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| black box function optimization: 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. |
| black box function optimization: Introduction to Derivative-Free Optimization Andrew R. Conn, Katya Scheinberg, Luis N. Vicente, 2009-04-16 The first contemporary comprehensive treatment of optimization without derivatives. This text explains how sampling and model techniques are used in derivative-free methods and how they are designed to solve optimization problems. It is designed to be readily accessible to both researchers and those with a modest background in computational mathematics. |
| black box function optimization: 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. |
| black box function optimization: Theory of Evolutionary Computation Benjamin Doerr, Frank Neumann, 2019-11-20 This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influences performance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming. Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing. |
| black box function optimization: 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. |
| black box function optimization: Theory of Randomized Search Heuristics Anne Auger, Benjamin Doerr, 2011 This volume covers both classical results and the most recent theoretical developments in the field of randomized search heuristics such as runtime analysis, drift analysis and convergence. |
| black box function optimization: Operations Research Proceedings 2019 Janis S. Neufeld, Udo Buscher, Rainer Lasch, Dominik Möst, Jörn Schönberger, 2020-09-24 This book gathers a selection of peer-reviewed papers presented at the International Conference on Operations Research (OR 2019), which was held at Technische Universität Dresden, Germany, on September 4-6, 2019, and was jointly organized by the German Operations Research Society (GOR) the Austrian Operations Research Society (ÖGOR), and the Swiss Operational Research Society (SOR/ASRO). More than 600 scientists, practitioners and students from mathematics, computer science, business/economics and related fields attended the conference and presented more than 400 papers in plenary presentations, parallel topic streams, as well as special award sessions. The respective papers discuss classical mathematical optimization, statistics and simulation techniques. These are complemented by computer science methods, and by tools for processing data, designing and implementing information systems. The book also examines recent advances in information technology, which allow big data volumes to be processed and enable real-time predictive and prescriptive business analytics to drive decisions and actions. Lastly, it includes problems modeled and treated while taking into account uncertainty, risk management, behavioral issues, etc. |
| black box function optimization: Constrained Global Optimization Panos M. Pardalos, Judah Ben Rosen, 1987 |
| black box function optimization: Numerical Computations: Theory and Algorithms Yaroslav D. Sergeyev, Dmitri E. Kvasov, 2020-02-13 The two-volume set LNCS 11973 and 11974 constitute revised selected papers from the Third International Conference on Numerical Computations: Theory and Algorithms, NUMTA 2019, held in Crotone, Italy, in June 2019. This volume, LNCS 11974, consists of 19 full and 32 short papers chosen among regular papers presented at the the Conference including also the paper of the winner (Lorenzo Fiaschi, Pisa, Italy) of The Springer Young Researcher Prize for the best NUMTA 2019 presentation made by a young scientist. The papers in part II explore the advanced research developments in such interconnected fields as local and global optimization, machine learning, approximation, and differential equations. A special focus is given to advanced ideas related to methods and applications using emerging computational paradigms. |
| black box function optimization: Optimization Methods and Applications Sergiy Butenko, Panos M. Pardalos, Volodymyr Shylo, 2018-02-20 Researchers and practitioners in computer science, optimization, operations research and mathematics will find this book useful as it illustrates optimization models and solution methods in discrete, non-differentiable, stochastic, and nonlinear optimization. Contributions from experts in optimization are showcased in this book showcase a broad range of applications and topics detailed in this volume, including pattern and image recognition, computer vision, robust network design, and process control in nonlinear distributed systems. This book is dedicated to the 80th birthday of Ivan V. Sergienko, who is a member of the National Academy of Sciences (NAS) of Ukraine and the director of the V.M. Glushkov Institute of Cybernetics. His work has had a significant impact on several theoretical and applied aspects of discrete optimization, computational mathematics, systems analysis and mathematical modeling. |
| black box function optimization: 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. |
