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Optimization Techniques in Computer Vision
by Mongi A. Abidi Andrei V. Gribok Joonki PaikThis book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc. Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision.
Optimization Techniques in Engineering: Advances and Applications (Sustainable Computing and Optimization)
by Dheeraj Joshi Prasenjit Chatterjee Anita Khosla Ikbal AliOPTIMIZATION TECHNIQUES IN ENGINEERING The book describes the basic components of an optimization problem along with the formulation of design problems as mathematical programming problems using an objective function that expresses the main aim of the model, and how it is to be either minimized or maximized; subsequently, the concept of optimization and its relevance towards an optimal solution in engineering applications, is explained. This book aims to present some of the recent developments in the area of optimization theory, methods, and applications in engineering. It focuses on the metaphor of the inspired system and how to configure and apply the various algorithms. The book comprises 30 chapters and is organized into two parts: Part I — Soft Computing and Evolutionary-Based Optimization; and Part II — Decision Science and Simulation-Based Optimization, which contains application-based chapters. Readers and users will find in the book: An overview and brief background of optimization methods which are used very popularly in almost all applications of science, engineering, technology, and mathematics; An in-depth treatment of contributions to optimal learning and optimizing engineering systems; Maps out the relations between optimization and other mathematical topics and disciplines; A problem-solving approach and a large number of illustrative examples, leading to a step-by-step formulation and solving of optimization problems. Audience Researchers, industry professionals, academicians, and doctoral scholars in major domains of engineering, production, thermal, electrical, industrial, materials, design, computer engineering, and natural sciences. The book is also suitable for researchers and postgraduate students in mathematics, applied mathematics, and industrial mathematics.
Optimization Under Stochastic Uncertainty: Methods, Control and Random Search Methods (International Series in Operations Research & Management Science #296)
by Kurt MartiThis book examines application and methods to incorporating stochastic parameter variations into the optimization process to decrease expense in corrective measures. Basic types of deterministic substitute problems occurring mostly in practice involve i) minimization of the expected primary costs subject to expected recourse cost constraints (reliability constraints) and remaining deterministic constraints, e.g. box constraints, as well as ii) minimization of the expected total costs (costs of construction, design, recourse costs, etc.) subject to the remaining deterministic constraints.After an introduction into the theory of dynamic control systems with random parameters, the major control laws are described, as open-loop control, closed-loop, feedback control and open-loop feedback control, used for iterative construction of feedback controls. For approximate solution of optimization and control problems with random parameters and involving expected cost/loss-type objective, constraint functions, Taylor expansion procedures, and Homotopy methods are considered, Examples and applications to stochastic optimization of regulators are given. Moreover, for reliability-based analysis and optimal design problems, corresponding optimization-based limit state functions are constructed. Because of the complexity of concrete optimization/control problems and their lack of the mathematical regularity as required of Mathematical Programming (MP) techniques, other optimization techniques, like random search methods (RSM) became increasingly important.Basic results on the convergence and convergence rates of random search methods are presented. Moreover, for the improvement of the – sometimes very low – convergence rate of RSM, search methods based on optimal stochastic decision processes are presented. In order to improve the convergence behavior of RSM, the random search procedure is embedded into a stochastic decision process for an optimal control of the probability distributions of the search variates (mutation random variables).
Optimization and Applications: 10th International Conference, OPTIMA 2019, Petrovac, Montenegro, September 30 – October 4, 2019, Revised Selected Papers (Communications in Computer and Information Science #1145)
by Michael Khachay Milojica Jaćimović Vlasta Malkova Mikhail PosypkinThis book constitutes the refereed proceedings of the 10th International Conference on Optimization and Applications, OPTIMA 2019, held in Petrovac, Montenegro, in September-October 2019.The 35 revised full papers presented were carefully reviewed and selected from 117 submissions. The papers cover such topics as optimization, operations research, optimal control, game theory, and their numerous applications in practical problems of operations research, data analysis, and software development.
Optimization and Applications: 11th International Conference, OPTIMA 2020, Moscow, Russia, September 28 – October 2, 2020, Proceedings (Lecture Notes in Computer Science #12422)
by Michael Khachay Vlasta Malkova Nicholas Olenev Yuri EvtushenkoThis book constitutes the refereed proceedings of the 11th International Conference on Optimization and Applications, OPTIMA 2020, held in Moscow, Russia, in September-October 2020.*The 21 full and 2 short papers presented were carefully reviewed and selected from 60 submissions. The papers cover such topics as mathematical programming, combinatorial and discrete optimization, optimal control, optimization in economics, finance, and social sciences, global optimization, and applications. * The conference was held virtually due to the COVID-19 pandemic.
