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| bayesian optimization with inequality constraints: Lectures on Stochastic Programming Alexander Shapiro, Darinka Dentcheva, Andrzej Ruszczy?ski, 2009-01-01 Optimization problems involving stochastic models occur in almost all areas of science and engineering, such as telecommunications, medicine, and finance. Their existence compels a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. Readers will find coverage of the basic concepts of modeling these problems, including recourse actions and the nonanticipativity principle. The book also includes the theory of two-stage and multistage stochastic programming problems; the current state of the theory on chance (probabilistic) constraints, including the structure of the problems, optimality theory, and duality; and statistical inference in and risk-averse approaches to stochastic programming. |
| bayesian optimization with inequality constraints: Bayesian Optimization and Data Science Francesco Archetti, Antonio Candelieri, 2019-09-25 This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities. |
| bayesian optimization with inequality constraints: Bayesian Optimization Roman Garnett, 2023-01-31 Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications. |
| bayesian optimization with inequality constraints: Bayesian Optimization in Action Quan Nguyen, 2024-01-09 Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy. Put its advanced techniques into practice with this hands-on guide. In Bayesian Optimization in Action you will learn how to: Train Gaussian processes on both sparse and large data sets Combine Gaussian processes with deep neural networks to make them flexible and expressive Find the most successful strategies for hyperparameter tuning Navigate a search space and identify high-performing regions Apply Bayesian optimization to cost-constrained, multi-objective, and preference optimization Implement Bayesian optimization with PyTorch, GPyTorch, and BoTorch Bayesian Optimization in Action shows you how to optimize hyperparameter tuning, A/B testing, and other aspects of the machine learning process by applying cutting-edge Bayesian techniques. Using clear language, illustrations, and concrete examples, this book proves that Bayesian optimization doesn’t have to be difficult! You’ll get in-depth insights into how Bayesian optimization works and learn how to implement it with cutting-edge Python libraries. The book’s easy-to-reuse code samples let you hit the ground running by plugging them straight into your own projects. Forewords by Luis Serrano and David Sweet. About the technology In machine learning, optimization is about achieving the best predictions—shortest delivery routes, perfect price points, most accurate recommendations—in the fewest number of steps. Bayesian optimization uses the mathematics of probability to fine-tune ML functions, algorithms, and hyperparameters efficiently when traditional methods are too slow or expensive. About the book Bayesian Optimization in Action teaches you how to create efficient machine learning processes using a Bayesian approach. In it, you’ll explore practical techniques for training large datasets, hyperparameter tuning, and navigating complex search spaces. This interesting book includes engaging illustrations and fun examples like perfecting coffee sweetness, predicting weather, and even debunking psychic claims. You’ll learn how to navigate multi-objective scenarios, account for decision costs, and tackle pairwise comparisons. What's inside Gaussian processes for sparse and large datasets Strategies for hyperparameter tuning Identify high-performing regions Examples in PyTorch, GPyTorch, and BoTorch About the reader For machine learning practitioners who are confident in math and statistics. About the author Quan Nguyen is a research assistant at Washington University in St. Louis. He writes for the Python Software Foundation and has authored several books on Python programming. Table of Contents 1 Introduction to Bayesian optimization 2 Gaussian processes as distributions over functions 3 Customizing a Gaussian process with the mean and covariance functions 4 Refining the best result with improvement-based policies 5 Exploring the search space with bandit-style policies 6 Leveraging information theory with entropy-based policies 7 Maximizing throughput with batch optimization 8 Satisfying extra constraints with constrained optimization 9 Balancing utility and cost with multifidelity optimization 10 Learning from pairwise comparisons with preference optimization 11 Optimizing multiple objectives at the same time 12 Scaling Gaussian processes to large datasets 13 Combining Gaussian processes with neural networks |
| bayesian optimization with inequality constraints: Bayesian Optimization with Application to Computer Experiments Tony Pourmohamad, Herbert K. H. Lee, 2021-10-04 This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field. This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning. |
