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This book provides a complete and comprehensive reference/guide to
Pyomo (Python Optimization Modeling Objects) for both beginning and
advanced modelers, including students at the undergraduate and
graduate levels, academic researchers, and practitioners. The text
illustrates the breadth of the modeling and analysis capabilities
that are supported by the software and support of complex
real-world applications. Pyomo is an open source software package
for formulating and solving large-scale optimization and operations
research problems. The text begins with a tutorial on simple linear
and integer programming models. A detailed reference of Pyomo's
modeling components is illustrated with extensive examples,
including a discussion of how to load data from data sources like
spreadsheets and databases. Chapters describing advanced modeling
capabilities for nonlinear and stochastic optimization are also
included. The Pyomo software provides familiar modeling features
within Python, a powerful dynamic programming language that has a
very clear, readable syntax and intuitive object orientation. Pyomo
includes Python classes for defining sparse sets, parameters, and
variables, which can be used to formulate algebraic expressions
that define objectives and constraints. Moreover, Pyomo can be used
from a command-line interface and within Python's interactive
command environment, which makes it easy to create Pyomo models,
apply a variety of optimizers, and examine solutions. The software
supports a different modeling approach than commercial AML
(Algebraic Modeling Languages) tools, and is designed for
flexibility, extensibility, portability, and maintainability but
also maintains the central ideas in modern AMLs.
Optimization problems in practice are diverse and evolve over time,
giving rise to - quirements both for ready-to-use optimization
software packages and for optimization software libraries, which
provide more or less adaptable building blocks for app-
cation-specific software systems. In order to apply optimization
methods to a new type of problem, corresponding models and
algorithms have to be "coded" so that they are accessible to a
computer. One way to achieve this step is the use of a mod- ing
language. Such modeling systems provide an excellent interface
between models and solvers, but only for a limited range of model
types (in some cases, for example, linear) due, in part, to
limitations imposed by the solvers. Furthermore, while m- eling
systems especially for heuristic search are an active research
topic, it is still an open question as to whether such an approach
may be generally successful. Modeling languages treat the solvers
as a "black box" with numerous controls. Due to variations, for
example, with respect to the pursued objective or specific problem
properties, - dressing real-world problems often requires special
purpose methods. Thus, we are faced with the difficulty of
efficiently adapting and applying appropriate methods to these
problems. Optimization software libraries are intended to make it
relatively easy and cost effective to incorporate advanced planning
methods in application-specific software systems. A general
classification provides a distinction between callable packages,
nume- cal libraries, and component libraries.
On March 15, 2002 we held a workshop on network interdiction and
the more general problem of stochastic mixed integer programming at
the University of California, Davis. Jesus De Loera and I
co-chaired the event, which included presentations of on-going
research and discussion. At the workshop, we decided to produce a
volume of timely work on the topics. This volume is the result.
Each chapter represents state-of-the-art research and all of them
were refereed by leading investigators in the respective fields.
Problems - sociated with protecting and attacking computer,
transportation, and social networks gain importance as the world
becomes more dep- dent on interconnected systems. Optimization
models that address the stochastic nature of these problems are an
important part of the research agenda. This work relies on recent
efforts to provide methods for - dressing stochastic mixed integer
programs. The book is organized with interdiction papers first and
the stochastic programming papers in the second part. A nice
overview of the papers is provided in the Foreward written by Roger
Wets.
Computer Science and Operations Research continue to have a
synergistic relationship and this book - as a part of the
Operations Research and Computer Science Interface Series - sits
squarely in the center of the confluence of these two technical
research communities. The research presented in the volume is
evidence of the expanding frontiers of these two intersecting
disciplines and provides researchers and practitioners with new
work in the areas of logic programming, stochastic optimization,
heuristic search and post-solution analysis for integer programs.
The chapter topics span the spectrum of application level. Some of
the chapters are highly applied and others represent work in which
the application potential is only beginning. In addition, each
chapter contains expository material and reviews of the literature
designed to enhance the participation of the reader in this
expanding interface.