Optimization and Applications: 14th International Conference, OPTIMA 2023, Petrovac, Montenegro, September 18–22, 2023, Revised Selected Papers (Lecture Notes in Computer Science #14395)
by Michael Khachay Milojica Jaćimović Vlasta Malkova Nicholas Olenev Yuri EvtushenkoThis book constitutes the refereed proceedings of the 14th International Conference on Optimization and Applications, OPTIMA 2023, held in Petrovac, Montenegro, during September 18–22, 2023.The 27 full papers included in this book were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: mathematical programming; global optimization; discrete and combinatorial optimization; game theory and mathematical economics; optimization in economics and finance; and applications.
Optimization and Applications: 15th International Conference, OPTIMA 2024, Petrovac, Montenegro, September 16–20, 2024, Revised Selected Papers (Lecture Notes in Computer Science #15218)
by Michael Khachay Milojica Jaćimović Vlasta Malkova Nicholas Olenev Yuri EvtushenkoThis book constitutes the refereed proceedings of the 15th International Conference on Optimization and Applications, OPTIMA 2024, held in Petrovac, Montenegro, during September 16–20, 2024. The 24 full papers presented in this volume were carefully reviewed and selected from 60 submissions. They are grouped into the following topics: Mathematical Programming; Global Optimization; Optimal Control; Game Theory and Mathematical Economics; Optimization in Economics and Finance; and Applications.
Optimization and Applications: 9th International Conference, OPTIMA 2018, Petrovac, Montenegro, October 1–5, 2018, Revised Selected Papers (Communications in Computer and Information Science #974)
by Yury Kochetov Michael Khachay Yury Evtushenko Milojica Jaćimović Vlasta Malkova Mikhail PosypkinThis book constitutes the refereed proceedings of the 9th International Conference on Optimization and Applications, OPTIMA 2018, held in Petrovac, Montenegro, in October 2018.The 35 revised full papers and the one short paper presented were carefully reviewed and selected from 103 submissions. The papers are organized in topical sections on mathematical programming; combinatorial and discrete optimization; optimal control; optimization in economy, finance and social sciences; applications.
Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making: Artificial Intelligence Applications (Intelligent Data-Driven Systems and Artificial Intelligence)
by Harish Garg Irfan Ali Umar Muhammad Modibbo Asaju La’aro BolajiThis book comprehensively discusses nature-inspired algorithms, deep learning methods, applications of mathematical programming and artificial intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0, and social responsibility.This book: Addresses solving practical problems such as supply chain management, take-off, and healthcare analytics using intelligent computing Presents a comparative analysis of machine learning algorithms for power consumption prediction Discusses a machine learning-based multi-objective optimization technique for load balancing in an integrated fog cloud environment Illustrates a data-driven optimization concept for modeling environmental and economic sustainability Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals The text is primarily written for graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, mathematics and statistics, computer science and engineering.
Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making: Optimization Applications (Intelligent Data-Driven Systems and Artificial Intelligence)
by Harish Garg Irfan Ali Umar Muhammad Modibbo Asaju La’aro BolajiThis book comprehensively discusses nature‑inspired algorithms, deep learning methods, applications of mathematical programming, and artificial intelligence techniques. It further covers important topics such as the use of machine learning and the Internet of Things and multi‑objective optimization under Fermatean hesitant fuzzy and uncertain environment.This book: Addresses solving practical problems such as supply chain management, smart manufacturing, and healthcare analytics using intelligent computing and discusses solving the fuzzy inference system in ant colony optimization for traveling salesman problem Presents an overview of artificial intelligence (AI) and explainable AI decision‑making (XAIDM) and illustrates a data‑driven optimization concept for modeling environmental and economic sustainability Discusses machine learning‑based multi‑objective optimization technique for load balancing in integrated fog‑cloud environment Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals Discusses sustainable transit of hazardous waste, green fractional transportation system, perishable inventory, M‑estimation of functional regression operator, and intuitionistic fuzzy sets applications The text is primarily written for graduate students and academic researchers in diverse fields, including operations research, mathematics, statistics, computer science, information and communication technology, and industrial engineering.
