Thanks to the revival by von Neumann and Morgenstern, maximization of the expected value of a concave utility function of outcomes has for the last third of a century generally been accepted as the correct criterion for optimal portfolio selection. Operational theorems for the general case were delayed in becoming recognized, and it was appropriate that the seminal breakthroughs of the 1950's be largely preoccupied with the special case of mean-variance analysis. Not only could the fruitful Sharpe-Lintner-Mossin capital asset pricing model be based on it, but in addition, it gave rise to simple linear rules of portfolio optimizing. In the mean-variance model, the well-known Separation or Mutual-Fund Theorem holds; and with suitable additional assumptions, the model can be used to define a complete micro-economic framework for the capital market, and a number of empirically testable hypotheses can be derived. As a result, an overwhelming majority of the literature on portfolio theory have been based on this criterion.
Preface. About the Authors. Foreword. Prologue. Part I: Fundamentals. 1. Sampling Plans. 1.1 The 'Curse of Dimensionality' and How to Avoid It. 1.2 Physical versus Computational Experiments. 1.3 Designing Preliminary Experiments (Screening). 1.3.1 Estimating the Distribution of Elementary Effects. 1.4 Designing a Sampling Plan. 1.4.1 Stratification. 1.4.2 Latin Squares and Random Latin Hypercubes. 1.4.3 Space-filling Latin Hypercubes. 1.4.4 Space-filling Subsets. 1.5 A Note on Harmonic Responses. 1.6 Some Pointers for Further Reading. References. 2. Constructing a Surrogate. 2.1 The Modelling Process. 2.1.1 Stage One: Preparing the Data and Choosing a Modelling Approach. 2.1.2 Stage Two: Parameter Estimation and Training. 2.1.3 Stage Three: Model Testing. 2.2 Polynomial Models. 2.2.1 Example One: Aerofoil Drag. 2.2.2 Example Two: a Multimodal Testcase. 2.2.3 What About the k -variable Case? 2.3 Radial Basis Function Models. 2.3.1 Fitting Noise-Free Data. 2.3.2 Radial Basis Function Models of Noisy Data. 2.4 Kriging. 2.4.1 Building the Kriging Model. 2.4.2 Kriging Prediction. 2.5 Support Vector Regression. 2.5.1 The Support Vector Predictor. 2.5.2 The Kernel Trick. 2.5.3 Finding the Support Vectors. 2.5.4 Finding . 2.5.5 Choosing C and epsilon. 2.5.6 Computing epsilon : v -SVR 71. 2.6 The Big(ger) Picture. References. 3. Exploring and Exploiting a Surrogate. 3.1 Searching the Surrogate. 3.2 Infill Criteria. 3.2.1 Prediction Based Exploitation. 3.2.2 Error Based Exploration. 3.2.3 Balanced Exploitation and Exploration. 3.2.4 Conditional Likelihood Approaches. 3.2.5 Other Methods. 3.3 Managing a Surrogate Based Optimization Process. 3.3.1 Which Surrogate for What Use? 3.3.2 How Many Sample Plan and Infill Points? 3.3.3 Convergence Criteria. 3.3.4 Search of the Vibration Isolator Geometry Feasibility Using Kriging Goal Seeking. References. Part II: Advanced Concepts. 4. Visualization. 4.1 Matrices of Contour Plots. 4.2 Nested Dimensions. Reference. 5. Constraints. 5.1 Satisfaction of Constraints by Construction. 5.2 Penalty Functions. 5.3 Example Constrained Problem. 5.3.1 Using a Kriging Model of the Constraint Function. 5.3.2 Using a Kriging Model of the Objective Function. 5.4 Expected Improvement Based Approaches. 5.4.1 Expected Improvement With Simple Penalty Function. 5.4.2 Constrained Expected Improvement. 5.5 Missing Data. 5.5.1 Imputing Data for Infeasible Designs. 5.6 Design of a Helical Compression Spring Using Constrained Expected Improvement. 5.7 Summary. References. 6. Infill Criteria With Noisy Data. 6.1 Regressing Kriging. 6.2 Searching the Regression Model. 6.2.1 Re-Interpolation. 6.2.2 Re-Interpolation With Conditional Likelihood Approaches. 6.3 A Note on Matrix Ill-Conditioning. 6.4 Summary. References. 7. Exploiting Gradient Information. 7.1 Obtaining Gradients. 7.1.1 Finite Differencing. 7.1.2 Complex Step Approximation. 7.1.3 Adjoint Methods and Algorithmic Differentiation. 7.2 Gradient-enhanced Modelling. 7.3 Hessian-enhanced Modelling. 7.4 Summary. References. 8. Multi-fidelity Analysis. 8.1 Co-Kriging. 8.2 One-variable Demonstration. 8.3 Choosing X c and X e . 8.4 Summary. References. 9. Multiple Design Objectives. 9.1 Pareto Optimization. 9.2 Multi-objective Expected Improvement. 9.3 Design of the Nowacki Cantilever Beam Using Multi-objective, Constrained Expected Improvement. 9.4 Design of a Helical Compression Spring Using Multi-objective, Constrained Expected Improvement. 9.5 Summary. References. Appendix: Example Problems. A.1 One-Variable Test Function. A.2 Branin Test Function. A.3 Aerofoil Design. A.4 The Nowacki Beam. A.5 Multi-objective, Constrained Optimal Design of a Helical Compression Spring. A.6 Novel Passive Vibration Isolator Feasibility. References. Index.
