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This book contains problems of stochastic optimization and identification. Results concerning uniform law of large numbers, convergence of approximate estimates of extreme points, as well as empirical estimates of functionals with probability 1 and in probability are presented. Audience: Specialists in stochastic optimization and estimations, postgraduate students, and graduate students studying such topics
This monograph focuses on the construction of regression models with linear and non-linear constrain inequalities from the theoretical point of view. Unlike previous publications, this volume analyses the properties of regression with inequality constrains, investigating the flexibility of inequality constrains and their ability to adapt in the presence of additional a priori information The implementation of inequality constrains improves the accuracy of models, and decreases the likelihood of errors. Based on the obtained theoretical results, a computational technique for estimation and prognostication problems is suggested. This approach lends itself to numerous applications in various practical problems, several of which are discussed in detail The book is useful resource for graduate students, PhD students, as well as for researchers who specialize in applied statistics and optimization. This book may also be useful to specialists in other branches of applied mathematics, technology, econometrics and finance
Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering.
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.
The Routledge Handbook of Transportation offers a current and comprehensive survey of transportation planning and engineering research. It provides a step-by-step introduction to research related to traffic engineering and control, transportation planning, and performance measurement and evaluation of transportation alternatives. The Handbook of Transportation demonstrates models and methods for predicting travel and freight demand, planning future transportation networks, and developing traffic control systems. Readers will learn how to use various engineering concepts and approaches to make future transportation safer, more efficient, and more sustainable. Edited by Dušan Teodorović and featuring 29 chapters from more than 50 leading global experts, with more than 200 illustrations, the Routledge Handbook of Transportation is designed as an invaluable resource for professionals and students in transportation planning and engineering.
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.
Issue 07 Jan-Feb-Mar 2016 Assessment Of Irregularities-Bursts And Catastrophic Changes In Compressor Units A.M. Pashayev, A.Kh. Janahmadov, N.G. Javadov, M.Y. Javadov The paper assesses the type of irregularities of type bursts and disastrous wear during operation of the tested CU equipment. Using the flicker-noise spectroscopy (FNS), which is used to estimated the parameters of the singular component of the power spectrum of the signal and find significant changes in the dimensionless parameters of unsteadiness providing an indication of the approaching moments of a catastrophic deterioration of the equipment. Mineral Composition And Textural-Structural Peculiarities Of Ore, And Mineral Formation Stage Of The Gedabey Gold-Copper Deposit (Lesser Caucasus) M. Aliyev, G. Huseynov The mineral composition and textural and structural characteristics of ores are studied, also the phases and stages of mineralization, which are an important source of information on the conditions of formation of the deposit, time allocation of gold and its spatial association with certain mineral assemblages and associations. Consideration of these issues can come to an understanding of the factors behind the differences in the scale and extent of gold deposits of various types, as well as to form a mineralogical search features gold-bearing mineralization. Development Of Decision-Making Algorithm On Efficiency Of Operators And Traffic Controllers Of Air Transport Based On Their Psycho-Physiological Conditions And Productivities R.M. Jafarzade, T.R. Jafarzade By processing the data on the human-operator active performance with respect to their psycho-physiological conditions and productivities in the human-machine systems, we developed the algorithm for the possibilities of further execution of their (operator) duties in the incomplete and unclear initial data. Using fuzzy clustering and the interval fuzzy sets of the second type, and the coordinated assessment of expert opinions on the binary relationship of objects from the class with recommendations on the further implementation of their activities, we obtained the individual assessment of the alternative recommendations for each of the tested objects from the set of objects. The calculation for the experimental data is provided. The proposed approach can be used for the adaptive selection of recommendations on the continuation of duties of air operators and air traffic controllers by taking into account the dynamic of changes of their psycho-physiological states and productivities. Management Of Portfolio Of Securities On The Basis Of Minimization Of The Conditional Expected Losses S.M. Javadova On the basis of a method of empirical averages for the general problem of stochastic programming, convergence of the solution of an approximating task to the solution of a problem of conditional minimization of the expected losses when forming an investment portfolio is proved. Numerical calculations on a concrete example of two joint stock companies are received by means of the program of linear programming in MATLAB system.