| bayesian optimization with inequality constraints: Bayesian and High-Dimensional Global Optimization Anatoly Zhigljavsky, Antanas Žilinskas, 2021-03-02 Accessible to a variety of readers, this book is of interest to specialists, graduate students and researchers in mathematics, optimization, computer science, operations research, management science, engineering and other applied areas interested in solving optimization problems. Basic principles, potential and boundaries of applicability of stochastic global optimization techniques are examined in this book. A variety of issues that face specialists in global optimization are explored, such as multidimensional spaces which are frequently ignored by researchers. The importance of precise interpretation of the mathematical results in assessments of optimization methods is demonstrated through examples of convergence in probability of random search. Methodological issues concerning construction and applicability of stochastic global optimization methods are discussed, including the one-step optimal average improvement method based on a statistical model of the objective function. A significant portion of this book is devoted to an analysis of high-dimensional global optimization problems and the so-called ‘curse of dimensionality’. An examination of the three different classes of high-dimensional optimization problems, the geometry of high-dimensional balls and cubes, very slow convergence of global random search algorithms in large-dimensional problems , and poor uniformity of the uniformly distributed sequences of points are included in this book. |
| bayesian optimization with inequality constraints: Hierarchical Bayesian Optimization Algorithm Martin Pelikan, 2005-02 This book provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The book focuses on two algorithms that replace traditional variation operators of evolutionary algorithms by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). BOA and hBOA are theoretically and empirically shown to provide robust and scalable solution for broad classes of nearly decomposable and hierarchical problems. A theoretical model is developed that estimates the scalability and adequate parameter settings for BOA and hBOA. The performance of BOA and hBOA is analyzed on a number of artificial problems of bounded difficulty designed to test BOA and hBOA on the boundary of their design envelope. The algorithms are also extensively tested on two interesting classes of real-world problems: MAXSAT and Ising spin glasses with periodic boundary conditions in two and three dimensions. Experimental results validate the theoretical model and confirm that BOA and hBOA provide robust and scalable solution for nearly decomposable and hierarchical problems with only little problem-specific information. |
| bayesian optimization with inequality constraints: Black Box Optimization, Machine Learning, and No-Free Lunch Theorems Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, 2021-05-27 This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem. |
| bayesian optimization with inequality constraints: Numerical Methods for Constrained Optimization Philip E. Gill, P. E. Gill, William Allan Murray, Institute of Mathematics and Its Applications, National Physical Laboratory (Great Britain), 1974 |
| bayesian optimization with inequality constraints: ECAI 2023 K. Gal, A. Nowé, G.J. Nalepa, 2023-10-18 Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field. |
| bayesian optimization with inequality constraints: Derivative-Free and Blackbox Optimization Charles Audet, Warren Hare, 2017-12-02 This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization. The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead). Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region). Part V discusses dealing with constraints, using surrogates, and bi-objective optimization. End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix. |
| bayesian optimization with inequality constraints: Many-Criteria Optimization and Decision Analysis Dimo Brockhoff, Michael Emmerich, Boris Naujoks, Robin Purshouse, 2023-07-28 This book presents the state-of-the-art, current challenges, and future perspectives for the field of many-criteria optimization and decision analysis. The field recognizes that real-life problems often involve trying to balance a multiplicity of considerations simultaneously – such as performance, cost, risk, sustainability, and quality. The field develops theory, methods and tools that can support decision makers in finding appropriate solutions when faced with many (typically more than three) such criteria at the same time. The book consists of two parts: key research topics, and emerging topics. Part I begins with a general introduction to many-criteria optimization, perspectives from research leaders in real-world problems, and a contemporary survey of the attributes of problems of this kind. This part continues with chapters on fundamental aspects of many-criteria optimization, namely on order relations, quality measures, benchmarking, visualization, and theoretical considerations. Part II offers more specialized chapters on correlated objectives, heterogeneous objectives, Bayesian optimization, and game theory. Written by leading experts across the field of many-criteria optimization, this book will be an essential resource for researchers in the fields of evolutionary computing, operations research, multiobjective optimization, and decision science. |
