Download Classic LINDO User's Manual

Total Page:16

File Type:pdf, Size:1020Kb

Download Classic LINDO User's Manual LINDO User’s Manual LINDO Systems, Inc. 1415 North Dayton Street, Chicago, Illinois 60622 Phone: (312)988-7422 Fax: (312)988-9065 E-mail: [email protected] WWW: http://www.lindo.com COPYRIGHT LINDO software and its related documentation are copyrighted. You may not copy the LINDO software or related documentation except in the manner authorized in the related documentation or with the written permission of LINDO systems, Inc. TRADEMARKS LINGO is a trademark, and LINDO and What’sBest! are registered trademarks, of LINDO Systems, Inc. Other product and company names mentioned herein are the property of their respective owners. DISCLAIMER LINDO Systems, Inc. warrants that on the date of receipt of your payment, the disk enclosed in the disk envelope contains an accurate reproduction of the LINDO software and that the copy of the related documentation is accurately reproduced. Due to the inherent complexity of computer programs and computer models, the LINDO software may not be completely free of errors. You are advised to verify your answers before basing decisions on them. NEITHER LINDO SYSTEMS, INC. NOR ANYONE ELSE ASSOCIATED IN THE CREATION, PRODUCTION, OR DISTRIBUTION OF THE LINDO SOFTWARE MAKES ANY OTHER EXPRESSED WARRANTIES REGARDING THE DISKS OR DOCUMENTATION AND MAKES NO WARRANTIES AT ALL, EITHER EXPRESSED OR IMPLIED, REGARDING THE LINDO SOFTWARE, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE OR OTHERWISE. Further, LINDO Systems, Inc. reserves the right to revise this software and related documentation and make changes to the content hereof without obligation to notify any person of such revisions or changes. ACKNOWLEDGMENTS We gratefully acknowledge Mr. Fritz Raffensperger for his contributions to the LINDO Systems, Inc. user’s manuals. Copyright © 2003 by LINDO Systems, Inc. All rights reserved. Published by LINDO SYSTEMS INC. 1415 North Dayton Street Chicago, Illinois 60622 Technical Support: (312)988-9421 E-mail: [email protected] WWW: http://www.lindo.com iii Table Of Contents Table of Contents ........................................................................................ iii Preface........................................................................................................... v Ch 1 Getting Started with LINDO................................................................. 1 A Note About This Manual....................................................................................... 2 Installing .................................................................................................................. 3 Entering a Model in Windows.................................................................................. 4 Entering a Model from the Command Line.............................................................. 8 Model Syntax......................................................................................................... 12 A Staff Scheduling example .................................................................................. 21 Ch 2 LINDO for Windows ........................................................................... 25 LINDO for Windows Commands in Brief ............................................................... 25 The Commands in Depth....................................................................................... 28 File Menu ........................................................................................................... 29 Edit Menu ........................................................................................................... 41 Solve Menu ........................................................................................................ 58 Reports Menu..................................................................................................... 71 Window Menu................................................................................................... 102 Help Menu........................................................................................................ 110 Ch 3 LINDO for Command-line Environments ....................................... 115 Commands in Brief .............................................................................................. 115 The Commands in Depth..................................................................................... 118 Information Commands.................................................................................... 118 Input Commands.............................................................................................. 120 Display Commands .......................................................................................... 127 File Output Commands .................................................................................... 144 Solution Commands......................................................................................... 151 Problem Editing Commands............................................................................. 159 Integer, Quadratic, & Parametric Programs Commands.................................. 167 Conversational Parameters Commands .......................................................... 180 User Supplied Subroutines .............................................................................. 182 Miscellaneous Commands ............................................................................... 183 Quit................................................................................................................... 189 Ch 4 Integer Programming....................................................................... 