Simulation and Statistics with Excel
Simulation and Statistics with Excel: An Introduction to Business Students uses a versatile and accessible resource, the Microsoft Excel spreadsheet, to cover the concepts essential to understanding the basic principles and approaches of statistical simulation
Recent Trends in AI Enabled Technologies
This book constitutes the refereed proceedings of the First International Conference on Recent Trends in AI Enabled Technologies, ThinkAI 2023, which took place in Hyderabad, India, in December 2023. The 7 full papers presented in these proceedings were carefully reviewed and selected from 51 submissions. The conference focuses on on up to date topics and recent trends in artificial intelligence and related technologies.
Statistical Thinking in Clinical Trials
The authors present the essence of statistical reasoning in clinical trials, peppered with well-known clinical trials This book uses only a small number of important principles to develop the entire statistical foundation of clinical trials.
From Concepts to Code
The breadth of problems that can be solved with data science is astonishing, and this book provides the required tools and skills to a broad audience. The necessary background in computer science, mathematics, and statistics is provided in an approachable manner.
Dynamic Network Flows with Adaptive Route Choice Based on Current Information
In this book Lukas Graf studies dynamic network flows which are a model for individual car traffic in road networks. It is assumed that drivers choose their routes based on information about the current state of the network in such a way as to selfishly minimize their own arrival time at their destination. Whilst on their journey the drivers adapt their current route choices based on the changing state of the network. A dynamic flow wherein every (infinitesimally small) flow particle behaves in this way is then called an instantaneous dynamic equilibrium. After giving a mathematically precise definition of this equilibrium concept the author shows existence of those equilibrium flows, studies their computational complexity and derives bounds on their quality.
Mathematical Sciences and Applications
The papers appearing in these proceedings are part of talks, oral presentations and poster presentations given at the International Conference on Mathematical Sciences and Applications held in the Department of Mathematics, Dr. Bhimrao Ambedkar University, Agra (India) from March 24-26, 2023.
Numerical Methods for Scientists and Engineers
This book is designed as a primary textbook for a one-semester course on Numerical Methods for sophomore or junior-level students. It covers the fundamental numerical methods required for scientists and engineers, as well as some advanced topics which are left to the discretion of instructors.
Actuarial Loss Models
This book covers part of the learning outcomes of the Fundamentals of Actuarial Mathematics (FAM) exam and the Advanced Short-Term Actuarial Mathematics (ASTAM) exam administered by the Society of Actuaries. It can be used by students and practitioners who prepare for actuarial exams.
The Eternal Life
Applied Math to Life ResearchUsing the objectiveness of the Mathematical Language to analyze, study and conclude on the spiritual eternal life, the successive lifetimes, life, death and related subjects.A "must not miss" book, quite deep on its subjects but joyfully presented on a youngish style.
Reliability
This text provides an elementary introduction to the probabilistic models and statistical methods used by reliability engineers that are applied to a system of components. Probability models include the exponential distribution, Weibull distribution, competing risks, mixtures, accelerated life model, proportional hazards model, and repairable systems models. Statistical methods emphasize determining point and interval estimates for parameters from censored data sets. Applications are drawn from a variety of disciplines. Over 600 exercises make this text appropriate for a class on reliability.
Functional Analysis with Applications
This book on functional analysis covers all the basics of the subject (normed, Banach and Hilbert spaces, Lebesgue integration and spaces, linear operators and functionals, compact and self-adjoint operators, small parameters, fixed point theory) with a strong focus on examples, exercises and practical problems, thus making it ideal as course material but also as a reference for self-study.
Complex Analysis
This book is an in-depth and modern presentation of important classical results in complex analysis and is suitable for a first course on the topic, as taught by the authors at several universities. The level of difficulty of the material increases gradually from chapter to chapter, and each chapter contains many exercises with solutions and applications of the results, with the particular goal of showcasing a variety of solution techniques.
Single Variable Calculus
A computer algebra system such as Mathematica(R) is able to do much more than just numerics: This text shows how to tackle real mathematical problems from basic analysis. The reader learns how Mathematica(R) represents domains, qualifiers and limits to implement actual proofs - a requirement to unlock the huge potential of Mathematica(R) for a variety of applications.