| bayesian optimization with inequality constraints: Computer Simulation Dragan Cvetković, 2017-06-07 The first chapter provides an overview of the development of a novel agent-based simulation model of socio-environmental innovation diffusion. The second chapter shows the study about rendering of colours with three rendering engines. The third and fourth chapters are devoted to modelling clothes at different levels. The fifth chapter describes the modelling of computer simulation in the optimization of bioprocess technology. Chapters 6 and 7 formulate a physical model of deformation of steel and idea of constructing a scientific workshop focused on high-temperature processes. Chapter 8 formulates surrogate models. Chapter 9 shows computer simulation of high-frequency electromagnetic fields. Chapter 10 proposes the modelling of the task allocation problem by the use of Petri Nets. Chapter 11 presents various scenarios whose ranking is done according to defined criteria and weight coefficients. |
| bayesian optimization with inequality constraints: Advances in Artificial Intelligence Ebrahim Bagheri, Jackie C.K. Cheung, 2018-04-23 This book constitutes the refereed proceedings of the 31th Canadian Conference on Artificial Intelligence, Canadian AI 2018, held in Toronto, ON, Canada, in May 2018. The 16 regular papers and 18 short papers presented together with 7 Graduate Student Symposium papers and 4 Industry Track papers were carefully reviewed and selected from 72 submissions. The focus of the conference was on artificial intelligence research and advanced information and communications technology. |
| bayesian optimization with inequality constraints: Constrained Markov Decision Processes Eitan Altman, 2021-12-17 This book provides a unified approach for the study of constrained Markov decision processes with a finite state space and unbounded costs. Unlike the single controller case considered in many other books, the author considers a single controller with several objectives, such as minimizing delays and loss, probabilities, and maximization of throughputs. It is desirable to design a controller that minimizes one cost objective, subject to inequality constraints on other cost objectives. This framework describes dynamic decision problems arising frequently in many engineering fields. A thorough overview of these applications is presented in the introduction. The book is then divided into three sections that build upon each other. |
| bayesian optimization with inequality constraints: Automated Machine Learning Frank Hutter, Lars Kotthoff, Joaquin Vanschoren, 2019-05-17 This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. |
| bayesian optimization with inequality constraints: Bayesian Approach to Global Optimization Jonas Mockus, 2012-12-06 ·Et moi ... si j'avait su comment en revcnir. One service mathematics has rendered the je o'y semis point alle.' human race. It has put common sense back Jules Verne where it beloogs. on the topmost shelf next to the dusty canister labelled 'discarded non The series is divergent; therefore we may be sense', able to do something with it. Eric T. BclI O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics ... '; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series. |
| bayesian optimization with inequality constraints: Bayesian Heuristic Approach to Discrete and Global Optimization Jonas Mockus, William Eddy, Gintaras Reklaitis, 2013-03-09 Bayesian decision theory is known to provide an effective framework for the practical solution of discrete and nonconvex optimization problems. This book is the first to demonstrate that this framework is also well suited for the exploitation of heuristic methods in the solution of such problems, especially those of large scale for which exact optimization approaches can be prohibitively costly. The book covers all aspects ranging from the formal presentation of the Bayesian Approach, to its extension to the Bayesian Heuristic Strategy, and its utilization within the informal, interactive Dynamic Visualization strategy. The developed framework is applied in forecasting, in neural network optimization, and in a large number of discrete and continuous optimization problems. Specific application areas which are discussed include scheduling and visualization problems in chemical engineering, manufacturing process control, and epidemiology. Computational results and comparisons with a broad range of test examples are presented. The software required for implementation of the Bayesian Heuristic Approach is included. Although some knowledge of mathematical statistics is necessary in order to fathom the theoretical aspects of the development, no specialized mathematical knowledge is required to understand the application of the approach or to utilize the software which is provided. Audience: The book is of interest to both researchers in operations research, systems engineering, and optimization methods, as well as applications specialists concerned with the solution of large scale discrete and/or nonconvex optimization problems in a broad range of engineering and technological fields. It may be used as supplementary material for graduate level courses. |