Optimization Criteria. Finite Horizon Optimization. Infinite Horizon Discounted Cost Optimization. An Inventory Model. Average Cost Optimization for Finite State Spaces. Average Cost Optimization Theory for Countable State Spaces. Computation of Average Cost Optimal Policies for Infinite State Spaces. Optimization Under Actions at Selected Epochs. Average Cost Optimization of Continuous Time Processes. Appendices. Bibliography. Index.
A hugely topical collection of essays from a cross-disciplinary group of leading academics focussing on the implications for an understanding of human identity in light of the current possibilities in medical science. The book brings together an international body of medical experts alongside philosophers, sociologists, theologians and ethicists in order to discuss these vital issues. The ensuing discussion will allow public debate to be more informed about the actual possibilities inherent in medical science, alongside a sophisticated treatment of ethical and theological issues. The result is a focused collection of essays that raises new and challenging questions.
Transform your business by improving customer experience across the entire end-to-end value chain with this practical guide, including 100 tips and case studies from brands like Amazon, AirBnB and Uber.
Focusing on polarization matrix optics in many forms, this book includes coverage of a wide range of methods which have been applied to LCD modeling, ranging from the simple Jones matrix method to elaborate and high accuracy algorithms suitable for off-axis optics. Researchers and scientists are constantly striving for improved performance, faster response times, wide viewing angles, improved colour in liquid crystal display development, and with this comes the need to model LCD devices effectively. The authors have significant experience in dealing with the problems related to the practical application of liquid crystals, in particular their optical performance. Key features: Explores analytical solutions and approximations to important cases in the matrix treatment of different LC layer configurations, and the application of these results to improve the computational method Provides the analysis of accuracies of the different approaches discussed in the book Explains the development of the Eigenwave Jones matrix method which offers a path to improved accuracy compared to Jones matrix and extended Jones matrix formalisms, while achieving significant improvement in computational speed and versatility compared to full 4x4 matrix methods Includes a companion website hosting the authors' program library LMOPTICS (FORTRAN 90), a collection of routines for calculating the optical characteristics of stratified media, the use of which allows for the easy implementation of the methods described in this book. The website also contains a set of sample programs (source codes) using LMOPTICS, which exemplify the application of these methods in different situations
The fundamental needs of rural life in America may be grouped under main divisions; (1) Educational, (2) economic, and (3) social and ational. Educational is of first importance as the progress of a n depends upon the intellectual development of its people. Of most tance in a program of rural-life improvement is the public school since the foundation for further improvement is laid.
Recent developments in multi-parametric optimization and control Multi-Parametric Optimization and Control provides comprehensive coverage of recent methodological developments for optimal model-based control through parametric optimization. It also shares real-world research applications to support deeper understanding of the material. Researchers and practitioners can use the book as reference. It is also suitable as a primary or a supplementary textbook. Each chapter looks at the theories related to a topic along with a relevant case study. Topic complexity increases gradually as readers progress through the chapters. The first part of the book presents an overview of the state-of-the-art multi-parametric optimization theory and algorithms in multi-parametric programming. The second examines the connection between multi-parametric programming and model-predictive control—from the linear quadratic regulator over hybrid systems to periodic systems and robust control. The third part of the book addresses multi-parametric optimization in process systems engineering. A step-by-step procedure is introduced for embedding the programming within the system engineering, which leads the reader into the topic of the PAROC framework and software platform. PAROC is an integrated framework and platform for the optimization and advanced model-based control of process systems.
This volume of essays draws together research on different types of collective actions: group actions, representative actions, test case procedures, derivative actions and class actions. The main focus is on how these actions can enhance access to justice and on how to balance the interests of private actors in protecting their rights with the interests of society as a whole. Rather than focusing on collective actions only as a procedural device per se, the contributors to this book also examine how these mechanisms relate to their broader social context. Bringing together a broad range of scholarship from the areas of competition, consumer, environmental, company and securities law, the book includes contributions from Asian, European and North American scholars and therefore expands the scope of the traditional European and/or American debate.
In understanding human behavior, psychologists have long been interested in what motivates specific actions. Debates have pitted extrinsic motivators (e.g. rewards/punishment) against intrinsic motivation in attempting to determine what best motivates individuals. This book provides a summary view of what research has determined about both extrinsic and intrinsic motivation, and clarifies what questions remain unanswered. Divided into three sections, section I revisits the debate about the effects of extrinsic incentives or constraints on intrinsic motivation and creativity, and identifies theoretical advances in motivational research. Section II focuses on the hidden costs and benefits of different types of achievement goals on motivation and performance. Section III discusses theory and research findings on how extrinsic and intrinsic motivators may work in everyday life and over time. This book is of interest to researchers in psychology, education, and business, as well as to a wider audience interested in promoting optimal motivation and performance. Coverage in this book includes: * Debates and controversies in motivational research * Developmental nature of intrinsic and extrinsic motivation over time * Influences of parents, educators, and employers in facilitating motivation * Effect of achievement goals on learning and performance *
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