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Time-Varying Convex optimization: Time-Structured Algorithms and Applications

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arXiv 2020年

作者： Simonetto, Andrea Dall’Anese, Emiliano Paternain, Santiago Leus, Geert Giannakis, Georgios B. Optimization and Control Group of IBM Research Ireland Dublin Ireland College of Engineering and Applied Science University of Colorado BoulderCO80309 United States Department of Electrical and Systems Engineering University of Pennsylvania Faculty of Electrical Mathematics and Computer Science Delft University of Technology Delft2628CD Netherlands Digital Technology Center University of Minnesota MinneapolisMN55455 United States

optimization underpins many of the challenges that science and technology face on a daily basis. Recent years have witnessed a major shift from traditional optimization paradigms grounded on batch algorithms for medium-scale problems to challenging dynamic, time-varying, and even huge-size settings. This is driven by technological transformations that converted infrastructural and social platforms into complex and dynamic networked systems with even pervasive sensing and computing capabilities. The present paper reviews a broad class of state-of-the-art algorithms for time-varying optimization, with an eye to both algorithmic development and performance analysis. It offers a comprehensive overview of available tools and methods, and unveils open challenges in application domains of broad interest. The real-world examples presented include smart power systems, robotics, machine learning, and data analytics, highlighting domain-specific issues and solutions. The ultimate goal is to exempify wide engineering relevance of analytical tools and pertinent theoretical foundations. Copyright © 2020, The Authors. All rights reserved.

关键词： Data Analytics

A hidden Markov model for route and destination prediction

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arXiv 2018年

作者： Lassoued, Yassine Monteil, Julien Gu, Yingqi Russo, Giovanni Shorten, Robert Mevissen, Martin IBM Research - Ireland Lab Control and Optimization Group IBM Technology Campus Mulhuddart Dublin 15 University College Dublin School of Electrical Engineering Belfield Dublin 4 Ireland

We present a simple model and algorithm for predicting driver destinations and routes, based on the input of the latest road links visited as part of an ongoing trip. The algorithm may be used to predict any clusters previously observed in a driver's trip history. It assumes that the driver's historical trips are grouped into clusters sharing similar patterns. Given a new trip, the algorithm attempts to predict the cluster in which the trip belongs. The proposed algorithm has low temporal complexity. In addition, it does not require the transition and emission matrices of the Markov chain to be computed. Rather it relies on the frequencies of co-occurrences of road links and trip clusters. We validate the proposed algorithm against an experimental dataset. We discuss the success and convergence of the algorithm and show that our algorithm has a high prediction success rate. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

How to desynchronize quorum-sensing networks

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Physical Review E 2017年第4期95卷 042312-042312页

作者： Giovanni Russo Control and Optimization Group IBM Research Ireland

In this paper we investigate how so-called quorum-sensing networks can be desynchronized. Such networks, which arise in many important application fields, such as systems biology, are characterized by the fact that direct communication between network nodes is superimposed to communication with a shared, environmental variable. In particular, we provide a new sufficient condition ensuring that the trajectories of these quorum-sensing networks diverge from their synchronous evolution. Then, we apply our result to study two applications.

关键词： Network stability Synchronization Collective dynamics

Dual Prediction-Correction Methods for Linearly Constrained Time-Varying Convex Programs

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arXiv 2017年

作者： Simonetto, Andrea Optimization and Control Group of IBM Research Ireland Dublin Ireland

—Devising efficient algorithms to solve continuously-varying strongly convex optimization programs is key in many applications, from control systems to signal processing and machine learning. In this context, solving means to find and track the optimizer trajectory of the continuously-varying convex optimization program. Recently, a novel prediction-correction methodology has been put forward to set up iterative algorithms that sample the continuously-varying optimization program at discrete time steps and perform a limited amount of computations to correct their approximate optimizer with the new sampled problem and predict how the optimizer will change at the next time step. Prediction-correction algorithms have been shown to outperform more classical strategies, i.e., correction-only methods. Typically, prediction-correction methods have asymptotical tracking errors of the order of h2, where h is the sampling period, whereas classical strategies have order of h. Up to now, Prediction-correction algorithms have been developed in the primal space, both for unconstrained and simply constrained convex programs. In this paper, we show how to tackle linearly constrained continuously-varying problem by prediction-correction in the dual space and we prove similar asymptotical error bounds as their primal versions. Copyright © 2017, The Authors. All rights reserved.