| bayesian optimization with inequality constraints: Surrogates Robert B. Gramacy, 2020-03-10 Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments. Features: • Emphasis on methods, applications, and reproducibility. • R code is integrated throughout for application of the methods. • Includes more than 200 full colour figures. • Includes many exercises to supplement understanding, with separate solutions available from the author. • Supported by a website with full code available to reproduce all methods and examples. The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc. |
| bayesian optimization with inequality constraints: Advanced Methods in Statistics, Data Science and Related Applications Matilde Bini, |
| bayesian optimization with inequality constraints: MM Optimization Algorithms Kenneth Lange, 2016-07-11 MM Optimization Algorithms?offers an overview of the MM principle, a device for deriving optimization algorithms satisfying the ascent or descent property. These algorithms can separate the variables of a problem, avoid large matrix inversions, linearize a problem, restore symmetry, deal with equality and inequality constraints gracefully, and turn a nondifferentiable problem into a smooth problem.? The author presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics; derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining; and summarizes a large amount of literature that has not reached book form before.? |
| bayesian optimization with inequality constraints: Feasibility and Infeasibility in Optimization: John W. Chinneck, 2007-10-25 Written by a world leader in the field and aimed at researchers in applied and engineering sciences, this brilliant text has as its main goal imparting an understanding of the methods so that practitioners can make immediate use of existing algorithms and software, and so that researchers can extend the state of the art and find new applications. It includes algorithms on seeking feasibility and analyzing infeasibility, as well as describing new and surprising applications. |
| bayesian optimization with inequality constraints: Applied Optimal Control A. E. Bryson, 1975-01-01 This best-selling text focuses on the analysis and design of complicated dynamics systems. CHOICE called it “a high-level, concise book that could well be used as a reference by engineers, applied mathematicians, and undergraduates. The format is good, the presentation clear, the diagrams instructive, the examples and problems helpful...References and a multiple-choice examination are included.” |
| bayesian optimization with inequality constraints: Multimodal and Tensor Data Analytics for Industrial Systems Improvement Nathan Gaw, |
| bayesian optimization with inequality constraints: 31st European Symposium on Computer Aided Process Engineering Metin Türkay, Rafiqul Gani, 2021-07-22 The 31st European Symposium on Computer Aided Process Engineering: ESCAPE-31, Volume 50 contains the papers presented at the 31st European Symposium of Computer Aided Process Engineering (ESCAPE) event held in Istanbul, Turkey. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students and consultants in the chemical industries. - Presents findings and discussions from the 31st European Symposium of Computer Aided Process Engineering (ESCAPE) event |
| bayesian optimization with inequality constraints: Machine Learning and Knowledge Discovery in Databases. Research Track Nuria Oliver, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, Jose A. Lozano, 2021-09-10 The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media. |
| bayesian optimization with inequality constraints: Algorithms for Optimization Mykel J. Kochenderfer, Tim A. Wheeler, 2019-03-12 A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals. |
| bayesian optimization with inequality constraints: Bayesian Network Ahmed Rebai, 2010-08-18 Bayesian networks are a very general and powerful tool that can be used for a large number of problems involving uncertainty: reasoning, learning, planning and perception. They provide a language that supports efficient algorithms for the automatic construction of expert systems in several different contexts. The range of applications of Bayesian networks currently extends over almost all fields including engineering, biology and medicine, information and communication technologies and finance. This book is a collection of original contributions to the methodology and applications of Bayesian networks. It contains recent developments in the field and illustrates, on a sample of applications, the power of Bayesian networks in dealing the modeling of complex systems. Readers that are not familiar with this tool, but have some technical background, will find in this book all necessary theoretical and practical information on how to use and implement Bayesian networks in their own work. There is no doubt that this book constitutes a valuable resource for engineers, researchers, students and all those who are interested in discovering and experiencing the potential of this major tool of the century. |
