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         Linear Programming:     more books (100)
  1. Computer Solution of Linear Programs (Numerical Mathematics and Scientific Computation) by J. L. Nazareth, 1988-01-07
  2. Linear Programming by James P. Ignizio, Tom M. Cavalier, 1993-11-02
  3. Matrices in control theory: with applications to linear programming, by S Barnett, 1971
  4. Linear Programming by Chung, An-min, 1963
  5. Linear Programming with Fortran by Carvel S. Wolfe, 1973-12
  6. Basic Linear Programming by Brian Bunday, 1984-10
  7. The simplex method of linear programming by F. A Ficken, 1961
  8. Practical linear programming with computer applications by Mitchell O Locks, 1974
  9. Applied linear programming by J. Ronald Frazer, 1968
  10. Methods and applications of linear programming by Leon Cooper, 1974
  11. Readings in linear programming by S Vajda, 1958
  12. Primer on Linear Programming by Neebe, 1991-06
  13. Elementary Linear Programming With Applications/Instructors Manual (Computer Science and Applied Mathematics) by Bernard Kolman, Robert Beck, 1980-02
  14. Iterative Methods for Sparse Linear Systems, Second Edition by Yousef Saad, 2003-04-30

101. Www2.math.bas.bg/~keleved/lpform.html
PDF linear programming
http://www2.math.bas.bg/~keleved/lpform.html

102. Anima-LP Java
Animated linear programming Applet. Use this to help you visualize and solve standard linear programming problems.
http://www.cs.stedwards.edu/~wright/linprog/AnimaLP.html
Animated Linear Programming Applet
Use this to help you visualize and solve standard Linear Programming problems.

103. Linear Programming (MENTOR Module), Management Science, Strathclyde University,
linear programming » Free Evaluation of Software. Examples of the kinds of problems tackled by linear programming; What is linear programming definition;
http://www.managementscience.org/mentor/lp.asp
www.ManagementScience.org Sunday 6 June, 2004 MENTOR FAQ Free Evaluation of Software Getting Started Guide ... Network Installation Mentor Modules Conflict Analysis Dynamic Programming Forecasting Heuristic Search Methods ... Stock Control
MENTOR Module
Linear Programming
Module Authors: Valerie Belton and Mark Elder , Management Science, University of Strathclyde, Glasgow. The module covers the following topics:
  • Examples of the kinds of problems tackled by linear programming What is linear programming - definition Formulating a practical problem as a linear program Solving LPs using the graphical method Solving LPs using the simplex method Using a computer package to solve LPs Interpreting the results of analysis - including the sensitivity of the solution to chnages in the data
General Learning Objectives The lecturer will usually define the specific learning objectives for their course as a whole. The students should:
  • have a general appreciation of the types of resource allocation problems which are amenable to analysis using mathematical programming be familiar with the concept of a mathematical model be able to formulate a problem described verbally as a mathematical model understand the techniques of linear programming be able to identify an appropriate technique of analysis be able to interpret the solutions derived from these approaches and on the basis of these be able to make practical recommendations

104. A Few Open Source Codes
Index. TNC A nonlinear optimization package; PuLP A Python linear programming modeler; PuLP A linear programming modeler in Python.
http://www.jeannot.org/~js/code/index.en.html
Back to JS's page (in french)
A few open source codes
Few codes for the moment, but sooner or later I'll add my HP48 programs (if I succeed in locating the sources) and some Mac programs. Cette page existe aussi
Index
  • TNC : A nonlinear optimization package PuLP : A Python Linear Programming modeler COBYLA : A derivative free constrained optimization package MapKit : Dictionnaries / Hash tables in C RandomKit : Cross-platform, thread-safe, random number generation MDMalloc : A multi dimensionnal array allocator in C TextTable : Delimited Text files I/O in C NetCDFStruct : NetCDF files as C strutures PyCestac : The Cestac in Python Countdown game : in C Analog Clock : an analog clock for GIMP : TE / T3 aganda backgrounds
TNC : A Truncated-Newton optimization package
TNC is a C implementation of TNBC, a truncated newton optimization package originally developed by Stephen G. Nash in Fortran. The original source code can be found at Stephen G. Nash Software Page This software aims at minimizing the value of a nonlinear function whose variables are subject to bound constraints. It requires to be able to evaluate the function and its gradient and is especially useful for solving large scale problems. Since version 1.0.5, a Python interface module is provided.

