This book treats dynamic stability of structures under nonconservative forces. it is not a mathematics-based, but rather a dynamics-phenomena-oriented monograph, written with a full experimental background. Starting with fundamentals on stability of columns under nonconservative forces, it then deals with the divergence of Euler's column under a dead (conservative) loading from a view point of dynamic stability. Three experiments with cantilevered columns under a rocket-based follower force are described to present the verifiability of nonconservative problems of structural stability. Dynamic stability of columns under pulsating forces is discussed through analog experiments, and by analytical and experimental procedures together with related theories. Throughout the volume the authors retain a good balance between theory and experiments on dynamic stability of columns under nonconservative loading, offering a new window to dynamic stability of structures, promoting student- and scientist-friendly experiments.

In this monograph, the authors develop a comprehensive approach for the mathematical analysis of a wide array of problems involving moving interfaces. It includes an in-depth study of abstract quasilinear parabolic evolution equations, elliptic and parabolic boundary value problems, transmission problems, one- and two-phase Stokes problems, and the equations of incompressible viscous one- and two-phase fluid flows. The theory of maximal regularity, an essential element, is also fully developed. The authors present a modern approach based on powerful tools in classical analysis, functional analysis, and vector-valued harmonic analysis. The theory is applied to problems in two-phase fluid dynamics and phase transitions, one-phase generalized Newtonian fluids, nematic liquid crystal flows, Maxwell-Stefan diffusion, and a variety of geometric evolution equations. The book also includes a discussion of the underlying physical and thermodynamic principles governing the equations of fluid flows and phase transitions, and an exposition of the geometry of moving hypersurfaces.

This book offers an ideal introduction to singular perturbation problems, and a valuable guide for researchers in the field of differential equations. It also includes chapters on new contributions to both fields: differential equations and singular perturbation problems. Written by experts who are active researchers in the related fields, the book serves as a comprehensive source of information on the underlying ideas in the construction of numerical methods to address different classes of problems with solutions of different behaviors, which will ultimately help researchers to design and assess numerical methods for solving new problems. All the chapters presented in the volume are complemented by illustrations in the form of tables and graphs.

This book introduces methods of robust optimization in multivariate adaptive regression splines (MARS) and Conic MARS in order to handle uncertainty and non-linearity. The proposed techniques are implemented and explained in two-model regulatory systems that can be found in the financial sector and in the contexts of banking, environmental protection, system biology and medicine. The book provides necessary background information on multi-model regulatory networks, optimization and regression. It presents the theory of and approaches to robust (conic) multivariate adaptive regression splines - R(C)MARS - and robust (conic) generalized partial linear models - R(C)GPLM - under polyhedral uncertainty. Further, it introduces spline regression models for multi-model regulatory networks and interprets (C)MARS results based on different datasets for the implementation. It explains robust optimization in these models in terms of both the theory and methodology. In this context it studies R(C)MARS results with different uncertainty scenarios for a numerical example. Lastly, the book demonstrates the implementation of the method in a number of applications from the financial, energy, and environmental sectors, and provides an outlook on future research.

This book presents the derivation of the fluctuation theorems with divergent entropy production and their application to fundamental problems in statistical physics. It explores the two basic aspects of the fluctuation theorems: i) Applicability in extreme situations with divergent entropy production, concluding that the fluctuation theorems remain valid under the notion of absolute irreversibility, and ii) utility in the investigation of classical enigmas in the framework of statistical physics, i.e., Gibbs and Loschmidt paradoxes. The book offers readers an overview of the research in fundamental statistical physics. Firstly it briefly but skillfully reviews the modern development of fluctuation theorems to found the key theme of the book. Secondly it concisely discusses historical issues of statistical physics in chronological order, along with the key literature in the field. They help readers easily follow the key developments in the fundamental research of statistical physics.