| bayesian optimization with inequality constraints: Computer Vision – ECCV 2018 Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, Yair Weiss, 2018-10-06 The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions. |
| bayesian optimization with inequality constraints: Information Science for Materials Discovery and Design Turab Lookman, Francis J. Alexander, Krishna Rajan, 2015-12-12 This book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a “fourth leg’’ to our toolkit to make the “Materials Genome'' a reality, the science of Materials Informatics. |
| bayesian optimization with inequality constraints: Optimization Rajesh Kumar Arora, 2015-05-06 Choose the Correct Solution Method for Your Optimization ProblemOptimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and co |
| bayesian optimization with inequality constraints: Topics in Modal Analysis & Testing, Volume 8 Michael L. Mains, Brandon J. Dilworth, 2019-05-14 Topics in Modal Analysis & Testing, Volume 8: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics, 2019, the eighth volume of eight from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Modal Analysis, including papers on: Analytical Methods Modal Applications Basics of Modal Analysis Experimental Techniques Multi Degree of Freedom Testing Boundary Conditions in Environmental Testing Operational Modal Analysis Modal Parameter Identification Novel Techniques |
| bayesian optimization with inequality constraints: Advanced Human-Robot Collaboration in Manufacturing Lihui Wang, Xi Vincent Wang, József Váncza, Zsolt Kemény, 2021-06-10 This book presents state-of-the-art research, challenges and solutions in the area of human–robot collaboration (HRC) in manufacturing. It enables readers to better understand the dynamic behaviour of manufacturing processes, and gives more insight into on-demand adaptive control techniques for industrial robots. With increasing complexity and dynamism in today’s manufacturing practice, more precise, robust and practical approaches are needed to support real-time shop-floor operations. This book presents a collection of recent developments and innovations in this area, relying on a wide range of research efforts. The book is divided into five parts. The first part presents a broad-based review of the key areas of HRC, establishing a common ground of understanding in key aspects. Subsequent chapters focus on selected areas of HRC subject to intense recent interest. The second part discusses human safety within HRC. The third, fourth and fifth parts provide in-depth views of relevant methodologies and algorithms. Discussing dynamic planning and monitoring, adaptive control and multi-modal decision making, the latter parts facilitate a better understanding of HRC in real situations. The balance between scope and depth, and theory and applications, means this book appeals to a wide readership, including academic researchers, graduate students, practicing engineers, and those within a variety of roles in manufacturing sectors. |
| bayesian optimization with inequality constraints: Optimal Design and Related Areas in Optimization and Statistics Luc Pronzato, Anatoly Zhigljavsky, 2010-07-25 The present volume is a collective monograph devoted to applications of the optimal design theory in optimization and statistics. The chapters re?ect the topics discussed at the workshop “W-Optimum Design and Related Statistical Issues” that took place in Juan-les-Pins, France, in May 2005. The title of the workshop was chosen as a light-hearted celebration of the work of Henry Wynn. It was supported by the Laboratoire I3S (CNRS/Universit ́ e de Nice, Sophia Antipolis), to which Henry is a frequent visitor. The topics covered partly re?ect the wide spectrum of Henry’s research - terests. Algorithms for constructing optimal designs are discussed in Chap. 1, where Henry’s contribution to the ?eld is acknowledged. Steepest-ascent - gorithms used to construct optimal designs are very much related to general gradientalgorithmsforconvexoptimization. Inthelasttenyears,asigni?cant part of Henry’s research was devoted to the study of the asymptotic prop- ties of such algorithms. This topic is covered by Chaps. 2 and 3. The work by Alessandra Giovagnoli concentrates on the use of majorization and stoch- tic ordering, and Chap. 4 is a hopeful renewal of their collaboration. One of Henry’s major recent interests is what is now called algebraic statistics, the application of computational commutative algebra to statistics, and he was partly responsible for introducing the experimental design sub-area, reviewed in Chap. 5. One other sub-area is the application to Bayesian networks and Chap. 6 covers this, with Chap. 7 being strongly related. |
| bayesian optimization with inequality constraints: Proceedings of the International Conference on Intelligent Vision and Computing (ICIVC 2021) Harish Sharma, Vijay Kumar Vyas, Rajesh Kumar Pandey, Mukesh Prasad, 2022-03-23 This book gathers outstanding research papers presented at the International Conference on Intelligent Vision and Computing (ICIVC 2021), held online during October 03–04, 2021. ICIVC 2021 is organised by Sur University, Oman. The book presents novel contributions in intelligent vision and computing and serves as reference material for beginners and advanced research. The topics covered are intelligent systems, intelligent data analytics and computing, intelligent vision and applications collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal natural language processing. |
