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Linear Programming:     more books (100)

Linear Programming by James P. Ignizio, Tom M. Cavalier, 1993-11-12 Production Planning by Mixed Integer Programming (Springer Series in Operations Research and Financial Engineering) by Yves Pochet, Laurence A. Wolsey, 2010-11-02 Linear Programming: Basic Theory and Applications (McGraw-Hill series in quantitative methods for management) by Leonard W. Swanson, 1979-10-01 Basic Linear Programming by Brian Bunday, 1984-10 Applied Optimization with MATLAB Programming by P. Venkataraman, 2009-03-23 Fuzzy Geometric Programming (Applied Optimization) by Bing-Yuan Cao, 2002-10-31 Linear Programming with MATLAB (MPS-SIAM Series on Optimization) by Michael C. Ferris, Olvi L. Mangasarian, et all 2008-01-31 Elementary Linear Programming with Applications, Second Edition (Computer Science and Scientific Computing) by Bernard Kolman, Robert E. Beck, 1995-07-06 Linear Programming: 1: Introduction (Springer Series in Operations Research and Financial Engineering) (v. 1) by George B. Dantzig, Mukund N. Thapa, 1997-01-27 Theory of Linear and Integer Programming by Alexander Schrijver, 1998-06-04 Linear-Fractional Programming: Theory, Methods, Applications and Software (Applied Optimization) by E.B. Bajalinov, 2003-11-30 Basic Linear Partial Differential Equations (Dover Books on Mathematics) by Francois Treves, 2006-11-17 An Introduction to Linear Programming and Game Theory by Paul R. Thie, Gerard E. Keough, 2008-08-11 Introduction to Linear Programming by Leonid N. Vaserstein, 2002-11-17

lists with details

21. Linear Programming Packages
    BPMPD, Csaba Meszaros, Hungarian Academy of Sciences, linear programming, Interior Point method 2001 linear programming Software Survey by Robert Fourer.  
    http://www.ici.ro/camo/hlp.htm

22. Linear Programming - Ignizio Cavalier
    linear programming by James P. Ignizio and Tom M. Cavalier, Prentice Hall International Series in Industrial and Systems Engineering, 666pp (1994).  
    http://www.personal.psu.edu/tmc7/lpbook.html

23. Linear Programming --  Britannica Online Encyclopedia
    Britannica online encyclopedia article on linear programming mathematical modeling technique useful for guiding quantitative decisions in business planning  
    http://www.britannica.com/eb/article-9048382/linear-programming
    
    Extractions: Close Enable free complete viewings of Britannica premium articles when linked from your website or blog-post. Now readers of your website, blog-post, or any other web content can enjoy full access to this article on linear programming , or any Britannica premium article for free, even those readers without a premium membership. Just copy the HTML code fragment provided below to create the link and then paste it within your web content. For more details about this feature, visit our Webmaster and Blogger Tools page Copy and paste this code into your page

24. MIT OpenCourseWare | Electrical Engineering And Computer Science | 6.252J Nonlin
    Textbook cover of D. P. Bertsekas, Nonlinear programming 2nd Edition, The materials are largely based on the textbook, Nonlinear programming 2nd  
    http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-252JNon-
    
    Extractions: skip to content Home Courses Donate ... Electrical Engineering and Computer Science Nonlinear Programming Textbook cover of Nonlinear Programming: 2nd Edition , by D.P. Bertsekas, Athena Scientific, 2000. (Image courtesy of Dimitri Bertsekas and Athena Scientific.) This course features a full set of lecture notes , in addition to other materials used by students in the course. The materials are largely based on the textbook, Nonlinear Programming: 2nd Edition publisher's site for more information). 6.252J is a course in the department's "Communication, Control, and Signal Processing" concentration. This course provides a unified analytical and computational approach to nonlinear optimization problems. The topics covered in this course include: unconstrained optimization methods, constrained optimization methods, convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. There is also a comprehensive treatment of optimality conditions, Lagrange multiplier theory, and duality theory. Throughout the course, applications are drawn from control, communications, power systems, and resource allocation problems. Any number of development tools can be used to compile and run the .fortran files found on this course site. Please refer to the course materials for any specific instructions or recommendations.

25. Linear Programming With Matlab
    This textbook provides a selfcontained introduction to linear programming using MATLAB® software to elucidate the development of algorithms and theory.  
    http://www.ec-securehost.com/SIAM/MP07.html
    
    Extractions: This textbook provides a self-contained introduction to linear programming using MATLAB® software to elucidate the development of algorithms and theory. Early chapters cover linear algebra basics, the simplex method, duality, the solving of large linear problems, sensitivity analysis, and parametric linear programming. In later chapters, the authors discuss quadratic programming, linear complementarity, interior-point methods, and selected applications of linear programming to approximation and classification problems. Exercises are interwoven with the theory presented in each chapter, and two appendices provide additional information on linear algebra, convexity, and nonlinear functions and on available MATLAB commands, respectively. Readers can access MATLAB codes and associated mex files at a Web site maintained by the authors.

