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Linear Programming:     more books (100)

Dynamic Programming (Princeton Landmarks in Mathematics) by Richard Bellman, 2010-07-21 Optimization for Profit: A Decision Maker's Guide to Linear Programming by Filmore E Bender, Gerald Kahan, et all 1992-01-10 Mathematical Introduction to Linear Programming and Game Theory (Undergraduate Texts in Mathematics) by Louis Brickman, 1989-06-23 Integer and Non-Linear (Nonlinear) Programming by J. (ed.) Abadie, 1970 Non-linear Programming by M.S. Bazaraa, C.M. Shetty, 1993-03-03 Linear and Nonlinear Programming by Stephen G. Nash, Ariela Sofer, 1995-12-01 Linear and Nonlinear Optimization, Second Edition by Igor Griva, Stephen G. Nash, et all 2008-12-03 Linear Goal Programming by Marc J. Schniederjans, 1984-09 Linear Optimization Problems with Inexact Data by Miroslav Fiedler, Josef Nedoma, et all 2010-11-02 Linear Programming (Modern Birkhäuser Classics) by Howard Karloff, 2008-11-19 Interior Point Methods for Linear Optimization by Cornelis Roos, Tamás Terlaky, et all 2010-11-02 Decomposition Techniques in Mathematical Programming: Engineering and Science Applications by Antonio J. Conejo, Enrique Castillo, et all 2009-12-15 Methods of Mathematical Economics: Linear and Nonlinear Programming, Fixed-Point Theorems (Classics in Applied Mathematics, 37) by Joel N. Franklin, 2002-01-15 Stochastic Linear Programming: Models, Theory, and Computation by Peter Kall, 2010-10

lists with details

41. ILOG CPLEX: High-performance Software For Mathematical Programming And Optimizat
    ILOG CPLEX provides flexible, highperformance optimizers used when solving linear programming, quadratic programming, quadratically constrained programming  
    http://www.ilog.com/products/cplex/
    
    Extractions: BRMS Optimization Visualization ... ILOG CPLEX Product Info Latest version CPLEX interfaces CPLEX algorithms ILOG Parallel CPLEX ... Datasheet Solutions Customers Manufacturing Transportation and travel Newsletters ... Contact info ILOG CPLEX The World's Leading Mathematical Programming Optimizers MM_preloadImages('../../images/imgSmall_CPLEX.jpg','../../images/imgSmall_ODM.jpg','../../images/imgSmall_OPL.jpg','../../images/imgSmall_CP.jpg'); Improve efficiency, quickly implement strategies and increase profitability. ILOG CPLEX's mathematical optimization technology enables better decision-making for efficient resource utilization. With ILOG CPLEX, complex business problems can be represented as mathematical programming models. Advanced optimization algorithms allow you to rapidly find solutions to these models. Today, over 1,000 corporations and government agencies use ILOG CPLEX, along with researchers at over 1,000 universities. ILOG CPLEX helps solve planning and scheduling problems in virtually every industry. More than 100 of the world's leading software companies are also ILOG CPLEX customers, including market leaders like SAP, Oracle and Sabre. Robust algorithms for demanding problems

42. Linear Programming - Formulation
    linear programming formulation. You will recall from the Two Mines example that the conditions for a mathematical model to be a linear program (LP) were  
    http://people.brunel.ac.uk/~mastjjb/jeb/or/lp.html
    
    Extractions: OR-Notes are a series of introductory notes on topics that fall under the broad heading of the field of operations research (OR). They were originally used by me in an introductory OR course I give at Imperial College. They are now available for use by any students and teachers interested in OR subject to the following conditions A full list of the topics available in OR-Notes can be found here You will recall from the Two Mines example that the conditions for a mathematical model to be a linear program (LP) were: LP's are important - this is because: We will return later to the simplex algorithm for solving LP's but for the moment we will concentrate upon formulating LP's.

43. Virtual Optima
    Information on LPL, a mathematical modeling language, related product, projects and links.  
    http://www.virtual-optima.com/

44. The Diet Problem
    An Application of linear programming. cute icon The problem is formulated as a linear program where the objective is to minimize cost and meet  
    http://www-neos.mcs.anl.gov/CaseStudies/dietpy/WebForms/index.html
    
    Extractions: An Application of Linear Programming CLICK HERE for the demo! 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.

45. Dantzig, G.: Linear Programming And Extensions.
    of the book linear programming and Extensions by Dantzig, G., published by Princeton University Press.  
    http://pup.princeton.edu/titles/413.html
    
    Extractions: In real-world problems related to finance, business, and management, mathematicians and economists frequently encounter optimization problems. In this classic book, George Dantzig looks at a wealth of examples and develops linear programming methods for their solutions. He begins by introducing the basic theory of linear inequalities and describes the powerful simplex method used to solve them. Treatments of the price concept, the transportation problem, and matrix methods are also given, and key mathematical concepts such as the properties of convex sets and linear vector spaces are covered. George Dantzig is properly acclaimed as the "father of linear programming." Linear programming is a mathematical technique used to optimize a situation. It can be used to minimize traffic congestion or to maximize the scheduling of airline flights. He formulated its basic theoretical model and discovered its underlying computational algorithm, the "simplex method," in a pathbreaking memorandum published by the United States Air Force in early 1948. Linear Programming and Extensions provides an extraordinary account of the subsequent development of his subject, including research in mathematical theory, computation, economic analysis, and applications to industrial problems.

46. Problem 8: Linear Programming: Strongly Polynomial?
    Problem 8 linear programming Strongly Polynomial?  
    http://maven.smith.edu/~orourke/TOPP/P8.html

47. Zuse Institute Berlin - Mathprog - Linear Programming Software
    HOPDM, Package for solving large scale linear programming problems, LIPSOL, linear programming Interior Point Solvers package by Yin Zhang.  
    http://elib.zib.de/pub/Packages/mathprog/linprog/

48. Mixed Integer Programming
    If all the variables can be rational (the set D is empty), this is a linear programming problem, which can be solved in polynomial time.  
    http://www.cs.sandia.gov/opt/survey/mip.html
    
    Extractions: A mixed-integer program is the minimization or maximization of a linear function subject to linear constraints. More explicitly, a mixed-integer program with n variables and m constraints has the form: T minimize c x subject to A x = b 1 1 A x <= b 2 2 l <= x If all the variables can be rational (the set D is empty), this is a linear programming problem, which can be solved in polynomial time. In practice linear programs can be solved efficiently for reasonable-sized problems, or even for big problems with special structure. However when some or all of the variables must be integer, corresponding to pure integer and mixed integer programming respectively, the problem becomes NP-complete (formally intractible). For the sake of this discussion, assume that all variables must be integral. The general methods can be easily extended to the mixed-integer case. The most widely used method for solving integer programs is branch and bound. Subproblems are created by restricting the range of the integer variables. For binary variables, there are only two possible restrictions: setting the variable to 0, or setting the variable to 1. More generally, a variable with lower bound l and upper bound u will be divided into two problems with ranges l to q and q+1 to u respectively. Lower bounds are provided by the linear-programming relaxation to the problem: keep the objective function and all constraints, but relax the integrality restrictions to derive a linear program. If the optimal solution to a relaxed problem is (coincidentally) integral, it is an optimal solution to the subproblem, and the value can be used to terminate searches of subproblems whose lower bound is higher. See the

49. Linear Programming
    Selected topics in linear programming, including problem formulation checklist, sensitivity analysis, binary variables, simulation, useful functions,  
    http://www.quickmba.com/ops/lp/
    
    Extractions: Problem Formulation Checklist The objective function and constraints are formulated from information extracted from the problem statement. The following checklist is useful for minimizing the risk of errors in problem formulation. Every number in problem statement should be either implemented in the formulation or rejected as irrelevant, e.g. sunk costs. Don't forget any initial conditions, e.g. initial staff on hand at beginning of first staffing period. Ensure every variable in the objective function is listed somewhere in the constraints. Ensure that any non-negativity constraints are listed. Ensure that binary integer variables are restricted to 0,1.

50. Linear Programming: Introduction
    Explains the terminology and demonstrates the basic techniques for linear programming ; that is, for maximizing or minimizing a linear relation subject to  
    http://www.purplemath.com/modules/linprog.htm
    
    Extractions: powered by FreeFind Print-friendly page Linear Programming: Introduction (page 1 of 5) Sections: Optimizing linear systems, Setting up word problems Linear programming is the process of taking various linear inequalities relating to some situation, and finding the "best" value obtainable under those conditions. A typical example would be taking the limitations of materials and labor, the determining the "best" production levels for maximal profits under those conditions. In "real life", linear programming is part of a very important area of mathematics called "optimization techniques". This field of study (or at least the applied results of it) are used every day in the organization and allocation of resources. These "real life" systems can have dozens or hundreds of variables, or more. In algebra, though, you'll only work with the simple (and graphable) two-variable linear case. The general process is to graph the inequalities (which, in this context, are called "constraints") to form a walled-off area on the

51. Two Dimensional Linear Programming
    linear programming is the problem of deciding whether there exists a solution to a system of linear ineqalities. Alot of work was gone into this problem,  
    http://valis.cs.uiuc.edu/~sariel/research/CG/applets/linear_prog/main.html

52. BPMPD Home Page
    I m collecting linear programming problems (in MPS format, formulated as STOCHLP, deterministic equvivalent of stochastic linear programming problems  
    http://www.sztaki.hu/~meszaros/bpmpd/
    
    Extractions: BPMPD Home Page This page is devoted to the interior point solver called BPMPD. The solver is developed by at the MTA SZTAKI, Computer and Automation Research Institute, Hungarian Academy of Sciences Budapest Hungary Newest version: 2.21 (June, 1998) New features: The standard BPMPD package has the following features: Sophisticated heuristic to make decision between normal equation and augmented system approach Advanced sparsity analyzer for sparse quadratic problems Scaling the problem for better numerical properties Advanced symbolic ordering for normal equations approach Left-looking supernodal factorization algorithm with loop unrolling