| bayesian optimization with inequality constraints: The 1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems (QuGOMS'11) Hansjörg Kutterer, Florian Seitz, Hamza Alkhatib, Michael Schmidt, 2014-12-06 These proceedings contain 25 papers, which are the peer-reviewed versions of presentations made at the 1st International Workshop on the Quality of Geodetic Observation and Monitoring (QuGOMS’11), held 13 April to 15 April 2011 in Garching, Germany. The papers were drawn from five sessions which reflected the following topic areas: (1) Uncertainty Modeling of Geodetic Data, (2) Theoretical Studies on Combination Strategies and Parameter Estimation, (3) Recursive State-Space Filtering, (4) Sensor Networks and Multi Sensor Systems in Engineering Geodesy, (5) Multi-Mission Approaches With View to Physical Processes in the Earth System. |
| bayesian optimization with inequality constraints: Issues in Statistics, Decision Making, and Stochastics: 2013 Edition , 2013-05-01 Issues in Statistics, Decision Making, and Stochastics: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Regular and Chaotic Dynamics. The editors have built Issues in Statistics, Decision Making, and Stochastics: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Regular and Chaotic Dynamics in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Statistics, Decision Making, and Stochastics: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/. |
| bayesian optimization with inequality constraints: Model Predictive Control James Blake Rawlings, David Q. Mayne, 2009 |
| bayesian optimization with inequality constraints: Entropy Optimization and Mathematical Programming Shu-Cherng Fang, J.R. Rajasekera, H.S.J. Tsao, 2012-12-06 Entropy optimization is a useful combination of classical engineering theory (entropy) with mathematical optimization. The resulting entropy optimization models have proved their usefulness with successful applications in areas such as image reconstruction, pattern recognition, statistical inference, queuing theory, spectral analysis, statistical mechanics, transportation planning, urban and regional planning, input-output analysis, portfolio investment, information analysis, and linear and nonlinear programming. While entropy optimization has been used in different fields, a good number of applicable solution methods have been loosely constructed without sufficient mathematical treatment. A systematic presentation with proper mathematical treatment of this material is needed by practitioners and researchers alike in all application areas. The purpose of this book is to meet this need. Entropy Optimization and Mathematical Programming offers perspectives that meet the needs of diverse user communities so that the users can apply entropy optimization techniques with complete comfort and ease. With this consideration, the authors focus on the entropy optimization problems in finite dimensional Euclidean space such that only some basic familiarity with optimization is required of the reader. |
| bayesian optimization with inequality constraints: Constraint Processing Rina Dechter, 2003-05-05 Constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. In Constraint Processing, Rina Dechter synthesizes these contributions, as well as her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. |
yz2547@cornell.edu arXiv:2112.02833v1 [stat.ML] 6 Dec 2021
Two-step lookahead Bayesian optimization with inequality constraints A PREPRINT optimization [12], constrained BO with max-value entropy search [13], and augmented Lagrangian …
Advances in Bayesian Optimization with Applications in …
III. Lookahead Bayesian Optimization with Inequality Constraints In this section, we consider the problem of optimizing an objective function subject to nonlinear constraints when a nite budget …
A BAYESIAN OPTIMIZATION ALGORITHM FOR …
In this research, we develop a Bayesian optimization algorithm to solve expensive constrained problems. We consider the presence of heteroscedastic noise in the evaluations, and propose …
No-Regret Bayesian Optimization with Unknown Equality and …
state-of-the-art constrained BO methods on problems with and without equality constraints. Keywords: Bayesian optimization; Gaussian processes; Constrained black-box optimization. 1. …
Lookahead Bayesian Optimization with Inequality Constraints …
We present numerical experiments demonstrating the perfor-mance improvements of such a lookahead approach compared to several greedy BO algorithms, including constrained …
Two-step Lookahead Bayesian Optimization with Inequality …
In response to this challenge, there are two widely-used special-purpose methods for optimizing Monte Carlo acquisition functions: infinitesimal perturbation analysis (IPA), and sample …
MIT Open Access Articles - Massachusetts Institute of …
Constrained Bayesian optimization (CBO) extends BO algorithms to handle nonlinear constraints. To do so, CBO models the objective function and the constraints with a statistical model.