| black box function optimization: Optimization and Optimal Control Panos M. Pardalos, Rentsen Enkhbat, Ider Tseveendorj, 2003 This volume gives the latest advances in optimization and optimal control which are the main part of applied mathematics. It covers various topics of optimization, optimal control and operations research. |
| black box function optimization: Learning and Intelligent Optimization Dimitris E. Simos, Panos M. Pardalos, Ilias S. Kotsireas, 2021-12-09 This book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 15, held in Athens, Greece, in June 2021. The 30 full papers presented have been carefully reviewed and selected from 35 submissions. LION deals with designing and engineering ways of learning about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components. |
| black box function optimization: Convex Optimization Sébastien Bubeck, 2015-11-12 This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. It begins with the fundamental theory of black-box optimization and proceeds to guide the reader through recent advances in structural optimization and stochastic optimization. The presentation of black-box optimization, strongly influenced by the seminal book by Nesterov, includes the analysis of cutting plane methods, as well as (accelerated) gradient descent schemes. Special attention is also given to non-Euclidean settings (relevant algorithms include Frank-Wolfe, mirror descent, and dual averaging), and discussing their relevance in machine learning. The text provides a gentle introduction to structural optimization with FISTA (to optimize a sum of a smooth and a simple non-smooth term), saddle-point mirror prox (Nemirovski's alternative to Nesterov's smoothing), and a concise description of interior point methods. In stochastic optimization it discusses stochastic gradient descent, mini-batches, random coordinate descent, and sublinear algorithms. It also briefly touches upon convex relaxation of combinatorial problems and the use of randomness to round solutions, as well as random walks based methods. |
| black box function optimization: Optimization for Machine Learning Suvrit Sra, Sebastian Nowozin, Stephen J. Wright, 2012 An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community. |
| black box function optimization: Modern Methods of Crystal Structure Prediction Artem R. Oganov, 2011-08-04 Gathering leading specialists in the field of structure prediction, this book provides a unique view of this complex and rapidly developing field, reflecting the numerous viewpoints of the different authors. A summary of the major achievements over the last few years and of the challenges still remaining makes this monograph very timely. |
| black box function optimization: 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. |
| black box function optimization: Mathematical Optimization Theory and Operations Research Michael Khachay, Yury Kochetov, Panos Pardalos, 2019-06-12 This book constitutes the proceedings of the 18th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2019, held in Ekaterinburg, Russia, in July 2019. The 48 full papers presented in this volume were carefully reviewed and selected from 170 submissions. MOTOR 2019 is a successor of the well-known International and All-Russian conference series, which were organized in Ural, Siberia, and the Far East for a long time. The selected papers are organized in the following topical sections: mathematical programming; bi-level optimization; integer programming; combinatorial optimization; optimal control and approximation; data mining and computational geometry; games and mathematical economics. |
| black box function optimization: AI 2019: Advances in Artificial Intelligence Jixue Liu, James Bailey, 2019-11-25 This book constitutes the proceedings of the 32nd Australasian Joint Conference on Artificial Intelligence, AI 2019, held in Adelaide, SA, Australia, in December 2019. The 48 full papers presented in this volume were carefully reviewed and selected from 115 submissions. The paper were organized in topical sections named: game and multiagent systems; knowledge acquisition, representation, reasoning; machine learning and applications; natural language processing and text analytics; optimization and evolutionary computing; and image processing. |
| black box function optimization: Applications of Intelligent Systems N. Petkov, N. Strisciuglio, C.M. Travieso-González, 2018-12-21 The deployment of intelligent systems to tackle complex processes is now commonplace in many fields from medicine and agriculture to industry and tourism. This book presents scientific contributions from the 1st International Conference on Applications of Intelligent Systems (APPIS 2018) held at the Museo Elder in Las Palmas de Gran Canaria, Spain, from 10 to 12 January 2018. The aim of APPIS 2018 was to bring together scientists working on the development of intelligent computer systems and methods for machine learning, artificial intelligence, pattern recognition, and related techniques with an emphasis on their application to various problems. The 34 peer-reviewed papers included here cover an extraordinarily wide variety of topics – everything from semi-supervised learning to matching electro-chemical sensor information with human odor perception – but what they all have in common is the design and application of intelligent systems and their role in tackling diverse and complex challenges. The book will be of particular interest to all those involved in the development and application of intelligent systems. |
