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Simulation Conference

作者： Giulia Pedrielli Andrea Matta Arianna Alfieri National University of Singapore SG Singapore Shanghai Jiao Tong University Shanghai China Politecnico di Torino Torino Italy

ISBN: (纸本)9781467397414

optimization of discrete event systems conventionally uses simulation as a black-box oracle to estimate performance at design points generated by a separate optimization algorithm. This decoupled approach fails to exploit an important advantage: simulation codes are white-boxes, at least to their creators. In fact, the full integration of the simulation model and the optimization algorithm is possible in many situations. In this contribution, a framework previously proposed by the authors, based on the mathematical programming methodology, is presented under a wider perspective. We show how to derive mathematical models for solving optimization problems while simultaneously considering the dynamics of the system to be optimized. Concerning the solution methodology, we refer back to retrospective optimization (RO) and sample path optimization (SPO) settings. Advantages and drawbacks deriving from the use of mathematical programming as work models within the RO (SPO) framework will be analyzed and its convergence properties will be discussed.

关键词： mathematical programming Simulation models optimization algorithms design point optimization methods REPORTABLE QUANTITIES simulating

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Novel optimisation algorithm of electrical machines

Novel optimisation algorithm of electrical machines

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8th IET International Conference on Power Electronics, Machines and Drives (PEMD 2016)

作者： Zheng Tan N. J. Baker Wenping Cao Electrical Power Research Group School of Electrical and Electronic Engineering Newcastle University NE1 7RU UK Newcastle University Newcastle upon Tyne Tyne and Wear GB Aston University United Kingdom

ISBN: (纸本)9781510825666

This paper presents a case study for multi-variable and multimodal design optimisation of a doubly fed induction generator (DFIG) based on surrogate-model optimisation algorithm. The DFIG's winding of stator and rotor are optimised to obtain higher efficiency for rewinding purposes. First, a Latin hypercube design is selected as the design of experiments to obtain sampling points. Then, the surrogate model is constructed using Kriging Model (KRG) method based on the Latin hypercube design. Finally, the particle swarm optimisation algorithm is applied in conjunction with the finite element method to achieve the machine design optimisation.

关键词： DFIG Kriging model Particle swarm optimisation Particle swarm optimization optimization algorithms Optimal design Finite element method electric machines double-fed induction generator Case studies Hypercube Latin

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On Uncertainty Quantification for Near-Bayes Optimal algorithms

arXiv

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arXiv 2024年

作者： Wang, Ziyu Holmes, Chris University of Oxford United Kingdom

Bayesian modelling allows for the quantification of predictive uncertainty which is crucial in safety-critical applications. Yet for many machine learning algorithms, it is difficult to construct or implement their Bayesian counterpart. In this work we present a promising approach to address this challenge, based on the hypothesis that commonly used ML algorithms are efficient across a wide variety of tasks and may thus be near Bayes-optimal w.r.t. an unknown task distribution. We prove that it is possible to recover the Bayesian posterior defined by the task distribution, which is unknown but optimal in this setting, by building a martingale posterior using the algorithm. We further propose a practical uncertainty quantification method that apply to general ML algorithms. Experiments based on a variety of neural network (NN) and non-NN algorithms demonstrate the efficacy of our method. © 2024, CC BY.

关键词： optimization algorithms

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Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks

arXiv

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arXiv 2024年

作者： Tsilivis, Nikolaos Vardi, Gal Kempe, Julia New York University United States Weizmann Institute of Science Israel Meta FAIR United States

We study the implicit bias of the general family of steepest descent algorithms, which includes gradient descent, sign descent and coordinate descent, in deep homogeneous neural networks. We prove that an algorithm-dependent geometric margin starts increasing once the networks reach perfect training accuracy and characterize the late-stage bias of the algorithms. In particular, we define a generalized notion of stationarity for optimization problems and show that the algorithms progressively reduce a (generalized) Bregman divergence, which quantifies proximity to such stationary points of a margin-maximization problem. We then experimentally zoom into the trajectories of neural networks optimized with various steepest descent algorithms, highlighting connections to the implicit bias of Adam. © 2024, CC BY.

