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arXiv 2024年

作者： Picard-Weibel, Antoine Moscoviz, Roman Guedj, Benjamin Inria France Centre International de Recherche Sur l’Eau et l’Environnement SUEZ France Department of Computer Science Centre for Artificial Intelligence University College London United Kingdom

PAC-Bayes learning is a comprehensive setting for (i) studying the generalisation ability of learning algorithms and (ii) deriving new learning algorithms by optimising a generalisation bound. However, optimising generalisation bounds might not always be viable for tractable or computational reasons, or both. For example, iteratively querying the empirical risk might prove computationally expensive. In response, we introduce a novel principled strategy for building an iterative learning algorithm via the optimisation of a sequence of surrogate training objectives, inherited from PAC-Bayes generalisation bounds. The key argument is to replace the empirical risk (seen as a function of hypotheses) in the generalisation bound by its projection onto a constructible low dimensional functional space: these projections can be queried much more efficiently than the initial risk. On top of providing that generic recipe for learning via surrogate PAC-Bayes bounds, we (i) contribute theoretical results establishing that iteratively optimising our surrogates implies the optimisation of the original generalisation bounds, (ii) instantiate this strategy to the framework of meta-learning, introducing a meta-objective offering a closed form expression for meta-gradient, (iii) illustrate our approach with numerical experiments inspired by an industrial biochemical problem. © 2024, CC BY-NC-SA.

关键词： optimization algorithms

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Resilient Energy Management for Residential Communities under Grid Outages

Resilient Energy Management for Residential Communities unde...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Fangfei Zhang Zhao Yang Dong Fengji Luo Gianluca Ranzi Yan Xu School of Electrical and Telecommunication The University of New South Wales Sydney Australia School of Civil Engineering The University of Sydney Sydney Australia School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

The ever-increasing power demand and more frequent extreme weather results in increasing number of grid outage events. This paper proposes a resilient energy management scheme for a building community prototype installed with community-scale photovoltaic solar panel and Battery Energy Storage System (BESS), aiming to enhance self-power supply capability of the community during the grid outage period. The scheme harnesses the community-scale renewable energy source and Battery Energy Storage System (BESS), and flexibility of building load shifting to maximize a warfare objective, i.e. the sum of served load of all buildings in the community over the grid outage horizon. An optimization algorithm previously proposed by the authors - Natural Aggregation Algorithm (NAA) is applied to solve the energy management model. Simulations are conducted based on the Australian "Smart Grid, Smart City" dataset to validate the effectiveness of the proposed method.

关键词： Smart building demand response natural aggregation algorithm demand side management smart grid Demand-Responsive Smart grids Energy management Demand-side management intelligent buildings battery energy storage system Communities residential communities Power demand grids Grid Buildings optimization algorithms

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Novel Model Calibration Method Based on Differential Evolution Used for SCR Model Fitting

Novel Model Calibration Method Based on Differential Evoluti...

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IEEE International Symposium on Design and Diagnostics of Electronic Circuits & Systems

作者： Tomas Napravnik Premysl Ziska Jiri Jakovenko Department of Microelectronics Faculty of Electrical Engineering CTU in Prague ASICentrum s.r.o.

ISBN: (纸本)9781467361354

This paper presents the possibility to use modern and very effective optimization algorithm called differential evolutionary optimization algorithm (DEOA) enhanced by Nelder-Mead Simplex algorithm (NMSA) for fully automatic ESD protection model calibration. This novel method requires only TLP measurement of specific protection device for optimal model calibration. Presented approach is currently being actively developed by authors and tested for various ESD protection devices to verify its robustness. It was in past successfully tested on electrostatic discharge (ESD) MOSFET [1]. Description of used macro-model and novel calibration method along with results of ESD Silicon-controlled rectifier (SCR) macro-model calibration to empirical data are presented.