105. Optimality Pays: An Introduction To Linear Programming An Application Of Mathema
Optimality Pays An Introduction to linear programming. Jeganathan Sriskandaraajah. Mathematics Topic linear programming, Precalculus.
http://www.comap.com/product/?idx=19

106. Second Moment: Linear Modeling To The Max: An Interview With UPS Operations Rese
On the optimization side and I think this is what your readers will be most interested in - is the linear programming that we’ve been developing to address
http://www.secondmoment.org/articles/ups.php
home about us contact us past features ... Resource Links
9/11 Remembered Linear Modeling to the Max: An Interview with UPS Operations Research Manager Keith Ware
Posted by Bill Abrams, Co-editor SecondMoment

United Parcel Service, better known as UPS, is the world's largest package distribution company. Serving more than 200 countries and territories, UPS transports more than 3 billion parcels annually, including upwards of 325 million during Peak, the four to five weeks between Thanksgiving and Christmas. To do this, UPS operates more than 600 aircraft, 88,000 vehicles, and 1,700 facilities. It is a logistics problem of the highest order, and UPS analysts like Keith Ware are taking linear modeling to the maximum in order to solve it. BA: Hello Keith. Thanks for taking the time to speak with us. KW: You’re quite welcome. BA: So getting right to it, tell us a little bit about your group. KW: Essentially the Operations Research group has two sides to it, an optimization group and a simulation group. On the simulation side, our main focus right now is a huge new facility that we’re in the process of building in Louisville. We started work on it about three years ago and it’s scheduled to be fully operational next summer, at which time it will be the largest automated sorting facility in the world. The simulation group does a lot of work using models to analyze various ways of aligning the building’s sorting systems, determining which load goes where, and looking at how to feed containers to the unloads from the ramps where they’re taken off the aircraft. The goal is to minimize the time it takes to run the sort operation.

107. DRA Systems OR-Objects 1.2.4 Package Drasys.or.mp.lp
. LinearProgrammingI, Abstract interface to linear programming algorithms. Class Summary.......Package drasys.or.mp.lp. linear programming Algorithms. See
http://opsresearch.com/OR-Objects/api/drasys/or/mp/lp/package-summary.html
Overview Package Class Tree Deprecated Index Help ... NO FRAMES
Package drasys.or.mp.lp
Linear Programming Algorithms. See:
Description
Interface Summary LinearProgrammingI
Abstract interface to linear programming algorithms. Class Summary DenseLPBase Abstract class containing common dense data structures. DenseSimplex Simple simplex algorithm for dense coefficients.
Package drasys.or.mp.lp Description
Linear Programming Algorithms.
Author:
DRA Systems
Overview Package Class Tree Deprecated Index Help ... OpsResearch.com

108. Potential Function Methods For Approximately Solving Linear Programming Problems
Potential Function Methods for Approximately Solving linear programming Problems Theory and Practice. Add to cart. by Daniel Bienstock Dept.
http://www.wkap.nl/prod/b/1-4020-7173-6
Title Authors Affiliation ISBN ISSN advanced search search tips Books Potential Function Methods for Approximately Solving Linear Programming Problems
Potential Function Methods for Approximately Solving Linear Programming Problems
Theory and Practice

Add to cart

by
Daniel Bienstock
Dept. of Industrial Engineering and OR, Columbia University, New York, USA
Book Series: INTERNATIONAL SERIES IN OPERATIONS RESEARCH AND MANAGEMENT SCIENCE Volume 53
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. Contents
Kluwer Academic Publishers, Boston

109. Wiley::Introduction To Practical Linear Programming
By Keyword, Wiley Mathematics Statistics Discrete Mathematics Introduction to Practical linear programming.
http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471517895.html
Shopping Cart My Account Help Contact Us
By Keyword By Title By Author By ISBN By ISSN Wiley Discrete Mathematics Introduction to Practical Linear Programming Related Subjects General Interest Computer Science
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Related Titles Discrete Mathematics
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by Hosam M. Mahmoud Modified Lagrangians and Monotone Maps in Optimization (Hardcover) by E. G. Golshtein, N. V. Tretyakov Coding Theory, Design Theory, Group Theory: Proceedings of The Marshall Hall Conference (Hardcover) by D. Jungnickel (Editor), S. A. Vanstone (Editor) Combinatorial Optimization (Hardcover) by William J. Cook, William H. Cunningham, William R. Pulleyblank, Alexander Schrijver Optimization Methods for Logical Inference (Hardcover) by Vijay Chandru, John Hooker Random Graphs: Volume 2 (Hardcover) by Alan Frieze (Editor), Tomasz Luczak (Editor) Join a Discrete Mathematics Introduction to Practical Linear Programming David J. Pannell