26. Teaching Linear Programming With Excel Solver (CHEER V9 N3)
    linear programming (LP) is one of the most widely applied O.R. techniques and owes its popularity principally to George Danzig s simplex method (Danzig  
    http://www.economicsnetwork.ac.uk/cheer/ch9_3/ch9_3p07.htm
    
    Extractions: University of Leicester Linear programming (LP) is one of the most widely applied O.R. techniques and owes its popularity principally to George Danzig's simplex method (Danzig 1963) and the revolution in computing. It is a very powerful technique for solving allocation problems and has become a standard tool for many businesses and organisations. Although Danzig's simplex method allows solutions to be generated by hand, the iterative nature of producing solutions is so tedious that had the computer never been invented then linear programming would have remained an interesting academic idea, relegated to the mathematics classroom. Fortunately, computers were invented and as they have become so powerful for so little cost, linear programming has become possibly one of the most widespread uses for a personal PC. There are of course numerous software packages which are dedicated to solving linear programs (and other types of mathematical program), of which possibly LINDO, GAMS and XPRESS-MP are the most popular. All these packages tend to be DOS based and are intended for a specialist market which requires tools dedicated to solving LPs. In recent years, however, several standard business packages, such as spreadsheets, have started to include an LP solving option, and Microsoft Excel is no exception. The inclusion of an LP solving capability into applications such as Excel is attractive for at least two reasons. Firstly, Excel is perhaps the most popular spreadsheet used both in business and in universities and as such is very accessible. Second to this, the spreadsheet offers very convenient data entry and editing features which allows the student to gain a greater understanding of how to construct linear programs.

27. Test-Problem Collection For Stochastic Linear Programming
    TestProblem Collection for Stochastic linear programming multistage stochastic linear program in the introduction to the other written descriptions  
    http://www.uwsp.edu/math/afelt/slptestset.html
    
    Extractions: University of Wisconsin-Stevens Point This is a modern test-problem collection for stochastic programming, with emphasis on a close connection between the test problems and their associated real world applications. The problem descriptions were collected from the literature, with focus on variety of problem structure and application. Each of the 11 problems has a short description, mathematical problem statement, and notational reconciliation to a standard problem format. In addition, there are 21 specific test cases with data in SMPS format. The test set is expanding. Indeed, submissions of new problems and descriptions from colleagues are encouraged. You are welcome to freely download any and all of the test cases. I would love to expand the test set. If you would like to submit a problem, please email me the following: Data files in SMPS format A written description in LaTeX, following the format of the other written descriptions. (See the

28. Math Forum: Game Theory / Linear / Non-Linear
    Internet resources for game theory and linear or nonlinear programming Web sites, software, Internet projects, and public forums for discussion.  
    http://mathforum.org/advanced/game.html
    
    Extractions: num. analysis This list contains some of the best resources for game theory and linear and non-linear programming. For a more exhaustive list, or to find materials that fit your specific needs, see also the Forum's Internet Mathematics Library: Operations Research. CPLEX Optimization, Inc. - ILOG, Inc. A provider of large-scale mathematical programming software and services for linear, mixed integer, and quadratic programming. Products: a linear ... David Eppstein's Teaching - David Eppstein Archives of Information and Computer Science courses in computational statistics, computability, design and analysis of algorithms, computational ... Decision Tree for Optimization Software - Hans D. Mittelmann, P. Spellucci Information on optimization software and optimization research, including links to a dictionary, tutorials, testcases, interfaces, and ...

29. Pattern Classification Via Linear Programming
    The purpose of this html document is to show how linear programming is used to classify patterns. Specifically, we show how to formulate the problem of  
    http://cgm.cs.mcgill.ca/~beezer/cs644/main.html
    
    Extractions: Introduction The purpose of this html document is to show how Linear Programming is used to classify patterns. Specifically, we show how to formulate the problem of finding a linear discriminant function as a problem in Linear Programming. We consider two cases: separable sets and inseparable sets. In addition, the reader will find a number of interesting links to web pages and applets about Linear Programming and Operations Research. Among these is a nice interactive applet that shows the geometric-algebraic relationship of Linear Programming in 2 Dimensions. Latest additions: The Problem Informally: The fundamental problem we wish to address is that of distinguishing between elements of two disjoint pattern sets. A linear programming solution is attractive since many general purpose linear programming packages exist, thus requiring little or no implementation. Formally (Mathematically): Given two disjoint point sets H and M in the n-dimensional real space R n , we wish to construct a discriminant function f , from R n into the real line R, such that