Bayesian Optimization With Inequality Constraints
Constrained Bayesian Optimization and Applications Michael Adam Gelbart,2015 Bayesian optimization is an approach for globally optimizing black box functions that are expensive to …
A Framework for Nonlinearly-Constrained Gradient …
Jun 3, 2025 · In this paper, two alternative methods are developed that enable rapid and deep convergence of nonlinearly-constrained local optimization problems using a Bayesian …
Applications of constrained Bayesian optimization to process …
The bayesian optimization was done using the Trieste library in Python, the approach was used on two different cases, an algebraic approach with inequality and equality constraints, and a …
Two-step lookahead Bayesian optimization with inequality …
the discovery of optimal solutions with tight constraints. In this paper, we propose a computationally efficient two-step lookahead constrained Bayesian optimization acquisition …
Bayesian optimization under mixed constraints with a slack …
Recently, surrogate-based Bayesian optimization (BO) sub-solvers have been successfully deployed in the AL framework for a more global search in the presence of inequality …
c-TPE: Tree-structured Parzen Estimator with Inequality …
In this work, we propose constrained TPE (c-TPE), an extension of the widely-used versatile Bayesian optimization method, tree-structured Parzen estimator (TPE), to handle these …
A General Framework for Constrained Bayesian Optimization …
We present an information-theoretic framework for solving global black-box optimization problems that also have black-box constraints. Of particular interest to us is to e ciently solve problems …
Constrained Preferential Bayesian Optimization and Its …
To fill this gap, we propose con-strained preferential Bayesian optimization (CPBO), an extension of PBO that incorporates inequality constraints for the first time. Specifically, we present a …
Scalable Constrained Bayesian Optimization
We propose the scalable constrained Bayesian op-timization algorithm (SCBO), the rst scalable al-gorithm for the optimization of high-dimensional expensive functions under expensive …
MATH2640 Introduction to Optimisation 4. Inequality …
If we want to minimise f(x) subject to the inequality constraints, we need to write the inequality con-straints in the form g 1(x) ≥ b 1, ···, g k(x) ≥ b k, i = 1,k. The key point is the ≥, different …
Bayesian optimization under mixed constraints with a slack …
Recently, surrogate-based Bayesian optimization (BO) sub-solvers have been successfully deployed in the AL framework for a more global search in the presence of inequality …
Bayesian Optimization with Inequality Constraints
In this paper, we extend the Bayesian optimization frame-work naturally to scenarios of optimizing an expensive-to-evaluate function under equally …
yz2547@cornell.edu arXiv:2112.02833v1 [stat.ML…
Two-step lookahead Bayesian optimization with inequality constraints A PREPRINT optimization [12], constrained BO with max-value …
ADMMBO: Bayesian Optimization with Unknow…
We introduce an equivalent optimization by augmenting the objective function with constraints, introducing auxiliary variables for …
Advances in Bayesian Optimization with Applicat…
III. Lookahead Bayesian Optimization with Inequality Constraints In this section, we consider the problem of optimizing an objective function …
A BAYESIAN OPTIMIZATION ALGORITHM FOR CONSTRAI…
In this research, we develop a Bayesian optimization algorithm to solve expensive constrained problems. We consider the presence of …