This book is a substantially revised and expanded edition reflecting major developments in stochastic numerics since the first edition was published in 2004. The new topics, in particular, include mean-square and weak approximations in the case of nonglobally Lipschitz coefficients of Stochastic Differential Equations (SDEs) including the concept of rejecting trajectories; conditional probabilistic representations and their application to practical variance reduction using regression methods; multi-level Monte Carlo method; computing ergodic limits and additional classes of geometric integrators used in molecular dynamics; numerical methods for FBSDEs; approximation of parabolic SPDEs and nonlinear filtering problem based on the method of characteristics. SDEs have many applications in the natural sciences and in finance. Besides, the employment of probabilistic representations together with the Monte Carlo technique allows us to reduce the solution of multi-dimensional problems for partial differential equations to the integration of stochastic equations. This approach leads to powerful computational mathematics that is presented in the treatise. Many special schemes for SDEs are presented. In the second part of the book numerical methods for solving complicated problems for partial differential equations occurring in practical applications, both linear and nonlinear, are constructed. All the methods are presented with proofs and hence founded on rigorous reasoning, thus giving the book textbook potential. An overwhelming majority of the methods are accompanied by the corresponding numerical algorithms which are ready for implementation in practice. The book addresses researchers and graduate students in numerical analysis, applied probability, physics, chemistry, and engineering as well as mathematical biology and financial mathematics.

With contributions from a number of pioneering researchers in the field, this collection is aimed not only at researchers and scientists in nonlinear dynamics but also at a broader audience interested in understanding and exploring how modern chaos theory has developed since the days of Poincare. This book was motivated by and is an outcome of the CHAOS 2015 meeting held at the Henri Poincare Institute in Paris, which provided a perfect opportunity to gain inspiration and discuss new perspectives on the history, development and modern aspects of chaos theory. Henri Poincare is remembered as a great mind in mathematics, physics and astronomy. His works, well beyond their rigorous mathematical and analytical style, are known for their deep insights into science and research in general, and the philosophy of science in particular. The Poincare conjecture (only proved in 2006) along with his work on the three-body problem are considered to be the foundation of modern chaos theory.

Following the recent financial crisis, risk management in financial institutions, particularly in banks, has attracted widespread attention and discussion. Novel modeling approaches and courses to educate future professionals in industry, government, and academia are of timely relevance. This book introduces an innovative concept and methodology developed by the authors: active risk management. It is suitable for graduate students in mathematical finance/financial engineering, economics, and statistics as well as for practitioners in the fields of finance and insurance. The book s website features the data sets used in the examples along with various exercises."

This textbook provides concise coverage of the basics of linear and integer programming which, with megatrends toward optimization, machine learning, big data, etc., are becoming fundamental toolkits for data and information science and technology. The authors' approach is accessible to students from almost all fields of engineering, including operations research, statistics, machine learning, control system design, scheduling, formal verification and computer vision. The presentations enables the basis for numerous approaches to solving hard combinatorial optimization problems through randomization and approximation. Readers will learn to cast various problems that may arise in their research as optimization problems, understand the cases where the optimization problem will be linear, choose appropriate solution methods and interpret results appropriately.

The special volume offers a global guide to new concepts and approaches concerning the following topics: reduced basis methods, proper orthogonal decomposition, proper generalized decomposition, approximation theory related to model reduction, learning theory and compressed sensing, stochastic and high-dimensional problems, system-theoretic methods, nonlinear model reduction, reduction of coupled problems/multiphysics, optimization and optimal control, state estimation and control, reduced order models and domain decomposition methods, Krylov-subspace and interpolatory methods, and applications to real industrial and complex problems. The book represents the state of the art in the development of reduced order methods. It contains contributions from internationally respected experts, guaranteeing a wide range of expertise and topics. Further, it reflects an important effor t, carried out over the last 12 years, to build a growing research community in this field. Though not a textbook, some of the chapters can be used as reference materials or lecture notes for classes and tutorials (doctoral schools, master classes).