| black box function optimization: Stochastic Adaptive Search for Global Optimization Z.B. Zabinsky, 2013-11-27 The field of global optimization has been developing at a rapid pace. There is a journal devoted to the topic, as well as many publications and notable books discussing various aspects of global optimization. This book is intended to complement these other publications with a focus on stochastic methods for global optimization. Stochastic methods, such as simulated annealing and genetic algo rithms, are gaining in popularity among practitioners and engineers be they are relatively easy to program on a computer and may be cause applied to a broad class of global optimization problems. However, the theoretical performance of these stochastic methods is not well under stood. In this book, an attempt is made to describe the theoretical prop erties of several stochastic adaptive search methods. Such a theoretical understanding may allow us to better predict algorithm performance and ultimately design new and improved algorithms. This book consolidates a collection of papers on the analysis and de velopment of stochastic adaptive search. The first chapter introduces random search algorithms. Chapters 2-5 describe the theoretical anal ysis of a progression of algorithms. A main result is that the expected number of iterations for pure adaptive search is linear in dimension for a class of Lipschitz global optimization problems. Chapter 6 discusses algorithms, based on the Hit-and-Run sampling method, that have been developed to approximate the ideal performance of pure random search. The final chapter discusses several applications in engineering that use stochastic adaptive search methods. |
| black box function optimization: Advances and Trends in Optimization with Engineering Applications Tamas Terlaky, Miguel F. Anjos, Shabbir Ahmed, 2017-04-26 Optimization is of critical importance in engineering. Engineers constantly strive for the best possible solutions, the most economical use of limited resources, and the greatest efficiency. As system complexity increases, these goals mandate the use of state-of-the-art optimization techniques. In recent years, the theory and methodology of optimization have seen revolutionary improvements. Moreover, the exponential growth in computational power, along with the availability of multicore computing with virtually unlimited memory and storage capacity, has fundamentally changed what engineers can do to optimize their designs. This is a two-way process: engineers benefit from developments in optimization methodology, and challenging new classes of optimization problems arise from novel engineering applications. Advances and Trends in Optimization with Engineering Applications reviews 10 major areas of optimization and related engineering applications, providing a broad summary of state-of-the-art optimization techniques most important to engineering practice. Each part provides a clear overview of a specific area and discusses a range of real-world problems. The book provides a solid foundation for engineers and mathematical optimizers alike who want to understand the importance of optimization methods to engineering and the capabilities of these methods. |
| black box function optimization: Mathematical Optimization Theory and Operations Research Alexander Kononov, Michael Khachay, Valery A Kalyagin, Panos Pardalos, 2020-06-29 This book constitutes the proceedings of the 19th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2020, held in Novosibirsk, Russia, in July 2020. The 31 full papers presented in this volume were carefully reviewed and selected from 102 submissions. The papers are grouped in these topical sections: discrete optimization; mathematical programming; game theory; scheduling problem; heuristics and metaheuristics; and operational research applications. |
| black box function optimization: Learning and Intelligent Optimization Dimitris E. Simos, Panos M. Pardalos, Ilias S. Kotsireas, 2021-12-08 This book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 15, held in Athens, Greece, in June 2021. The 30 full papers presented have been carefully reviewed and selected from 35 submissions. LION deals with designing and engineering ways of learning about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components. |
| black box function optimization: Bayesian Optimization and Data Science Francesco Archetti, Antonio Candelieri, 2019-10-07 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. |
