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Author: Napat Rujeerapaiboon
Publisher:
ISBN:
Category :
Languages : en
Pages : 177
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Book Description
Mots-clés de l'auteur: convex optimization ; conic programming ; distributionally robust optimization ; stochastic programming ; linear decision rules ; portfolio optimization ; growth-optimal portfolio ; value-at-risk ; Chebyshev inequality ; electricity market.
Author: Napat Rujeerapaiboon
Publisher:
ISBN:
Category :
Languages : en
Pages : 177
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Book Description
Mots-clés de l'auteur: convex optimization ; conic programming ; distributionally robust optimization ; stochastic programming ; linear decision rules ; portfolio optimization ; growth-optimal portfolio ; value-at-risk ; Chebyshev inequality ; electricity market.
Author: Christopher George McCord
Publisher:
ISBN:
Category :
Languages : en
Pages : 190
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Book Description
Optimization under uncertainty forms the foundation for many of the fundamental problems the operations research community seeks to solve. In this thesis, we develop and analyze algorithms that incorporate ideas from machine learning to optimize uncertain objectives directly from data. In the first chapter, we consider problems in which the decision affects the observed outcome, such as in personalized medicine and pricing. We present a framework for using observational data to learn to optimize an uncertain objective over a continuous and multi-dimensional decision space. Our approach accounts for the uncertainty in predictions, and we provide theoretical results that show this adds value. In addition, we test our approach on a Warfarin dosing example, and it outperforms the leading alternative methods. In the second chapter, we develop an approach for solving dynamic optimization problems with covariates that uses machine learning to approximate the unknown stochastic process of the uncertainty. We provide theoretical guarantees on the effectiveness of our method and validate the guarantees with computational experiments. In the third chapter, we introduce a distributionally robust approach for incorporating covariates in large-scale, data-driven dynamic optimization. We prove that it is asymptotically optimal and provide a tractable general-purpose approximation scheme that scales to problems with many temporal stages. Across examples in shipment planning, inventory management, and finance, our method achieves improvements of up to 15% over alternatives. In the final chapter, we apply the techniques developed in previous chapters to the problem of optimizing the operating room schedule at a major US hospital. Our partner institution faces significant census variability throughout the week, which limits the amount of patients it can accept due to resource constraints at peak times. We introduce a data-driven approach for this problem that combines machine learning with mixed integer optimization and demonstrate that it can reliably reduce the maximal weekly census.
Author: Warren B. Powell
Publisher: John Wiley & Sons
ISBN: 0470182954
Category : Mathematics
Languages : en
Pages : 487
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Book Description
A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.
Author: Isaac E Elishakoff
Publisher: World Scientific
ISBN: 190897818X
Category : Technology & Engineering
Languages : en
Pages : 425
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Book Description
The volume presents a collaboration between internationally recognized experts on anti-optimization and structural optimization, and summarizes various novel ideas, methodologies and results studied over 20 years. The book vividly demonstrates how the concept of uncertainty should be incorporated in a rigorous manner during the process of designing real-world structures. The necessity of anti-optimization approach is first demonstrated, then the anti-optimization techniques are applied to static, dynamic and buckling problems, thus covering the broadest possible set of applications. Finally, anti-optimization is fully utilized by a combination of structural optimization to produce the optimal design considering the worst-case scenario. This is currently the only book that covers the combination of optimization and anti-optimization. It shows how various optimization techniques are used in the novel anti-optimization technique, and how the structural optimization can be exponentially enhanced by incorporating the concept of worst-case scenario, thereby increasing the safety of the structures designed in various fields of engineering./a
Author: Massimiliano Vasile
Publisher: Springer Nature
ISBN: 3030805425
Category : Technology & Engineering
Languages : en
Pages : 448
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Book Description
The 2020 International Conference on Uncertainty Quantification & Optimization gathered together internationally renowned researchers in the fields of optimization and uncertainty quantification. The resulting proceedings cover all related aspects of computational uncertainty management and optimization, with particular emphasis on aerospace engineering problems. The book contributions are organized under four major themes: Applications of Uncertainty in Aerospace & Engineering Imprecise Probability, Theory and Applications Robust and Reliability-Based Design Optimisation in Aerospace Engineering Uncertainty Quantification, Identification and Calibration in Aerospace Models This proceedings volume is useful across disciplines, as it brings the expertise of theoretical and application researchers together in a unified framework.
Author: Ikjin Lee
Publisher: LAP Lambert Academic Publishing
ISBN: 9783843354585
Category : Dimension reduction (Statistics)
Languages : en
Pages : 156
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Book Description
The objective of this book is to study new methods for inverse reliability analysis and robust design optimization using the univariate dimension reduction method (DRM). The current research effort involves: (1) reliability-based robust design optimization (RBRDO) method using the mean-based DRM; (2) reliability-based design optimization (RBDO) method using the inverse reliability analysis and the most probable point (MPP)-based DRM; (3) design sensitivity analyses for RBDO using the MPP-based DRM; and (4) system inverse reliability analysis and RBDO using the MPP-based DRM and Ditlevsen s second order upper bound. For robust design optimization (RDO), the mean-based DRM improves the accuracy of estimation of statistical moments which are used as a cost function of RDO. For component and system RBDO, the MPP-based DRM improves the accuracy and efficiency of estimation of probability of failure.
Author: Warren B. Powell
Publisher: John Wiley & Sons
ISBN: 1119815037
Category : Mathematics
Languages : en
Pages : 1090
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Book Description
REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
Author: Oliver Kramer
Publisher: Springer Science & Business Media
ISBN: 3642386520
Category : Technology & Engineering
Languages : en
Pages : 137
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Book Description
This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.
Author: Chao Hu
Publisher: Springer
ISBN: 3319925741
Category : Technology & Engineering
Languages : en
Pages : 350
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Book Description
This book presents state-of-the-art probabilistic methods for the reliability analysis and design of engineering products and processes. It seeks to facilitate practical application of probabilistic analysis and design by providing an authoritative, in-depth, and practical description of what probabilistic analysis and design is and how it can be implemented. The text is packed with many practical engineering examples (e.g., electric power transmission systems, aircraft power generating systems, and mechanical transmission systems) and exercise problems. It is an up-to-date, fully illustrated reference suitable for both undergraduate and graduate engineering students, researchers, and professional engineers who are interested in exploring the fundamentals, implementation, and applications of probabilistic analysis and design methods.
Author: C. A. Mota Soares
Publisher: Springer Science & Business Media
ISBN: 1402053703
Category : Technology & Engineering
Languages : en
Pages : 861
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Book Description
III European Conference on Computational Mechanics: Solids, Structures and Coupled Problem in Engineering Computational Mechanics in Solid, Structures and Coupled Problems in Engineering is today a mature science with applications to major industrial projects. This book contains the edited version of the Abstracts of Plenary and Keynote Lectures and Papers, and a companion CD-ROM with the full-length papers, presented at the III European Conference on Computational Mechanics: Solids, Structures and Coupled Problems in Engineering (ECCM-2006), held in the National Laboratory of Civil Engineering, Lisbon, Portugal 5th - 8th June 2006. The book reflects the state-of-art of Computation Mechanics in Solids, Structures and Coupled Problems in Engineering and it includes contributions by the world most active researchers in this field.