Beyond Multiple Linear Regression
For advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II and courses in Generalized Linear Models, Longitudinal Data Analysis, Correlated Data, Multilevel Models. Material on R at the end of each chapter. Solutions manual for qualified instructors.
Principles of Uncertainty
Like the De Groot winning first edition, the second edition of Principles of Uncertainty is an accessible, comprehensive guide to the theory of Bayesian Statistics written in an appealing, inviting style, and packed with interesting examples.
Analysis of Distributional Data
In a time when increasingly larger and complex data collections are being produced, it is clear that new and adaptive forms of data representation and analysis have to be conceived and implemented. Distributional data, i.e., data where a distribution rather than a single value is recorded for each descriptor, on each unit, come into this framework. Distributional data may result from the aggregation of large amounts of open/collected/generated data, or it may be directly available in a structured or unstructured form, describing the variability of some features. This book provides models and methods for the representation, analysis, interpretation, and organization of distributional data, taking into account its specific nature, and not relying on a reduction to single values, to be conform to classical paradigms. Conceived as an edited book, gathering contributions from multiple authors, the book presents alternative representations and analysis' methods for distributional data of different types, and in particular, -Uni- and bi-variate descriptive statistics for distributional data-Clustering and classification methodologies-Methods for the representation in low-dimensional spaces-Regression models and forecasting approaches for distribution-valued variablesFurthermore, the different chapters -Feature applications to show how the proposed methods work in practice, and how results are to be interpreted, -Often provide information about available software.The methodologies presented in this book constitute cutting-edge developments for stakeholders from all domains who produce and analyse large amounts of complex data, to be analysed in the form of distributions. The book is hence of interest for companies operating not only in the area of data analytics, but also on logistics, energy and finance. It also concerns national statistical institutes and other institutions at European and international level, where microdata is aggregated to preserve confidentiality and allow for analysis at the appropriate regional level. Academics will find in the analysis of distributional data a challenging up-to-date field of research.
Analysis of Longitudinal Data with Examples
Development in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. This book will present a discussion of the modern approaches to inference, including the links between the theories of estimators and various types of efficient statistical models including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case-studies from a number of different areas.Key Features: -Includes the most up-to-date methods-Use simple examples to demonstrate complex methods-Uses real data from a number of areas-Examples utilize R code
Martingale Methods in Statistics
This gives a comprehensive introduction to the (standard) statistical analysis based on the theory of martingales and develops entropy methods in order to treat dependent data in the framework of martingales. The author starts a summary of the martingale theory, and then proceeds to give full proofs of the martingale central limit theorems.
Disruptive Technologies and Optimization Towards Industry 4.0 Logistics
This contributed volume guides researchers and practitioners on resource collaborative management of supply chains and manufacturing enterprises within an industrial internet technological environment. The book comprises 10 chapters that cover two major topics in the subject of logistics 4.0, namely the utilization of both disruptive technologies and optimization techniques in smart logistic management. With global research on the book's topic expanding rapidly across various directions and disciplines, it provides a structured framework for international experts to showcase outstanding work and unique approaches. Researchers and students will find the comprehensive outline on collaborative optimization and management of smart manufacturing and production, warehousing, inventory, logistics, transportation, integrated supply chain, and supply network within the industrial internet platform a beneficial guide to understanding current and future practical problems that arise in manufacturing and supply chain management.
Mathematics of Information
Ausgehend vom Shannon-Wiener-Zugang zur mathematischen Informationstheorie beginnt das Buch mit einer Abgrenzung der Begriffe Nachricht und Information und der axiomatischen Zuordnung einer Informationsmenge zu einer Wahrscheinlichkeit. Im zweiten Teil werden abz瓣hlbare Wahrscheinlichkeitsr瓣ume untersucht, deren mittlere Informationsmenge zur Definition der Shannon-Entropie f羹hrt; dabei werden drei klassische Anwendungen der Shannon-Entropie in der statistischen Physik, der mathematischen Statistik und der Nachrichtentechnik vorgestellt, und es wird ein erster Einblick in den Bereich Quanteninformation gegeben. Der dritte Teil behandelt die informationstheoretische Analyse dynamischer Systeme.Das Buch baut auf Bachelor-Wissen auf und legt gro?en Wert auf exakte Beweisf羹hrung.