| black box function optimization: Network Flows and Monotropic Optimization R. Tyrell Rockafellar, 1999-06-01 A rigorous and comprehensive treatment of network flow theory and monotropic optimization by one of the world's most renowned applied mathematicians. This classic textbook covers extensively the duality theory and the algorithms of linear and nonlinear network optimization optimization, and their significant extensions to monotropic programming (separable convex constrained optimization problems, including linear programs). It complements our other book on the subject of network optimization Network Optimization: Continuous and Discrete Models (Athena Scientific, 1998). Monotropic programming problems are characterized by a rich interplay between combinatorial structure and convexity properties. Rockafellar develops, for the first time, algorithms and a remarkably complete duality theory for these problems. Among its special features the book: (a) Treats in-depth the duality theory for linear and nonlinear network optimization (b) Uses a rigorous step-by-step approach to develop the principal network optimization algorithms (c) Covers the main algorithms for specialized network problems, such as max-flow, feasibility, assignment, and shortest path (d) Develops in detail the theory of monotropic programming, based on the author's highly acclaimed research (e) Contains many examples, illustrations, and exercises (f) Contains much new material not found in any other textbook |
| black box function optimization: Constraint-Handling in Evolutionary Optimization Efrén Mezura-Montes, 2009-04-07 This book is the result of a special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007. It presents recent research in constraint-handling in evolutionary optimization. |
| black box function optimization: Learning and Intelligent Optimization Nikolaos F. Matsatsinis, Yannis Marinakis, Panos Pardalos, 2020-01-21 This book constitutes the thoroughly refereed pChania, Crete, Greece, in May 2019. The 38 full papers presented have been carefully reviewed and selected from 52 submissions. The papers focus on advancedresearch developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence and describe advanced ideas, technologies, methods, and applications in optimization and machine learning. |
| black box function optimization: Learning and Intelligent Optimization Ilias S. Kotsireas, Panos M. Pardalos, 2020-07-17 This book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 14, held in Athens, Greece, in May 2020. The 37 full papers presented together with one invited paper have been carefully reviewed and selected from 75 submissions. LION deals with designing and engineering ways of learning about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components. Due to the COVID-19 pandemic, LION 14 was not held as a physical meeting. |
| black box function optimization: Local Search in Combinatorial Optimization Emile H. L. Aarts, Jan Karel Lenstra, 2003-08-03 1. Introduction -- 2. Computational complexity -- 3. Local improvement on discrete structures -- 4. Simulated annealing -- 5. Tabu search -- 6. Genetic algorithms -- 7. Artificial neural networks -- 8. The traveling salesman problem: A case study -- 9. Vehicle routing: Modern heuristics -- 10. Vehicle routing: Handling edge exchanges -- 11. Machine scheduling -- 12. VLSI layout synthesis -- 13. Code design. |
| black box function optimization: Advances in Mechanical Design Jianrong Tan, |
| black box function optimization: Evolutionary Multi-Criterion Optimization Hisao Ishibuchi, Qingfu Zhang, Ran Cheng, Ke Li, Hui Li, Handing Wang, Aimin Zhou, 2021-03-24 This book constitutes the refereed proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021 held in Shenzhen, China, in March 2021. The 47 full papers and 14 short papers were carefully reviewed and selected from 120 submissions. The papers are divided into the following topical sections: theory; algorithms; dynamic multi-objective optimization; constrained multi-objective optimization; multi-modal optimization; many-objective optimization; performance evaluations and empirical studies; EMO and machine learning; surrogate modeling and expensive optimization; MCDM and interactive EMO; and applications. |
| black box function optimization: 100 Optimization Techniques Subrata Pandey, 2023-02-23 100 optimization techniques is intended as a handbook for optimization techniques. Optimization techniques and algorithms are methods used to find the most efficient solution to a problem. Different techniques and algorithms may be used to solve a particular problem, depending on the nature of the problem. Researchers from varieties of domains are using optimization algorithms to solve problems in their domain. Different optimization techniques have their pros and cons. This book serves as a handbook for researchers who wants to know about different optimization methods currently available and their operating principles. One hundred optimization techniques are arranged in an alphabetical order. Researchers and students who want to use different optimization techniques for solving their domain related problems will find this book helpful. |
