An Introduction To Nonlinear Optimization Theory Marius Durea
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An Introduction to Nonlinear Optimization Theory
Author: Marius Durea
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2014-01-01
The goal of this book is to present the main ideas and techniques in the field of continuous smooth and nonsmooth optimization. Starting with the case of differentiable data and the classical results on constrained optimization problems, and continuing with the topic of nonsmooth objects involved in optimization theory, the book concentrates on both theoretical and practical aspects of this field. This book prepares those who are engaged in research by giving repeated insights into ideas that are subsequently dealt with and illustrated in detail.
Environmental Data Analysis
Author: Zhihua Zhang
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2016-11-21
Most environmental data involve a large degree of complexity and uncertainty. Environmental Data Analysis is created to provide modern quantitative tools and techniques designed specifically to meet the needs of environmental sciences and related fields. This book has an impressive coverage of the scope. Main techniques described in this book are models for linear and nonlinear environmental systems, statistical & numerical methods, data envelopment analysis, risk assessments and life cycle assessments. These state-of-the-art techniques have attracted significant attention over the past decades in environmental monitoring, modeling and decision making. Environmental Data Analysis explains carefully various data analysis procedures and techniques in a clear, concise, and straightforward language and is written in a self-contained way that is accessible to researchers and advanced students in science and engineering. This is an excellent reference for scientists and engineers who wish to analyze, interpret and model data from various sources, and is also an ideal graduate-level textbook for courses in environmental sciences and related fields. Contents: Preface Time series analysis Chaos and dynamical systems Approximation Interpolation Statistical methods Numerical methods Optimization Data envelopment analysis Risk assessments Life cycle assessments Index
Concepts of Machine Learning
Author: Ajay Kumar Goyal
language: en
Publisher: Xoffencerpublication
Release Date: 2023-08-14
Since AI has dominated data innovation for more than 20 years, it has played a significant but frequently unnoticed role in our lives. Shrewd information examination will probably turn out to be considerably more common as a prerequisite for mechanical progression because of the steadily growing amounts of information that are becoming available. The main objectives of this chapter are to organise the zoo of issues and to provide the reader with a comprehensive overview of the numerous applications that have machine learning challenges at their core. The language where various AI issues should be figured out to become agreeable to arrangement, measurements and likelihood hypothesis will next be covered in detail along with some fundamental tools from these fields. Finally, we will present a series of simple yet effective techniques to address a significant categorization challenge. Later chapters of the book will look at more advanced methods, broad concerns, and a complete analysis. A variety of structures can be used with AI. We currently cover the types of information that various projects need, and we categorise the problems in a somewhat more tailored way. The last option is pivotal assuming we are to forestall making the wheel without any preparation for each new application. All things being equal, a huge piece of the craft of AI is to consolidate a wide assortment of rather unique issues into few models. The inquiry into artificial intelligence is then heavily concentrated on finding convincing evidence for the solutions to such problems. The majority of readers will understand page positioning on a website. This method involves typing a search word into a web search tool, which then searches the Internet for pages matching the term and returns them in a list organised by pertinence. For a delineation of the query items for "AI," see Figure 1.2. As such, because of an inquiry, the web crawler gives an arranged rundown of sites. To achieve this, a web search tool must "know" which pages fit the inquiry and which pages are significant.