110. ILOG CPLEX
Mathematical programming Software for resource optimization. Our linear, mixedinteger and quadratic programming solvers are known for superior performance and reliabilityparticularly on large, difficult problems.
http://www.cplex.com/
ilog.com Products ILOG Optimization Suite ILOG CPLEX Product Info New in version 9.0 ILOG CPLEX Suite ILOG Parallel CPLEX Cooperative and hybrid optimization ... Technical papers Solutions Academic Manufacturing Telecommunications Transportation and travel ... Contact info ILOG CPLEX ILOG CPLEX delivers high-performance, robust, flexible optimizers for solving linear, mixed-integer and quadratic programming problems in mission-critical resource allocation applications. Virtually every leading end-user and software provider in supply chain planning, telecommunication network design and transportation logistics depends on the unequaled solving power of ILOG CPLEX. Robust algorithms for demanding problems
ILOG pioneered development of the world's fastest, most robust implementations of the fundamental algorithms needed to solve today's most demanding mathematical optimization problems. ILOG CPLEX has solved problems with millions of constraints and variables, and consistently sets new records for mathematical programming software performance. Leading hardware vendors use ILOG CPLEX to measure the computational performance of their latest CPUs. Industry-leading support
No other vendor can match ILOG's reputation in the industry for performance, reliability, flexibility and support. ILOG's ongoing commitment to pushing the performance envelope ensures ILOG customers that their investment in ILOG CPLEX is protected well into the future.

111. Linear Algebra And Econometrics
SPARSEM is a collection of sparse matrix classes that makes programming with sparse matrices (and large problems) almost as easy as a matrix language. BLUPF90 is a BLUP program written using SPARSEM. REMLF90 is a REML version of BLUPF90 that uses accelerated EM algorithm. Other programs included are dense matrix module DENSEOP, Gibbs sampling program GIBBS90, and several versions of linearthreshold models. By Ignacy Misztal and collaborators.
http://nce.ads.uga.edu:80/~ignacy/numpub/blupf

112. Bibliography On The Solution Of Sparse Linear Systems And Related Areas Of Compu
With emphasis on Computational linear Algebra, High Performance Computing, Mathematical programming and Graph Theory. Searchable.
http://liinwww.ira.uka.de/bibliography/Math/sparse.linear.systems.html
The Collection of
Computer Science Bibliographies Up: Bibliographies on Mathematics Collection Home
Bibliography on the Solution of Sparse Linear Systems and Related Areas of Computation
About Browse Statistics Number of references: Last update: September 28, 2001 Number of online publications: Supported: yes Most recent reference: September 1997 Search the Bibliography Query: Options case insensitive Case Sensitive partial word(s) exact online papers only Results Citation BibTeX Count Only Maximum of matches Help on: [ Syntax Options Improving your query Query examples
Boolean operators: and and or . Use to group boolean subexpressions.
Example: (specification or verification) and asynchronous Information on the Bibliography
Author:
Dr. Ricardo Duarte Arantes (email mangled to prevent spamming)
National Laboratory for Scientific Computation

Rua Lauro Muller, 455, Botafogo
22290-160, Rio de Janeiro
Brazil
Abstract:
This bibliography includes references on the Solution of Sparse Linear Systems, and Related Areas of Computation, with emphasis on: Computational Linear Algebra, High Performance Computing, Mathematical Programming, Graph Theory.
Keywords:
Sparse Linear Systems, Direct Methods, Computational Linear Algebra, High Performance Computing

113. Logique De La Programmation
The Logic of programming research team is interested in proof theory and its relations with theoretical computer science. The main topic is mathematical interpretation of proofs nets (proof = graph), denotational semantics (proof = function), and game semantics (proof = strategy). Two realisations of this working programm are linear Logic and Ludics.
http://iml.univ-mrs.fr/ldp/welcome.html
This page uses frames, but your navigator does not take them into account.