This book provides a complete and comprehensive guide to Pyomo
(Python Optimization Modeling Objects) for beginning and advanced
modelers, including students at the undergraduate and graduate
levels, academic researchers, and practitioners. Using many
examples to illustrate the different techniques useful for
formulating models, this text beautifully elucidates the breadth of
modeling capabilities that are supported by Pyomo and its handling
of complex real-world applications. In the third edition, much of
the material has been reorganized, new examples have been added,
and a new chapter has been added describing how modelers can
improve the performance of their models. The authors have also
modified their recommended method for importing Pyomo. A big change
in this edition is the emphasis of concrete models, which provide
fewer restrictions on the specification and use of Pyomo models.
Pyomo is an open source software package for formulating and
solving large-scale optimization problems. The software extends the
modeling approach supported by modern AML (Algebraic Modeling
Language) tools. Pyomo is a flexible, extensible, and portable AML
that is embedded in Python, a full-featured scripting language.
Python is a powerful and dynamic programming language that has a
very clear, readable syntax and intuitive object orientation. Pyomo
includes Python classes for defining sparse sets, parameters, and
variables, which can be used to formulate algebraic expressions
that define objectives and constraints. Moreover, Pyomo can be used
from a command-line interface and within Python's interactive
command environment, which makes it easy to create Pyomo models,
apply a variety of optimizers, and examine solutions.
provide models that could be used by do-it-yourselfers and also can
be used
toprovideunderstandingofthebackgroundissuessothatonecandoabetter
job of working with the (proprietary) algorithms of the software
vendors. In this book we strive to provide models that capture many
of the - tails faced by ?rms operating in a modern supply chain,
but we stop short of proposing models for economic analysis of the
entire multi-player chain. In other words, we produce models that
are useful for planning within a supply chain rather than models
for planning the supply chain. The usefulness of the models is
enhanced greatly by the fact that they have been implemented - ing
computer modeling languages. Implementations are shown in Chapter
7, which allows solutions to be found using a computer. A
reasonable question is: why write the book now? It is a combination
of opportunities that have recently become available. The
availability of mod-
inglanguagesandcomputersthatprovidestheopportunitytomakepractical
use of the models that we develop. Meanwhile, software companies
are p- viding software for optimized production planning in a
supply chain. The opportunity to make use of such software gives
rise to a need to understand some of the issues in computational
models for optimized planning. This is best done by considering
simple models and examples.
On March 15, 2002 we held a workshop on network interdiction and
the more general problem of stochastic mixed integer programming at
the University of California, Davis. Jesus De Loera and I
co-chaired the event, which included presentations of on-going
research and discussion. At the workshop, we decided to produce a
volume of timely work on the topics. This volume is the result.
Each chapter represents state-of-the-art research and all of them
were refereed by leading investigators in the respective fields.
Problems - sociated with protecting and attacking computer,
transportation, and social networks gain importance as the world
becomes more dep- dent on interconnected systems. Optimization
models that address the stochastic nature of these problems are an
important part of the research agenda. This work relies on recent
efforts to provide methods for - dressing stochastic mixed integer
programs. The book is organized with interdiction papers first and
the stochastic programming papers in the second part. A nice
overview of the papers is provided in the Foreward written by Roger
Wets."
Stefan VoC and David Woodruff have edited a carefully refereed
volume by experts on optimization software class libraries. The
book focuses on flexible and powerful collections of computational
objects for addressing complex optimization problems. These
component class libraries are suitable for use in the increasing
number of optimization applications that stand alone or are
imbedded in advanced planning, engineering, and bioinformatics
applications. Most researchers today use a number of modeling
language software packages and a number of software solvers to
solve computational problems. This book outlines packaged software
class libraries to enable researchers to find cost-effective and
efficient methods of getting problems coded into the computer, or
into a modeling language package or into optimizing solvers - hence
providing software coding solutions to whatever specialized needs a
specific problem might require. Optimization Software Class
Libraries provides the reader with a rich overview of the variety
of components for framing problems. With the growing number of
application-specific software systems and advance planning methods
for specific classes of problems, class libraries for optimization
are increasingly useful, practical, and needed. Benefits of
Optimization Software Class Libraries are: Researchers will be able
to invest more effort in examining better algorithms, performing
experiments, and making use of problem-specific knowledge; The
libraries that encapsulate general-purpose algorithms as reusable,
high-quality software components are themselves significant
contributions to ongoing research; and In addition to the research
benefits, the libraries described providesubstantial practical
value to organizations that adopt them.
