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| julia programming for operations research: Julia Programming for Operations Research Changhyun Kwon, 2019-03-03 Last Updated: December 2020 Based on Julia v1.3+ and JuMP v0.21+ The main motivation of writing this book was to help the author himself. He is a professor in the field of operations research, and his daily activities involve building models of mathematical optimization, developing algorithms for solving the problems, implementing those algorithms using computer programming languages, experimenting with data, etc. Three languages are involved: human language, mathematical language, and computer language. His team of students need to go over three different languages, which requires translation among the three languages. As this book was written to teach his research group how to translate, this book will also be useful for anyone who needs to learn how to translate in a similar situation. The Julia Language is as fast as C, as convenient as MATLAB, and as general as Python with a flexible algebraic modeling language for mathematical optimization problems. With the great support from Julia developers, especially the developers of the JuMP—Julia for Mathematical Programming—package, Julia makes a perfect tool for students and professionals in operations research and related areas such as industrial engineering, management science, transportation engineering, economics, and regional science. For more information, visit: http://www.chkwon.net/julia |
| julia programming for operations research: Beginning Julia Programming Sandeep Nagar, 2017-11-25 Get started with Julia for engineering and numerical computing, especially data science, machine learning, and scientific computing applications. This book explains how Julia provides the functionality, ease-of-use and intuitive syntax of R, Python, MATLAB, SAS, or Stata combined with the speed, capacity, and performance of C, C++, or Java. You’ll learn the OOP principles required to get you started, then how to do basic mathematics with Julia. Other core functionality of Julia that you’ll cover, includes working with complex numbers, rational and irrational numbers, rings, and fields. Beginning Julia Programming takes you beyond these basics to harness Julia’s powerful features for mathematical functions in Julia, arrays for matrix operations, plotting, and more. Along the way, you also learn how to manage strings, write functions, work with control flows, and carry out I/O to implement and leverage the mathematics needed for your data scienceand analysis projects. Julia walks like Python and runs like C. This phrase explains why Julia is quickly growing as the most favored option for data analytics and numerical computation. After reading and using this book, you'll have the essential knowledge and skills to build your first Julia-based application. What You'll Learn Obtain core skills in Julia Apply Julia in engineering and science applications Work with mathematical functions in Julia Use arrays, strings, functions, control flow, and I/O in Julia Carry out plotting and display basic graphics Who This Book Is For Those who are new to Julia; experienced users may also find this helpful as a reference. |
| julia programming for operations research: 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. |
| julia programming for operations research: Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages Tanmay Bakshi, 2019-12-06 Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. A quick guide to start writing your own fun and useful Julia apps—no prior experience required! This engaging guide shows, step by step, how to build custom programs using Julia, the open-source, intuitive scripting language. Written by 15-year-old technology phenom Tanmay Bakshi, the book is presented in an accessible style that makes learning easy and enjoyable. Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages clearly explains the basics of Julia programming and takes a look at cutting-edge machine learning applications. You will also discover how to interface your Julia apps with code written in Python. Inside, you’ll learn to: • Set up and configure your Julia environment • Get up and running writing your own Julia apps • Define variables and use them in your programs • Use conditions, iterations, for-loops, and while-loops • Create, go through, and modify arrays • Build an app to manage things you lend and get back from your friends • Create and utilize dictionaries • Simplify maintenance of your code using functions • Apply functions on arrays and use functions recursively and generically • Understand and program basic machine learning apps |
| julia programming for operations research: Learning Julia Anshul Joshi, Rahul Lakhanpal, 2017-11-24 Learn Julia language for data science and data analytics About This Book Set up Julia's environment and start building simple programs Explore the technical aspects of Julia and its potential when it comes to speed and data processing Write efficient and high-quality code in Julia Who This Book Is For This book allows existing programmers, statisticians and data scientists to learn the Julia and take its advantage while building applications with complex numerical and scientific computations. Basic knowledge of mathematics is needed to understand the various methods that will be used or created in the book to exploit the capabilities for which Julia is made. What You Will Learn Understand Julia's ecosystem and create simple programs Master the type system and create your own types in Julia Understand Julia's type system, annotations, and conversions Define functions and understand meta-programming and multiple dispatch Create graphics and data visualizations using Julia Build programs capable of networking and parallel computation Develop real-world applications and use connections for RDBMS and NoSQL Learn to interact with other programming languages–C and Python—using Julia In Detail Julia is a highly appropriate language for scientific computing, but it comes with all the required capabilities of a general-purpose language. It allows us to achieve C/Fortran-like performance while maintaining the concise syntax of a scripting language such as Python. It is perfect for building high-performance and concurrent applications. From the basics of its syntax to learning built-in object types, this book covers it all. This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Julia to explore its wide and ever-growing package ecosystem and also for experienced developers/statisticians/data scientists who want to add Julia to their skill-set. The book presents the fundamentals of programming in Julia and in-depth informative examples, using a step-by-step approach. You will be taken through concepts and examples such as doing simple mathematical operations, creating loops, metaprogramming, functions, collections, multiple dispatch, and so on. By the end of the book, you will be able to apply your skills in Julia to create and explore applications of any domain. Style and approach This book demonstrates the basics of Julia along with some data structures and testing tools that will give you enough material to get started with the language from an application standpoint. |
| julia programming for operations research: Statistics with Julia Yoni Nazarathy, Hayden Klok, 2021-09-04 This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia. |
| julia programming for operations research: Stochastic Decomposition Julia L. Higle, Suvrajeet Sen, 1996-02-29 This book summarizes developments related to a class of methods called Stochastic Decomposition (SD) algorithms, which represent an important shift in the design of optimization algorithms. Unlike traditional deterministic algorithms, SD combines sampling approaches from the statistical literature with traditional mathematical programming constructs (e.g. decomposition, cutting planes etc.). This marriage of two highly computationally oriented disciplines leads to a line of work that is most definitely driven by computational considerations. Furthermore, the use of sampled data in SD makes it extremely flexible in its ability to accommodate various representations of uncertainty, including situations in which outcomes/scenarios can only be generated by an algorithm/simulation. The authors report computational results with some of the largest stochastic programs arising in applications. These results (mathematical as well as computational) are the `tip of the iceberg'. Further research will uncover extensions of SD to a wider class of problems. Audience: Researchers in mathematical optimization, including those working in telecommunications, electric power generation, transportation planning, airlines and production systems. Also suitable as a text for an advanced course in stochastic optimization. |
| julia programming for operations research: Optimization in Industry Tito A. Ciriani, Robert C. Leachman, 1994-09-06 The proceedings of an annual IBM Europe Institute held in Oberlech, Austria in August 1992 in which contributors from Europe and the US provide an overview of mathematical optimization applications in industry, considering the entire cycle of development, implementation, and operation. The 15 papers describe strategies and techniques used in various industries and recent advances in mathematical modeling and computer software with potential for industrial application. Annotation copyright by Book News, Inc., Portland, OR |
| julia programming for operations research: Julia for Machine Learning Zacharias Voulgaris, 2020-05-18 Unleash the power of Julia for your machine learning tasks. We reveal why Julia is chosen for more and more data science and machine learning projects, including Julia's ability to run algorithms at lightning speed. Next, we show you how to set up Julia and various IDEs such as Jupyter. Afterward, we explore key Julia libraries, which are useful for data science work, including packages related to visuals, data structures, and mathematical processes. After building a foundation in Julia, we dive into machine learning, with foundational concepts reinforced by Julia use cases. The use cases build upon each other, reaching the level where we code a machine learning model from scratch using Julia. All of these use cases are available in a series of Jupyter notebooks. After covering dimensionality reduction methods, we explore additional machine learning topics, such as parallelization and data engineering. Although knowing how to use Julia is essential, it is even more important to communicate our results to the business, which we cover next, including how to work efficiently with project stakeholders. Our Julia journey then ascends to the finer points, including improving machine learning transparency, reconciling machine learning with statistics, and continuing to innovate with Julia. The final chapters cover future trends in the areas of Julia, machine learning, and artificial intelligence. We explain machine learning and Bayesian Statistics hybrid systems, and Julia's Gen language. We share many resources so you can continue to sharpen your Julia and machine learning skills. Each chapter concludes with a series of questions designed to reinforce that chapter's material, with answers provided in an appendix. Other appendices include an extensive glossary, bridge packages between Julia and other programming languages, and an overview of three data science-related heuristics implemented in Julia, which aren't in any of the existing packages. |
| julia programming for operations research: Julia Quick Syntax Reference Antonello Lobianco, 2019-11-11 This quick Julia programming language guide is a condensed code and syntax reference to the Julia 1.x programming language, updated with the latest features of the Julia APIs, libraries, and packages. It presents the essential Julia syntax in a well-organized format that can be used as a handy reference. This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. You will learn how to use Julia packages for data analysis, numerical optimization and symbolic computation, and how to disseminate your results in dynamic documents or interactive web pages. In this book, the focus is on providing important information as quickly as possible. It is packed with useful information and is a must-have for any Julia programmer. What You Will Learn Set up the software needed to run Julia and your first Hello World example Work with types and the different containers that Julia makes available for rapid application development Use vectorized, classical loop-based code, logical operators, and blocks Explore Julia functions by looking at arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts Build custom structures in Julia Interface Julia with other languages such as C/C++, Python, and R Program a richer API, modifying the code before it is executed using expressions, symbols, macros, quote blocks, and more Maximize your code’s performance Who This Book Is For Experienced programmers new to Julia, as well as existing Julia coders new tothe now stable Julia version 1.0 release. |
| julia programming for operations research: Julia High Performance Avik Sengupta, 2016-04-26 Design and develop high performing programs with Julia About This Book Learn to code high reliability and high performance programs Stand out from the crowd by developing code that runs faster than your peers' codes This book is intended for developers who are interested in high performance technical programming. Who This Book Is For This book is for beginner and intermediate Julia programmers who are interested in high performance technical computing. You will have a basic familiarity with Julia syntax, and have written some small programs in the language. What You Will Learn Discover the secrets behind Julia's speed Get a sense of the possibilities and limitations of Julia's performance Analyze the performance of Julia programs Measure the time and memory taken by Julia programs Create fast machine code using Julia's type information Define and call functions without compromising Julia's performance Understand number types in Julia Use Julia arrays to write high performance code Get an overview of Julia's distributed computing capabilities In Detail Julia is a high performance, high-level dynamic language designed to address the requirements of high-level numerical and scientific computing. Julia brings solutions to the complexities faced by developers while developing elegant and high performing code. Julia High Performance will take you on a journey to understand the performance characteristics of your Julia programs, and enables you to utilize the promise of near C levels of performance in Julia. You will learn to analyze and measure the performance of Julia code, understand how to avoid bottlenecks, and design your program for the highest possible performance. In this book, you will also see how Julia uses type information to achieve its performance goals, and how to use multuple dispatch to help the compiler to emit high performance machine code. Numbers and their arrays are obviously the key structures in scientific computing – you will see how Julia's design makes them fast. The last chapter will give you a taste of Julia's distributed computing capabilities. Style and approach This is a hands-on manual that will give you good explanations about the important concepts related to Julia programming. |
| julia programming for operations research: Python for Scientists John M. Stewart, 2017-07-20 Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets. Everything the working scientist needs to know is covered, quickly providing researchers and research students with the skills to start using Python effectively. |
| julia programming for operations research: Julia for Data Science Zacharias Voulgaris, 2016 After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialized script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia Options for Julia IDEs Programming structures and functions Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data preprocessing Data visualization and some simple yet powerful statistics for data exploration purposes Dimensionality reduction and feature evaluation Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines) Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia. |
| julia programming for operations research: Stochastic Programming Horand Gassmann, W. T. Ziemba, 2013 This book shows the breadth and depth of stochastic programming applications. All the papers presented here involve optimization over the scenarios that represent possible future outcomes of the uncertainty problems. The applications, which were presented at the 12th International Conference on Stochastic Programming held in Halifax, Nova Scotia in August 2010, span the rich field of uses of these models. The finance papers discuss such diverse problems as longevity risk management of individual investors, personal financial planning, intertemporal surplus management, asset management with benchmarks, dynamic portfolio management, fixed income immunization and racetrack betting. The production and logistics papers discuss natural gas infrastructure design, farming Atlantic salmon, prevention of nuclear smuggling and sawmill planning. The energy papers involve electricity production planning, hydroelectric reservoir operations and power generation planning for liquid natural gas plants. Finally, two telecommunication papers discuss mobile network design and frequency assignment problems. |
| julia programming for operations research: Numerical Methods for Scientific Computing Kyle Novak, 2022-03-13 A comprehensive guide to the theory, intuition, and application of numerical methods in linear algebra, analysis, and differential equations. With extensive commentary and code for three essential scientific computing languages: Julia, Python, and Matlab. |
| julia programming for operations research: Hands-On Design Patterns and Best Practices with Julia Tom Kwong, 2020-01-17 Design and develop high-performance, reusable, and maintainable applications using traditional and modern Julia patterns with this comprehensive guide Key FeaturesExplore useful design patterns along with object-oriented programming in Julia 1.0Implement macros and metaprogramming techniques to make your code faster, concise, and efficientDevelop the skills necessary to implement design patterns for creating robust and maintainable applicationsBook Description Design patterns are fundamental techniques for developing reusable and maintainable code. They provide a set of proven solutions that allow developers to solve problems in software development quickly. This book will demonstrate how to leverage design patterns with real-world applications. Starting with an overview of design patterns and best practices in application design, you'll learn about some of the most fundamental Julia features such as modules, data types, functions/interfaces, and metaprogramming. You'll then get to grips with the modern Julia design patterns for building large-scale applications with a focus on performance, reusability, robustness, and maintainability. The book also covers anti-patterns and how to avoid common mistakes and pitfalls in development. You'll see how traditional object-oriented patterns can be implemented differently and more effectively in Julia. Finally, you'll explore various use cases and examples, such as how expert Julia developers use design patterns in their open source packages. By the end of this Julia programming book, you'll have learned methods to improve software design, extensibility, and reusability, and be able to use design patterns efficiently to overcome common challenges in software development. What you will learnMaster the Julia language features that are key to developing large-scale software applicationsDiscover design patterns to improve overall application architecture and designDevelop reusable programs that are modular, extendable, performant, and easy to maintainWeigh up the pros and cons of using different design patterns for use casesExplore methods for transitioning from object-oriented programming to using equivalent or more advanced Julia techniquesWho this book is for This book is for beginner to intermediate-level Julia programmers who want to enhance their skills in designing and developing large-scale applications. |
| julia programming for operations research: Julia High Performance Avik Sengupta, 2019-06-11 |
| julia programming for operations research: Optimization Problems and Their Applications Anton Eremeev, Michael Khachay, Yury Kochetov, Panos Pardalos, 2018-06-29 This book constitutes extended, revised and selected papers from the 7th International Conference on Optimization Problems and Their Applications, OPTA 2018, held in Omsk, Russia in July 2018. The 27 papers presented in this volume were carefully reviewed and selected from a total of 73 submissions. The papers are listed in thematic sections, namely location problems, scheduling and routing problems, optimization problems in data analysis, mathematical programming, game theory and economical applications, applied optimization problems and metaheuristics. |
| julia programming for operations research: Applications of Stochastic Programming Stein W. Wallace, William T. Ziemba, 2005-01-01 Consisting of two parts, this book presents papers describing publicly available stochastic programming systems that are operational. It presents a diverse collection of application papers in areas such as production, supply chain and scheduling, gaming, environmental and pollution control, financial modeling, telecommunications, and electricity. |
| julia programming for operations research: Chemical Production Scheduling Christos Maravelias, 2021-05-06 Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners. |
| julia programming for operations research: Fundamentals of Optimization Techniques with Algorithms Sukanta Nayak, 2020-08-25 Optimization is a key concept in mathematics, computer science, and operations research, and is essential to the modeling of any system, playing an integral role in computer-aided design. Fundamentals of Optimization Techniques with Algorithms presents a complete package of various traditional and advanced optimization techniques along with a variety of example problems, algorithms and MATLAB© code optimization techniques, for linear and nonlinear single variable and multivariable models, as well as multi-objective and advanced optimization techniques. It presents both theoretical and numerical perspectives in a clear and approachable way. In order to help the reader apply optimization techniques in practice, the book details program codes and computer-aided designs in relation to real-world problems. Ten chapters cover, an introduction to optimization; linear programming; single variable nonlinear optimization; multivariable unconstrained nonlinear optimization; multivariable constrained nonlinear optimization; geometric programming; dynamic programming; integer programming; multi-objective optimization; and nature-inspired optimization. This book provides accessible coverage of optimization techniques, and helps the reader to apply them in practice. |
| julia programming for operations research: Nonlinear Assignment Problems Panos M. Pardalos, L.S. Pitsoulis, 2013-03-09 Nonlinear Assignment Problems (NAPs) are natural extensions of the classic Linear Assignment Problem, and despite the efforts of many researchers over the past three decades, they still remain some of the hardest combinatorial optimization problems to solve exactly. The purpose of this book is to provide in a single volume, major algorithmic aspects and applications of NAPs as contributed by leading international experts. The chapters included in this book are concerned with major applications and the latest algorithmic solution approaches for NAPs. Approximation algorithms, polyhedral methods, semidefinite programming approaches and heuristic procedures for NAPs are included, while applications of this problem class in the areas of multiple-target tracking in the context of military surveillance systems, of experimental high energy physics, and of parallel processing are presented. Audience: Researchers and graduate students in the areas of combinatorial optimization, mathematical programming, operations research, physics, and computer science. |
| julia programming for operations research: Architectural Research Methods Linda N. Groat, David Wang, 2013-04-12 ARCHITECTURAL RESEARCH METHODS ARCHITECTURE/GENERAL A PRACTICAL GUIDE TO RESEARCH FOR ARCHITECTS AND DESIGNERS—NOW UPDATED AND EXPANDED! From searching for the best glass to prevent glare to determining how clients might react to the color choice for restaurant walls, research is a crucial tool that architects must master in order to effectively address the technical, aesthetic, and behavioral issues that arise in their work. This book’s unique coverage of research methods is specifically targeted to help professional designers and researchers better conduct and understand research. Part I explores basic research issues and concepts, and includes chapters on relating theory to method and design to research. Part II gives a comprehensive treatment of specific strategies for investigating built forms. In all, the book covers seven types of research, including historical, qualitative, correlational, experimental, simulation, logical argumentation, and case studies and mixed methods. Features new to this edition include: Strategies for investigation, practical examples, and resources for additional information A look at current trends and innovations in research Coverage of design studio–based research that shows how strategies described in the book can be employed in real life A discussion of digital media and online research New and updated examples of research studies A new chapter on the relationship between design and research Architectural Research Methods is an essential reference for architecture students and researchers as well as architects, interior designers, landscape architects, and building product manufacturers. |
| julia programming for operations research: Introduction to Operations Research Frederick S. Hillier, Gerald J. Lieberman, 1990 |
| julia programming for operations research: ISE: ESSEN OF COMPUTER ORGZTN & ARCH 4E INTL VERS Linda Null, Julia Lobur, 2014-02-12 Updated and revised, The Essentials of Computer Organization and Architecture, Third Edition is a comprehensive resource that addresses all of the necessary organization and architecture topics, yet is appropriate for the one-term course. |
| julia programming for operations research: Introduction to Operations Research Frederick S. Hillier, Gerald J. Lieberman, 2021 Introduction to Operations Research is the worldwide gold standard for textbooks in operations research. This famous text, around since the early days of the field, has grown into a contemporary 21st century eleventh edition with the infusion of new state-of-the-art content.-- |
| julia programming for operations research: Stochastic Linear Programming P. Kall, 2012-12-06 Todaymanyeconomists, engineers and mathematicians are familiar with linear programming and are able to apply it. This is owing to the following facts: during the last 25 years efficient methods have been developed; at the same time sufficient computer capacity became available; finally, in many different fields, linear programs have turned out to be appropriate models for solving practical problems. However, to apply the theory and the methods of linear programming, it is required that the data determining a linear program be fixed known numbers. This condition is not fulfilled in many practical situations, e. g. when the data are demands, technological coefficients, available capacities, cost rates and so on. It may happen that such data are random variables. In this case, it seems to be common practice to replace these random variables by their mean values and solve the resulting linear program. By 1960 various authors had already recog nized that this approach is unsound: between 1955 and 1960 there were such papers as Linear Programming under Uncertainty, Stochastic Linear Pro gramming with Applications to Agricultural Economics, Chance Constrained Programming, Inequalities for Stochastic Linear Programming Problems and An Approach to Linear Programming under Uncertainty. |
| julia programming for operations research: Decision Making Under Uncertainty Mykel J. Kochenderfer, 2015-07-24 An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines. |
| julia programming for operations research: Introduction to Management Science with Spreadsheets William J. Stevenson, Ceyhun Ozgur, 2007 |
| julia programming for operations research: Profiles in Operations Research Arjang A. Assad, Saul I. Gass, 2011-06-30 Profiles in Operations Research: Pioneers and Innovators recounts the development of the field of Operations Research (OR), the science of decision making. The book traces the development of OR from its military origins to a mature discipline that is recognized worldwide for its contributions to managerial planning and complex global operations. Over the past six decades, OR analyses have impacted our daily lives: when making an airline or hotel reservation, waiting in line at a bank, getting the correctly blended fuel at the gas station, and ensuring that the book you are holding arrived at its destination on time. OR originated in the late 1930s when British scientists from various disciplines joined Royal Air Force officers to determine the most effective way to employ new radar technology for intercepting enemy aircraft. During World War II, similar applied research groups were formed to study, test, and evaluate military operations on both sides of the Atlantic. Their work resulted in great improvements—OR helped the Allies win the war. The scientific field that emerged from these studies was called operational research in the U.K. and operations research in the U.S. Today, OR provides a broad and powerful science to aid decision making. Profiles describes the lives and contributions of 43 OR pioneers and innovators and relates how these individuals, with varying backgrounds and diverse interests, were drawn to the nascent field of OR. The profiles also describe how OR techniques and applications expanded considerably beyond the military context to find new domains in business and industry. In addition to their scientific contributions, these profiles capture the life stories of the individuals—interwoven with personal tales, vivid vignettes, family backgrounds, and views of the mission and future of OR. Collectively, the profiles recount the fascinating story of the growth and development of a field enriched by the convergence of different disciplines. The Editors: Arjang A. Assad is Dean of the School of Management, University at Buffalo, State University of New York. Saul I. Gass is Professor Emeritus, Department of Decision, Operations & Information Technologies, Smith School of Business, University of Maryland, College Park. From the Reviews Profiles In Operations Research: Pioneers and Innovators. Book Review by Nigel Cummings: U.K. OR Society's e-journal, Inside OR., Sept 2011. I can thoroughly recommend this book. I found it both enlighteningand undeniably gripping, so much so in fact, you may find it difficultto put it down once you have commenced reading it. Arjang A. Assad and Saul I. Gass have created a masterwork whichwill serve to immortalise [stet] the pioneers of O.R. for many years to come. *For a list of all known typos, plus further discussion on the book, please visit http://profilesinoperationsresearch.com. |
| julia programming for operations research: Engineering and Scientific Computing with Scilab Claude Gomez, 2012-12-06 Supplementary files run on UNIX and Windows 95/98/NT |
| julia programming for operations research: The Guide to Greening Cities Sadhu Aufochs Johnston, Steven S. Nicholas, Julia Parzen, 2013-10-01 Superstorm Sandy sent a strong message that a new generation of urban development and infrastructure is desperately needed, and it must be designed with resilience in mind. As cities continue to face climate change impacts while growing in population, they find themselves at the center of resilience and green city solutions, yet political and budgetary obstacles threaten even the best-planned initiatives. In The Guide to Greening Cities, seasoned green city leaders Sadhu Johnston, Steven Nicholas, and Julia Parzen use success stories from across North America to show how to turn a green city agenda into reality. The Guide to Greening Cities is the first book written from the perspective of municipal leaders with successful, on-the-ground experience working to advance green city goals. Through personal reflections and interviews with leading municipal staff in cities from San Antonio to Minneapolis, the authors share lessons for cities to lead by example in their operations, create programs, implement high-priority initiatives, develop partnerships, measure progress, secure funding, and engage the community. Case studies and chapters highlight strategies for overcoming common challenges such as changes of leadership and fiscal austerity. The book is augmented by a companion website, launching with the publication of the book, which offers video interviews of municipal leaders, additional case studies, and other resources. Rich in tools, insights, and tricks of the trade, The Guide to Greening Cities helps professionals, policymakers, community leaders, and students understand which approaches have worked and why and demonstrates multidisciplinary solutions for creating healthy, just, and green communities. |
| julia programming for operations research: 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. |