| black box function optimization: Machine Learning Crash Course for Engineers Eklas Hossain, 2023-12-26 Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly. |
| black box function optimization: Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis, 2019-09-09 The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions. |
| black box function optimization: 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 |
| black box function optimization: A Toolbox for Digital Twins Mark Asch , 2022-08-04 This book brings together the mathematical and numerical frameworks needed for developing digital twins. Starting from the basics—probability, statistics, numerical methods, optimization, and machine learning—and moving on to data assimilation, inverse problems, and Bayesian uncertainty quantification, the book provides a comprehensive toolbox for digital twins. Emphasis is also placed on the design process, denoted as the “inference cycle,” the aim of which is to propose a global methodology for complex problems. Readers will find guidelines and decision trees to help them choose the right tools for the job; a comprehensive reference section with all recent methods, covering both model-based and data-driven approaches; a vast selection of examples and all accompanying code; and a companion website containing updates, case studies, and extended material. A Toolbox for Digital Twins: From Model-Based to Data-Driven is for researchers and engineers, engineering students, and scientists in any domain where data and models need to be coupled to produce digital twins. |
| black box function optimization: Multi-agent Optimization Angelia Nedić, Jong-Shi Pang, Gesualdo Scutari, Ying Sun, 2018-11-01 This book contains three well-written research tutorials that inform the graduate reader about the forefront of current research in multi-agent optimization. These tutorials cover topics that have not yet found their way in standard books and offer the reader the unique opportunity to be guided by major researchers in the respective fields. Multi-agent optimization, lying at the intersection of classical optimization, game theory, and variational inequality theory, is at the forefront of modern optimization and has recently undergone a dramatic development. It seems timely to provide an overview that describes in detail ongoing research and important trends. This book concentrates on Distributed Optimization over Networks; Differential Variational Inequalities; and Advanced Decomposition Algorithms for Multi-agent Systems. This book will appeal to both mathematicians and mathematically oriented engineers and will be the source of inspiration for PhD students and researchers. |
| black box function optimization: Enhancing Surrogate-Based Optimization Through Parallelization Frederik Rehbach, 2023-05-29 This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible. Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case. Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently. |
| black box function optimization: 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. |
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Optimization for Black-box Objective Functions
Optimization and Optimal Control, pp. 185-210 P.M. Pardalos, I. Tsevendorj, and R. Enkhbat, Editors c 2003 World Scientific Publishing Co. Optimization for Black-box Objective Functions …
Black-box Optimizer with Implicit Natural Gradient
black-box optimization; while our methods and analysis focus on black-box optimization. To the ... The distribution pis the sampling distribution for black-box function queries. Note, pcan be …
Derivative Free and Black Box Optimization
Blackbox optimization (BBO) is the study of optimization problems where the objective function is a blackbox. That is, no analytic description of the function is available, but given an arbitrary …
arXiv:2402.03243v1 [cs.LG] 5 Feb 2024
Feb 6, 2024 · Black-box optimization is a powerful approach for discovering global optima in noisy and expen-sive black-box functions, a problem widely encountered in real-world scenarios. ...
Optimum-statistical Collaboration Towards General and Ecient …
of a black-box optimization algorithm is to gradually nd x n such that f(x n) ... Given the objective function fand hierarchical partition P, we introduce a generalized denition of near-optimality …
Diffusion Models for Black-Box Optimization - arXiv.org
volve optimization of an expensive black-box function, such as optimal experimental design and product optimization. Since evaluating the black-box function is expensive, re-cent works …
Batched Large-scale Bayesian Optimization in High …
Global optimization of black-box and non-convex functions is an important component of modern machine learning and has wide applications in many areas of science and engineering [2, 19, …
Minimizing UCB: a Better Local Search Strategy in Local …