191 Branch-and-Bound Solution Method ................................................................... 192 Solving Difficult Integer Programs ....................................................................... 193 Setting an Optimality Tolerance ....................................................................... 194 iv TABLE OF CONTENTS Exploiting a Known Good Solution to an IP...................................................... 194 Benders Decomposition ................................................................................... 194 Tightening Loose IP Formulations ................................................................... 194 Ch 5 Quadratic Programming .................................................................. 197 Debugging Quadratic Programs and the POSD Command ................................ 201 Parametric Analysis of Quadratic Programs........................................................ 202 Ch 6 Analyzing & Debugging a Model..................................................... 205 Model Statistics ................................................................................................... 205 Perusing a Model for Errors................................................................................. 206 Debug Command ................................................................................................ 209 Ch 7 Interfacing with the Outside World................................................. 211 Using External Editors with LINDO...................................................................... 211 Running Command Scripts with the Take Command.......................................... 217 Integrating LINDO Into Other Applications .......................................................... 219 Conclusion........................................................................................................... 229 Ch 8 LINDO Callable Libraries ................................................................. 231 LINDO Callable Library Routines ........................................................................ 231 General Application Requirements...................................................................... 244 Sample Matrix Generators................................................................................... 245 Calling LINDO from Other Languages................................................................. 264 Integer Programming User Interface ................................................................... 264 Monitoring the Solver........................................................................................... 265 Ch 9 Numerical Considerations............................................................... 267 Appendix A Error Messages (List) ......................................................... 271 INDEX......................................................................................................... 291 v Preface We have been busy adding a number of features to LINDO since the manual was last published. The most significant new features include: Windows version with pull down menus, full editing capabilities, graphics, and numerous additional user-friendly features. Improved performance and robustness of the linear and integer solvers. Windows DLL version of the callable library. Model debugging capability for finding a minimal set of constraints (variables) leading to an infeasible (unbounded) model. Additional callable routines in the programming library. Lexico optimization feature for performing goal programming. Improved basis save and retrieve functions. Permuted nonzero picture for viewing models in (as close as possible to) lower triangular form. MPSX compatible solution reports. We hope you enjoy this new release of the LINDO software. Please feel free to contact us at any time regarding questions
Recommended publications
  • How to Use Your Favorite MIP Solver: Modeling, Solving, Cannibalizing
    How to use your favorite MIP Solver: modeling, solving, cannibalizing Andrea Lodi University of Bologna, Italy [email protected] January-February, 2012 @ Universit¨atWien A. Lodi, How to use your favorite MIP Solver Setting • We consider a general Mixed Integer Program in the form: T maxfc x : Ax ≤ b; x ≥ 0; xj 2 Z; 8j 2 Ig (1) where matrix A does not have a special structure. A. Lodi, How to use your favorite MIP Solver 1 Setting • We consider a general Mixed Integer Program in the form: T maxfc x : Ax ≤ b; x ≥ 0; xj 2 Z; 8j 2 Ig (1) where matrix A does not have a special structure. • Thus, the problem is solved through branch-and-bound and the bounds are computed by iteratively solving the LP relaxations through a general-purpose LP solver. A. Lodi, How to use your favorite MIP Solver 1 Setting • We consider a general Mixed Integer Program in the form: T maxfc x : Ax ≤ b; x ≥ 0; xj 2 Z; 8j 2 Ig (1) where matrix A does not have a special structure. • Thus, the problem is solved through branch-and-bound and the bounds are computed by iteratively solving the LP relaxations through a general-purpose LP solver. • The course basically covers the MIP but we will try to discuss when possible how crucial is the LP component (the engine), and how much the whole framework is built on top the capability of effectively solving LPs. • Roughly speaking, using the LP computation as a tool, MIP solvers integrate the branch-and-bound and the cutting plane algorithms through variations of the general branch-and-cut scheme [Padberg & Rinaldi 1987] developed in the context of the Traveling Salesman Problem (TSP).