114. Linear Scan Register Allocation In The HiPE Compiler.
Erik Johansson and Konstantinos Sagonas. Precented at the International Workshop on Functional and (Constraint) Logic programming (WFLP 2001), Kiel.
http://user.it.uu.se/~happi/publications/wflp.ps

115. Applications Of Linear Logic To Computation: An Overview - Alexiev (ResearchInde
Survey article by Vladimir Alexiev providing an overview of existing applications of linear Logic to issues of computation. Discusses implications of the theory in several fields of theoretical computer science, such as functional programming, and the correct treatment of negation in logic programming.
http://citeseer.nj.nec.com/alexiev93applications.html
Applications of Linear Logic to Computation: An Overview (1993) (Make Corrections) (35 citations)
Vladimir Alexiev Bulletin of the IGPL
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(Enter summary) Abstract: This paper is an overview of existing applications of Linear Logic (LL) to issues of computation. After a substantial introduction to LL, it discusses the implications of LL to functional programming, logic programming, concurrent and object-oriented programming and some other applications of LL, like semantics of negation in LP, non-monotonic issues in AI planning, etc. Although the overview covers pretty much the state-of-the-art in this area, by necessity many of the works are only mentioned ... (Update) Context of citations to this paper: More ...false. There is far more to linear logic than can be discussed in this paper; for a more complete introduction see the papers

116. Gregory Gutin, TSP, Combinatorial Optimization, Digraphs
Royal Holloway, University of London. Graph theory and algorithms, combinatorial optimisation, linear and integer programming.
http://www.cs.rhbnc.ac.uk/home/gutin/
Professor Gregory Gutin
CV.pdf Digraph Monograph Page DIMACS TSP Challenge RHUL Library ... Oper. Res. Lett.
Biography
Gregory received his MSc in Mathematics in 1979 from Gomel State University, Belarus. He worked in high school and research institutes of Belarus from 1979-1990. He studied for PhD under Professor Noga Alon at the School of Mathematics, Tel Aviv University, Israel and received his PhD (with distinction) in 1993. Between 1993 and 1996 he held visiting positions in the Department of Mathematics and Computer Science, Odense University, Denmark and then became a lecturer at the Department of Mathematics, Brunel University, UK. Since 1 September 2000, Gregory has been Professor in Computer Science, Department of Computer Science, Royal Holloway. Gutin's main research interests include graph theory and algorithms, combinatorial optimisation, linear and integer programming. He has published more than sixty research papers, and a monograph : J. Bang-Jensen and G. Gutin, Digraphs. Theory, Algorithms and Applications. Springer-Verlag, London, 2000 (ISBN: 1-85233-268-9). He co-edited (with A.P. Punnen) a new TSP book : Traveling Salesman Problem and its Variations, Kluwer, 2002 (ISBN: 1-4020-0664-0). A preliminary version of Chapter 6 is available in

117. Miller, Dale
Penn State University linear logic, proof search and declarative programming languages.
http://www.cse.psu.edu/~dale/

118. Luke Ong's Home Page
Merton College, Oxford Semantics of programming languages, lambda calculus, categorical logic and type theory, game semantics, linear logic.
http://web.comlab.ox.ac.uk/oucl/work/luke.ong/

Luke Ong's new OUCL web page

oucl work luke.ong
Updated April 2004 Home Search SiteMap Feedback ... News

119. Charles Stewart
Boston University programming language theory, optimal reductions, graph reduction, linear logic, semantics of logic, formulae-as-types correspondence, continuation semantics.
http://www.linearity.org/cas/
Charles Alexander Stewart
Personal Information
I am a postdoctoral researcher in theoretical computer science associated with the Institute of Artifical Intelligence at Technische Universitaet Dresden. In the past, I have been associated with the Theory and Formal Specifications group of Technische Universitaet Berlin, the Linear Naming and Computation section of the Church Project at Boston University, the Department of Computer Science at Brandeis University, and the Foundations of Computation section of the Programming Research Group at Oxford University.
Research Interests
My research interests include:
  • Structural proof theory:
    • Deep inference and the Calculus of structures;
    • Natural deduction, sequent calculus, and applications to programming language design and implementation;
    • Modal logic and display logic;
  • Programming language theory:
    • Optimal reductions in the lambda calculus;
    • Linear naming and graph reduction, interaction nets;
    • Continuations in theory and practice;
    • Relationships between functional and logic programming;
  • Graph transformation:
    • Graph transformation and the design of distributed algorithms;

120. Cube
3D visual dataflow programming language programs consist of an arrangement of 3dimensional shapes instead of a linear stream of text. Interesting example images.
http://www.cs.berkeley.edu/~maratb/cs263/paper/node16.html

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