Physical and Physiological Analysis of Soya Seeds from the 2016/2017 Harvest
The use of high quality seeds allows access to genetic advances, with guarantees of quality and adaptation technologies in the various producing regions. The factors that influence seed quality can occur during the production phase in the field, during harvesting, drying, processing, storage, transport and sowing. Quality control must therefore be established, which includes analysing and certifying the seed in order to guarantee the genetic purity of the cultivars, thus assuring the farmer of a pure batch with high vigour, which can establish a uniform stand in the field. The quality of soya bean seed is essential for obtaining an adequate plant stand, because when the seed is placed in the soil to germinate and emerge, it usually encounters adverse conditions of humidity and temperature, among other factors. Considering the importance of seed for agricultural production and aiming to achieve higher levels of profitability, this study will present data on the physical and physiological quality of soya bean seeds from the 2017/2018 harvest in northern Rio Grande do Sul.
Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume II, Part II
This title is part of UC Press's Voices Revived program, which commemorates University of California Press's mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1967.
Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume II, Part II
This title is part of UC Press's Voices Revived program, which commemorates University of California Press's mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1967.
Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume III
This title is part of UC Press's Voices Revived program, which commemorates University of California Press's mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1972.
Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume III
This title is part of UC Press's Voices Revived program, which commemorates University of California Press's mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1972.
Game Theory and Applications
This textbook provides an overview of the fundamentals of game theory and its applications in various fields. It introduces game theory as an established toolkit for the mathematical analysis and evaluation of strategic decisions. Through applied exercises, it introduces the basic concepts of game theory and offers students from various disciplines the opportunity to practice the concepts through in-depth training. The textbook addresses advanced students of economics, business administration, and related disciplines, university graduates with basic mathematical training as well as interested readers from all fields. For this, it provides student-friendly explanations, a variety of exercises and problems, and useful references to further reading. The book is divided into a beginner-friendly theory section, in which the most important aspects are presented in a compact and clear manner, and an application-oriented problem section, in which the readers can directly check what they have learned and find many application examples. The latter can also be used as a source of inspiration for instructors.
Parametric Optimization and Related Topics
No detailed description available for "Parametric Optimization and Related Topics".
Finite Difference Methods on Irregular Networks
No detailed description available for "Finite Difference Methods on Irregular Networks".
The Arithmetic of Life and Death
Whether you realize it or not, numbers are everywhere--and integral to almost every facet of your life . . . from your next raise in pay to the inevitable rise of inflation, your weekly family budget to your end of the national debt. And as George Shaffner amazingly reveals, there are discerning answers (and a great measure of comfort) in numbers. In The Arithmetic of Life, he applies the basic principles of mathematics--addition, subtraction, multiplication, and division--to some of the most profound and just plain puzzling questions of our time. Illuminated with anecdotes, humor, and insight, each chapter explains a unique part of life that can be understood only through the magic of numbers. Whether it's an unconventional theory on why more things go wrong than right, a simple calculation of how much it will cost you to smoke for a lifetime, why crime (accumulatively) doesn't pay, or a glimpse into the probability of life after death, this enlightening and lucidly reasoned book will forever change the way you think about numbers--and the world around you.
Complex Heterogeneous Systems
The author's research on energy storage systems generally was confronted with five characteristics, i.e., complex, interacting, transporting, reacting, and heterogeneous systems. Hence, we refer to these kind of systems as Complex Heterogeneous Systems (CHeSs). The work considers interacting systems that exchange energy, mass, information, etc. in various ways. The elementary building blocks of CHeSs are based on fundamental thermodynamic, chemical, material, physical, and mathematical principles such as variational and graph-theoretic concepts. It investigates ways of defining complexity, computing percolation thresholds, making smart decisions also by learning from data/past experiences (e.g., providing a systematic approach towards battery management systems), and identifying battery life (e.g., by blow-up analysis of highly nonlinear concentrated solutions). Ultimately, the elaborated tools shall allow the reader to obtain a general understanding for simulating (also on quantum computers), controlling, and developing CHeSs as well as to pave the way for a general theory on CHeSs generalizing the view on complexity, measurement, estimation, and control.
Comparative Genomics
This book constitutes the proceedings of the 21st International Conference on Comparative Genomics, RECOMB-CG 2024, which was held in Boston, MA, USA, during April 27-28, 2024. The 13 full papers presented in this book were carefully reviewed and selected from 21 submissions. The papers are divided into the following topical sections: phylogenetic networks; homology and phylogenetic reconstruction; tools for evolution reconstruction; genome rearrangements; and genome evolution.
Objective Bayesian Inference
Bayesian analysis is today understood to be an extremely powerful method of statistical analysis, as well an approach to statistics that is particularly transparent and intuitive. It is thus being extensively and increasingly utilized in virtually every area of science and society that involves analysis of data.A widespread misconception is that Bayesian analysis is a more subjective theory of statistical inference than what is now called classical statistics. This is true neither historically nor in practice. Indeed, objective Bayesian analysis dominated the statistical landscape from roughly 1780 to 1930, long before 'classical' statistics or subjective Bayesian analysis were developed. It has been a subject of intense interest to a multitude of statisticians, mathematicians, philosophers, and scientists. The book, while primarily focusing on the latest and most prominent objective Bayesian methodology, does present much of this fascinating history.The book is written for four different audiences. First, it provides an introduction to objective Bayesian inference for non-statisticians; no previous exposure to Bayesian analysis is needed. Second, the book provides an overview of the development and current state of objective Bayesian analysis and its relationship to other statistical approaches, for those with interest in the philosophy of learning from data. Third, the book presents a careful development of the particular objective Bayesian approach that we recommend, the reference prior approach. Finally, the book presents as much practical objective Bayesian methodology as possible for statisticians and scientists primarily interested in practical applications.
So You Think You Have Brains?
Really Challenging Mathematical Puzzles Newspapers and similar media often feature crossword puzzles, word-searches, general knowledge quizzes, problems on word games like Scrabble and other puzzles, typically on 'brain' games like chess and bridge. As one who has always been fascinated by mathematics, the author offers testing examples from the world of numbers which will require a fair amount of brain-work if you are to find correct solutions. The world of numbers displays much variety and some strange traits. That world forms the first of our group of problems and then the book focuses attention on the three "p's" palindromes, primes, and powers. Later, geometry problems and examples where numbers can be substituted for letters conclude the book. We hope readers enjoy being challenged!
Upper Bounds for Grothendieck Constants, Quantum Correlation Matrices and CCP Functions
This book concentrates on the famous Grothendieck inequality and the continued search for the still unknown best possible value of the real and complex Grothendieck constant (an open problem since 1953). It describes in detail the state of the art in research on this fundamental inequality, including Krivine's recent contributions, and sheds light on related questions in mathematics, physics and computer science, particularly with respect to the foundations of quantum theory and quantum information theory. Unifying the real and complex cases as much as possible, the monograph introduces the reader to a rich collection of results in functional analysis and probability. In particular, it includes a detailed, self-contained analysis of the multivariate distribution of complex Gaussian random vectors. The notion of Completely Correlation Preserving (CCP) functions plays a particularly important role in the exposition.The prerequisites are a basic knowledge of standard functional analysis, complex analysis, probability, optimisation and some number theory and combinatorics. However, readers missing some background will be able to consult the generous bibliography, which contains numerous references to useful textbooks. The book will be of interest to PhD students and researchers in functional analysis, complex analysis, probability, optimisation, number theory and combinatorics, in physics (particularly in relation to the foundations of quantum mechanics) and in computer science (quantum information and complexity theory).
Extreme Values in Random Sequences
The main subject is the probabilistic extreme value theory. The purpose is to present recent results related to limiting distributions of maxima in incomplete samples from stationary sequences, and results related to extremal properties of different combinatorial configurations. The necessary contents related to regularly varying functions and basic results of extreme value theory are included in the first two chapters with examples, exercises and supplements. The motivation for consideration maxima in incomplete samples arises from the fact that real data are often incomplete. A sequence of observed random variables from a stationary sequence is also stationary only in very special cases. Hence, the results provided in the third chapter are also related to non-stationary sequences. The proof of theorems related to joint limiting distribution of maxima in complete and incomplete samples requires a non-trivial combination of combinatorics and point process theory. Chapter four provides results on the asymptotic behavior of the extremal characteristics of random permutations, the coupon collector's problem, the polynomial scheme, random trees and random forests, random partitions of finite sets, and the geometric properties of samples of random vectors. The topics presented here provide insight into the natural connections between probability theory and algebra, combinatorics, graph theory and combinatorial geometry. The contents of the book may be useful for graduate students and researchers who are interested in probabilistic extreme value theory and its applications.
The Art of Finding Hidden Risks
This text gives a comprehensive, largely self-contained treatment of multivariate heavy tail analysis. Emphasizing regular variation of measures means theory can be presented systematically and without regard to dimension. Tools are developed that allow a flexible definition of "extreme" in higher dimensions and permit different heavy tails to coexist on the same state space leading to "hidden regular variation" and "steroidal regular variation". This emphasizes when estimating risks, it is important to choose the appropriate heavy tail. Theoretical foundations lead naturally to statistical techniques; examples are drawn from risk estimation, finance, climatology and network analysis. Treatments target a broad audience in insurance, finance, data analysis, network science and probability modeling. The prerequisites are modest knowledge of analysis and familiarity with the definition of a measure; regular variation of functions is reviewed but is not a focal point.
Elements of Stochastic Modelling (Third Edition)
This is a thoroughly revised and expanded third edition of a successful university textbook that provides a broad introduction to key areas of stochastic modelling. The previous edition was developed from lecture notes for two one-semester courses for third-year science and actuarial students at the University of Melbourne.This book reviews the basics of probability theory and presents topics on Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. It also features elements of stochastic calculus and introductory mathematical finance. This makes the book suitable for a larger variety of university courses presenting the fundamentals of modern stochastic modelling.To make the text covering a lot of material more appealing and accessible to the reader, instead of rigorous proofs we often give only sketches of the arguments, with indications as to why a particular result holds and also how it is related to other results, and illustrate them by examples. It is in this aspect that the present, third edition differs from the second one: the included background material and argument sketches have been extended, made more graphical and informative. The whole text was reviewed and streamlined wherever possible to make the book more attractive and useful for readers. Where appropriate, the book includes references to more specialised texts on respective topics that contain both complete proofs and more advanced material.
Elements of Stochastic Modelling
This is a thoroughly revised and expanded third edition of a successful university textbook that provides a broad introduction to key areas of stochastic modelling. The previous edition was developed from lecture notes for two one-semester courses for third-year science and actuarial students at the University of Melbourne.This book reviews the basics of probability theory and presents topics on Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. It also features elements of stochastic calculus and introductory mathematical finance. This makes the book suitable for a larger variety of university courses presenting the fundamentals of modern stochastic modelling.To make the text covering a lot of material more appealing and accessible to the reader, instead of rigorous proofs we often give only sketches of the arguments, with indications as to why a particular result holds and also how it is related to other results, and illustrate them by examples. It is in this aspect that the present, third edition differs from the second one: the included background material and argument sketches have been extended, made more graphical and informative. The whole text was reviewed and streamlined wherever possible to make the book more attractive and useful for readers. Where appropriate, the book includes references to more specialised texts on respective topics that contain both complete proofs and more advanced material.
Notes on Tug-Of-War Games and the P-Laplace Equation
This book addresses the interplay between stochastic processes and partial differential equations. More specifically, it focuses on the connection between the nonlinear p-Laplace equation and the stochastic game called tug-of-war with noise. The connection in this context was discovered approximately 15 years ago and has since provided new insights and approaches. These lecture notes provide a brief but detailed and accessible introduction to the subject and to the more research-oriented literature. The book also presents the parabolic case side by side with the elliptic case, highlighting the fact that elliptic and parabolic equations are close in spirit in certain aspects. Moreover, it covers some parts of the regularity theory for these problems. Graduate students and advanced undergraduate students with a basic understanding of probability and partial differential equations will find this book useful.
Industrial Engineering and Industrial Management
This CCIS post conference volume constitutes the proceedings of the 5th International Conference, IEIM 2024, in Nice, France, in January 2024. The 18 full papers together with 3 short papers in this volume were carefully reviewed and selected from 71 submissions. The were organized in 5 tracks as follows: five topics of IEIM were classified as follows: "Data Analysis and Demand Calculation in Industrial Production", "Process Optimization and Intelligence in Green Manufacturing Systems", "Lean Manufacturing and Process Optimization", "Enterprise Digital Transformation and Business Management" and "Modern Logistics Information Systems and Distribution Services".
Introduction to Statistics and Data Analysis
Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference.The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.
Mathematics Beyond the Calculus
This text is about solving various types of equations using practical mathematical methods. Only the essentials of each topic are discussed. This is not about proving theorems, taking limits, or other matters important to mathematicians. "However, the emphasis should be somewhat more on how to do the mathematics quickly and easily, and what formulas are true, rather than the mathematicians' interest in methods of rigorous proof." Richard Feynman Concepts from Linear Algebra - the determinant, the finite matrix, the eigenvalue - are presented without the distractions of mathematical rigor. You learn solution methods that do not involve guesses. Methods you implement in a straightforward manner. The operational calculus can be traced back to Oliver Heaviside. Though many scientists preceded Heaviside in introducing operational methods, the systematic use of operational methods in physical problems was stimulated only by Heaviside's work. The methods he created are undoubtedly among the most important ever created. Heaviside was criticized for his lack of mathematical rigor. Yet his numerous mathematical and physical methods and results proved to be correct when mathematical rigor was incorporated. The Laplace Transform, a basis for a modern day operational calculus, is a straightforward technique for solving ordinary, partial differential, and, with a few complications, difference equations and a type of integral equation. On the other hand the Z transform solves difference equations without complications. And, Heaviside's differential operator D = d/dt augments the transform methods. The Laplace Transform transforms equations in one real variable domain, usually the time t domain, to a complex variable domain where the problem at hand is essentially solved. The inverse transform from the complex variable domain to the real variable domain completes the solution. Understanding the inverse transform requires knowledge of the theory of functions of complex variables. Our main interest in functions of a complex variable is integration, because integration of the complicated integrals of inverse transforms is amazingly simplified. The methods of the differential and integral calculus are extended to complex numbers and functions of complex variables. The results produce tremendous analytic methods. We show how ordinary differential equations. systems of ordinary differential equations, partial differential equations, and difference equations are readily solved by transform and/or differential operational methods. We show that each type of equation is solved in essentially the same way. We just define the Fourier Series, and show how to create Fourier series representing waveforms. Integral Equations - This is a hugh subject, which we limit to how the Laplace transform solves integral equations that include the convolution integral. Galois Finite Fields GF(2m) are not used to solve equations per se. They are used to implement functions such as error correcting codes, speech recognition, phase array antennas, and Doppler radar. Functions NOT implemented here.
A Modern Approach to Teaching an Introduction to Optimization
Optimization should be the science of making the best possible decisions. Making decisions is a virtually universal human activity encountered by professionals (in any field) or people in their everyday lives. You would think, then, that the study of making good decisions is a subject that should be taught broadly to students throughout engineering, the physical and social sciences, business, and policy. Yet today, "optimization" is widely taught as a mathematically sophisticated subject, often limited to graduate students in specialized fields. In operations research (or industrial engineering), "optimization" is equivalent to deterministic math programming, starting with linear programs (and the simplex algorithm), and then transitioning through integer linear programs and nonlinear programs. If you are in departments like electrical or mechanical engineering, optimization means teaching optimal control. And if you are in computer science, optimization today could be interpreted in the context of machine learning (such as fitting models to data) or as reinforcement learning. This book claims that the traditional style of teaching optimization is misguided and out of date. First, while the simplex algorithm is a powerful strategy for solving linear programs, the details of the simplex algorithm are completely inappropriate in an introductory course in optimization. Second, while linear programs are appropriate for solving many problems, they are only applicable to a tiny fraction of all decisions. Third, linear programs (along with integer and nonlinear programs) are static models for problems with (typically) vector-valued decisions. By contrast, most decisions are sequential since they are made periodically over time as new information is arriving. In addition, the vast majority of these decisions are scalar (possibly continuous or discrete). This book is designed for instructors (or potential instructors) looking to introduce the science of making good decisions to the broadest possible audience. It should also be of interest to anyone who has already had a traditional course in optimization of any type. The presentation is organized around a series of topics that suggest a fundamentally different approach to teaching "optimization" spanning both sequential decision problems (which offer the simplest problem settings) before transitioning to more complex vector-valued decisions. It also makes the case that most problems which are modeled as linear (or integer, or nonlinear programs) are actually methods for making decisions in a sequential setting. For this reason, these topics are introduced with much less emphasis on algorithms than is traditionally used, both in static and sequential settings.