Optimization and Control of Bilinear Systems
by Panos M. Pardalos Vitaliy A. YatsenkoCovers developments in bilinear systems theory Focuses on the control of open physical processes functioning in a non-equilibrium mode Emphasis is on three primary disciplines: modern differential geometry, control of dynamical systems, and optimization theory Includes applications to the fields of quantum and molecular computing, control of physical processes, biophysics, superconducting magnetism, and physical information science
Optimization and Data Science in Industrial Engineering: First International Conference, ODSIE 2023, Istanbul, Turkey, November 16–17, 2023, Proceedings, Part I (Communications in Computer and Information Science #2204)
by Gerhard-Wilhelm Weber Zohreh Molamohamadi A. Mirzazadeh Efran Babaee Tirkolaee Janny LeungThis two-volume set CCIS 2204 and 2205 constitutes the refereed proceedings of the First International Conference on Optimization and Data Science in Industrial Engineering, ODSIE 2023, held in Istanbul, Turkey, during November 16–17, 2023. The 33 full papers and 2 short papers presented in these proceedings were carefully reviewed and selected from 311 submissions. The papers were organized in the following topical sections: Part I: smart and intelligent transportation systems; machine/deep/reinforcement learning in industries; and advances of artificial intelligence/operational research tools in healthcare. Part II: technology, learning and analytics in intelligent systems; expert systems, decision analysis, and advanced optimization; digital transformation of supply chain and logistics systems.
Optimization and Data Science in Industrial Engineering: First International Conference, ODSIE 2023, Istanbul, Turkey, November 16–17, 2023, Proceedings, Part II (Communications in Computer and Information Science #2205)
by Gerhard-Wilhelm Weber Zohreh Molamohamadi A. Mirzazadeh Efran Babaee Tirkolaee Janny LeungThis two-volume set CCIS 2204 and 2205 constitutes the refereed proceedings of the First International Conference on Optimization and Data Science in Industrial Engineering, ODSIE 2023, held in Istanbul, Turkey, during November 16–17, 2023. The 33 full papers and 2 short papers presented in these proceedings were carefully reviewed and selected from 311 submissions. The papers were organized in the following topical sections: Part I: smart and intelligent transportation systems; machine/deep/reinforcement learning in industries; and advances of artificial intelligence/operational research tools in healthcare. Part II: technology, learning and analytics in intelligent systems; expert systems, decision analysis, and advanced optimization; digital transformation of supply chain and logistics systems.
Optimization and Data Science: 5th AIROYoung Workshop and AIRO PhD School 2021 Joint Event (AIRO Springer Series #6)
by Adriano Masone Veronica Dal Sasso Valentina MorandiThis proceedings volume collects contributions from the 5th AIRO Young Workshop and AIRO PhD School 2021 joint event on “Optimization and Data Science: Trends and Applications”, held online, from February 8 to 12, 2021. The joint event was organized by AIROYoung representatives and the Operations Research Group of the Department of Electrical Engineering and Information Technology of the University “Federico II” of Naples.The selected contributions represent the state-of-the-art knowledge related to different branches of research, such as data science, machine learning and combinatorial optimization. Therefore, this book is primarily addressed to researchers and PhD students of the operations research community. However, due to its interdisciplinary content, it will be of high interest for other closely related research communities. Moreover, this volume not only presents theoretical results but also covers real applications in computer science, engineering, economics, healthcare, and logistics, making it interesting for practitioners facing complex decision-making problems in these areas.
Optimization and Decision Science: ODS, Virtual Conference, November 19, 2020 (AIRO Springer Series #7)
by Dario Pacciarelli Antonio Sforza Raffaele Cerulli Mauro Dell’Amico Francesca GuerrieroThis book collects selected contributions from the international conference “Optimization and Decision Science” (ODS2020), which was held online on November 19, 2020, and organized by AIRO, the Italian Operations Research Society. The book offers new and original contributions on optimization, decisions science and prescriptive analytics from both a methodological and applied perspective, using models and methods based on continuous and discrete optimization, graph theory and network optimization, analytics, multiple criteria decision making, heuristics, metaheuristics, and exact methods.In addition to more theoretical contributions, the book chapters describe models and methods for addressing a wide diversity of real-world applications, spanning health, transportation, logistics, public sector, manufacturing, and emergency management.Although the book is aimed primarily at researchers and PhD students in the Operations Research community, the interdisciplinary content makes it interesting for practitioners facing complex decision-making problems in the afore-mentioned areas, as well as for scholars and researchers from other disciplines, including artificial intelligence, computer sciences, economics, mathematics, and engineering.
