The theory of self-concordant barriers was introduced by Nesterov and Nemirovskii (1994) in the context of interior-point methods for convex optimisation. Their development is general, elegant and enjoys widespread im...
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The theory of self-concordant barriers was introduced by Nesterov and Nemirovskii (1994) in the context of interior-point methods for convex optimisation. Their development is general, elegant and enjoys widespread implementation in state-of-the-art algorithms. In this paper we exploit the theory of self-concordant functions with application to nonlinear MPC. In particular we construct an invariant terminal constraint set via properties of self-concordant functions. We also extend earlier results on recent red barrier function MPC to nonlinear MPC (model predictive control) with state constraints. We show nominal closed-loop stability for a wide class of nonlinear systems under full state feedback.
作者：
Iida, FumiyaPfeifer, RolfSteels, LucKuniyoshi, YasuoUniversity of Zurich
Artificial Intelligence Laboratory Department of Informatics Andreasstr. 15 Zurich Switzerland Vrije Universiteit Brussel
Artificial Intelligence Laboratory and Sony Computer Science Laboratory Department of Informatics Pleinlaan 2 Brussels Belgium University of Tokyo
School of Information Science and Technology Dept. of Mechano-Informatics Laboratory for Intelligent Systems and Informatics Engineering Bldg. 8 7-3-1 Hongo Bunkyo-kuTokyo Japan
Phase transition phenomena are often observed in various kinds of fields of engineering, including chemical, biological and fluid engineering. To construct precise models of phase transitions and their analysis are on...
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The purpose of this paper is to construct the estimator system for the stochastic parabolic system with the free boundary. The free boundary problem is one of important nonlinear problems in engineering. The intrinsic...
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Autonomic computing - self-configuring, self-healing, self-optimizing applications, systems and networks - is widely believed to be a promising solution to ever-increasing system complexity and the spiraling costs of ...
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Autonomic computing - self-configuring, self-healing, self-optimizing applications, systems and networks - is widely believed to be a promising solution to ever-increasing system complexity and the spiraling costs of human system management as systems scale to global proportions. Most results to date, however, suggest ways to architect new software constructed from the ground up as autonomic systems, whereas in the real world organizations continue to use stovepipe legacy systems and/or build ''systems of systems'' that draw from a gamut of new and legacy components involving disparate technologies from numerous vendors. Our goal is to retrofit autonomic computing onto such systems, externally, without any need to understand or modify the code, and in many cases even when it is impossible to recompile. We present a meta-architecture implemented as active middleware infrastructure to explicitly add autonomic services via an attached feedback loop that provides continual monitoring and, as needed, reconfiguration and/or repair. Our lightweight design and separation of concerns enables easy adoption of individual components, as well as the full infrastructure, for use with a large variety of legacy, new systems, and systems of systems. We summarize several experiments spanning multiple domains.
Huge amounts of data are stored in autonomous, geographically distributed sources. The discovery of previously unknown, implicit and valuable knowledge is a key aspect of the exploitation of such sources. In recent ye...
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Huge amounts of data are stored in autonomous, geographically distributed sources. The discovery of previously unknown, implicit and valuable knowledge is a key aspect of the exploitation of such sources. In recent years several approaches to knowledge discovery and data mining, and in particular to clustering, have been developed, but only a few of them are designed for distributed data sources. We propose a novel distributed clustering algorithm based on non-parametric kernel density estimation, which takes into account the issues of privacy and communication costs that arise in a distributed environment.
作者：
Fiege, LudgerGärtner, Felix C.Kasten, OliverZeidler, Andreas
Department of Computer Science Databases Distributed System Group D-64283 Darmstadt Germany
School of Computer and Communication Sciences Distributed Programming Laboratory CH-1015 Lausanne Switzerland
Department of Computer Science Distributed Systems Group CH-8092 Zurich Switzerland
Publish/subscribe (pub/sub) is considered a valuable middleware architecture that proliferates loose coupling and leverages reconfigurability and evolution. Up to now, existing pub/sub middleware was optimized for sta...
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The purpose of Generic Evolutionary Algorithms programming Library (GEA1) system is to provide researchers with an easy-to-use, widely applicable and extendable programming library which solves real-world optimization...
The purpose of Generic Evolutionary Algorithms programming Library (GEA1) system is to provide researchers with an easy-to-use, widely applicable and extendable programming library which solves real-world optimization problems by means of evolutionary algorithms. It contains algorithms for various evolutionary methods, implemented genetic operators for the most common representation forms for individuals, various selection methods, and examples on how to use and expand the library. All these functions assure that GEA can be effectively applied on many problems. GraphGEA is a graphical user interface to GEA written with the GTK API. The numerous parameters of the evolutionary algorithm can be set in appropriate dialog boxes. The program also checks the correctness of the parameters and saving/restoring of parameter sets is also possible. The selected evolutionary algorithm can be executed interactively on the specified optimization problem through the graphical user interface of GraphGEA, and the results and behavior of the EA can be observed on several selected graphs and drawings. While the main purpose of GEA is solving optimization problems, that of GraphGEA is education and analysis. It can be of great help for students understanding the characteristics of evolutionary algorithms and researchers of the area can use it to analyze an EA's behavior on particular problems.
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