关键词： optimization algorithms

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Thompson Sampling for 1-Dimensional Exponential Family Bandits 13

Thompson Sampling for 1-Dimensional Exponential Family Bandi...

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Annual Conference on Neural Information Processing Systems

作者： Nathaniel Korda Emilie Kaufmann Remi Munos INRIA Lille - Nord Europe Team SequeL Institut Mines-Telecom Telecom ParisTech

ISBN: (纸本)9781632660244

Thompson Sampling has been demonstrated in many complex bandit models, however the theoretical guarantees available for the parametric multi-armed bandit are still limited to the Bernoulli case. Here we extend them by proving asymptotic optimality of the algorithm using the Jeffreys prior for 1-dimensional exponential family bandits. Our proof builds on previous work, but also makes extensive use of closed forms for Kullback-Leibler divergence and Fisher information (through the Jeffreys prior) available in an exponential family. This allow us to give a finite time exponential concentration inequality for posterior distributions on exponential families that may be of interest in its own right. Moreover our analysis covers some distributions for which no optimistic algorithm has yet been proposed, including heavy-tailed exponential families.

关键词： Exponential family closed forms Fisher information optimization algorithms Proof Sampling Posterior distribution

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Probabilistic Iterative Hard Thresholding for Sparse Learning

arXiv

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arXiv 2024年

作者： Bergamaschi, Matteo Cristofari, Andrea Kungurtsev, Vyacheslav Rinaldi, Francesco Department of Mathematics "Tullio Levi-Civita" University of Padua Italy Department of Civil Engineering and Computer Science Engineering University of Rome "Tor Vergata" Italy Czech Technical University in Prague Czech Republic

For statistical modeling wherein the data regime is unfavorable in terms of dimensionality relative to the sample size, finding hidden sparsity in the ground truth can be critical in formulating an accurate statistical model. The so-called "0 norm", which counts the number of non-zero components in a vector, is a strong reliable mechanism of enforcing sparsity when incorporated into an optimization problem. However, in big data settings wherein noisy estimates of the gradient must be evaluated out of computational necessity, the literature is scant on methods that reliably converge. In this paper we present an approach towards solving expectation objective optimization problems with cardinality constraints. We prove convergence of the underlying stochastic process, and demonstrate the performance on two Machine Learning problems. © 2024, CC BY.

关键词： optimization algorithms

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Power Allocation for Cognitive Relay Systems

Power Allocation for Cognitive Relay Systems

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World Congress on Intelligent Control and Automation

作者： Lina. Fan Jinkuan. Wang Jing. Gao Engineer Optimization& Smart Antenna Institute Northeastern University at Qinhuangdao Qin huang dao China Northeastern University Shenyang China

ISBN: (纸本)9781467384155

In this paper, the power allocation problem for Orthogonal frequency division multiplexing (OFDM)-based relay cognitive radio systems is investigated. An optimization power allocation algorithm is developed. In the algorithm, objective function is studied and simplified. The channel capacity of secondary users is maximized while keeping the total interference introduced to primary user less than a given threshold with a specified power. Compared to the optimal algorithm and the average power allocation algorithm, the proposed algorithm is close to the optimal algorithm and has a better performance than average power allocation algorithm, and has low complexity.

关键词： Relays Resource management Cognitive radio Interference Signal processing algorithms OFDM optimization Signal processing algorithms Relay systems power splitting relay Cognitive radio management of resources Orthogonal frequency division multiplexing optimization algorithms Allocation Algorithm Channel capacity interference average power

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Fixed-Point Constrained Model Predictive Control of Spacecraft Attitude

Fixed-Point Constrained Model Predictive Control of Spacecra...

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American Control Conference

作者： Alberto Guiggiani Ilya Kolmanovsky Panagiotis Patrinos Alberto Bemporad IMT Institute for Advanced Studies Lucca Italy Department of Aerospace Engineering University of Michigan Ann Arbor MI 48109

ISBN: (纸本)9781479917730

The paper develops a Model Predictive Controller for constrained control of spacecraft attitude with reaction wheel actuators. The controller exploits a special formulation of the cost with the reference governor like term, a low complexity addition of integral action to guarantee offset-free tracking of attitude set points, and an online optimization algorithm for the solution of the Quadratic Programming problem which is tailored to run in fixed-point arithmetic. Simulations on a nonlinear space-craft model demonstrate that the MPC controller achieves good tracking performance while satisfying reaction wheel torque constraints. The controller also has low computational complexity and is suitable for implementation in spacecrafts with fixed-point processors.

关键词： Spacecraft optimization algorithms Fixed point reaction wheels Predictive controllers visual tracking Attitude Model predictive control Predictive control Controllers complexity classes spacecraft control

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Low-rank Modifications of Riccati Factorizations with Applications to Model Predictive Control

Low-rank Modifications of Riccati Factorizations with Applic...

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IEEE Annual Conference on Decision and Control

作者： Isak Nielsen Daniel Ankelhed Daniel Axehill Division of Automatic Control Linkoping University SE-58183 Linkoping Sweden

ISBN: (纸本)9781467357159

In optimization algorithms used for on-line Model Predictive Control (MPC), the main computational effort is spent while solving linear systems of equations to obtain search directions. Hence, it is of greatest interest to solve them efficiently, which commonly is performed using Riccati recursions or generic sparsity exploiting algorithms. The focus in this work is efficient search direction computation for activeset methods. In these methods, the system of equations to be solved in each iteration is only changed by a low-rank modification of the previous one. This highly structured change of the system of equations from one iteration to the next one is an important ingredient in the performance of active-set solvers. It seems very appealing to try to make a structured update of the Riccati factorization, which has not been presented in the literature so far. The main objective of this paper is to present such an algorithm for how to update the Riccati factorization in a structured way in an active-set solver. The result of the work is that the computational complexity of the step direction computation can be significantly reduced for problems with bound constraints on the control signal. This in turn has important implications for the computational performance of active-set solvers used for linear, nonlinear as well as hybrid MPC.

关键词： System of equations Predictive control Solvers on-line model iterative methods Model predictive control computational performance optimization algorithms factorization Linear system of equations Linear system nonlinear model predictive control complexity classes

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Optimal Design of a Special Permanent Magnet Synchronous Machine for Magnetocaloric Refrigeration

Optimal Design of a Special Permanent Magnet Synchronous Mac...

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IEEE Energy Conversion Congress and Exposition

作者： Christophe Espanet Amine Mira Stefan Giurgea Frederic Gustin Christophe Kieffer University of Franche-Comte Femto-ST Intitute ENERGY department Belfort France

ISBN: (纸本)9781479903375

As the refrigeration technology consumes more than 15% of worldwide consumption of electricity, the development of technologies with improved efficiency remains an important challenge. In this context, magnetic refrigeration is an interesting solution and this paper proposes an original magnetic refrigerator. The aim - and also the originality - of this new device is to integrate the magnetocaloric regenerator in the air gap of a Permanent Magnet Synchronous Machine (PMSM). The conventional design of PMSM stator and rotor is presented and the results lead to the construction of a prototype that enables the validation of the proposed concept. Finally, thanks to the coupling of an optimization algorithm and a 2D FEM simulation, the rotor is optimized and the air-gap flux density is greatly improved (from 0.9 T to 1.35 T).

关键词： PMSM coupling Permanent magnet synchronous machines coupling Optimal design Couplings air-gap flux density electricity consumption optimization algorithms AIR GAP refrigeration Creativity

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