    [Show full text]
  • Process Optimization
    Process Optimization Mathematical Programming and Optimization of Multi-Plant Operations and Process Design Ralph W. Pike Director, Minerals Processing Research Institute Horton Professor of Chemical Engineering Louisiana State University Department of Chemical Engineering, Lamar University, April, 10, 2007 Process Optimization • Typical Industrial Problems • Mathematical Programming Software • Mathematical Basis for Optimization • Lagrange Multipliers and the Simplex Algorithm • Generalized Reduced Gradient Algorithm • On-Line Optimization • Mixed Integer Programming and the Branch and Bound Algorithm • Chemical Production Complex Optimization New Results • Using one computer language to write and run a program in another language • Cumulative probability distribution instead of an optimal point using Monte Carlo simulation for a multi-criteria, mixed integer nonlinear programming problem • Global optimization Design vs. Operations • Optimal Design −Uses flowsheet simulators and SQP – Heuristics for a design, a superstructure, an optimal design • Optimal Operations – On-line optimization – Plant optimal scheduling – Corporate supply chain optimization Plant Problem Size Contact Alkylation Ethylene 3,200 TPD 15,000 BPD 200 million lb/yr Units 14 76 ~200 Streams 35 110 ~4,000 Constraints Equality 761 1,579 ~400,000 Inequality 28 50 ~10,000 Variables Measured 43 125 ~300 Unmeasured 732 1,509 ~10,000 Parameters 11 64 ~100 Optimization Programming Languages • GAMS - General Algebraic Modeling System • LINDO - Widely used in business applications
    [Show full text]
  • Notes 1: Introduction to Optimization Models
    Notes 1: Introduction to Optimization Models IND E 599 September 29, 2010 IND E 599 Notes 1 Slide 1 Course Objectives I Survey of optimization models and formulations, with focus on modeling, not on algorithms I Include a variety of applications, such as, industrial, mechanical, civil and electrical engineering, financial optimization models, health care systems, environmental ecology, and forestry I Include many types of optimization models, such as, linear programming, integer programming, quadratic assignment problem, nonlinear convex problems and black-box models I Include many common formulations, such as, facility location, vehicle routing, job shop scheduling, flow shop scheduling, production scheduling (min make span, min max lateness), knapsack/multi-knapsack, traveling salesman, capacitated assignment problem, set covering/packing, network flow, shortest path, and max flow. IND E 599 Notes 1 Slide 2 Tentative Topics Each topic is an introduction to what could be a complete course: 1. basic linear models (LP) with sensitivity analysis 2. integer models (IP), such as the assignment problem, knapsack problem and the traveling salesman problem 3. mixed integer formulations 4. quadratic assignment problems 5. include uncertainty with chance-constraints, stochastic programming scenario-based formulations, and robust optimization 6. multi-objective formulations 7. nonlinear formulations, as often found in engineering design 8. brief introduction to constraint logic programming 9. brief introduction to dynamic programming IND E 599 Notes 1 Slide 3 Computer Software I Catalyst Tools (https://catalyst.uw.edu/) I AIMMS - optimization software (http://www.aimms.com/) Ming Fang - AIMMS software consultant IND E 599 Notes 1 Slide 4 What is Mathematical Programming? Mathematical programming refers to \programming" as a \planning" activity: as in I linear programming (LP) I integer programming (IP) I mixed integer linear programming (MILP) I non-linear programming (NLP) \Optimization" is becoming more common, e.g.
    [Show full text]
  • Co-Optimization of Transmission and Other Supply Resources
    Co-optimization of Transmission and Other Supply Resources September, 2013 Illinois Institute of Technology Iowa State University Johns Hopkins University Purdue University West Virginia University For EISPC and NARUC Funded by the U.S Department of Energy Co-optimization of Transmission and Other Supply Resources prepared for Eastern Interconnection States’ Planning Council and National Association of Regulatory Utility Commissioners prepared by Dr. Andrew (Lu) Liu, Purdue University Dr. Benjamin H. Hobbs and Jonathan Ho, Johns Hopkins University Dr. James D. McCalley and Venkat Krishnan, Iowa State University Dr. Mohammad Shahidehpour, Illinois Institute of Technology Dr. Qipeng P. Zheng, University of Central Florida Acknowledgement: This material is based upon work supported by the Department of Energy, National Energy Technology Laboratory, under Award Number DE-OE0000316. The project team would like to thank Bob Pauley, Doug Gotham and Stan Hadley as well as the EISPC and NARUC organizations, for their support throughout this project. Patrick Sullivan of NREL provided crucial perspectives, advice, and resources during the project. We would also like to acknowledge inputs on earlier versions of our work by members of NARUC’s Studies and Whitepaper Workgroup. The earlier versions as well benefited from the editorial assistance provided by Jingjie Xiao, a former PhD student with the School of Industrial Engineering at Purdue University, and her generous efforts are greatly appreciated. In addition, the team gratefully acknowledge the generous and timely assistance of several organizations that have provided important background information on transmission planning, including but not limited to: Parveen Baig, Iowa Utilities Board Jaeseok Choi, Gyeongsang National University, South Korea Bruce Fardanesh, New York Power Authority Flora Flydt, ATC Ian Grant, TVA Bruce Hamilton, Smart Grid Network Ben Harrison, Duke Energy Michael Henderson, ISO New England Len Januzik, Quanta Technology David Kelley, SPP Raymond Kershaw, ITC Holdings Corp.