53. Practical Optimization: A Gentle Introduction
    Chapter 2 Introduction to linear programming. The basic notions of linear programming and the simplex method. The simplex method is the easiest way to  
    http://www.sce.carleton.ca/faculty/chinneck/po.html
    
    Extractions: Canada http://www.sce.carleton.ca/faculty/chinneck/po.html The chapters appearing below are draft chapters of an introductory textbook on optimization. The ultimate objective is the creation of a complete, yet compact, introductory survey text on the major topics in optimization. The material is derived from the lecture notes used by the author in engineering courses at Carleton University, and reflects the design considerations for those courses: Students need to have a solid intuitive understanding of how and why optimization methods work. This enables them to recognize when things have gone wrong, and to diagnose the source of the difficulty and take appropriate action. It also permits students to see how methods can be combined or modified to solve non-standard problems. Explanation and diagrams are more effective in transmitting a real understanding than a total reliance on mathematics. There should be some exposure to how things are done in practice, which can be significantly different than how they are usually done in textbooks.

54. Annotated Bibliography On Linear Programming Models
    This bibliography consists of the early papers on linear programming model formulations. It includes some papers that are not about linear programming  
    http://itorms.iris.okstate.edu/index-10.html

55. GNU Linear Programming Kit - Summary [Savannah]
    GLPK is a callable library in ANSI C intended for solving large scale linear programming (LP), mixed integer programming (MIP), and other related problems.  
    http://savannah.gnu.org/projects/glpk/
    
    Extractions: Hosted Projects Register New Project Full List Contributors Wanted Statistics Site Help User Docs: Cookbook User Docs: In Depth Guide Get Support Contact Us ... FAQ Developer Info Cryptographic software legal notice Anonymous CVS GNU Webmasters Documentation Organization Projects map www project GNU Project GNU GPL v3 Coming Events Free Software Directory Help GNU ... GNU Mirrors Group Show feedback again Membership Info Project Admins: Andrew Makhorin Andrew Makhorin members View Members

56. Egwald Mathematics: Operations Research - Linear Programming
    Operations research entry page, online linear programming solver, online solver of two person games, solve lpproblems online.  
    http://www.egwald.com/operationsresearch/orpage.php3
    
    Extractions: Elmer G. Wiens A. Linear Programming. 1. Step through a linear programming graphical example . Then solve a problem by hand using the simplex algorithm , or the dual simplex algorithm 2. Use either the primal simplex tableaux generator , or the dual simplex tableaux generator , to set up and solve your own linear programming problem, with a user-friendly interface. 3. Perform a sensitivity and/or postoptimal analysis of the solution to the data of an economics application of linear programming, with a user-friendly interface.

57. Optimization Toolbox - Quadratic, Linear, And Binary Integer Programming
    linear programming problems consist of a linear expression for the objective function, and linear equality and inequality constraints.  
    http://www.mathworks.com/products/optimization/description5.html
    
    Extractions: var host_pre = "http://www"; Home Select Country Contact Us Store Search Create Account Log In Industries Academia ... Documentation Other Resources Technical Literature User Stories Related Books Product Description Quadratic programming problems involve minimizing a multivariate quadratic function subject to linear equality and inequality constraints. The toolbox implements three methods for solving these problems: trust-region, preconditioned conjugate gradient, and active set. Trust-region is used for bound constrained problems. Preconditioned conjugate gradient is used for problems subject to equality constraints. Active set minimizes the objective at each iteration over the active set (a subset of the constraints that are locally active) until it reaches a solution. Linear programming problems consist of a linear expression for the objective function and linear equality and inequality constraints. Two methods are used to solve this type of problem: simplex and interior point.

58. Linear Programming With Equality Constraints
    linear programming with equality constraints. linear programming with equality constraints. Consider the following classical optimization problem  
    http://www.scilab.org/doc/lmidoc/node11.html
    
    Extractions: Next: Sylvester Equation Up: Examples Previous: Descriptor Riccati equations Consider the following classical optimization problem where A and C are matrices and e b and d are vectors with appropriate dimensions. Here the sign is to be understood elementwise. This problem can be formulated in LMITOOL as follows: and solved in Scilab by (assuming A C e b and d and an initial guess x0 exist in the environment):

59. Mixed Integer Linear Programming (MILP) For Control
    Mixedinteger linear programming (MILP) is a very general framework for capturing problems with both discrete decisions and continuous variables.  
    http://acl.mit.edu/milp/
    
    Extractions: Mixed-integer linear programming (MILP) is a very general framework for capturing problems with both discrete decisions and continuous variables. This includes: You can obtain some sample code for trajectory design and assignment problem for UAV guidance. It requires Matlab and AMPL/CPLEX system to run the code. The MILP problems are sized down so that AMPL CPLEX student version can solve them. Trajectory optimization for an autonomous UAV

60. Linear Programming - Randomization And Abstract Frameworks
    Recent years have brought some progress in the knowledge of the complexity of linear programming in the unit cost model, and the best result known at this  
    http://citeseer.ist.psu.edu/grtner96linear.html

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