Optimization and Inventory Management (Asset Analytics)
by Nita H. Shah Mandeep MittalThis book discusses inventory models for determining optimal ordering policies using various optimization techniques, genetic algorithms, and data mining concepts. It also provides sensitivity analyses for the models’ robustness. It presents a collection of mathematical models that deal with real industry scenarios. All mathematical model solutions are provided with the help of various optimization techniques to determine optimal ordering policy. The book offers a range of perspectives on the implementation of optimization techniques, inflation, trade credit financing, fuzzy systems, human error, learning in production, inspection, green supply chains, closed supply chains, reworks, game theory approaches, genetic algorithms, and data mining, as well as research on big data applications for inventory management and control. Starting from deterministic inventory models, the book moves towards advanced inventory models. The content is divided into eight major sections: inventory control and management – inventory models with trade credit financing for imperfect quality items; environmental impact on ordering policies; impact of learning on the supply chain models; EOQ models considering warehousing; optimal ordering policies with data mining and PSO techniques; supply chain models in fuzzy environments; optimal production models for multi-items and multi-retailers; and a marketing model to understand buying behaviour. Given its scope, the book offers a valuable resource for practitioners, instructors, students and researchers alike. It also offers essential insights to help retailers/managers improve business functions and make more accurate and realistic decisions.
Optimization and Its Applications in Control and Data Sciences
by Boris GoldengorinThis book focuses on recent research in modern optimization and its implications in control and data analysis. This book is a collection of papers from the conference "Optimization and Its Applications in Control and Data Science" dedicated to Professor Boris T. Polyak, which was held in Moscow, Russia on May 13-15, 2015. This book reflects developments in theory and applications rooted by Professor Polyak's fundamental contributions to constrained and unconstrained optimization, differentiable and nonsmooth functions, control theory and approximation. Each paper focuses on techniques for solving complex optimization problems in different application areas and recent developments in optimization theory and methods. Open problems in optimization, game theory and control theory are included in this collection which will interest engineers and researchers working with efficient algorithms and software for solving optimization problems in market and data analysis. Theoreticians in operations research, applied mathematics, algorithm design, artificial intelligence, machine learning, and software engineering will find this book useful and graduate students will find the state-of-the-art research valuable.
Optimization and Learning: 4th International Conference, OLA 2021, Catania, Italy, June 21-23, 2021, Proceedings (Communications in Computer and Information Science #1443)
by Lionel Amodeo Bernabé Dorronsoro Patricia Ruiz Mario PavoneThis volume constitutes the refereed proceedings of the 4th International Conference on Optimization and Learning, OLA 2021, held in Catania, Italy, in June 2021. Due to the COVID-19 pandemic the conference was held online. The 27 full papers were carefully reviewed and selected from 62 submissions. The papers presented in the volume are organized in topical sections on synergies between optimization and learning; learning for optimization; machine learning and deep learning; transportation and logistics; optimization; applications of learning and optimization methods.
Optimization and Learning: 5th International Conference, OLA 2022, Syracuse, Sicilia, Italy, July 18–20, 2022, Proceedings (Communications in Computer and Information Science #1684)
by Bernabé Dorronsoro Mario Pavone El-Ghazali Talbi Amir NakibThis book constitutes the refereed proceedings of the 5th International Conference on Optimization and Learning, OLA 2022, which took place in Syracuse, Sicilia, Italy, in July 2022. The 19 full papers presented in this volume were carefully reviewed and selected from 52 submissions. The papers are organized in the following topical sections: Optimization and Learning; Novel Optimization Techniques; Logistics; and Applications.
Optimization and Learning: 7th International Conference, OLA 2024, Dubrovnik, Croatia, May 13–15, 2024, Revised Selected Papers (Communications in Computer and Information Science #2311)
by Bernabé Dorronsoro El-Ghazali Talbi Martin ZagarThis book constitutes the refereed proceedings of the 7th International Conference on Optimization and Learning, OLA 2024, held in Dubrovnik, Croatia, during May 13–15, 2024. The 24 full papers presented here were carefully reviewed and selected from 64 submissions. They were organized in the following topical sections: synergies between optimization and machine learning; enhancing optimization and learning techniques; transportation and routing; and applications.