30. Linear Programming
    linear programming, a specific class of mathematical problems, in which a linear function is maximized (or minimized) subject to given linear constraints.  
    http://cs.nyu.edu/overton/g22_lp/encyc/article_web.html
    
    Extractions: December 20, 1997 LINEAR PROGRAMMING , a specific class of mathematical problems, in which a linear function is maximized (or minimized) subject to given linear constraints. This problem class is broad enough to encompass many interesting and important applications, yet specific enough to be tractable even if the number of variables is large. History. Overview. The general form of a linear program is Here and are given numbers, and are variables whose values are to be determined, maximizing the given objective subject to the given constraints. There are n variables and m constraints, in addition to the nonnegativity restrictions on the variables. The constraints are called linear because they involve only linear functions of the variables. Quadratic terms such as or are not permitted. If minimization is desired instead of maximization, this can be accomplished by reversing the signs of An example is very helpful. Consider the linear program

31. DRA Systems OR-Objects 1.2.4 Package Drasys.or.mp.lp
    Interface Summary. LinearProgrammingI, Abstract interface to linear programming algorithms. linear programming Algorithms. Author DRA Systems  
    http://opsresearch.com/OR-Objects/api/drasys/or/mp/lp/package-summary.html
    
    Extractions: 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. Linear Programming Algorithms. Overview Package Class Tree Deprecated Index Help ... OpsResearch.com

32. The GNU Linear Programming Kit, Part 1 Introduction To Linear
    The GNU linear programming Kit is a powerful, proven tool for solving numeric problems with multiple constraints. This article introduces GLPK,  
    http://www-128.ibm.com/developerworks/linux/library/l-glpk1/

33. Linear Programming Environment
    linear programming environment GIPALS for constrained optimization.  
    http://www.optimalon.com/product_gipals.htm
    
    Extractions: Home Woodcutting Products GNCutter GIPALS Features Screen Shots Examples Download ... Contacts GIPALS (General Interior-Point Algorithm Linear Solver) GIPALS is linear programming environment that incorporates large-scale linear programs solver and easy, intuitive graphical user interface to direct specify or import and solve any type of constrained optimization problems arising in various industrial, financial and educational areas. A constrained optimization problem is stated as a linear program that size can be unlimited. The linear program solver is based on interior-point method (Mehrotra predictor - corrector algorithm) and optimized for large sparse linear programs. The solver exploits a sparsity of the constraint matrix by implementing state-of-art ordering algorithm to preserve the matrix sparsity and hence reduce the calculation time dramatically. Almost every stage of the linear programming calculation can be saved in the debug files or traced for the deep analysis. The user can specify the linear program constraints in a dense form using the grids or in a sparse form using the particular constraints editor. The dense form is suitable for small and medium linear programs with non-zeros prevail over zeros. In this form the constraints can be directly copied/pasted from/to MS Excel spreadsheets by Windows clipboard. The sparse form is designed to specify / view / edit large linear programs with tens and hundreds of thousand variables and constraints. Any constraint can be disabled / enabled from the calculation by the user at any time.

34. The LP Formulation And The Underlying Assumptions
    A linear programming problem is a special case of a Mathematical Programming linear programming is the specialization of mathematical programming to the  
    http://www2.isye.gatech.edu/~spyros/LP/node1.html
    
    Extractions: Next: A prototype LP problem: Up: No Title Previous: No Title A Linear Programming problem is a special case of a Mathematical Programming problem. From an analytical perspective, a mathematical program tries to identify an extreme (i.e., minimum or maximum) point of a function , which furthermore satisfies a set of constraints, e.g., . Linear programming is the specialization of mathematical programming to the case where both, function f - to be called the objective function - and the problem constraints are linear From an applications perspective, mathematical (and therefore, linear) programming is an optimization tool, which allows the rationalization of many managerial and/or technological decisions required by contemporary techno-socio-economic applications. An important factor for the applicability of the mathematical programming methodology in various application contexts, is the computational tractability of the resulting analytical models. Under the advent of modern computing technology, this tractability requirement translates to the existence of effective and efficient algorithmic procedures able to provide a systematic and fast solution to these models. For Linear Programming problems, the Simplex algorithm, discussed later in the text, provides a powerful computational tool, able to provide fast solutions to very large-scale applications, sometimes including hundreds of thousands of variables (i.e., decision factors). In fact, the Simplex algorithm was one of the first Mathematical Programming algorithms to be developed (George Dantzig, 1947), and its subsequent successful implementation in a series of applications significantly contributed to the acceptance of the broader field of

