Download or read book Mathematical Programming with Data Perturbations II Second Edition written by Fiacco and published by CRC Press. This book was released on 2020-09-24 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents theoretical results, including an extension of constant rank and implicit function theorems, continuity and stability bounds results for infinite dimensional problems, and the interrelationship between optimal value conditions and shadow prices for stable and unstable programs.
Download or read book Mathematical Programming with Data Perturbations written by Anthony V. Fiacco and published by CRC Press. This book was released on 1997-09-19 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents research contributions and tutorial expositions on current methodologies for sensitivity, stability and approximation analyses of mathematical programming and related problem structures involving parameters. The text features up-to-date findings on important topics, covering such areas as the effect of perturbations on the performance of algorithms, approximation techniques for optimal control problems, and global error bounds for convex inequalities.
Download or read book Perturbation Analysis of Optimization Problems written by J.Frederic Bonnans and published by Springer Science & Business Media. This book was released on 2000-05-11 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: A presentation of general results for discussing local optimality and computation of the expansion of value function and approximate solution of optimization problems, followed by their application to various fields, from physics to economics. The book is thus an opportunity for popularizing these techniques among researchers involved in other sciences, including users of optimization in a wide sense, in mechanics, physics, statistics, finance and economics. Of use to research professionals, including graduate students at an advanced level.
Download or read book Mathematical Programming with Data Perturbations written by Anthony V. Fiacco and published by CRC Press. This book was released on 2020-09-24 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents research contributions and tutorial expositions on current methodologies for sensitivity, stability and approximation analyses of mathematical programming and related problem structures involving parameters. The text features up-to-date findings on important topics, covering such areas as the effect of perturbations on the performance of algorithms, approximation techniques for optimal control problems, and global error bounds for convex inequalities.
Download or read book Mathematical Programming with Data Perturbations II Second Edition written by Fiacco and published by CRC Press. This book was released on 1983-01-24 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theorem of constant rank to lipschitzian maps; Lipschitzian perturbations of infinite optimization problems; On the continuity of the optimum set in parametric semiinfinite programming; Optimality conditions and shadow prices; Optimal value continuity and differential stability bounds under the mangasarian-fromovitz constraint qualification; Iteration and sensitivity for a nonlinear spatial equilibrium problem; A sensitivity analysis approach to iteration skipping in the harmonic mean algorithm; Least squares optimization with implicit model equations.
Download or read book Stochastic Recursive Algorithms for Optimization written by S. Bhatnagar and published by Springer. This book was released on 2012-08-11 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value.
Download or read book Advances in Knowledge Discovery and Data Mining written by De-Nian Yang and published by Springer Nature. This book was released on with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Introduction to Sensitivity and Stability Analysis in Nonlinear Programming written by Fiacco and published by Academic Press. This book was released on 1983-11-02 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Sensitivity and Stability Analysis in Nonlinear Programming
Download or read book Robust Data Mining written by Petros Xanthopoulos and published by Springer Science & Business Media. This book was released on 2012-11-28 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field.
Download or read book Perturbations Optimization and Statistics written by Tamir Hazan and published by MIT Press. This book was released on 2023-12-05 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.
Download or read book Mathematical Reviews written by and published by . This book was released on 2002 with total page 964 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nature Inspired Algorithms for Optimisation written by Raymond Chiong and published by Springer Science & Business Media. This book was released on 2009-04-28 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nature-Inspired Algorithms have been gaining much popularity in recent years due to the fact that many real-world optimisation problems have become increasingly large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficiency is measured by their ability to find acceptable results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This volume 'Nature-Inspired Algorithms for Optimisation' is a collection of the latest state-of-the-art algorithms and important studies for tackling various kinds of optimisation problems. It comprises 18 chapters, including two introductory chapters which address the fundamental issues that have made optimisation problems difficult to solve and explain the rationale for seeking inspiration from nature. The contributions stand out through their novelty and clarity of the algorithmic descriptions and analyses, and lead the way to interesting and varied new applications.
Download or read book Graph Neural Networks Foundations Frontiers and Applications written by Lingfei Wu and published by Springer Nature. This book was released on 2022-01-03 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
Download or read book Applied Modeling Techniques and Data Analysis 1 written by Yiannis Dimotikalis and published by John Wiley & Sons. This book was released on 2021-05-11 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 1 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.
Download or read book Perturbation Methods with Applications in Science and Engineering written by İlkay Bakırtaş and published by BoD – Books on Demand. This book was released on 2018-10-17 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: The governing equations of mathematical, chemical, biological, mechanical and economical models are often nonlinear and too complex to be solved analytically. Perturbation theory provides effective tools for obtaining approximate analytical solutions to a wide variety of such nonlinear problems, which may include differential or difference equations. In this book, we aim to present the recent developments and applications of the perturbation theory for treating problems in applied mathematics, physics and engineering. The eight chapters cover a variety of topics related to perturbation methods. The book is intended to draw attention of researchers and scientist in academia and industry.
Download or read book Handbook of Sharing Confidential Data written by Jörg Drechsler and published by CRC Press. This book was released on 2024-10-09 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature—specifically, synthetic data, formal privacy, and secure computation—can be used to manage trade-offs in disclosure risk and data usefulness. Key features: • Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation • Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives • Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy • Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approaches The handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis.
Download or read book Perturbation theory for linear operators written by Tosio Kato and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt:
