Proceedings of the 8th China High Resolution Earth Observation Conference (CHREOC 2022)

Proceedings of the 8th China High Resolution Earth Observation Conference (CHREOC 2022)
Author: Liheng Wang
Publisher: Springer Nature
Total Pages: 415
Release: 2022-11-29
Genre: Technology & Engineering
ISBN: 9811982023
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This proceedings has four thematic parts: advanced aerospace manufacturing technology, near space system and application technology, accurate perception technology of the earth, and collaborative fusion application technology. The CHREOCs (China High-resolution Earth Observation Conference) focus on the popular topics including military-civilian integration, the One Belt and One Road project, the transformation of scientific research achievements, and it also discusses the new ideas, new technologies, new methods, and new developments. The CHREOCs have effectively promoted high-level institutional mechanisms, technological innovation, and industrial upgrading in the high-resolution earth observation area, and arouse the influence of the national-sponsored major project. All papers in this proceeding are from the 8th CHREOC, and most authors are the researchers and experts participating the state major project CHEOS. The papers are the extraction of research results and reflect the technique level and research direction of the field high-resolution earth observation. All articles have gone through the scientific and strict reviews for several rounds by the experts from the related fields, and therefore reflect the research level and technology innovation of the high-resolution field earth observation. It will be an informative and valuable reference for both academic research and engineering practice. The year 2022 is the final year of high-resolution special projects. After more than ten years of construction, the task of high-resolution special projects has been basically completed, the core technology has been comprehensively breakthrough, and the typical achievements have been rapidly transformed, providing strong support for national security, national defense construction and national economic development.

Proceedings of the 5th China High Resolution Earth Observation Conference (CHREOC 2018)

Proceedings of the 5th China High Resolution Earth Observation Conference (CHREOC 2018)
Author: Liheng Wang
Publisher: Springer
Total Pages: 185
Release: 2019-03-23
Genre: Technology & Engineering
ISBN: 9811365539
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This book is the proceedings of the 5th China High-resolution Earth Observation Conference (CHREOC). The series conference of China High Resolution Earth Observation has been becoming the influential academic event in the earth detection area, and attracting more and more top experts and industry users of related fields. The CHREOCs focus on the popular topics including military-civilian integration, the One Belt and One Road project, the transformation of scientific research achievements, and it also discusses the new ideas, new technologies, new methods, and new developments. The CHREOCs have effectively promoted high-level institutional mechanisms, technological innovation, and industrial upgrading in the high-resolution earth observation area, and arouse the influence of the national-sponsored major project. All papers in this proceeding are from the 5th CHREOC, and most authors are the researchers and experts participating the state major project CHEOS. The papers are the extraction of research results and reflect the technique level and research direction of the field high-resolution earth observation. All articles have gone through the scientific and strict reviews for several rounds by the experts from the related fields, and therefore reflect the research level and technology innovation of the high-resolution field earth observation. This proceedings will be an informative and valuable reference for both academic research and engineering practice.

Proceedings of the 7th China High Resolution Earth Observation Conference (CHREOC 2020)

Proceedings of the 7th China High Resolution Earth Observation Conference (CHREOC 2020)
Author: Liheng Wang
Publisher: Springer Nature
Total Pages: 569
Release: 2022-01-11
Genre: Technology & Engineering
ISBN: 9811657351
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This book is the proceedings of the 7th China High-resolution Earth Observation Conference (CHREOC). The series conference of China High Resolution Earth Observation has become an influential academic event in the earth detection area, attracting more and more top experts and industry users of related fields. The CHREOCs focus on the popular topics including military-civilian integration, the One Belt and One Road project, the transformation of scientific research achievements. They also discuss the new ideas, new technologies, new methods, and new developments. The CHREOCs have effectively promoted high-level institutional mechanisms, technological innovation, and industrial upgrading in the high-resolution earth observation area, and extend the influences of the state-sponsored major projects.

Introduction to Stochastic Search and Optimization

Introduction to Stochastic Search and Optimization
Author: James C. Spall
Publisher: John Wiley & Sons
Total Pages: 620
Release: 2005-03-11
Genre: Mathematics
ISBN: 0471441902
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* Unique in its survey of the range of topics. * Contains a strong, interdisciplinary format that will appeal to both students and researchers. * Features exercises and web links to software and data sets.

Optimization for Machine Learning

Optimization for Machine Learning
Author: Suvrit Sra
Publisher: MIT Press
Total Pages: 509
Release: 2012
Genre: Computers
ISBN: 026201646X
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An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Adaptive Algorithms and Stochastic Approximations

Adaptive Algorithms and Stochastic Approximations
Author: Albert Benveniste
Publisher: Springer Science & Business Media
Total Pages: 373
Release: 2012-12-06
Genre: Mathematics
ISBN: 3642758940
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Adaptive systems are widely encountered in many applications ranging through adaptive filtering and more generally adaptive signal processing, systems identification and adaptive control, to pattern recognition and machine intelligence: adaptation is now recognised as keystone of "intelligence" within computerised systems. These diverse areas echo the classes of models which conveniently describe each corresponding system. Thus although there can hardly be a "general theory of adaptive systems" encompassing both the modelling task and the design of the adaptation procedure, nevertheless, these diverse issues have a major common component: namely the use of adaptive algorithms, also known as stochastic approximations in the mathematical statistics literature, that is to say the adaptation procedure (once all modelling problems have been resolved). The juxtaposition of these two expressions in the title reflects the ambition of the authors to produce a reference work, both for engineers who use these adaptive algorithms and for probabilists or statisticians who would like to study stochastic approximations in terms of problems arising from real applications. Hence the book is organised in two parts, the first one user-oriented, and the second providing the mathematical foundations to support the practice described in the first part. The book covers the topcis of convergence, convergence rate, permanent adaptation and tracking, change detection, and is illustrated by various realistic applications originating from these areas of applications.

Frontiers in PDE-Constrained Optimization

Frontiers in PDE-Constrained Optimization
Author: Harbir Antil
Publisher: Springer
Total Pages: 434
Release: 2018-10-12
Genre: Mathematics
ISBN: 1493986368
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This volume provides a broad and uniform introduction of PDE-constrained optimization as well as to document a number of interesting and challenging applications. Many science and engineering applications necessitate the solution of optimization problems constrained by physical laws that are described by systems of partial differential equations (PDEs)​. As a result, PDE-constrained optimization problems arise in a variety of disciplines including geophysics, earth and climate science, material science, chemical and mechanical engineering, medical imaging and physics. This volume is divided into two parts. The first part provides a comprehensive treatment of PDE-constrained optimization including discussions of problems constrained by PDEs with uncertain inputs and problems constrained by variational inequalities. Special emphasis is placed on algorithm development and numerical computation. In addition, a comprehensive treatment of inverse problems arising in the oil and gas industry is provided. The second part of this volume focuses on the application of PDE-constrained optimization, including problems in optimal control, optimal design, and inverse problems, among other topics.

First-order and Stochastic Optimization Methods for Machine Learning

First-order and Stochastic Optimization Methods for Machine Learning
Author: Guanghui Lan
Publisher: Springer Nature
Total Pages: 591
Release: 2020-05-15
Genre: Mathematics
ISBN: 3030395685
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This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.

Stochastic Decomposition

Stochastic Decomposition
Author: Julia L. Higle
Publisher: Springer Science & Business Media
Total Pages: 237
Release: 2013-11-27
Genre: Mathematics
ISBN: 1461541158
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Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications.