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         Linear Programming:     more books (100)
  1. The Linear Complementarity Problem (Classics in Applied Mathematics) by Richard W. Cottle, Jong-Shi Pang, et all 2009-07-28
  2. Theory of Linear Models (Chapman & Hall/CRC Texts in Statistical Science) by Bent Jorgensen, 1993-10-01
  3. The Logical Basis for Computer Programming, Volume 1 by Zohar Manna, Richard Waldinger, 1985-01-11
  4. Linear and Nonlinear Programming with Maple: An Interactive, Applications-Based Approach (Textbooks in Mathematics) by Paul E. Fishback, 2009-12-09
  5. Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3) by Dimitri P. Bertsekas, John N. Tsitsiklis, 1996-05
  6. Iterative Methods for Sparse Linear Systems, Second Edition by Yousef Saad, 2003-04-30
  7. Linear Programs & Related Problems: A Volume in the COMPUTER SCIENCE and SCIENTIFIC COMPUTING Series by Evar D. Nering, Albert W. Tucker, 1992-10-26
  8. Mathematical Programming by Steven Vajda, 2009-06-22
  9. Mathematical Programming for Industrial Engineers (Industrial Engineering: A Series of Reference Books and Textboo)
  10. Applications of Linear Algebra by Chris Rorres, Howard Anton, 1984-06
  11. Linear Programming and its Applications by H.A. Eiselt, C.-L. Sandblom, 2010-11-30
  12. Stochastic Linear Programming: Models, Theory, and Computation (International Series in Operations Research & Management Science) by Peter Kall, János Mayer, 2010-11-02
  13. Introduction to Linear Programming: Methods and Cases by Thomas Herbert Naylor, etc., 1971-12-10
  14. In-Depth Analysis of Linear Programming by F.P. Vasilyev, A.Y. Ivanitskiy, 2010-11-02

The TOMNET Optimization Platform provides a standardized environment for general operations research development for the Microsoft .NET Framework. Well-known optimization solvers, such as SNOPT and MINOS are fully integrated.
LATEST NEWS Oct 1st 2010
TOMLAB 7.5 released. PROPT now supports binary and integer variables! Mar 24th 2010
TOMLAB 7.4 released. PROPT now has an automated scaling module. Dec 7th 2009
TOMLAB 7.3 released. GUROBI 2.0 released. Several Base Module updates! Aug 18th 2009
TOMLAB 7.2 released. New GUROBI solver now available. Aug 6th 2009
TOMLAB switches to BITROCK for multi-platform installation support. Mar 25th 2009
TOMLAB v7.1 released. Many additional PROPT examples, MINLP support in KNITRO and more. Nov 17th 2008
TOMLAB v7 finally out! New modeling platform with source transformation and lightning fast optimal control! Jun 11th 2008
TOMLAB v6.1 released. New optimal control package available. Jan 31st 2008 TOMLAB is now a registered trademark of Tomlab Optimization. PARTNERS PEN OPT
What is TOMNET?
The TOMNET Optimization Environment enables the solution of a new range of problem types in .NET. A solver-independent design introduces full flexibility in solving large-scale nonlinear data fitting, transportation and planning problems. The system also features a standardized input-output format, thereby enabling seamless execution of prototyping work. Structures for managing sparse data is included in the general Base Module functionality.

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123. Linear Programming With O-Matrix And Lp_solve
Tools for integrating lp_solve, a free, public domain linear programming solver with the O-Matrix technical computing environment.
::lpSolve:: Overview
Application Samples

Using O-Matrix
with lp_solve ...
lp_solve user group

:: Other Toolboxes Image Processing
Time-Series Analysis

Signal Processing

SigmaPlot Interface Toolkit
Data Visualizer

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.
The lp_solve Interface Toolbox for O-Matrix is written completely in the C programming language to provide maximum performance. The interface uses the O-Matrix DLL linking capability to integrate seamlessly into the O-Matrix language. The integration of lpsolve with O-Matrix is a win-win situation for both packages. For

124. Interior Path Following Primal-dual Algorithms. Part I Linear
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125. Optimization And Modeling Software And Consulting
Information on LPL, a mathematical modeling language, related product, projects and links.

126. Lp_solve Reference Guide
The simple answer is, lp_solve is a Mixed Integer Linear Programming See What is Linear Programming? and Oh, and we also want to solve it as an
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127. Borges Home
Ruby Web application framework based on Smalltalk Seaside 2, allows linear programming of applications. Open source
Borges Home
Borges is a continuation-based web application framework originally ported from on Seaside 2 that allows a linear style of programming of web applications. Components of a Borges web page can call and return from each other in a natural way, allowing complex interaction between components from simple methods. Backtracking is supported seamlessly, allowing a simple approach to building web applications that does not get in the developer's way. Borges requires Ruby 1.8 . Examples are provided, and are typically installed in /usr/local/share/examples/ruby/borges/.
Borges News RSS Feed
Borges-1.1.0 Released
Borges-1.1.0 fixes several memory issues allowing Borges to maintain a more stable memory size. Many Unit Tests have been added, and the Renderer API is mostly documented. You can download Borges-1.1.0 from the Borges filelist
Borges Quickref
Kaspar Schiess has collected this nice Borges Quick Reference document.
You can read the Borges Quickref , or browse the Borges RDoc You may also be interested in the Seaside 2 documentation:
Borges-Users Mailing List
If you have any questions or comments about Borges, you can

128. GraphTest
Sep 23, 1998 The SIMPLEX method is a well known algorithm for solving linear programs. (see V .Chvatal, Linear Programming, Freeman, 1983)
LP SOLVER A linear program consists of a linear objective function to be optimized and a set of linear constraints (inequations and/or equations). The SIMPLEX method is a well known algorithm for solving linear programs. (see V.Chvatal, Linear Programming, Freeman, 1983) Once you have selected the number of variables and the number of constraints, select the GO button to display the input grid.
  • Solve delivers the optimal solution. Tableau lets you run the algorithm one iteration at a time. Random generates random coefficients, rhs, and upper-bound values. Dual generates the dual problem. Because the applet assumes that all variables are non-negative, no dual is generated if the primal contains one or more equalities
  • Remarks
Please send your remarks and comments to
Olivier Goldschmidt

Last Updated: Sep 23, 1998

129. Linear Programming 2 Theory And Extensions
File Format PDF/Adobe Acrobat Quick View Programming, 2, Theory and Extension

130. Topics In Linear Algebra
An approach unifying the notions of system of equations, matrix inversion, and linear programming.
Unification of System of Linear Equations,
Matrix Inversion, and Linear Programming
Europe Site
Site for Asia Site for Middle East UK Site ... USA Site

This site extends the existing one-way connections among the solving linear systems of equations, matrix inversion, and linear programming. The additional linkages empower the user to understand the wholeness and manifoldness of these topics. They also assist the user to model and solve a problem modeled as any one of the above topics by having access to a computer package solver. The goals are theoretical unification as well as advancements in applications. Illustrative numerical examples are presented. 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. " inverse" or " equations" If the first appearance of the word/phrase is not what you are looking for, try F ind Next MENU
  • Introduction
  • LP Problem Solved by System of Equation Solver
  • System of Equations Solution by LP Solver
  • Solving for the Inverse of a Matrix Using LP Solver ...
  • References and Further Readings Companion Sites:
    Linear programming (LP), Linear systems of equations, and Matrix inversion are often favorite topics for both instructors and students. The ability to solve these problems by Gauss-Jordan pivoting (GJP), the widespread availability of software packages, and their wide range of applications make these topics accessible even for students with relatively weak mathematical backgrounds. The typical textbooks on LP usually devote separate sections for each topic. However, the relationships among these closely related topics are often not presented or thoroughly discussed. This article extends the existing one-way connections among these topics to construct a comprehensive two-way relationship as in the following figure. For each topic, it is shown how the problem may be modeled and solved by either of the associated methodologies.
  • 131. Improved Linear Programming Models For Discriminant Analysis* - Glover - 2007 -
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    Decision Sciences Volume 21, Issue 4 , Article first published online: 7 JUN 2007
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    132. Arageli - Main
    C++ template library for computations in ARithmetics, Algebra, GEometry, Linear and Integer linear programming. Supports arbitrary length integers, rationals, vectors, matrices.
    Main Download Documentation Webdemo ... Links
    About Arageli
    Arageli is C++ library for computations in ar ithmetic, a lgebra, ge ometry, l inear and i nteger linear programming. Arageli provides routines supporting precise, i.e. symbolic or algebraic, computations. It contains definitions of basic algebraic structures such as integer numbers with arbitrary precision, rational numbers, vectors, matrices, polynomials etc. You can download the library and documentation and read online documentation . Also you can learn more about us . If you already use the library and expirience problems or you have questions or suggestions to the developers, please, contact Arageli Support Service or go to our help forum
    Latest News
    New prealpha drop is available for download. It is a hot fix for recently released version Go to download page New prealpha drop is available for download. Go to download page Preparing for the next prealpha drop was resumed. We are fixing bugs and select worthy features from working branches for including to this release. Read the previous news in the news archive If you have any questions about information on this page or you have noticed an error

    Your browser may not have a PDF reader available. Google recommends visiting our text version of this document.

    134. Dipartimento Di Informatica: Home
    Department of Informatics. Research groups concentrate on knowledge representation and reasoning, machine learning, natural language processing, databases and information systems, decision making models and management systems, informatic technology, linear programming, integer linear programming, game theory, logic programming and automated reasoning, mathematical logic, performance analysis, modelling in biology and medicine, cooperative systems, multidimensional signal processing, security and computer networks, semantics and logics of computation.
    vai al contenuto
    Dipartimento di Informatica
    Chiave di ricerca: document.getElementById('q').style.color='black'; document.getElementById('q').value='cognome o altro argomento di interesse'; scopriDiv('cercaMyunito_json'); Chiave di ricerca: [Menu di navigazione]
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    C.So Svizzera, 185 - 10149 Torino Tel.: 0116706711 - Fax: 011751603 E-mail: segreteria[at] direttore[at]

    135. ScienceDirect - Stochastic Processes And Their Applications : Limit Distribution
    by PD Feigin 1994 - Cited by 28 - Related articles
    window.onresize = resizeWindow; Username: Password: Remember me Not Registered? Forgotten your username or password? Go to Athens / Institution login All fields Author Advanced search Journal/Book title Volume Issue Page Search tips Font Size: Related Articles Many multivariate records
    Stochastic Processes and their Applications

    Many multivariate records
    Original Research Article
    Stochastic Processes and their Applications Volume 59, Issue 2 October 1995 Pages 185-216
    Charles M. Goldie, Sidney I. Resnick
    X n n is a sequence of independent, identically distributed random vectors in . Say X n is a record if there is a record simultaneously in both coordinates at index n . The total number of records of a sequence is frequently finite so we consider the behaviour of the records in a fixed rectangle A conditional on there being a large number of records N A in the rectangle. We join the records up to form a random parametrized curve, and prove that, under suitable conditions, as the number of records in the rectangle goes to infinity the random curves converge conditionally in probability to a non-random limit parametrized curve, which we characterize. We also prove a large-deviation result for the random curves.
    PDF (1325 K) Gauss-Newton and M-estimation for ARMA processes with i...

    136. Calculated Risk, Inc.: Evidence-based Decision Support For The Health Care Indus
    Uses techniques to measure financial risk, optimize staffing, and predict wait times. Customized analyses and training feature Monte Carlo simulation, linear programming, and queuing models.
    LEARN MORE Our Approach
    Case Studies

    Publications and Studies

    About Us
    Contact Us

    We are a founding signatory of Stand for Quality.
    New: Online Quality Reporting Reveals Variation in Care
    Public performance reports compare medical organizations along fundamental dimensions of care quality. Read more
    Evidence-based decision support for the Health Care Industry
    With expertise in Health Care Operations, Economics, and Biomedical Statistics, we work to upgrade decision-making from an anecdotal activity to an evidence-based pursuit.
    Realistic solutions to complex issues
    We help proactive decision-makers formulate realistic operating and strategic plans that incorporate diverse stakeholder concerns such as Access, Productivity, Quality and Solvency. Clients range from Providers and Purchasers to Insurers and Administrative Entities.
    Contact Calculated Risk, Inc.