This book is a collection of selected papers presented at the 10th International Conference on Scientific Computing in Electrical Engineering (SCEE), held in Wuppertal, Germany in 2014. The book is divided into five parts, reflecting the main directions of SCEE 2014: 1. Device Modeling, Electric Circuits and Simulation, 2. Computational Electromagnetics, 3. Coupled Problems, 4. Model Order Reduction, and 5. Uncertainty Quantification. Each part starts with a general introduction followed by the actual papers. The aim of the SCEE 2014 conference was to bring together scientists from academia and industry, mathematicians, electrical engineers, computer scientists, and physicists, with the goal of fostering intensive discussions on industrially relevant mathematical problems, with an emphasis on the modeling and numerical simulation of electronic circuits and devices, electromagnetic fields, and coupled problems. The methodological focus was on model order reduction and uncertainty quantification.

This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in the properties of differential equations estimated from data will find rather less to work with. This book fills that gap.

This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website.

Scholars in various fields are exploring similar ideas to combat indeterminism when conditions are chaotic and prohibit the use of a rigid program approach, even with probabilities. Many do not realize that they are dealing with the same issues that appear between chaos and full order (or stochastic processes) in a phase that lends itself to the same formal treatment. Examples are observed in the development of social systems, in the evaluation of the performance of a corporation or a position in chess, in the perception of artworks. Conceptualization of this treatment requires a better understanding of the category of indeterminism. Confirmation of this is the absence of separation between indeterminism and, especially, uncertainty. One indirect confirmation of this is the lack of a developed concept of the degree of indeterminism.

The author contends that the category of indeterminism has its own meaning dealing with unavoidability. There are several phases in the spectrum of measurement of indeterminism, among which is a phase--a key phase of this book--which requires the introduction of the category of predisposition and a corresponding calculus of predisposition. By means of the aesthetic method, the degree of beauty (ugliness) measures perception by the given subjet of the predisposition for development of observable objects.

In 1967 Walter K. Hayman published 'Research Problems in Function Theory', a list of 141 problems in seven areas of function theory. In the decades following, this list was extended to include two additional areas of complex analysis, updates on progress in solving existing problems, and over 520 research problems from mathematicians worldwide. It became known as 'Hayman's List'. This Fiftieth Anniversary Edition contains the complete 'Hayman's List' for the first time in book form, along with 31 new problems by leading international mathematicians. This list has directed complex analysis research for the last half-century, and the new edition will help guide future research in the subject. The book contains up-to-date information on each problem, gathered from the international mathematics community, and where possible suggests directions for further investigation. Aimed at both early career and established researchers, this book provides the key problems and results needed to progress in the most important research questions in complex analysis, and documents the developments of the past 50 years.

Written by the pioneer and foremost authority on the subject, this new book is both a comprehensive university textbook and professional/research reference on the finite-difference time-domain (FD-TD) computational solution method for Maxwell's equations. It presents in-depth discussions of: The revolutionary Berenger PML absorbing boundary condition; FD-TD modelling of nonlinear, dispersive, and gain optical materials used in lasers and optical microchips; unstructured FD-TD meshes for modelling of complex systems; 2.5-dimensional body-of-revolution FD-TD algorithms; Linear and nonlinear electronic circuit models, including a seamless tie-in to SPICE; Digital signal postprocessing of FD-TD data; FD-TD modelling of microlaser cavities; and FD-TD software development for the latest Intel and Cray massively parallel computers.

Preface. From Integrable Models to Conformal Field Theory via Quantum Groups; L.D. Faddeev. A Brief History of Hidden Quantum Symmetries in Conformal Field Theories; C. Gomez, G. Sierra. q-Deformation of Semisimple and Non-Semisimple Lie Algebras; H. Ruegg. Quantum Algebras and Quantum Groups in q-Special Function Theory; R. Floreanini, L. Vinet. Lectures in Topological Quantum Field Theory; D.S. Freed. Canonical Quantum Gravity and the Problem of Time; C.J. Isham. Higher Symmetries in Lower-Dimensional Models; R. Jackiw. Three Lectures on Supersymmetry and Extended Objects; P.K. Townsend. The Geometric Phase in Quantum Physics; A. Bohm. Quantum Mechanics on Phase Space; M. Gadella. Lie Groups and Solutions of Nonlinear Partial Differential Equations; P. Winternitz. Index.

