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Optimisation mathématique. --- Programmation linéaire --- Linear programming. --- Robust optimization.

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"Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image classification applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. [ . . . ] While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems."--

Machine learning. --- Robust optimization. --- Optimization, Robust --- RO (Robust optimization) --- Mathematical optimization --- Learning, Machine --- Artificial intelligence --- Machine theory --- Machine Learning --- Machine Learning.

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This reprint contains fourteen chosen articles on robust design optimization of electrical machines and devices. Optimization is essential for the research and design of electromechanical devices, especially electrical machines. Finding optimal solutions may lead to cheaper and more efficient production of electrical machines. However, optimizing such a complex system as an electrical machine is a computationally expensive optimization problem, where many physical domains should be considered together. However, a good, practical design should be insensitive to parameter changes and the manufacturing tolerances. The collected papers show how modern artificial intelligence (AI) tools can be used for the robust design optimization of electric machines and electrical devices. The articles which are published in this Special Issue present the latest results of current research fields. Hopefully, the presented models and various application fields will provide useful information for researchers and professionals interested in these techniques themselves or who have other problems from different fields.

Mathematical optimization. --- Robust optimization. --- Optimization, Robust --- RO (Robust optimization) --- Mathematical optimization --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis

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Static hedge portfolios for barrier options are very sensitive with respect to changes of the volatility surface. To prevent potentially significant hedging losses this book develops a static super-replication strategy with market-typical robustness against volatility, skew and liquidity risk as well as model errors. Empirical results and various numerical examples confirm that the static superhedge successfully eliminates the risk of a changing volatility surface. Combined with associated sub-replication strategies this leads to robust price bounds for barrier options which are also relevant in the context of dynamic hedging. The mathematical techniques used to prove appropriate existence, duality and convergence results range from financial mathematics, stochastic and semi-infinite optimization, convex analysis and partial differential equations to semidefinite programming.

Options (Finance) --- Hedging (Finance) --- Speculation --- Financial futures --- Mathematical models. --- Mathematical models --- Barrier Options. --- Robust Optimization. --- Semi-infinite Optimization. --- Semidefinite Programming. --- Static Hedging. --- Stochastic Volatility.

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The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted? Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics. Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.

Robust optimization --- Robust control --- Robust control. --- Robust optimization. --- -330.0151 --- 305.6 --- AA / International- internationaal --- Economics --- -Macroeconomics --- Economic theory --- Political economy --- Risicotheorie, speltheorie. Risicokapitaal. Beslissingsmodellen. --- Mathematical models --- Decision making --- Econometric models --- Kalman filtering --- Macroeconomics --- 330.0151 --- Optimization, Robust --- RO (Robust optimization) --- Mathematical optimization --- Robustness (Control systems) --- Automatic control --- Filtering, Kalman --- Control theory --- Estimation theory --- Prediction theory --- Stochastic processes --- Econometrics --- Risicotheorie, speltheorie. Risicokapitaal. Beslissingsmodellen --- E-books --- Kalman filtering. --- Econometric models. --- Decision making. --- Economics, Mathematical --- Econometría |2 UTDT --- Economía |x Modelos matemáticos |2 UTDT --- Macroeconomics - Decision making