    137. CCMs
    File Format Microsoft Powerpoint View as HTML
    <(LfҴ׮mMp9A P i^^Az%~faT>›?Qڜ! UN>U`.zA PU-6ti9vq5@C;'*bY`Ui]E[t%ꜝL @%++HX@їcr/

    138. N-genes: About
    An easy to learn evolutionary computing framework written for Java 5. Genetic programming is implemented through fast stack-based linear programs.
    n-genes is a powerful Genetic Algorithms and Programming toolkit written for Java 5 . Using advanced object oriented techniques, like generics and introspection, makes it one of the simplest systems to learn and use. Its design allows fast coding and a total flexibility. n-genes is an open-source project released under GPL . It is free of charges.
    Stack-based Genetic Programming
    The Genetic Programming implemented in n-genes relies on linear postfix programs, close to Forth or Postscript programming languages. This has the following advantages:
    • High-level and turing-complete language (through flow-control instructions); Extendable and customisable instruction set; Possibility of using faster and simpler GA operators; Efficient bloat removing/controlling algorithms.
    Modularity and Dynamic Config Files
    All parts of evolutionary computing have been made components, through "Design Patterns" methodology. The benefits are:
    • Separation between behaviour and representation, i.e.

    139. Speeding-up Linear Programming Using Fast Matrix Multiplication
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    140. Linear Programming
    Ratfor code for the primal-dual log barrier form of the interior point LP solver of Lustig, Marsten and Shanno, ORSA J Opt 1992.
    <'End of fnc.r' #Outer wrapper for the new rqn functioncalls a frisch-newton LP solver #Does preprocessing using the functions globit and checkit to reduce initial n subroutine rqm(n2,p,a,y,rhs,d,wn,wp,beta,eps,tau,s,aa,hist) integer n,p,s(n2),hist(3,32),kit,nit,mit,m,mm,n2,maxnit,maxmit,mlim double precision a(p,n2),y(n2),rhs(p),d(n2),wn(1),wp(p,p+3),aa(p,p) double precision beta,eps,tau,omega,sparsity #real ut,time,udt data zero/0.0d0/ data one/1.0d0/ data two/2.0d0/ maxmit=8 maxnit=4 n=n2-2 m=2*nint(n**(2./3.)) mlim = 5*m mit=0 kit=0 #outer loop on the initial sample size while(mit <=x <0)deltap=dmin1(deltap,-x(i)/dx(i)) if(ds(i) <0)deltap=dmin1(deltap,-s(i)/ds(i)) if(dz(i) <0)deltad=dmin1(deltad,-z(i)/dz(i)) if(dw(i) <0)deltap=dmin1(deltap,-x(i)/dx(i)) else deltap=dmin1(deltap,-s(i)/ds(i)) if(dz(i) <0)deltad=dmin1(deltad,-z(i)/dz(i)) if(dw(i) <'End of sparsity.r' #This is a Siddiqui sparsity function estimate based on residuals double precision function sparsity(n,u,tau) integer n,nd,enuf double precision u(n),tau,h,qhi,qlo,half data half/0.5d0/ data enuf/600/ #bandwidth: approximate Hall-Sheather method - quadratic approx max error 1% nd=nint((.05 + 3.65*tau - 3.65*tau**2)*(n**(2./3.))) h=dfloat(nd)/dfloat(n) #compute Siddiqui estimator call kuantile(n,u,tau+h,qhi,enuf,half,half) call kuantile(n,u,tau-h,qlo,enuf,half,half) sparsity=(qhi-qlo)/(h+h) return end #function to compute pth quantile of a sample of n observations subroutine kuantile(n,x,p,q,mmax,cs,cd) integer n,k,l,r,mmax double precision x(n),p,q,cs,cd if(p

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