Local Bayesian optimization is a promising practical approach to solve the high dimensional black-box function optimization problem. Among them is the approx-imated gradient class of …
OpenBox: A Python Toolkit for Generalized Black-box …
Optimization 1. Introduction Black-box optimization (BBO) (Munoz~ et al., 2015) deals with optimizing an objective function under a limited budget for function evaluation. However, since …
Black-Box Optimization with Implicit Constraints for Public …
surrogate into our black-box optimization algorithm, allow-ing for a two-way mapping between the feasible region in the original space and an unconstrained latent space. As a result, while …
High-Dimensional Bayesian Optimization with Sparse Axis …
capture enough features of the objective function. A com-promise between flexibility and parsimony is essential. In this work we focus on the setting where we aim to opti-mize a black …
A black-box optimization method with polynomial-based …
model function for the black-box function. 2.1 Expected value of the black-box function Here, we describe the details of the surrogate model construction for the black-box function (exploitation …
Fourier Representations for Black-Box Optimization over …
Therefore, we divide the black box optimization problem into two settings, depending on the constraint set: (i) Generic Black Box Optimization (BBO) problem referring to the un …
Bayesian Optimization as a Flexible and Efficient Design …
Jan 30, 2024 · the choice of internal optimization procedure used to select the next sample point, and the exploitation of problem structure to improve sample efficiency. Keywords: derivative …
Transfer Learning for Bayesian Optimization: A Survey
Black-box Optimization (BBO) is a kind of optimization problem when the objective function is a black-box function. On the contrary to white-box function, black-box function has no exact form …
Noisy Blackbox Optimization with Multi-Fidelity Queries: A …
selection [36] in machine learning can be cast as sequential optimization of a function f(:) over a domain X, with black-box access. A black-box optimization algorithm evaluates the function at …
Deterministic Global Optimization of the Acquisition …
Mar 6, 2025 · function is explorative, uncertain regions result in additional local/global minima. Thus, the global optimization of the acquisition function has been stated to be important for …
A General Recipe for Likelihood-free Bayesian Optimization
objective function and improve its optimization efficiency. 2. Background Bayesian optimization (BO) aims to find a strategy that effectively maximizes a black-box function g: X→R, given …
Generative Pretraining for Black-Box Optimization - arXiv.org
2 Pretraining Black-Box Optimizers via BOOMER 2.1 Problem Statement Let f: X!R be a black-box function, where X Rdis an arbitrary d-dimensional domain. In black-box optimization …
Ali Hebbal arXiv:1809.04632v1 [math.OC] 11 Sep 2018
Bayesian algorithms are widely used to deal with expensive black-box function optimization. They are based on surrogate models, allowing the emulation of the statistical relationship between …
Surrogate Model Guided Optimization of Expensive Black …
Here, we describe an optimization approach based on surrogate models and dive\ rse sampling strategies to accelerate the search for the Pareto solutions. We use a separate surrogate …
Constrained Discrete Black-Box Optimization using Mixed …
2.1. Model-Based Black-Box Optimization Model-based Black-box Optimization (MBO) is a broad family of methods that includes Bayesian optimization as a special case (Mockus et …
Team Optuna Developers’ Method for Black-Box …
black box functions, where gradients and smoothness parameters are unavailable. What makes these problems challenging is that the evaluation of these objective function oftentimes are …
B2Opt: Learning to Optimize Black-box Optimization with …
provement with fewer function evaluations even if the training dataset is a low-delity set of the target black-box function. • We design the B2Opt framework to realize the automated GA, …
Generative Pretraining for Black-box Optimization - arXiv.org
black-box function during optimization, unlike in online BBO where most approaches (Snoek et al.,2012;Shahriari et al.,2016) utilize iterative online solving. One natural approach for offline …
arXiv:2211.00162v4 [math.OC] 6 Feb 2025
to solve black-box optimization problems (Xu et al.,2022a), has recently been becoming more and more popular. The general idea of efficient global optimization is to build a surrogate model, …
Generative Pretraining for Black-box Optimization - arXiv.org
black-box function during optimization, unlike in online BBO where most approaches (Snoek et al.,2012;Shahriari et al.,2016) utilize iterative online solving. One natural approach for offline …
Lecture 1: Intrinsic complexity of Black-Box Optimization
Lecture 1: Intrinsic complexity of Black-Box Optimization Yurii Nesterov, CORE/INMA (UCL) February 24, 2012 Yu. Nesterov Complexity of Black-Box Optimization 1/26February 24, 2012 …
blackbox: A procedure for parallel optimization of expensive …
