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arXiv 2024年

作者： Folena, Giampaolo Charbonneau, Patrick Morse, Peter K. Rojas, Rafael Díaz Hernández Ricci-Tersenghi, Federico Department of Chemistry Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States Department of Physics Seton Hall University South OrangeNJ07079 United States Institute for Theoretical Physics Georg-August-Universität Göttingen Göttingen Germany Dipartimento di Fisica Sapienza Università di Roma INFN – Sezione di Roma1 CNR – Nanotec Piazzale Aldo Moro 5 Roma00185 Italy

Deterministic optimization algorithms unequivocally partition a complex energy landscape in inherent structures (ISs) and their respective basins of attraction. But can these basins be defined solely through geometric principles? This question is particularly important when examining the jamming of hard spheres. We here consider this issue by proposing a geometric class of gradient descent–like algorithms, which we use to study a system in the hard-sphere universality class, the random Lorentz gas. The statistics of the resulting IS is found to be strictly inherited from those of Poisson–Voronoi tessellations. The landscape roughness is further found to give rise to a hierarchical organization of ISs which various algorithms explore differently. In particular, greedy and reluctant schemes tend to favor ISs of markedly different densities. The resulting ISs nevertheless robustly exhibit a universal force distribution, thus confirming the geometric nature of the jamming universality class. © 2024, CC BY-NC-ND.

关键词： optimization algorithms

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Linear model predictive control based on approximate optimal control inputs and constraint tightening

Linear model predictive control based on approximate optimal...

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IEEE Annual Conference on Decision and Control

作者： Ion Necoara Valentin Nedelcu Tamas Keviczky Minh Dang Doan Bart de Schutter Automatic Control and Systems Engineering Department University Politehnica Bucharest Romania Delft Center for Systems and Control Delft University of Technology The Netherlands Can Tho University of Technology Vietnam

ISBN: (纸本)9781467357159

In this paper we propose a model predictive control scheme for discrete-time linear time-invariant systems based on inexact numerical optimization algorithms. We assume that the solution of the associated quadratic program produced by some numerical algorithm is possibly neither optimal nor feasible, but the algorithm is able to provide estimates on primal suboptimality and primal feasibility violation. By tightening the complicating constraints we can ensure the primal feasibility of the approximate solutions generated by the algorithm. Finally, we derive a control strategy that has the following properties: the constraints on the states and inputs are satisfied, asymptotic stability of the closed-loop system is guaranteed, and the number of iterations needed for a desired level of suboptimality can be determined.

关键词： nonlinear model predictive control Tightening Predictive control control strategy control input optimization algorithms asymptotic stability region Closed loop systems Approximate iterative methods linear models Optimal control Constraint

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Equation discovery framework EPDE: Towards a better equation discovery

arXiv

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arXiv 2024年

作者： Maslyaev, Mikhail Hvatov, Alexander NSS Lab Kronverksky pr. 49 Saint-Petersburg197101 Russia

Equation discovery methods hold promise for extracting knowledge from physics-related data. However, existing approaches often require substantial prior information that significantly reduces the amount of knowledge extracted. In this paper, we enhance the EPDE algorithm - an evolutionary optimization-based discovery framework. In contrast to methods like SINDy, which rely on pre-defined libraries of terms and linearities, our approach generates terms using fundamental building blocks such as elementary functions and individual differentials. Within evolutionary optimization, we may improve the computation of the fitness function as is done in gradient methods and enhance the optimization algorithm itself. By incorporating multi-objective optimization, we effectively explore the search space, yielding more robust equation extraction, even when dealing with complex experimental data. We validate our algorithm's noise resilience and overall performance by comparing its results with those from the state-of-the-art equation discovery framework SINDy. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Adaptive Coordinate-Wise Step Sizes for Quasi-Newton Methods: A Learning-to-Optimize Approach

arXiv

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arXiv 2024年

作者： Lin, Wei Song, Qingyu Xu, Hong CSE CUHK Hong Kong

Tuning effective step sizes is crucial for the stability and efficiency of optimization algorithms. While adaptive coordinate-wise step sizes tuning methods have been explored in first-order methods, second-order methods still lack efficient techniques. Current approaches, including hypergradient descent and cutting plane methods, offer limited improvements or encounter difficulties in second-order contexts. To address these challenges, we introduce a novel Learning-to-Optimize (L2O) model within the Broyden-Fletcher-Goldfarb-Shanno (BFGS) framework, which leverages neural networks to predict optimal coordinate-wise step sizes. Our model integrates a theoretical foundation that establishes conditions for the stability and convergence of these step sizes. Extensive experiments demonstrate that our approach achieves substantial improvements over traditional backtracking line search and hypergradient descent-based methods, offering up to 7× faster and stable performance across diverse optimization tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Learning of Closed-Loop Motion Control

