimator shows that three parallel Kalman filters enhance the estimation performance for the variables of interest (states, dynamic wheel load and road profile). The Kalman filter structure is validated in simulations and on a testrig for an active suspension configuration using measurements of real road profiles as disturbance input. The advantages of the concept are its low computational effort compared to Extended or Unscented Kalman filters and its good estimation accuracy despite the presence of nonlinearities in the suspension setup.

关键词： Automobile suspensions Kalman filters nonlinearity Road optimization algorithms

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Newton Losses: Using Curvature Information for Learning with Differentiable algorithms

arXiv

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arXiv 2024年

作者： Petersen, Felix Borgelt, Christian Sutter, Tobias Kuehne, Hilde Deussen, Oliver Ermon, Stefano Stanford University United States University of Salzburg Austria University of Konstanz Germany Tuebingen AI Center MIT-IBM Watson AI Lab United States

When training neural networks with custom objectives, such as ranking losses and shortest-path losses, a common problem is that they are, per se, non-differentiable. A popular approach is to continuously relax the objectives to provide gradients, enabling learning. However, such differentiable relaxations are often non-convex and can exhibit vanishing and exploding gradients, making them (already in isolation) hard to optimize. Here, the loss function poses the bottleneck when training a deep neural network. We present Newton Losses, a method for improving the performance of existing hard to optimize losses by exploiting their second-order information via their empirical Fisher and Hessian matrices. Instead of training the neural network with second-order techniques, we only utilize the loss function’s second-order information to replace it by a Newton Loss, while training the network with gradient descent. This makes our method computationally efficient. We apply Newton Losses to eight differentiable algorithms for sorting and shortest-paths, achieving significant improvements for less-optimized differentiable algorithms, and consistent improvements, even for well-optimized differentiable algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Modified Recursive QAOA for Exact Max-Cut Solutions on Bipartite Graphs: Closing the Gap Beyond QAOA Limit

arXiv

引用

arXiv 2024年

作者： Bae, Eunok Kwon, Hyukjoon Vijendran, V. Lee, Soojoon Seoul02455 Korea Republic of Department of Quantum Science and Technology Research School of Physics Australian National University Acton2601 Australia 2 Fusionopolis Way Innovis #08-03 Singapore138634 Singapore Department of Mathematics Research Institute for Basic Sciences Kyung Hee University Seoul02447 Korea Republic of

Quantum Approximate optimization Algorithm (QAOA) is a quantum-classical hybrid algorithm proposed with the goal of approximately solving combinatorial optimization problems such as the MAX-CUT problem. It has been considered a potential candidate for achieving quantum advantage in the Noisy Intermediate-Scale Quantum era and has been extensively studied. However, the performance limitations of low-level QAOA have also been demonstrated across various instances. In this work, we first analytically prove the performance limitations of level-1 QAOA in solving the MAX-CUT problem on bipartite graphs. To this end, we derive an upper bound for the approximation ratio based on the average degree of bipartite graphs. Second, we demonstrate that Recursive QAOA (RQAOA), which recursively reduces graph size using QAOA as a subroutine, outperforms the level-1 QAOA. However, the performance of RQAOA exhibits limitations as the graph size increases. Finally, we show that RQAOA with a restricted parameter regime can fully address these limitations. Surprisingly, this modified RQAOA always finds the exact maximum cut for any bipartite graphs and even for a more general graph with parity-signed weights. © 2024, CC BY.

关键词： optimization algorithms

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SMALL PERTURBATION ANALYSIS OF NETWORK TOPOLOGIES

SMALL PERTURBATION ANALYSIS OF NETWORK TOPOLOGIES

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Elena Ceci Sergio Barbarossa Dept. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Via Eudossiana 18 00184 Rome Italy

ISBN: (纸本)9781538646595

The goal of this paper is to derive a small perturbation analysis for networks subject to random changes of a small number of edges. Small perturbation theory allows us to derive, albeit approximate, closed form expressions that make possible the theoretical statistical characterization of the network topology changes. The analysis is instrumental to formulate a graph-based optimization algorithm, which is robust against edge failures. In particular, we focus on the optimal allocation of the overall transmit powers in wireless communication networks subject to fading, aimed at minimizing the variation of the network connectivity, subject to a constraint on the overall power necessary to maintain network connectivity.

关键词： Graph topologies network reliability information flow small perturbation analysis information flow small disturbance Network Network topology perturbation Wireless networks Transmitter power optimal allocation optimization algorithms fading color

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