| julia programming for operations research: State of the Art in Global Optimization Christodoulos A. Floudas, Panos M. Pardalos, 2013-12-01 Optimization problems abound in most fields of science, engineering, and tech nology. In many of these problems it is necessary to compute the global optimum (or a good approximation) of a multivariable function. The variables that define the function to be optimized can be continuous and/or discrete and, in addition, many times satisfy certain constraints. Global optimization problems belong to the complexity class of NP-hard prob lems. Such problems are very difficult to solve. Traditional descent optimization algorithms based on local information are not adequate for solving these problems. In most cases of practical interest the number of local optima increases, on the aver age, exponentially with the size of the problem (number of variables). Furthermore, most of the traditional approaches fail to escape from a local optimum in order to continue the search for the global solution. Global optimization has received a lot of attention in the past ten years, due to the success of new algorithms for solving large classes of problems from diverse areas such as engineering design and control, computational chemistry and biology, structural optimization, computer science, operations research, and economics. This book contains refereed invited papers presented at the conference on State of the Art in Global Optimization: Computational Methods and Applications held at Princeton University, April 28-30, 1995. The conference presented current re search on global optimization and related applications in science and engineering. The papers included in this book cover a wide spectrum of approaches for solving global optimization problems and applications. |
| julia programming for operations research: Principles and Practice in Second Language Acquisition Stephen D. Krashen, 1982 The present volume examines the relationship between second language practice and what is known about the process of second language acquisition, summarising the current state of second language acquisition theory, drawing general conclusions about its application to methods and materials and describing what characteristics effective materials should have. The author concludes that a solution to language teaching lies not so much in expensive equipment, exotic new methods, or sophisticated language analysis, but rather in the full utilisation of the most important resources - native speakers of the language - in real communication. |
| julia programming for operations research: History of Programming Languages Richard L. Wexelblat, 2014-05-27 History of Programming Languages presents information pertinent to the technical aspects of the language design and creation. This book provides an understanding of the processes of language design as related to the environment in which languages are developed and the knowledge base available to the originators. Organized into 14 sections encompassing 77 chapters, this book begins with an overview of the programming techniques to use to help the system produce efficient programs. This text then discusses how to use parentheses to help the system identify identical subexpressions within an expression and thereby eliminate their duplicate calculation. Other chapters consider FORTRAN programming techniques needed to produce optimum object programs. This book discusses as well the developments leading to ALGOL 60. The final chapter presents the biography of Adin D. Falkoff. This book is a valuable resource for graduate students, practitioners, historians, statisticians, mathematicians, programmers, as well as computer scientists and specialists. |
| julia programming for operations research: 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. |
| julia programming for operations research: Complementarity Modeling in Energy Markets Steven A. Gabriel, Antonio J. Conejo, J. David Fuller, Benjamin F. Hobbs, Carlos Ruiz, 2012-07-20 This addition to the ISOR series introduces complementarity models in a straightforward and approachable manner and uses them to carry out an in-depth analysis of energy markets, including formulation issues and solution techniques. In a nutshell, complementarity models generalize: a. optimization problems via their Karush-Kuhn-Tucker conditions b. on-cooperative games in which each player may be solving a separate but related optimization problem with potentially overall system constraints (e.g., market-clearing conditions) c. conomic and engineering problems that aren’t specifically derived from optimization problems (e.g., spatial price equilibria) d. roblems in which both primal and dual variables (prices) appear in the original formulation (e.g., The National Energy Modeling System (NEMS) or its precursor, PIES). As such, complementarity models are a very general and flexible modeling format. A natural question is why concentrate on energy markets for this complementarity approach? s it turns out, energy or other markets that have game theoretic aspects are best modeled by complementarity problems. The reason is that the traditional perfect competition approach no longer applies due to deregulation and restructuring of these markets and thus the corresponding optimization problems may no longer hold. Also, in some instances it is important in the original model formulation to involve both primal variables (e.g., production) as well as dual variables (e.g., market prices) for public and private sector energy planning. Traditional optimization problems can not directly handle this mixing of primal and dual variables but complementarity models can and this makes them all that more effective for decision-makers. |
| julia programming for operations research: Fitting the Human K. H. E. Kroemer, 2017 Body sizes -- Mobility -- Muscular work -- Body strength -- How we see -- How we hear -- How we sense objects and energy -- How we experience indoor and outside climates -- Mental activities -- Hard physical work -- Light and moderate work -- Task load and stress -- Working with others -- The organization and you -- Working hours and sleep -- Night and shift work -- Designing the home -- Office design -- Computer design and use -- Workplace design -- Load handling -- Health care for patients and providers -- Autonomous automobiles: emerging ergonomic issues -- Making work efficient and pleasant. |
The Julia Programming Language
The official website for the Julia Language. Julia is a language that is fast, dynamic, easy to use, and open source. Click here to learn more.