1 Reference Material.- 1.1 Introduction.- 1.2 Singular Integral Equations.- 1.3 Improper Integrals.- 1.3.1 The Gamma function.- 1.3.2 The Beta function.- 1.3.3 Another important improper integral.- 1.3.4 A few integral identities.- 1.4 The Lebesgue Integral.- 1.5 Cauchy Principal Value for Integrals.- 1.6 The Hadamard Finite Part.- 1.7 Spaces of Functions and Distributions.- 1.8 Integral Transform Methods.- 1.8.1 Fourier transform.- 1.8.2 Laplace transform.- 1.9 Bibliographical Notes.- 2 Abel's and Related Integral Equations.- 2.1 Introduction.- 2.2 Abel's Equation.- 2.3 Related Integral Equations.- 2.4 The equation $$\int_{0}^{s} {{{{(s - t)}}^{\beta }}g(t)dt = f(s), \Re e \beta > - 1}$$.- 2.5 Path of Integration in the Complex Plane.- 2.6 The Equation $$\int_{{{ {C}_{{a\xi }}}}} {\frac{{g(z)dz}}{{ {{{(z - \xi )}}^{\nu }}}}} + k\int_{ {{{C}_{{\xi b}}}}} {\frac{ {g(z)dz}}{{{{{(\xi - z)}}^{\nu }}}}} = f(\xi )$$.- 2.7 Equations on a Closed Curve.- 2.8 Examples.- 2.9 Bibliographical Notes.- 2.10 Problems.- 3 Cauchy Type Integral Equations.- 3.1 Introduction.- 3.2 Cauchy Type Equation of the First Kind.- 3.3 An Alternative Approach.- 3.4 Cauchy Type Equations of the Second Kind.- 3.5 Cauchy Type Equations on a Closed Contour.- 3.6 Analytic Representation of Functions.- 3.7 Sectionally Analytic Functions (z?a)n?v(z?b)m+v.- 3.8 Cauchy's Integral Equation on an Open Contour.- 3.9 Disjoint Contours.- 3.10 Contours That Extend to Infinity.- 3.11 The Hilbert Kernel.- 3.12 The Hilbert Equation.- 3.13 Bibliographical Notes.- 3.14 Problems.- 4 Carleman Type Integral Equations.- 4.1 Introduction.- 4.2 Carleman Type Equation over a Real Interval.- 4.3 The Riemann-Hilbert Problem.- 4.4 Carleman Type Equations on a Closed Contour.- 4.5 Non-Normal Problems.- 4.6 A Factorization Procedure.- 4.7 An Operational Approach.- 4.8 Solution of a Related Integral Equation.- 4.9 Bibliographical Notes.- 4.10 Problems.- 5 Distributional Solutions of Singular Integral Equations.- 5.1 Introduction.- 5.2 Spaces of Generalized Functions.- 5.3 Generalized Solution of the Abel Equation.- 5.4 Integral Equations Related to Abel's Equation.- 5.5 The Fractional Integration Operators .- 5.6 The Cauchy Integral Equation over a Finite Interval.- 5.7 Analytic Representation of Distributions of ?'[a, b].- 5.8 Boundary Problems in A[a, b].- 5.9 Disjoint Intervals.- 5.9.1 The problem [RjF]j =hj.- 5.9.2 The equation A1?1(0F) + A2?2(F) = G.- 5.10 Equations Involving Periodic Distributions.- 5.11 Bibliographical Notes.- 5.12 Problems.- 6 Distributional Equations on the Whole Line.- 6.1 Introduction.- 6.2 Preliminaries.- 6.3 The Hilbert Transform of Distributions.- 6.4 Analytic Representation.- 6.5 Asymptotic Estimates.- 6.6 Distributional Solutions of Integral Equations.- 6.7 Non-Normal Equations.- 6.8 Bibliographical Notes.- 6.9 Problems.- 7 Integral Equations with Logarithmic Kernels.- 7.1 Introduction.- 7.2 Expansion of the Kernel In x-y.- 7.3 The Equation $$\int_{a}^{b} {\ln } \left {x - y} \rightg(y)dy = f(x)$$.- 7.4 Two Related Operators.- 7.5 Generalized Solutions of Equations with Logarithmic Kernels.- 7.6 The Operator $$\int_{a}^{b} {(P(x - y)\ln \left {x - y} \right + Q(x, y))g(y)dy}$$.- 7.7 Disjoint Intervals of Integration.- 7.8 An Equation Over a Semi-Infinite Interval.- 7.9 The Equation of the Second Kind Over a Semi-Infinite Interval.- 7.10 Asymptotic Behavior of Eigenvalues.- 7.11 Bibliographical Notes.- 7.12 Problems.- 8 Wiener-Hopf Integral Equations.- 8.1 Introduction.- 8.2 The Holomorphic Fourier Transform.- 8.3 The Mathematical Technique.- 8.4 The Distributional Wiener-Hopf Operators.- 8.5 Illustrations.- 8.6 Bibliographical Notes.- 8.7 Problems.- 9 Dual and Triple Integral Equations.- 9.1 Introduction.- 9.2 The Hankel Transform.- 9.3 Dual Equations with Trigonometric Kernels.- 9.4 Beltrami's Dual Integral Equations.- 9.5 Some Triple Integral Equations.- 9.6 Erdelyi-Koeber Operators.- 9.7 Dual Integral Equations of the Titchmarsh Type.- 9.8 D