    [Show full text]
  • MODELING LANGUAGES in MATHEMATICAL OPTIMIZATION Applied Optimization Volume 88
    MODELING LANGUAGES IN MATHEMATICAL OPTIMIZATION Applied Optimization Volume 88 Series Editors: Panos M. Pardalos University o/Florida, U.S.A. Donald W. Hearn University o/Florida, U.S.A. MODELING LANGUAGES IN MATHEMATICAL OPTIMIZATION Edited by JOSEF KALLRATH BASF AG, GVC/S (Scientific Computing), 0-67056 Ludwigshafen, Germany Dept. of Astronomy, Univ. of Florida, Gainesville, FL 32611 Kluwer Academic Publishers Boston/DordrechtiLondon Distributors for North, Central and South America: Kluwer Academic Publishers 101 Philip Drive Assinippi Park Norwell, Massachusetts 02061 USA Telephone (781) 871-6600 Fax (781) 871-6528 E-Mail <[email protected]> Distributors for all other countries: Kluwer Academic Publishers Group Post Office Box 322 3300 AlI Dordrecht, THE NETHERLANDS Telephone 31 78 6576 000 Fax 31 786576474 E-Mail <[email protected]> .t Electronic Services <http://www.wkap.nl> Library of Congress Cataloging-in-Publication Kallrath, Josef Modeling Languages in Mathematical Optimization ISBN-13: 978-1-4613-7945-4 e-ISBN-13:978-1-4613 -0215 - 5 DOl: 10.1007/978-1-4613-0215-5 Copyright © 2004 by Kluwer Academic Publishers Softcover reprint of the hardcover 1st edition 2004 All rights reserved. No part ofthis pUblication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photo-copying, microfilming, recording, or otherwise, without the prior written permission ofthe publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Permissions for books published in the USA: P"§.;r.m.i.§J?i..QD.§.@:w.k~p" ..,.g.Qm Permissions for books published in Europe: [email protected] Printed on acid-free paper.
    [Show full text]
  • Open Source and Free Software
    WP. 24 ENGLISH ONLY UNITED NATIONS STATISTICAL COMMISSION and EUROPEAN COMMISSION ECONOMIC COMMISSION FOR EUROPE STATISTICAL OFFICE OF THE CONFERENCE OF EUROPEAN STATISTICIANS EUROPEAN COMMUNITIES (EUROSTAT) Joint UNECE/Eurostat work session on statistical data confidentiality (Bilbao, Spain, 2-4 December 2009) Topic (iv): Tools and software improvements ON OPEN SOURCE SOFTWARE FOR STATISTICAL DISCLOSURE LIMITATION Invited Paper Prepared by Juan José Salazar González, University of La Laguna, Spain On open source software for Statistical Disclosure Limitation Juan José Salazar González * * Department of Statistics, Operations Research and Computer Science, University of La Laguna, 38271 Tenerife, Spain, e-mail: [email protected] Abstract: Much effort has been done in the last years to design, analyze, implement and compare different methodologies to ensure confidentiality during data publication. The knowledge of these methods is public, but the practical implementations are subject to different license constraints. This paper summarizes some concepts on free and open source software, and gives some light on the convenience of using these schemes when building automatic tools in Statistical Disclosure Limitation. Our results are based on some computational experiments comparing different mathematical programming solvers when applying controlled rounding to tabular data. 1 Introduction Statistical agencies must guarantee confidentiality of data respondent by using the best of modern technology. Today data snoopers may have powerful computers, thus statistical agencies need sophisticated computer programs to ensure that released information is protected against attackers. Recent research has designed different optimization techniques that ensure protection. Unfortunately the number of users of these techniques is quite small (mainly national and regional statistical agencies), thus there is not enough market to stimulate the development of several implementations, all competing for being the best.
