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Book Synopsis Active Robust Optimization by : Shaul Salomon
Download or read book Active Robust Optimization written by Shaul Salomon and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
This book presents a novel framework, known as Active Robust Optimization, which provides the tools for evaluating, comparing and optimizing changeable products. Since any product that can change its configuration during normal operation may be considered a “changeable product,” the framework is widely applicable. Further, the methodology enables designers to use adaptability to deal with uncertainties and so avoid over-conservative designs. Offering a comprehensive overview of the framework, including its unique features, such as its ability to optimally respond to uncertain situations, the book also defines a new class of optimization problem and examines the effects of changes in various parameters on their solution. Lastly, it discusses innovative approaches for solving the problem and demonstrates these with two examples from different fields in engineering design: optimization of an optical table and optimization of a gearbox.
Book Synopsis Active Robust Optimization: Optimizing for Robustness of Changeable Products by : Shaul Salomon
Download or read book Active Robust Optimization: Optimizing for Robustness of Changeable Products written by Shaul Salomon and published by Springer. This book was released on 2019-07-06 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a novel framework, known as Active Robust Optimization, which provides the tools for evaluating, comparing and optimizing changeable products. Since any product that can change its configuration during normal operation may be considered a “changeable product,” the framework is widely applicable. Further, the methodology enables designers to use adaptability to deal with uncertainties and so avoid over-conservative designs. Offering a comprehensive overview of the framework, including its unique features, such as its ability to optimally respond to uncertain situations, the book also defines a new class of optimization problem and examines the effects of changes in various parameters on their solution. Lastly, it discusses innovative approaches for solving the problem and demonstrates these with two examples from different fields in engineering design: optimization of an optical table and optimization of a gearbox.
Zusammenfassung: This book develops robust design and assessment of product and production from viewpoint of system theory, which is quantized with the introduction of brand new concept of preferable probability and its assessment. It aims to provide a new idea and novel way to robust design and assessment of product and production and relevant problems. Robust design and assessment of product and production is attractive to both customer and producer since the stability and insensitivity of a product's quality to uncontrollable factors reflect its value. Taguchi method has been used to conduct robust design and assessment of product and production for half a century, but its rationality is criticized by statisticians due to its casting of both mean value of a response and its dispersion into one index, which doesn't characterize the issue of simultaneous robust design of above two independent responses sufficiently, so an appropriate approach is needed. The preference or role of a response in the evaluation is indicated by using preferable probability as the unique index. Thus, the rational approach for robust design and assessment of product and production is formulated by means of probabilistic multi-objective optimization, which reveals the simultaneous robust designs of both mean value of a response and its dispersion in manner of joint probability. Besides, defuzzification and fuzzification measurements are involved as preliminary approaches for robust assessment, the latter provides miraculous treatment for the 'target the best' case flexibly
Book Synopsis Robust Design and Assessment of Product and Production by Means of Probabilistic Multi-objective Optimization by : Maosheng Zheng
Download or read book Robust Design and Assessment of Product and Production by Means of Probabilistic Multi-objective Optimization written by Maosheng Zheng and published by Springer Nature. This book was released on 2024 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Zusammenfassung: This book develops robust design and assessment of product and production from viewpoint of system theory, which is quantized with the introduction of brand new concept of preferable probability and its assessment. It aims to provide a new idea and novel way to robust design and assessment of product and production and relevant problems. Robust design and assessment of product and production is attractive to both customer and producer since the stability and insensitivity of a product's quality to uncontrollable factors reflect its value. Taguchi method has been used to conduct robust design and assessment of product and production for half a century, but its rationality is criticized by statisticians due to its casting of both mean value of a response and its dispersion into one index, which doesn't characterize the issue of simultaneous robust design of above two independent responses sufficiently, so an appropriate approach is needed. The preference or role of a response in the evaluation is indicated by using preferable probability as the unique index. Thus, the rational approach for robust design and assessment of product and production is formulated by means of probabilistic multi-objective optimization, which reveals the simultaneous robust designs of both mean value of a response and its dispersion in manner of joint probability. Besides, defuzzification and fuzzification measurements are involved as preliminary approaches for robust assessment, the latter provides miraculous treatment for the 'target the best' case flexibly
Download or read book Robust Optimization written by and published by . This book was released on 2006 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
Book Synopsis Robust Optimization by : Aharon Ben-Tal
Download or read book Robust Optimization written by Aharon Ben-Tal and published by Princeton University Press. This book was released on 2009-08-10 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
Download or read book On Robust Optimization written by and published by . This book was released on 2014 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust Optimization; Uncertainties; Scenarios; Multi-objective Optimization; Set Optimization; Scalarization; Vectorization; Optimality Conditions
Book Synopsis Robust Optimization: Complexity and Solution Methods by : André Chassein
Download or read book Robust Optimization: Complexity and Solution Methods written by André Chassein and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