Julia Documentation · The Julia Language
Julia provides ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming.
JuliaLang/julia: The Julia Programming Language - GitHub
Julia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. This is the GitHub repository of Julia source …
Julia Language Introduction - GeeksforGeeks
Apr 16, 2020 · Julia is a dynamic, high-performance programming language that is used to perform operations in scientific computing. Similar to R Programming Language, Julia is used …
Julia (programming language) - Wikipedia
Julia is a high-level, general-purpose [17] dynamic programming language, designed to be fast and productive, [18] for e.g. data science, artificial intelligence, machine learning, modeling …
Introduction to Julia - JuliaAcademy
This course curriculum is designed with the objective of enabling beginner level programmers in getting up to speed with Julia
Download Julia
The official website for the Julia Language. Julia is a language that is fast, dynamic, easy to use, and open source. Click here to learn more.
Julia Lab: Language, Composability, and Scientific Machine ...
Climate change refers to long-term shifts in temperatures and weather patterns. Human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like …
The Julia Programming Language: Uses, Benefits, and Careers
Sep 27, 2024 · Julia is an open-source project known for its high performance and ability to generate highly efficient code at speeds approaching those of languages like C and Fortran. A …
Getting Started · The Julia Language
The easiest way to learn and experiment with Julia is by starting an interactive session (also known as a read-eval-print loop or "REPL") by double-clicking the Julia executable or running …
The Julia Programming Language
The official website for the Julia Language. Julia is a language that is fast, dynamic, easy to use, and open source. Click here to learn more.
Julia Documentation · The Julia Language
Julia provides ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming.
JuliaLang/julia: The Julia Programming Language - GitHub
Julia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. This is the GitHub repository of Julia source …
Julia Language Introduction - GeeksforGeeks
Apr 16, 2020 · Julia is a dynamic, high-performance programming language that is used to perform operations in scientific computing. Similar to R Programming Language, Julia is used …
Julia (programming language) - Wikipedia
Julia is a high-level, general-purpose [17] dynamic programming language, designed to be fast and productive, [18] for e.g. data science, artificial intelligence, machine learning, modeling …
Introduction to Julia - JuliaAcademy
This course curriculum is designed with the objective of enabling beginner level programmers in getting up to speed with Julia
Download Julia
The official website for the Julia Language. Julia is a language that is fast, dynamic, easy to use, and open source. Click here to learn more.
Julia Lab: Language, Composability, and Scientific Machine ...
Climate change refers to long-term shifts in temperatures and weather patterns. Human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like …
The Julia Programming Language: Uses, Benefits, and Careers
Sep 27, 2024 · Julia is an open-source project known for its high performance and ability to generate highly efficient code at speeds approaching those of languages like C and Fortran. A …
Getting Started · The Julia Language
The easiest way to learn and experiment with Julia is by starting an interactive session (also known as a read-eval-print loop or "REPL") by double-clicking the Julia executable or running …