Learning of Closed-Loop Motion Control

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Farbod Farshidian Michael Neunert Jonas Buchli Agile & Dexterous Robotics Lab at the Institute of Robotics and Intelligent Systems ETH Zurich Switzerland

ISBN: (纸本)9781479969357

Learning motion control as a unified process of designing the reference trajectory and the controller is one of the most challenging problems in robotics. The complexity of the problem prevents most of the existing optimization algorithms from giving satisfactory results. While model-based algorithms like iterative linear-quadratic-Gaussian (iLQG) can be used to design a suitable controller for the motion control, their performance is strongly limited by the model accuracy. An inaccurate model may lead to degraded performance of the controller on the physical system. Although using machine learning approaches to learn the motion control on real systems have been proven to be effective, their performance depends on good initialization. To address these issues, this paper introduces a two-step algorithm which combines the proven performance of a model-based controller with a model-free method for compensating for model inaccuracy. The first step optimizes the problem using iLQG. Then, in the second step this controller is used to initialize the policy for our PI~2-01 reinforcement learning algorithm. This algorithm is a derivation of the PI~2 algorithm enabling more stable and faster convergence. The performance of this method is demonstrated both in simulation and experimental results.

关键词： Motor Control Controllers optimization algorithms degraded performance Learning initialization

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Restoration of Ultrasound Images Using Spatially-Variant Kernel Deconvolution

Restoration of Ultrasound Images Using Spatially-Variant Ker...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Mihai I. Florea Adrian Basarab Denis Kouame Sergiy A. Vorobyov Department of Signal Processing and Acoustics Aalto University Espoo Finland UMR CNRS 5505 University of Toulouse IRIT Toulouse France

ISBN: (纸本)9781538646595

Most of the existing ultrasound image restoration methods consider a spatially-invariant point-spread function (PSF) model and circulant boundary conditions. While computationally efficient, this model is not realistic and severely limits the quality of reconstructed images. In this work, we address ultrasound image restoration under the hypothesis of piece-wise linear vertical variation of the PSF based on a small number of prototypes. No assumption is made on the structure of the prototype PSFs. To regularize the solution, we use the classical elastic net constraint. Existing methodologies are rendered impractical either due to their reliance on matrix inversion or due to their inability to exploit the strong convexity of the objective. Therefore, we propose an optimization algorithm based on the Accelerated Composite Gradient Method, adapted and optimized for this task. Our method is guaranteed to converge at a linear rate and is able to adaptively estimate unknown problem parameters. We support our theoretical results with simulation examples.

关键词： Accelerated Composite Gradient Method point-spread function reconstruction restoration spatially varying ultrasound Point spread function restoration Ultrasonic Waves Ultrasonography optimization algorithms matrix inversion Reconstructions

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Localization of a moving non-cooperative RF target in NLOS environment using RSS and AOA measurements

Localization of a moving non-cooperative RF target in NLOS e...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： C. Cheng W. Hu W. P. Tay Sch. of Electr. & Electron. Eng. Nanyang Technol. Univ. Singapore Singapore

ISBN: (纸本)9781467369985

We propose an alternating optimization algorithm for localizing a mobile non-cooperative target using a wireless sensor network. We consider the scenario where sensors receive single-bounce non-line-of-sight signals from the moving target. Each sensor is able to measure the target signal's angle-of-arrival and received signal strength. The transmit powers of the non-cooperative target at different locations are unknown, and estimated jointly with its locations and the orientations of the scatterers off which the target signals are reflected before reaching the sensors. We formulate the problem as a non-convex least squares problem, and then transform and approximate it into a form that is solvable by an alternating algorithm. We show that our algorithm converges, and simulation results demonstrate that our algorithm is able to localize the target with good accuracy.