This book presents tensors and differential geometry in a comprehensive and approachable manner, providing a bridge from the place where physics and engineering mathematics end, and the place where tensor analysis begins. Among the topics examined are tensor analysis, elementary differential geometry of moving surfaces, and k-differential forms. The book includes numerous examples with solutions and concrete calculations, which guide readers through these complex topics step by step. Mindful of the practical needs of engineers and physicists, book favors simplicity over a more rigorous, formal approach. The book shows readers how to work with tensors and differential geometry and how to apply them to modeling the physical and engineering world. The authors provide chapter-length treatment of topics at the intersection of advanced mathematics, and physics and engineering: * General Basis and Bra-Ket Notation * Tensor Analysis * Elementary Differential Geometry * Differential Forms * Applications of Tensors and Differential Geometry * Tensors and Bra-Ket Notation in Quantum Mechanics The text reviews methods and applications in computational fluid dynamics; continuum mechanics; electrodynamics in special relativity; cosmology in the Minkowski four-dimensional space time; and relativistic and non-relativistic quantum mechanics. Tensor Analysis and Elementary Differential Geometry for Physicists and Engineers benefits research scientists and practicing engineers in a variety of fields, who use tensor analysis and differential geometry in the context of applied physics, and electrical and mechanical engineering. It will also interest graduate students in applied physics and engineering.

Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends and colleagues of Sid Yakowitz in his honor. Fifty internionally known scholars have collectively contributed 30 papers on modeling uncertainty to this volume. Each of these papers was carefully reviewed and in the majority of cases the original submission was revised before being accepted for publication in the book. The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others. There are papers with a theoretical emphasis and others that focus on applications. A number of papers survey the work in a particular area and in a few papers the authors present their personal view of a topic. It is a book with a considerable number of expository articles, which are accessible to a nonexpert - a graduate student in mathematics, statistics, engineering, and economics departments, or just anyone with some mathematical background who is interested in a preliminary exposition of a particular topic. Many of the papers present the state of the art of a specific area or represent original contributions which advance the present state of knowledge. In sum, it is a book of considerable interest to a broad range of academic researchers and students of stochastic systems.

Like the first Abel Symposium, held in 2004, the Abel Symposium 2015 focused on operator algebras. It is interesting to see the remarkable advances that have been made in operator algebras over these years, which strikingly illustrate the vitality of the field. A total of 26 talks were given at the symposium on a variety of themes, all highlighting the richness of the subject. The field of operator algebras was created in the 1930s and was motivated by problems of quantum mechanics. It has subsequently developed well beyond its initial intended realm of applications and expanded into such diverse areas of mathematics as representation theory, dynamical systems, differential geometry, number theory and quantum algebra. One branch, known as "noncommutative geometry", has become a powerful tool for studying phenomena that are beyond the reach of classical analysis. This volume includes research papers that present new results, surveys that discuss the development of a specific line of research, and articles that offer a combination of survey and research. These contributions provide a multifaceted portrait of beautiful mathematics that both newcomers to the field of operator algebras and seasoned researchers alike will appreciate.

