Design space exploration of RF-circuit blocks

ii iii Acknowledgments This thesis was written in the framework of an internship at NXP Semiconductors. It describes the results of a six months master project. I was supervised by Prof. Dr. W.H.A. Schilders of NXP Semiconductors and the Technical University Eindhoven and furthermore, by Dr. ir. J. A. Croon of NXP Semiconductors. Herewith, I want to express my deep gratitude to Prof. Schilders, who has guided me during the project and for proofreading of the thesis. Furthermore, I want to thank Dr. Croon sincerely for the helpful discussions, for the detailed corrections of the thesis and furthermore for the interesting introduction to semiconductor device modeling. Additionally, I want to thank Univ.-Prof. Dipl.-Ing. Dr. H. Gfrerer of the Johannes Kepler University Linz for reviewing this work and for his useful suggestions during the project. iv vContent

Metoda Škálovatelné Pravděpodobnostní Aproximace v aplikacích

This thesis aims to extend the research on the newly developed Scalable Probabilistic Approximation (SPA) method, with emphasis predominantly on classification problems. The SPA method is utilized to discretize continuous stochastic processes and, in conjunction with Bayesian causal inference modeling, leads to a multiobjective optimization problem that is capable of simultaneously resolving both objectives. The solution to this problem is formulated as a supervised machine learning algorithm that is suitable for various classification tasks. Although the algorithm is limited in terms of computational cost, a proposed estimation of the problem, which is closely related to the widely known K-means algorithm, is applicable even for large datasets. Preliminary experiments demonstrate that this framework is adaptable to the selected application of corrosion detection from image data.Cílem této práce je rozšířit výzkum nově vyvinuté metody Škálovatelné Pravděpodobnostní Aproximace (SPA) s důrazem převážně na klasifikační problémy. Metoda SPA je uplatněna k diskretizaci spojitých stochastických procesů a v kombinaci s bayesovským modelováním kauzální inference vede k vícekriteriálnímu optimalizačnímu problému, který umožňuje splnit obě kritéria současně. Řešení tohoto problému je navrženo jako algoritmus strojového učení s učitelem, který je vhodný pro různé klasifikační úlohy. Ačkoli je tento algoritmus omezen z hlediska výpočetní náročnosti, navržený odhad problému, který je úzce spjat s široce známým algoritmem K-means, je použitelný i pro velké soubory dat. Předběžné experimenty ukazují, že tuto metodiku lze přizpůsobit k vybrané aplikaci detekce koroze z obrazových dat.470 - Katedra aplikované matematikyvýborn

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Includes bibliographical references.2022 Fall.Multiobjective problems with a strict importance order over the objectives occur in many real-life scenarios. While Reinforcement Learning (RL) is a promising approach with a great potential to solve many real-life problems, the RL literature focuses primarily on single-objective tasks, and approaches that can directly address multiobjective with importance order have been scarce. The few proposed approach were noted to be heuristics without theoretical guarantees. However, we found that their practical applicability is very limited as they fail to find a good solution even in very common scenarios. In this work, we first investigate these shortcomings of the existing approaches and propose some solutions that could improve their practical performance. Finally, we propose a completely different approach based on policy optimization using our Lexicographic Projection Optimization (LPO) algorithm and show its performance on some benchmark problems

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The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Efficient Learning Machines

Computer scienc

Solution to the generalized lattice point and related problems to disjunctive programming

Issued as Pre-prints [1-5], Progress reports [1-2], Final summary report, and Final technical report, Project no. E-24-67