关键词： AOA Localization RSS alternating optimization wireless sensor network RSS gene Really Simple Syndication REACTOR SAFETY STUDY Wireless sensor networks ANGLE OF ATTACK Targets optimization algorithms Moving Sensors

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Fuzzy Robust and Non-fragile Minimax Control of a Trailer-Truck Model

Fuzzy Robust and Non-fragile Minimax Control of a Trailer-Tr...

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Proceedings of the 46th IEEE Conference on Decision and Control, Pages 3678-4134

作者： Yuanwei Jing Nan Jiang Yan Zheng Georgi M. Dimirovski Faculty of Information Science and Engineering Northeastern University Shenyang Liaoning China Faculty of Engineering Computer Engg.Dept Dogus University Istanbul Turkey

ISBN: (纸本)9781424414970;1424414970

This paper deals with a fuzzy robust and non-fragile minimax control problem of a trailer-truck model. By introducing parametric uncertainty terms into the T-S model for trailer-truck systems, the fuzzy model approaches to the original system more exactly. Existence conditions are derived for the robust and non-fragile minimax control in the sense of Lyapunov asymptotic stability and formulated in the form of Linear Matrix Inequalities (LMIs). The convex optimization algorithm is used to get the minimal upper bound of the performance cost and parameter of the optimal minimax controller. Then the closed-loop system will be asymptotically stable under the condition of the worst disturbance and uncertainty. Finally, an illustrative example is used to demonstrate the better robust and non-fragile performance of the controller design.

关键词： Minimax controller design fuzzy modeling Robustness Fuzzy optimization algorithms

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Cost Allocation Strategies for Wide-Area Control of Power Systems Using Nash Bargaining Solution

Cost Allocation Strategies for Wide-Area Control of Power Sy...

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IEEE Annual Conference on Decision and Control

作者： Feier Lian Alexandra Duel-Hallen Aranya Chakrabortty Electrical & Computer Engineering North Carolina State University

ISBN: (纸本)9781467360890

In this paper we present a novel game-theoretic strategy that guarantees fair cost allocation incurred by communication links in wide-area control for electric power systems. The underlying transmission network topology results in vastly diverse requirements for inter-area feedback for operating areas owned by different utility companies. Thus, it is unfair to divide the total communication cost equally among all companies. Our objective is to quantify these requirements and incorporate them into a fair cost distribution scheme. We formulate the wide-area control problem as a state-feedback based LQR minimization problem and cast it as a cooperative game with companies acting as game-players. We first apply sparsity-promoting optimization algorithms to construct the feedback gain matrix such that its off-diagonal blocks that characterize the inter-area feedback are as sparse as possible under a desired energy constraint. Assigning a fixed cost to every non-zero element in these off-diagonal blocks, we apply the Nash Bargaining Solution (NBS) to fairly allocate the total cost among the various game-players. Resulting insights into the wide-area communication requirements for different areas over a range of energy constraints are discussed.

关键词： Cost Allocation Power system control power systems Companies Total Communication Bargaining information matrix optimization algorithms off-diagonal Total Costs

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MODEL-BASED DEREVERBERATION IN THE LOGMELSPEC DOMAIN FOR ROBUST DISTANT-TALKING SPEECH RECOGNITION

MODEL-BASED DEREVERBERATION IN THE LOGMELSPEC DOMAIN FOR ROB...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Armin Sehr Roland Maas Walter Kellermann Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Cauerstr. 7 91058 Erlangen Germany

ISBN: (纸本)9781424442959

The REMOS (REverberation MOdeling for Speech recognition) concept for reverberation-robust distant-talking speech recognition, introduced in [1] for melspectral features, is extended in this contribution to logarithmic melspectral (logmelspec) features. Based on a combined acoustic model consisting of a hidden Markov model network and a reverberation model, REMOS determines clean-speech and reverberation estimates during recognition by an inner optimization operation. A reformulation of this inner optimization problem for logmelspec features, allowing an efficient solution by nonlinear optimization algorithms, is derived in this paper so that an efficient implementation of REMOS for logmelspec features becomes possible. Connected digit recognition experiments show that the proposed REMOS implementation significantly outperforms reverberantly-trained HMMs in highly reverberant environments.

关键词： Reverberation model-based dereverberation acoustic modeling distant-talking ASR robust ASR acoustic modeling Reverberation Speech recognition optimization algorithms Recognition (Psychology)

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