关键词： Convex optimization

Filtering random graph processes over random time-varying graphs

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arXiv 2017年

作者： Isufi, Elvin Loukas, Andreas Simonetto, Andrea Leus, Geert Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft28266 CD Netherlands Switzerland Optimisation and Control Group IBM Research Ireland

Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochasticity in both the graph topology as well as the signal itself. To bridge this gap, we examine the statistical behavior of the two key filter types, finite impulse response (FIR) and autoregressive moving average (ARMA) graph filters, when operating on random timevarying graph signals (or random graph processes) over random time-varying graphs. Our analysis shows that (i) in expectation, the filters behave as the same deterministic filters operating on a deterministic graph, being the expected graph, having as input signal a deterministic signal, being the expected signal, and (ii) there are meaningful upper bounds for the variance of the filter output. We conclude the paper by proposing two novel ways of exploiting randomness to improve (joint graph-time) noise cancellation, as well as to reduce the computational complexity of graph filtering. As demonstrated by numerical results, these methods outperform the disjoint average and denoise algorithm, and yield a (up to) four times complexity redution, with very little difference from the optimal solution. Copyright © 2017, The Authors. All rights reserved.

关键词： Graph theory

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arXiv 2025年

作者： Cif, Laura Demailly, Diane Lin, Jean-Pierre Barwick, Katy E. Sa, Mario Abela, Lucia Malhotra, Sony Chong, Wui K. Steel, Dora Sanchis-Juan, Alba Ngoh, Adeline Trump, Natalie Meyer, Esther Vasques, Xavier Rankin, Julia Allain, Meredith W. Applegate, Carolyn D. Isfahani, Sanaz Attaripour Baleine, Julien Balint, Bettina Bassetti, Jennifer A. Baple, Emma L. Bhatia, Kailash P. Blanchet, Catherine Burglen, Lydie Cambonie, Gilles Seng, Emilie Chan Bastaraud, Sandra Chantot Cyprien, Fabienne Coubes, Christine d’Hardemare, Vincent Doja, Asif Dorison, Nathalie Doummar, Diane Dy-Hollins, Marisela E. Farrelly, Ellyn Fitzpatrick, David R. Fearon, Conor Fieg, Elizabeth L. Fogel, Brent L. Forman, Eva B. Fox, Rachel G. Gahl, William A. Galosi, Serena Gonzalez, Victoria Graves, Tracey D. Gregory, Allison Hallett, Mark Hasegawa, Harutomo Hayflick, Susan J. Hamosh, Ada Hully, Marie Jansen, Sandra Jeong, Suh Young Krier, Joel B. Krystal, Sidney Kumar, Kishore R. Laurencin, Chloé Lee, Hane Lesca, Gaetan François, Laurence Lion Lynch, Timothy Mahant, Neil Martinez-Agosto, Julian A. Milesi, Christophe Mills, Kelly A. Mondain, Michel Morales-Briceno, Hugo Ostergaard, John R. Pal, Swasti Pallais, Juan C. Pavillard, Frédérique Perrigault, Pierre-Francois Petersen, Andrea K. Polo, Gustavo Poulen, Gaetan Rinne, Tuula Roujeau, Thomas Rogers, Caleb Roubertie, Agathe Sahagian, Michelle Schaefer, Elise Selim, Laila Selway, Richard Sharma, Nutan Signer, Rebecca Soldatos, Ariane G. Stevenson, David A. Stewart, Fiona Tchan, Michel Verma, Ishwar C. de Vries, Bert B.A. Wilson, Jenny L. Wong, Derek A. Zaitoun, Raghda Zhen, Dolly Znaczko, Anna Dale, Russell C. de Gusmão, Claudio M. Friedman, Jennifer Fung, Victor S.C. King, Mary D. Mohammad, Shekeeb S. Rohena, Luis Waugh, Jeff L. Toro, Camilo Lucy Raymond, F. Topf, Maya Coubes, Philippe Gorman, Kathleen M. Kurian, Manju A. Département de Neurochirurgie Unité des Pathologies Cérébrales Résistantes Unité de Recherche sur les Comportements et Mouvements Anormaux Hôpital Gui de Chauliac Centre Hospitalier Régional Montpellier Montpellier France Faculté de médecine Université de Montpellier France Complex Motor Disorder Service Children’s Neurosciences Department Evelina London Children’s Hospital Guy’s and St Thomas’ NHS Foundation Trust London United Kingdom King’s Health Partners London United Kingdom Molecular Neurosciences Developmental Neurosciences UCL Great Ormond Street Institute of Child Health London United Kingdom Institute of Structural and Molecular Biology Department of Biological Sciences Birkbeck College University of London London United Kingdom Developmental Imaging and Biophysics UCL Great Ormond Street Institute of Child Health London United Kingdom Department of Neurology Great Ormond Street Hospital London United Kingdom Department of Haematology NHS Blood and Transplant Centre University of Cambridge Cambridge United Kingdom European IBM Systems Center Montpellier France Clinical Genetics Royal Devon and Exeter NHS Foundation Trust Exeter United Kingdom Division of Medical Genetics Department of Pediatrics Stanford University Palo AltoCA United States McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine BaltimoreMD United States Human Motor Control Section National Institute of Neurological Disorders and Stroke National Institutes of Health BethesdaMD United States Unité de Soins Intensifs et Réanimation Pédiatrique et Néonatale Hôpital Universitaire de Montpellier Montpellier France Department of Clinical and Movement Neurosciences UCL Queen Square Institute of Neurology London United Kingdom Department of Neurology University Hospital Heidelberg Heidelberg Germany Division of Medical Genetics Department of Pediatrics Weill Cornell Medical College New YorkNY United States Institute of Biomedical and Clinica