35. Details: Linear Programming - Activity B Gizmo | ExploreLearning
    Details linear programming Activity B. Use the graph of the feasible region to find the maximum or minimum value of the objective function.  
    http://www.explorelearning.com/index.cfm?method=cResource.dspDetail&ResourceID=7

36. The Diet Problem
    An Application of linear programming. The recent change of web server at Argonne The problem is formulated as a linear program where the objective is to  
    http://www-new.mcs.anl.gov/otc/Guide/CaseStudies/diet/index.html
    
    Extractions: An Application of Linear Programming The recent change of web server at Argonne and the loss of some of the source code for this Case Study has rendered it inoperable. We are currently trying to fix it. We apologize for any inconvenience. CLICK HERE for the demo! Or try the following version without tables The goal of the diet problem is to find the cheapest combination of foods that will satisfy all the daily nutritional requirements of a person. The problem is formulated as a linear program where the objective is to minimize cost and meet constraints which require that nutritional needs be satisfied. We include constraints that regulate the number of calories and amounts of vitamins, minerals, fats, sodium and cholesterol in the diet. The mathematical formulation is simple, but you will find out by running the model that people do not actually choose their menus by solving this model. Our nutritional requirements can be met yet our concerns for taste and variety go unheeded. We would never drink gallons of vinegar nor include a few boullion cubes in our meals; however, such "optimal" menus have been created using this model. Read more about the history of the diet problem for more interesting facts.

37. Linear Optimization
    linear programming deals with a class of optimization problems, .. linear programming (LP) is a mathematical procedure for determining optimal allocation  
    http://home.ubalt.edu/ntsbarsh/opre640a/partVIII.htm
    
    Extractions: A mathematical optimization model consists of an objective function and a set of constraints in the form of a system of equations or inequalities. Optimization models are used extensively in almost all areas of decision-making, such as engineering design and financial portfolio selection. This site presents a focused and structured process for optimization problem formulation, design of optimal strategy, and quality-control tools that include validation, verification, and post-solution activities. Professor Hossein Arsham To search the site , try E F ind in page [Ctrl + f]. Enter a word or phrase in the dialogue box, e.g. " parameter " or " linear " If the first appearance of the word/phrase is not what you are looking for, try F ind Next MENU Optimization-Modeling Process Ingredients of Optimization Problems and Their Classification Linear Programming (LP) Dual Problem: Its Construction and Economics Implications ... Europe Mirror Site Companion Sites: Ingredients of Optimization Problems and Their Classification

38. Quick Review Of Linear Programming
    Quick Review of linear programming. Quick Review of linear programming. 15.966. Management Science Techniques for Consultants  
    http://mat.gsia.cmu.edu/mstc/sensitive/sensitive.html

39. LP Explorer 1.0
    LP Explorer enables the simplex method to be applied to a linear programming (LP) problem and allows the sensitivity of the solution to changes in the  
    http://www.maths.ed.ac.uk/LP-Explorer/
    
    Extractions: LP Explorer enables the simplex method to be applied to a linear programming (LP) problem and allows the sensitivity of the solution to changes in the problem data to be examined. LP Explorer is of particular value for problems with 2 variables when the simplex method and solution sensitivity are interpreted graphically. It is possible to enter the LP problem data via a sequence of two forms Before clicking on the box below (which runs LP Explorer for an example problem) it is suggested that you consult the user guide . This also links to further examples of LP Explorer in use and instructions on how to apply it to an LP problem of your own. LP Explorer was designed by Julian Hall (Mathematics) and Melanie Baird (Sellic On-Line), both of the University of Edinburgh. It was written by Melanie Baird in JDK1.02 and will run on any Java-enabled browser. Sellic On-Line is funded by Sun Microsystems. A review of LP Explorer 1.0 by the Java Applet Rating Service placed it in the top 25% of java-based resources. LP Explorer 1.0 has been reviewed by

40. Linear Programming With O-Matrix And Lp_solve
    The Lp_solve Interface Toolbox for OMatrix integrates the lp_solve linear programming Solver with O-Matrix.  
    http://www.omatrix.com/lpsolve.html
    
    Extractions: Linear Programming with O-Matrix and lp_solve The lp_solve Interface Toolbox for O-Matrix integrates lpsolve, a free, public domain linear programming solver with the O-Matrix matrix language. The lpsolve package solves pure linear, mixed integer/binary, semi-continuous and special ordered sets models. lpsolve has no limit on model size and has solved models with more than 100000 constraints. Input can be loaded directly from O-Matrix , from lp, xml or mps input files, and from dynamically called modeling languages.

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