关键词： safety devices CALIBRATION METHODS optimization algorithms macro model Thyristors electrostatic discharge electrostatic discharge protection mode matching empirical data EMERGENCY SHUTDOWN DEVICE Education for sustainability Nelder-Mead Simplex Algorithm

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An Evolutive Approach for Designing Thermal and Performance-Aware Heterogeneous 3D-NoCs

An Evolutive Approach for Designing Thermal and Performance-...

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Symposium on Integrated Circuits and Systems Design

作者： Johanna Sepulveda Guy Gogniat Ricardo Pires Wang Chau Marius Strum Microelectronics Laboratory LME University of Sao Paulo Information and Communication Science and Technology Laboratory Lab-STICC Universite Bretagne Sud

ISBN: (纸本)9781479911301

Three dimensional Multiprocessor System-on-Chip (3D-MPSoC) adoption. It is characterized by the integration of a large amount of hardware components on a single multilayer chip. However, heating is one of the major pitfalls of the 3D-MPSoCs. Three dimensional Network-on-Chip (3D-NoC) is used as the communication structure of 3D-MPSoCs. Its main role in system operation and performance makes the optimal 3D-NoC design a critical task. Final 3D-NoC configuration must fulfill all the application requirements and heating constraints of the system. Topology and mapping are some of the most critical parameters in 3D-NoC design, strongly influencing the 3D-MPSoC performance and cost. 3D-NoC topology and mapping has been solved for single application systems on homogeneous 3D-NoCs using single and multi-objective optimization algorithms. In this paper we use a multi-objective immune algorithm (MIA), to solve the multi-application 3D-NoC topology and mapping problems. Latency and power consumption are adopted as the target multi-objective functions constrained by the heating function. Our strategy has been applied on 8 3D-MPSoC benchmarks. Their final 3D-NoC configurations have up to 73% power and 42% latency enhancement when compared to previous reported results.

关键词： communication structure MAPPING(MATHEMATICAL) computer utility Operating systems topology optimization algorithms Power consumption Latency Heating

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Coordination Strategy for Harmonic Compensation using Multiple Distributed Resources

Coordination Strategy for Harmonic Compensation using Multip...

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IEEE Power and Energy Society General Meeting

作者： Hyun-Koo Kang Il-Yop Chung Seung-Il Moon School of Electrical Eng 301-Dong Seoul National University Seoul 151-742 S. Korea School of Electrical Engineering Kookmin University Seoul Korea

ISBN: (纸本)9781467327275

Nowadays, due to the development of power electronic technology, nonlinear electronic loads using power electronic circuits are widespread in power grids. The loads can consume more reliable and high-quality power through the power conversion circuits. However, the power grids can suffer significant harmonic currents caused by these power electronic circuits. This paper proposes a new harmonic compensation method of utilizing multiple power inverters that integrate distributed resources (DRs) to a microgrid. Therefore, harmonic problems can be solved without installing additional power filters to the grids. To coordinate multiple various DGs such as small distributed generators (DGs) and battery energy storage systems (BESSs), the proposed method considers multiple objectives in terms of energy cost, generation margins of DGs and energy margins of BESSs. The optimization algorithm is constructed based on fuzzy multi-objective optimization model so that the proposed method assigns the additional output power of each DR that is needed for harmonic elimination. Simulations of the suggested coordination strategy for harmonic compensation using multiple DRs are conducted in the microgrid including two inverter-based DGs and one BESS to verify the performance of the proposed method.

关键词： Harmonic compensation Power system harmonics Multiple distributed resources control Multi-objective optimization. harmonic compensation Power grids Output power distributed energy resources Electronic circuits MICRO-GRID Electric circuits Energy sources Power conversion optimization algorithms Power (Psychology) DR

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Reordering Buffers with Logarithmic Diameter Dependency for Trees 17

Reordering Buffers with Logarithmic Diameter Dependency for ...