Computer Science and Operations Research continue to have a
synergistic relationship and this book - as a part of the
Operations Research and Computer Science Interface Series - sits
squarely in the center of the confluence of these two technical
research communities. The research presented in the volume is
evidence of the expanding frontiers of these two intersecting
disciplines and provides researchers and practitioners with new
work in the areas of logic programming, stochastic optimization,
heuristic search and post-solution analysis for integer programs.
The chapter topics span the spectrum of application level. Some of
the chapters are highly applied and others represent work in which
the application potential is only beginning. In addition, each
chapter contains expository material and reviews of the literature
designed to enhance the participation of the reader in this
expanding interface.
This book provides a complete and comprehensive guide to Pyomo
(Python Optimization Modeling Objects) for beginning and advanced
modelers, including students at the undergraduate and graduate
levels, academic researchers, and practitioners. Using many
examples to illustrate the different techniques useful for
formulating models, this text beautifully elucidates the breadth of
modeling capabilities that are supported by Pyomo and its handling
of complex real-world applications. In the third edition, much of
the material has been reorganized, new examples have been added,
and a new chapter has been added describing how modelers can
improve the performance of their models. The authors have also
modified their recommended method for importing Pyomo. A big change
in this edition is the emphasis of concrete models, which provide
fewer restrictions on the specification and use of Pyomo models.
Pyomo is an open source software package for formulating and
solving large-scale optimization problems. The software extends the
modeling approach supported by modern AML (Algebraic Modeling
Language) tools. Pyomo is a flexible, extensible, and portable AML
that is embedded in Python, a full-featured scripting language.
Python is a powerful and dynamic programming language that has a
very clear, readable syntax and intuitive object orientation. Pyomo
includes Python classes for defining sparse sets, parameters, and
variables, which can be used to formulate algebraic expressions
that define objectives and constraints. Moreover, Pyomo can be used
from a command-line interface and within Python's interactive
command environment, which makes it easy to create Pyomo models,
apply a variety of optimizers, and examine solutions.
This book provides a complete and comprehensive guide to Pyomo
(Python Optimization Modeling Objects) for beginning and advanced
modelers, including students at the undergraduate and graduate
levels, academic researchers, and practitioners. Using many
examples to illustrate the different techniques useful for
formulating models, this text beautifully elucidates the breadth of
modeling capabilities that are supported by Pyomo and its handling
of complex real-world applications. This second edition provides an
expanded presentation of Pyomo's modeling capabilities, providing a
broader description of the software that will enable the user to
develop and optimize models. Introductory chapters have been
revised to extend tutorials; chapters that discuss advanced
features now include the new functionalities added to Pyomo since
the first edition including generalized disjunctive programming,
mathematical programming with equilibrium constraints, and bilevel
programming. Pyomo is an open source software package for
formulating and solving large-scale optimization problems. The
software extends the modeling approach supported by modern AML
(Algebraic Modeling Language) tools. Pyomo is a flexible,
extensible, and portable AML that is embedded in Python, a
full-featured scripting language. Python is a powerful and dynamic
programming language that has a very clear, readable syntax and
intuitive object orientation. Pyomo includes Python classes for
defining sparse sets, parameters, and variables, which can be used
to formulate algebraic expressions that define objectives and
constraints. Moreover, Pyomo can be used from a command-line
interface and within Python's interactive command environment,
which makes it easy to create Pyomo models, apply a variety of
optimizers, and examine solutions.
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