The procedure proposed here allows to perform e cient optimization of expensive black-box functions. Usage of the response surface methodology [1] based on radial basis functions [2] …
A Generative Neural Annealer for Black-Box Combinatorial …
the number of variables. When the objective function is a black box—accessible only through queries, each of which may be costly (e.g., due to long-running simulations or physical …
Batched Large-scale Bayesian Optimization in High …
tive approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a rela-tively small number of queries. …
High-dimensional Black-box Optimization Under Uncertainty
trade-o is required to nd the most promising data points for black-box function evaluation. In §2.9, we will elaborate on di erent input selection strategies for surrogate optimization. Most of the …
Solving Black-Box Optimization Challenge via Learning …
them as a black-box function is a part of black-box optimization. It has been successfully used for many different tasks such as hyper-parameter tuning for convolutional neural networks (Snoek …
Real-Parameter Black-Box Optimization Benchmarking 2009: …
benchmark function testbed, (2) design of an experimental set-up, (3) generation of data output for (4) post-processing and presentation of the results in graphs and tables. What remains to …
Trust Region Policy Optimization - Texas A&M University
policy iteration); and (3) derivative-free optimization meth-ods, such as the cross-entropy method (CEM) and covari-ance matrix adaptation (CMA), which treat the return as a black box function …
Monte Carlo Tree Search based Space Transfer for Black-box …
surrogate model and expected improvement (EI) as the acquisition function) in the experiments, and the performance can be further enhanced by advanced techniques. 2 Background 2.1 …
Black-Box Reductions for Zeroth-Order Gradient …
Black-Box Reduction Techniques: Allen-Zhu and Hazan (2016) proposed a black-box reduction method for convex problems. The reduction method is in a black-box manner, which means …
PDFO: Powell's Derivative-Free Optimization Solvers with …
Derivative-free optimization (DFO) • Minimize a function f using function values but not derivatives. • A typical case: f is a black box without an explicit formula. x f f(x) • Here, the …
Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box …
Black-box Optimization. While recent studies aim to solve offline Black-box Optimization (BBO) using a pre-collected dataset [Li et al., 2024, Krishnamoorthy et al., 2023, Fu and Levine, …
GENERATIVE PRETRAINING FOR BLACK-B OPTIMIZATION
to actively query the black-box function during optimization, unlike in online BBO where most approaches (Snoek et al., 2012; Shahriari et al., 2016) utilize iterative online solving. One …
Diffusion Models for Black-Box Optimization - OpenReview
volve optimization of an expensive black-box function, such as optimal experimental design and product optimization. Since evaluating the black-box function is expensive, re-cent works …
Combinatorial Black-Box Optimization with Expert Advice
There exists a vast literature on black-box function optimization when it comes to functions over the continuous domains. Bayesian Optimization (BO) [19] is a well-established paradigm for …
NeuralBO: A Black-box Optimization Algorithm using Deep …
struct an acquisition function that trades off between two potentially con-flicting objectives: exploration and exploitation. The acquisition function is then optimized to suggest a point …
Additive Tree-Structured Covariance Function for Conditional …
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization Xingchen Ma Matthew B. Blaschko ESAT-PSI, KU Leuven, Belgium ESAT-PSI, …
GENERATIVE PRETRAINING FOR BLACK-BOX …
to actively query the black-box function during optimization, unlike in online BBO where most approaches (Snoek et al., 2012; Shahriari et al., 2016) utilize iterative online solving. One …
Monte Carlo Tree Descent for Black-Box Optimization
Black-Box Optimization (BBO), also referred to as Derivative-free or Zeroth-order Optimization, ... progress with respect to the number of function evaluations. Existing work on BBO can be …
Chapter 10 Bayesian Optimization - Springer
Bayesian Optimization Hao Wang and Kaifeng Yang Abstract Bayesian Optimization (BO) is a sequential optimization strategy initially proposed to solve the single-objective black-box …
OpenBox: A Generalized Black-box Optimization Service
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. However, it remains a challenge for …
Surrogate-basedmethodsforblack-boxoptimization
In this paper, we survey about a special method for solving the following optimization problem: z = min {f(x) | x ∈ D}, (1) in which f : Rd → R is a continuous black-box and D is a compact subset …
Particle swarm with radial basis function surrogates for …
Particle Swarm with Radial Basis Function Surrogates for Expensive Black-Box Optimization Rommel G. Regis Department of Mathematics, Saint Joseph’s University, Philadelphia, PA …