Heterozygous mutations in KMT2B are associated with an early-onset, progressive and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5–37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke Fahn Marsden’s Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at 6 months, 1 year and last follow-up (motor, P = 0.001, P = 0.004, and P = 0.012;disability, P = 0.009, P = 0.002 and P = 0.012). At 1 year post-deep brain stimulation, 450% of subjects showed BFMDRS-M and BFMDRS-D improvements of 430%. In the long-

关键词： Risk assessment

Consensus with State Obfuscation: an Application to Speed Advisory Systems

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Consensus with State Obfuscation: an Application to Speed Ad...

IEEE International Conference on Intelligent Transportation Systems

作者： Wynita Griggs Giovanni Russo Robert Shorten University College Dublin School of Electrical Electronic and Communications Engineering IBM Research Ireland Optimisation and Control Group

ISBN: (纸本)9781509018901

In this paper, we present a new speed advisory system (SAS). The system provides indications to drivers which, if followed, guide the network of vehicles towards a common speed. The SAS we present is distributed and one of its key features is that the vehicles are guided towards the common speed by receiving obfuscated information. Essentially, this means that the desired network behaviour is achieved and, at the same time, there is no vehicle in the network that obtains clear knowledge of the state of any other vehicle. This results in a privacy-preserving feature which is particularly useful in smart cities applications, where drivers might not be willing to share their transient state with others.

关键词： Vehicles Base stations Roads Algorithm design and analysis Acceleration White noise Convergence

Aggregation of Energetic Flexibility Using Zonotopes

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Aggregation of Energetic Flexibility Using Zonotopes

IEEE Annual Conference on Decision and control

作者： Fabian L. Muller Olle Sundstrom Jacint Szabo John Lygeros Automatic Control Laboratory Swiss Federal Institute of Technology Zurich Switzerland and IBM Research Zurich Switzerland SmartGrid group at IBM Research Zurich Switzerland Optimization group at IBM Research Zurich Switzerland Automatic Control Laboratory Swiss Federal Institute of Technology Zurich Switzerland

ISBN: (纸本)9781479978878

The importance of energetic flexibility of distributed energy resources grows with the share of renewable generation in the power grid. However, the quantitative description and aggregation of flexible resources is challenging. This work proposes the use of zonotopes, a subclass of polytopes, to approximate flexibility. It is shown how optimal zonotopic approximations of flexibility can be computed efficiently for different objectives, and that the aggregation of those sets is tractable with regard to memory and computational complexity for long planning horizons and large populations of systems. In addition, we describe synergistic behavior exhibited by the aggregation of flexibility and illustrate that zonotopes can partly capture these synergy effects.

关键词： agglomeration Synergism distributed energy resources Power grids Polytopes complexity classes SETTING

Aerosol size distribution measurement using a SMPS: Scanning mode and uncertainty analysis

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Aerosol size distribution measurement using a SMPS: Scanning...

IEEE Instrumentation and Measurement Technology Conference

作者： L. Coquelin N. Fischer L. Le Brusquet G. Fleury C. Motzkus F. Gensdarmes Optimization and Control Group of IBM Research Ireland Dublin Ireland London School of Commerce Associate College of Cardiff Metropolitan University UK Department of Applied Physics Royal Institute of Technology Stockholm Sweden National Laboratory of Metrology and Testing France Institut de Radioprotection et de Surete Nucleaire Fontenay-aux-Roses ÃŽle-de-France FR

A model to simulate SMPS (Scanning Mobility Particle Sizer) measurement and the associated uncertainty analysis when axial DMA (Differential Mobility Analyser) classifier operates under scanning mode conditions is described. Starting from simulated SMPS raw data, a fast estimation of aerosol size distribution measurement using regularization technique is performed. Then, global sensitivity analysis is used to discriminate significant parameters of the system and, as a preliminary result, a 95% confidence region is obtained by Monte Carlo simulations on an atmospheric aerosol size distribution.

关键词： Aerosols Atmospheric measurements Uncertainty Vectors Switched-mode power supply Atmospheric modeling Particle measurements