Optimization and Learning: Third International Conference, OLA 2020, Cádiz, Spain, February 17–19, 2020, Proceedings (Communications in Computer and Information Science #1173)
by Bernabé Dorronsoro Patricia Ruiz El-Ghazali Talbi Juan Carlos de la Torre Daniel UrdaThis volume constitutes the refereed proceedings of the Third International Conference on Optimization and Learning, OLA 2020, held in Cádiz, Spain, in February 2020. The 23 full papers were carefully reviewed and selected from 55 submissions. The papers presented in the volume focus on the future challenges of optimization and learning methods, identifying and exploiting their synergies,and analyzing their applications in different fields, such as health, industry 4.0, games, logistics, etc.
Optimization and Machine Learning: Optimization for Machine Learning and Machine Learning for Optimization
by Patrick Siarry Rachid ChelouahMachine learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the field of optimization and machine learning, and to demonstrate how to apply them in the fields of engineering.Optimization and Machine Learning presents modern advances in the selection, configuration and engineering of algorithms that rely on machine learning and optimization. The first part of the book is dedicated to applications where optimization plays a major role, and the second part describes and implements several applications that are mainly based on machine learning techniques. The methods addressed in these chapters are compared against their competitors, and their effectiveness in their chosen field of application is illustrated.
Optimization by GRASP: Greedy Randomized Adaptive Search Procedures
by Celso C. Ribeiro Mauricio G.C. ResendeThis is the first book to cover GRASP (Greedy Randomized Adaptive Search Procedures), a metaheuristic that has enjoyed wide success in practice with a broad range of applications to real-world combinatorial optimization problems. The state-of-the-art coverage and carefully crafted pedagogical style lends this book highly accessible as an introductory text not only to GRASP, but also to combinatorial optimization, greedy algorithms, local search, and path-relinking, as well as to heuristics and metaheuristics, in general. The focus is on algorithmic and computational aspects of applied optimization with GRASP with emphasis given to the end-user, providing sufficient information on the broad spectrum of advances in applied optimization with GRASP. For the more advanced reader, chapters on hybridization with path-relinking and parallel and continuous GRASP present these topics in a clear and concise fashion. Additionally, the book offers a very complete annotated bibliography of GRASP and combinatorial optimization. For the practitioner who needs to solve combinatorial optimization problems, the book provides a chapter with four case studies and implementable templates for all algorithms covered in the text. This book, with its excellent overview of GRASP, will appeal to researchers and practitioners of combinatorial optimization who have a need to find optimal or near optimal solutions to hard combinatorial optimization problems.
Optimization for Computer Vision: An Introduction to Core Concepts and Methods
by Marco Alexander TreiberThis practical and authoritative text/reference presents a broad introduction to the optimization methods used specifically in computer vision. In order to facilitate understanding, the presentation of the methods is supplemented by simple flow charts, followed by pseudocode implementations that reveal deeper insights into their mode of operation. These discussions are further supported by examples taken from important applications in computer vision. Topics and features: provides a comprehensive overview of computer vision-related optimization; covers a range of techniques from classical iterative multidimensional optimization to cutting-edge topics of graph cuts and GPU-suited total variation-based optimization; describes in detail the optimization methods employed in computer vision applications; illuminates key concepts with clearly written and step-by-step explanations; presents detailed information on implementation, including pseudocode for most methods.
Optimization in Green Sustainability and Ecological Transition: ODS, Ischia, Italy, September 4–7, 2023 (AIRO Springer Series #12)
by Paola Festa Maurizio Bruglieri Giusy Macrina Ornella PisacaneThis book collects selected contributions of the “Optimization and Decision Science - ODS2023” international conference on the theme of optimization in green sustainability and ecological transition. ODS2023 was held in Ischia, 4–7 September 2023, and was organized by AIRO, the Italian Operations Research Society. The book offers new and original contributions on operations research, optimization, decision science, and prescriptive analytics from both a methodological and applied perspectives with a special focus on SDG related topics.It provides a state-of-the art on problem models and solving methods to address a widely class of real-world problems, arising in different application areas such as logistics, transportation, manufacturing, health, ICT and mobile networks, and emergency/disaster management. In addition, the scientific works collected in this book aim at providing significant contributions in the themes of sustainability, traffic and pollution reductions, and energy management.This book is aimed primarily at researchers and Ph.D. students in the Operations Research community. However, due to its interdisciplinary contents, this book is of high interest also for students and researchers from other disciplines, including artificial intelligence, computer sciences, finance, mathematics, and engineering as well as for practitioners facing complex decision-making problems in logistics, manufacturing production, and services.