Pyomo—Optimization Modeling in Python.pdf

发布时间：2022-06-13 发布人：admin 分类：说明书资料大小：2.10M 资料格式：pdf 举报版权申诉

a8e4dcb4-a000-45b5-b8fe-50bedb5d2120.pdf-第1页.png

第1页 / 共245页

a8e4dcb4-a000-45b5-b8fe-50bedb5d2120.pdf-第2页.png

第2页 / 共245页

a8e4dcb4-a000-45b5-b8fe-50bedb5d2120.pdf-第3页.png

第3页 / 共245页

a8e4dcb4-a000-45b5-b8fe-50bedb5d2120.pdf-第4页.png

第4页 / 共245页

a8e4dcb4-a000-45b5-b8fe-50bedb5d2120.pdf-第5页.png

第5页 / 共245页

a8e4dcb4-a000-45b5-b8fe-50bedb5d2120.pdf-第6页.png

第6页 / 共245页

a8e4dcb4-a000-45b5-b8fe-50bedb5d2120.pdf-第7页.png

第7页 / 共245页

a8e4dcb4-a000-45b5-b8fe-50bedb5d2120.pdf-第8页.png

第8页 / 共245页

Pyomo-Optimization Modeling in Python

Preface

Acknowledgements

Contents

Chapter 1 Introduction

Chapter 2 Pyomo Modeling Strategies

Chapter 3 Model Components: Variables, Objectives, and Constraints

Chapter 4 Model Components: Sets and Parameters

Chapter 5 Miscellaneous Model Components and Utility Functions

Chapter 6 Initializing Abstract Models with Data Command Files

Chapter 7 The Pyomo Command-line Interface

Chapter 8 Nonlinear Programming with Pyomo

Chapter 9 Stochastic Programming Extensions

Chapter 10 Scripting and Algorithm Development

Appendix A Installing Coopr

Appendix B A Brief Python Tutorial

Appendix C Pyomo and Coopr: The Bigger Picture

References

Index

Springer Optimization and Its Applications VOLUME 67 Managing Editor Panos M. Pardalos (University of Florida) Editor–Combinatorial Optimization Ding-Zhu Du (University of Texas at Dallas) Advisory Board J. Birge (University of Chicago) C.A. Floudas (Princeton University) F. Giannessi (University of Pisa) H.D. Sherali (Virginia Polytechnic and State University) T. Terlaky (Lehigh University) Y. Ye (Stanford University) Aims and Scope Optimization has been expanding in all directions at an astonishing rate during the last few decades. New algorithmic and theoretical techniques have been developed, the diffusion into other disciplines has proceeded at a rapid pace, and our knowledge of all aspects of the ﬁeld has grown even more profound. At the same time, one of the most striking trends in opti- mization is the constantly increasing emphasis on the interdisciplinary na- ture of the ﬁeld. Optimization has been a basic tool in all areas of applied mathematics, engineering, medicine, economics, and other sciences. The series Springer Optimization and Its Applications publishes under- graduate and graduate textbooks, monographs and state-of-the-art exposi- tory work that focus on algorithms for solving optimization problems and also study applications involving such problems. Some of the topics covered include nonlinear optimization (convex and nonconvex), network ﬂow prob- lems, stochastic optimization, optimal control, discrete optimization, multi- objective programming, description of software packages, approximation techniques and heuristic approaches. For further volumes: http://www.springer.com/series/7393

William E. Hart • Carl Laird • Jean-Paul Watson David L. Woodruff Pyomo—Optimization Modeling in Python

William E. Hart Data Analysis and Informatics Department Sandia National Laboratories Albuquerque, NM 87185 USA wehart@sandia.gov Jean-Paul Watson Discrete Mathematics and Complex Systems Department Sandia National Laboratories Albuquerque, NM 87185 USA jwatson@sandia.gov Carl Laird Department of Chemical Engineering Texas A&M College Station, TX 77843 USA carl.laird@tamu.edu David L. Woodruff Graduate School of Management University of California, Davis Davis, CA 95616 USA dlwoodruff@ucdavis.edu ISSN 1931-6828 - ISBN 978-1-4614- 3225 8 DOI 10.1007/978-1-4614- Springer New York Dordrecht Heidelberg London 3226 5 - e-ISBN 978-1-4614- - 3226 5 L ibrary of Congress Control Number: 2012930924 2 © Springer Science+Business Media, LLC 201 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identiﬁed as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

ForValerie,Christy,MichelleandBarbara.ThankyouforyoursupportandpatienceduringthemanynightsandweekendsthatwehavespentonPyomoandthisbook.

PrefaceThisbookdescribesanewtoolformathematicalmodeling:thePythonOptimizationModelingObjects(Pyomo)software.Pyomosupportstheformulationandanalysisofmathematicalmodelsforcomplexoptimizationapplications.Thiscapabilityiscommonlyassociatedwithalgebraicmodelinglanguages(AMLs),whichsupportthedescriptionandanalysisofmathematicalmodelswithahigh-levellanguage.AlthoughmostAMLsareimplementedincustommodelinglanguages,Pyomo’smodelingobjectsareembeddedwithinPython,afull-featuredhigh-levelprogram-minglanguagethatcontainsarichsetofsupportinglibraries.Modelingisafundamentalprocessinmanyaspectsofscientiﬁcresearch,engi-neeringandbusiness,andthewidespreadavailabilityofcomputingresourceshasmadethenumericalanalysisofmathematicalmodelsacommonplaceactivity.Fur-thermore,AMLshaveemergedasakeycapabilityforrobustlyformulatinglargemodelsforcomplex,real-worldapplications[40].AMLssimplifytheprocessofformulatingcomplexmodelsbysimplifyingthemanagementofsparsedataandsupportingthenaturalexpressionofmodelcomponents.Additionally,AMLslikePyomosupportscriptingwithmodelobjectswhichfacilitatesrapiddevelopmentofnewanalysistools.vii

分享到：

赞收藏

资料库

Pyomo—Optimization Modeling in Python.pdf

相关推荐

课程资源

热门标签

最新资料