In this dissertation, we develop and analyze algorithms for robustness in three different machine learning settings. In the first part of the dissertation, we introduce the problem of hidden stratification -- which is when a classification model substantially underperforms on certain unlabeled subclasses of the data -- and propose a method to detect and mitigate this issue. Previous works studied how to address this in the setting where the subclass labels are known. Based on the empirical observation that unlabeled subclasses are often separable in the feature space of deep neural networks, we instead estimate subclass labels for the data using clustering techniques. We then use the estimated subclass labels as a form of noisy supervision in a distributionally robust optimization objective, in order to train a model that is more robust to inter-subclass variations. We demonstrate the effectiveness of our approach on several robust image classification benchmarks. We briefly discuss alternative methods for 1) utilizing a limited number of subclass labels to further improve performance, and 2) using contrastive learning to learn representations less susceptible to hidden stratification. In the second part of the dissertation, we study the problem of evaluating classification models under structured distribution shifts. Given a labeled sample from a "source" distribution and an unlabeled sample from the "target" distribution, importance weighting is the standard approach to perform such evaluations; however, importance weighting can struggle in high-dimensional settings, and fails when the support of the target distribution is not contained in that of the source. We show that one can sidestep these issues with some foreknowledge of the nature of the distribution shift; specifically, we present an algorithm that uses user-defined "slicing functions" -- binary functions intended to capture possible axes of distribution shift -- to estimate performance on the target distribution. We theoretically characterize the robustness of our approach to noise and incompleteness in the slicing functions, and empirically verify its effectiveness on a variety of classification tasks. In the third part of the dissertation, we develop an accelerated gradient method to efficiently minimize a class of smooth structured nonconvex functions which we term "quasar-convex" functions. Our algorithm is a generalization of the classic accelerated gradient descent method for convex functions, and is robust to possible nonconvexity between algorithm iterates. We provide upper and lower bounds on the number of first-order evaluations that our algorithm requires to find an approximate optimum, which show that our algorithm has optimal complexity up to logarithmic factors.
Book Synopsis Robustness in Machine Learning and Optimization, with Limited Structural Knowledge by : Nimit Sharad Sohoni
Download or read book Robustness in Machine Learning and Optimization, with Limited Structural Knowledge written by Nimit Sharad Sohoni and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we develop and analyze algorithms for robustness in three different machine learning settings. In the first part of the dissertation, we introduce the problem of hidden stratification -- which is when a classification model substantially underperforms on certain unlabeled subclasses of the data -- and propose a method to detect and mitigate this issue. Previous works studied how to address this in the setting where the subclass labels are known. Based on the empirical observation that unlabeled subclasses are often separable in the feature space of deep neural networks, we instead estimate subclass labels for the data using clustering techniques. We then use the estimated subclass labels as a form of noisy supervision in a distributionally robust optimization objective, in order to train a model that is more robust to inter-subclass variations. We demonstrate the effectiveness of our approach on several robust image classification benchmarks. We briefly discuss alternative methods for 1) utilizing a limited number of subclass labels to further improve performance, and 2) using contrastive learning to learn representations less susceptible to hidden stratification. In the second part of the dissertation, we study the problem of evaluating classification models under structured distribution shifts. Given a labeled sample from a "source" distribution and an unlabeled sample from the "target" distribution, importance weighting is the standard approach to perform such evaluations; however, importance weighting can struggle in high-dimensional settings, and fails when the support of the target distribution is not contained in that of the source. We show that one can sidestep these issues with some foreknowledge of the nature of the distribution shift; specifically, we present an algorithm that uses user-defined "slicing functions" -- binary functions intended to capture possible axes of distribution shift -- to estimate performance on the target distribution. We theoretically characterize the robustness of our approach to noise and incompleteness in the slicing functions, and empirically verify its effectiveness on a variety of classification tasks. In the third part of the dissertation, we develop an accelerated gradient method to efficiently minimize a class of smooth structured nonconvex functions which we term "quasar-convex" functions. Our algorithm is a generalization of the classic accelerated gradient descent method for convex functions, and is robust to possible nonconvexity between algorithm iterates. We provide upper and lower bounds on the number of first-order evaluations that our algorithm requires to find an approximate optimum, which show that our algorithm has optimal complexity up to logarithmic factors.
Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice. Accompanied online by a solutions manual for instructors and source code for problems, this is ideal for a one- or two-semester graduate course on optimization in aerospace, civil, mechanical, electrical, and chemical engineering departments.
Book Synopsis Engineering Design Optimization by : Joaquim R. R. A. Martins
Download or read book Engineering Design Optimization written by Joaquim R. R. A. Martins and published by Cambridge University Press. This book was released on 2021-11-18 with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice. Accompanied online by a solutions manual for instructors and source code for problems, this is ideal for a one- or two-semester graduate course on optimization in aerospace, civil, mechanical, electrical, and chemical engineering departments.
Shows readers how to exploit the capabilities of the MATLAB® Robust Control and Control Systems Toolboxes to the fullest using practical robust control examples.
Book Synopsis Robust Control Design with MATLAB® by : Da-Wei Gu
Download or read book Robust Control Design with MATLAB® written by Da-Wei Gu and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Shows readers how to exploit the capabilities of the MATLAB® Robust Control and Control Systems Toolboxes to the fullest using practical robust control examples.