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Annual ACM-Society for Industrial and Applied Mathmatics Symposium on Discrete algorithms

作者： Matthias Englert Harald Racke Department of Computer Science and Centre for Discrete Mathematics and its Applications (DIMAP) University of Warwick Department of Informatics Technical University Munich

ISBN: (纸本)9781510836358

In the reordering buffer problem a sequence of items located in a metric space arrive online, and have to be processed by a single server moving within the metric space. At any point in time, the first k still unprocessed items from the sequence are available for processing and the server has to select one of these items and process it by visiting its location. The goal is to process all items while minimizing the total distance the server moves. Englert, Racke, Westermann (STOC'07) gave a deterministic O(D·log k)-competitive online algorithm for weighted tree metrics with hop-diameter D. We improve the analysis of this algorithm and significantly improve the dependency on D. Specifically, we show that the algorithm is in fact O(log D+log k)-competitive. Our analysis is quite robust. Even when an optimal algorithm, to which we compare the online algorithm, is allowed to choose between the first h > k unprocessed items, the online algorithm is still O(h·(log D+log h)/k)-competitive. For h = (1 + ε)·k, with constant ε > 0, this is optimal. Our results also imply better competitive ratio for general metric spaces, improving the randomized O(log n · log~2 k) result for n-point metric spaces from STOC'07 to O(log n·log k).

关键词： metric space Servers algorithms optimization algorithms DEPENDENCE analysis of algorithm Logarithmic

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UWB Dipole Antenna optimization with Neural Network Tuned Algorithm

UWB Dipole Antenna Optimization with Neural Network Tuned Al...

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European Conference on Antennas and Propagation

作者： Martin Mudroch Petr Cerny Pavel Hazdra Milos Mazanek Department of Electromagnetic Field Czech Technical University in Prague

This paper describes an improved approach in an optimization of different UWB dipole antennas. The new procedure of tuning different optimization algorithms is presented. Fine-tuning of optimizing algorithm which is n... 详细信息

ISBN: (纸本)9781424447534

关键词： optimization algorithms DIPOLE ANTENNAS Ultra wideband technologies tuning Neural network

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OPTIMIZING THE CGMS UPPER BOUND ON RAMSEY NUMBERS

arXiv

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arXiv 2024年

作者： Gupta, Parth Ndiaye, Ndiamé Norin, Sergey Wei, Louis Department of Mathematics and Statistics McGill University MontréalQC Canada

In a recent breakthrough Campos, Griffiths, Morris and Sahasrabudhe obtained the first exponential improvement of the upper bound on the diagonal Ramsey numbers since 1935. We shorten their proof, replacing the underlying book algorithm with a simple inductive statement. This modification allows us to give a very short proof of an improved upper bound on the off-diagonal Ramsey numbers, which extends to the multicolor setting, clarify the dependence of the bounds on underlying parameters and optimize these parameters, obtaining, in particular, an upper bound R(k, k) ≤ (3.8)k+o(k)on the diagonal Ramsey numbers. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Curvature and Optimal algorithms for Learning and Minimizing Submodular Functions 13

Curvature and Optimal Algorithms for Learning and Minimizing...

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Annual Conference on Neural Information Processing Systems

作者： Rishabh Iyer Stefanie Jegelka Jeff Bilmes University of Washington Dept. of EE Seattle U.S.A. University of California Dept. of EECS Berkeley U.S.A.

ISBN: (纸本)9781632660244

We investigate three related and important problems connected to machine learning: approximating a submodular function everywhere, learning a submodular function (in a PAC-like setting), and constrained minimization of submodular functions. We show that the complexity of all three problems depends on the "curvature" of the submodular function, and provide lower and upper bounds that refine and improve previous results. Our proof techniques are fairly generic. We either use a black-box transformation of the function (for approximation and learning), or a transformation of algorithms to use an appropriate surrogate function (for minimization). Curiously, curvature has been known to influence approximations for submodular maximization, but its effect on minimization, approximation and learning has hitherto been open. We complete this picture, and also support our theoretical claims by empirical results.

关键词： Learning optimization algorithms Curvature Function approximation

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Jamming the Random Lorentz Gas: Configurational Entropy, Crunching Geometry, and Critical Universality

arXiv

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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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