This book presents a methodology based on inverse problems for use in solutions for fault diagnosis in control systems, combining tools from mathematics, physics, computational and mathematical modeling, optimization and computational intelligence. This methodology, known as fault diagnosis - inverse problem methodology or FD-IPM, unifies the results of several years of work of the authors in the fields of fault detection and isolation (FDI), inverse problems and optimization. The book clearly and systematically presents the main ideas, concepts and results obtained in recent years. By formulating fault diagnosis as an inverse problem, and by solving it using metaheuristics, the authors offer researchers and students a fresh, interdisciplinary perspective for problem solving in these fields. Graduate courses in engineering, applied mathematics and computing also benefit from this work.

These are the proceedings of the 24th International Conference on Domain Decomposition Methods in Science and Engineering, which was held in Svalbard, Norway in February 2017. Domain decomposition methods are iterative methods for solving the often very large systems of equations that arise when engineering problems are discretized, frequently using finite elements or other modern techniques. These methods are specifically designed to make effective use of massively parallel, high-performance computing systems. The book presents both theoretical and computational advances in this domain, reflecting the state of art in 2017.

This monograph is devoted to the theory and approximation by finite volume methods of nonlinear hyperbolic systems of conservation laws in one or two space variables. It follows directly a previous publication on hyperbolic systems of conservation laws by the same authors. Since the earlier work concentrated on the mathematical theory of multidimensional scalar conservation laws, this book will focus on systems and the theoretical aspects which are needed in the applications, such as the solution of the Riemann problem and further insights into more sophisticated problems, with special attention to the system of gas dynamics. This new edition includes more examples such as MHD and shallow water, with an insight on multiphase flows. Additionally, the text includes source terms and well-balanced/asymptotic preserving schemes, introducing relaxation schemes and addressing problems related to resonance and discontinuous fluxes while adding details on the low Mach number situation.

INHALT LANG: Introduction: Introductory Survey; Vector Norm. Matrix Norm. Matrix Measure; Functional Analysis, Function Norms and Control Signals.- Differential Sensitivity. Small-Scale Perturbation: Kronecker Calculus in Control Theory; Analysis Using Matrices and Control Theory; Eigenvalue and Eigenvector Differential Sensitivity; Transition Matrix Differential Sensitivity; Characteristic Polynomial Differential Sensitivity; Optimal Control and Performance Sensitivity; Desensitizing Control.- Robustness in the Time Domain: General Stability Bounds in Perturbed Systems; Robust Dynamic Interval Systems; Lyapunov-Based Methods for Perturbed Continuous-Time Systems; Lyapunov-Based Methods for Perturbed Discrete-Time Systems; Robust Pole Assignment; Models for Optimal and Interconnected Systems; Robust State Feedback Using Ellipsoid Sets; Robustness of Observers and Kalman-Bucy Filters; Initial Condition Perturbation, Overshoot and Robustness; Lnp-Stability and Robust Nonlinear Control.- Robustness in the Frequency Domain: Uncertain Polynomials. Interval Polynomials; Eigenvalues and Singular Values of Complex Matrices; Resolvent Matrix and Stability Radius; Robustness Via Singular-Value Analysis; Generalized Nyquist Stability of Perturbed Systems; Block-Structured Uncertainty and Structured Singular Value; Performance Robustness; Robust Controllers Via Spectral Radius Technique.- Coprime Factorization and Minimax Frequency Optimization: Robustness Based on the Internal Model Principle; Parametrization and Factorization of Systems; Hardy Space Robust Design.- Robustness Via Approximative Models: Robust Hyperplane Design in Variable Structure Control; SIngular Perturbaitons. Unmodelled High-Frequendy Dynamics; Control Using Aggregation Models; Optimum Control of Approximate and Nonlinear Systems; System Analysis via Orthogonal Functions; System Analysis Via Pulse Functions and Piecewise Linear Functions; Orthogonal Decomposition Applications.