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Potential Function Methods For Approximately Solving Linear
Programming Problems breaks new ground in linear programming
theory. The book draws on the research developments in three broad
areas: linear and integer programming, numerical analysis, and the
computational architectures which enable speedy, high-level
algorithm design. During the last ten years, a new body of research
within the field of optimization research has emerged, which seeks
to develop good approximation algorithms for classes of linear
programming problems. This work both has roots in fundamental areas
of mathematical programming and is also framed in the context of
the modern theory of algorithms. The result of this work, in which
Daniel Bienstock has been very much involved, has been a family of
algorithms with solid theoretical foundations and with growing
experimental success. This book will examine these algorithms,
starting with some of the very earliest examples, and through the
latest theoretical and computational developments.
This volume contains the papers accepted for publication at IPCO X,
the Tenth InternationalConferenceonInteger
ProgrammingandCombinatorialOptimi- tion, held in New York City, New
York, USA, June 7-11, 2004.The IPCO series of conferences presents
recent results in theory, computation and applications of integer
programming and combinatorial optimization. These conferences are
sponsoredby the Mathematical ProgrammingSociety, andareheldin
thoseyearsin whichno InternationalSymposiumonMathema-
calProgrammingtakes place.IPCO VIII was held in Utrecht (The
Netherlands) and IPCO IX was held in Cambridge (USA). A total of
109 abstracts, mostly of very high quality, were submitted. The
ProgramCommittee accepted 32, in order to meet the goal of having
three days of talks with no parallel sessions. Thus, many excellent
abstracts could not be accepted. The papers in this volume havenot
been refereed. It is expected that revised versions of the accepted
papers will be submitted to standard scienti?c journals for
publication. The Program Committee thanks all authors of submitted
manuscripts for their support of IPCO. March 2004 George Nemhauser
Daniel Bienstock Organization IPCO X was hosted by the
Computational Optimization Research Center (CORC), Columbia
University.
Potential Function Methods For Approximately Solving Linear
Programming Problems breaks new ground in linear programming
theory. The book draws on the research developments in three broad
areas: linear and integer programming, numerical analysis, and the
computational architectures which enable speedy, high-level
algorithm design. During the last ten years, a new body of research
within the field of optimization research has emerged, which seeks
to develop good approximation algorithms for classes of linear
programming problems. This work both has roots in fundamental areas
of mathematical programming and is also framed in the context of
the modern theory of algorithms. The result of this work, in which
Daniel Bienstock has been very much involved, has been a family of
algorithms with solid theoretical foundations and with growing
experimental success. This book will examine these algorithms,
starting with some of the very earliest examples, and through the
latest theoretical and computational developments.
This book constitutes the refereed proceedings of the 21st
International Conference on Integer Programming and Combinatorial
Optimization, IPCO 2020, held in London, UK, in June 2020. The 33
full versions of extended abstracts presented were carefully
reviewed and selected from 126 submissions. The conference is a
forum for researchers and practitioners working on various aspects
of integer programming and combinatorial optimization. The aim is
to present recent developments in theory, computation, and
applications in these areas.
The power grid can be considered one of twentieth-century
engineering's greatest achievements, and as grids and populations
grow, robustness is a factor that planners must take into account.
Power grid robustness is a complex problem for two reasons: the
underlying physics is mathematically complex, and modeling is
complicated by lack of accurate data. This book sheds light on this
complex problem by introducing the engineering details of power
grid operations from the basic to the detailed; describing how to
use optimization and stochastic modeling, with special focus on the
modeling of cascading failures and robustness; providing numerical
examples that show "how things work"; and detailing the application
of a number of optimization theories to power grids.?????
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