| DC Field | Value | Language |
|---|
| dc.contributor.author | Vanderbei, Robert J. | - |
| dc.date.accessioned | 2021-04-20T09:48:19Z | - |
| dc.date.available | 2021-04-20T09:48:19Z | - |
| dc.date.issued | 2014 | - |
| dc.identifier.isbn | 978-1-4614-7630-6 | - |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/140 | - |
| dc.description | The programs that implement these algorithms are written in C and can be
easily compiled on most hardware platforms. Students/instructors are encouraged
to install and compile these programs on their local hardware. Great pains have
been taken to make the source code for these programs readable (see Appendix A).
In particular, the names of the variables in the programs are consistent with the
notation of this book.
There are two ways to run these programs. The first is to prepare the input in
a standard computer-file format, called MPS format, and to run the program using
such a file as input. The advantage of this input format is that there is an archive
of problems stored in this format, called the NETLIB suite, that one can download
and use immediately (a link to the NETLIB suite can be found at the web site men-
tioned below). But, this format is somewhat archaic and, in particular, it is not easy
to create these files by hand. Therefore, the programs can also be run from within a
problem modeling system called AMPL. AMPL allows one to describe mathemat-
ical programming problems using an easy to read, yet concise, algebraic notation.
To run the programs within AMPL, one simply tells AMPL the name of the solver-
program before asking that a problem be solved. The text that describes AMPL,
Fourer et al. (1993) makes an excellent companion to this book. It includes a dis-
cussion of many practical linear programming problems. It also has lots of exercises
to hone the modeling skills of the student. | en_US |
| dc.description.abstract | This book is about constrained optimization. It begins with a thorough treat-
ment of linear programming and proceeds to convex analysis, network flows, integer
programming, quadratic programming, and convex optimization. Along the way,
dynamic programming and the linear complementarity problem are touched on as
well.
The book aims to be a first introduction to the subject. Specific examples and
concrete algorithms precede more abstract topics. Nevertheless, topics covered are
developed in some depth, a large number of numerical examples are worked out
in detail, and many recent topics are included, most notably interior-point methods.
The exercises at the end of each chapter both illustrate the theory and, in some cases,
extend it. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | en_US |
| dc.subject | Management Science | en_US |
| dc.subject | Programming | en_US |
| dc.title | Linear Programming | en_US |
| dc.title.alternative | Foundations and Extensions | en_US |
| dc.type | Book | en_US |
| Appears in Collections: | ARTS & SCIENCE
|