    [Show full text]
  • Chipps Framework Applications Results and Conclusions
    Introduction The CHiPPS Framework Applications Results and Conclusions The COIN-OR High-Performance Parallel Search Framework (CHiPPS) TED RALPHS LEHIGH UNIVERSITY YAN XU SAS INSTITUTE CPAIOR, June 15, 2010 Thanks: Work supported in part by the National Science Foundation and IBM Ralphs& Xu CHiPPS Introduction The CHiPPS Framework Applications Results and Conclusions Outline 1 Introduction Tree Search Algorithms Parallel Computing Previous Work 2 The CHiPPS Framework Introduction ALPS: Abstract Library For Parallel Search BiCePS: Branch, Constrain, and Price Software BLIS: BiCePS Linear Integer Solver 3 Applications Knapsack Problem Vehicle Routing 4 Results and Conclusions Ralphs& Xu CHiPPS Introduction Tree Search Algorithms The CHiPPS Framework Parallel Computing Applications Previous Work Results and Conclusions Tree Search Algorithms Tree search algorithms systematically search the nodes of an acyclic graph for certain goal nodes. Root Initial State Goal State Tree search algorithms have been applied in many areas such as Constraint satisfaction, Game search, Constraint Programming, and Mathematical programming. Ralphs& Xu CHiPPS Introduction Tree Search Algorithms The CHiPPS Framework Parallel Computing Applications Previous Work Results and Conclusions Elements of Tree Search Algorithms A generic tree search algorithm consists of the following elements: Generic Tree Search Algorithm Processing method: Is this a goal node? Fathoming rule: Can node can be fathomed? Branching method: What are the successors of this node? Search strategy: What should we work on next? The algorithm consists of choosing a candidate node, processing it, and either fathoming or branching. During the course of the search, various information (knowledge) is generated and can be used to guide the search. Ralphs& Xu CHiPPS Introduction Tree Search Algorithms The CHiPPS Framework Parallel Computing Applications Previous Work Results and Conclusions Parallelizing Tree Search Algorithms In general, the search tree can be very large.
    [Show full text]
  • CME 338 Large-Scale Numerical Optimization Notes 2
    Stanford University, ICME CME 338 Large-Scale Numerical Optimization Instructor: Michael Saunders Spring 2019 Notes 2: Overview of Optimization Software 1 Optimization problems We study optimization problems involving linear and nonlinear constraints: NP minimize φ(x) n x2R 0 x 1 subject to ` ≤ @ Ax A ≤ u; c(x) where φ(x) is a linear or nonlinear objective function, A is a sparse matrix, c(x) is a vector of nonlinear constraint functions ci(x), and ` and u are vectors of lower and upper bounds. We assume the functions φ(x) and ci(x) are smooth: they are continuous and have continuous first derivatives (gradients). Sometimes gradients are not available (or too expensive) and we use finite difference approximations. Sometimes we need second derivatives. We study algorithms that find a local optimum for problem NP. Some examples follow. If there are many local optima, the starting point is important. x LP Linear Programming min cTx subject to ` ≤ ≤ u Ax MINOS, SNOPT, SQOPT LSSOL, QPOPT, NPSOL (dense) CPLEX, Gurobi, LOQO, HOPDM, MOSEK, XPRESS CLP, lp solve, SoPlex (open source solvers [7, 34, 57]) x QP Quadratic Programming min cTx + 1 xTHx subject to ` ≤ ≤ u 2 Ax MINOS, SQOPT, SNOPT, QPBLUR LSSOL (H = BTB, least squares), QPOPT (H indefinite) CLP, CPLEX, Gurobi, LANCELOT, LOQO, MOSEK BC Bound Constraints min φ(x) subject to ` ≤ x ≤ u MINOS, SNOPT LANCELOT, L-BFGS-B x LC Linear Constraints min φ(x) subject to ` ≤ ≤ u Ax MINOS, SNOPT, NPSOL 0 x 1 NC Nonlinear Constraints min φ(x) subject to ` ≤ @ Ax A ≤ u MINOS, SNOPT, NPSOL c(x) CONOPT, LANCELOT Filter, KNITRO, LOQO (second derivatives) IPOPT (open source solver [30]) Algorithms for finding local optima are used to construct algorithms for more complex optimization problems: stochastic, nonsmooth, global, mixed integer.
    [Show full text]
  • Financial Analysts
    Operations Research Analysts Financial Analysts TORQ Analysis of Operations Research Analysts to Financial Analysts INPUT SECTION: Transfer Title O*NET Filters Operations Research Importance LeveL: Weight: From Title: 15-2031.00 Abilities: Analysts 50 1 Importance LeveL: Weight: To Title: Financial Analysts 13-2051.00 Skills: 69 1 Labor Market Weight: Maine Statewide Knowledge: Importance Level: 69 Area: 1 OUTPUT SECTION: Grand TORQ: 83 Ability TORQ Skills TORQ Knowledge TORQ Level Level Level 96 81 71 Gaps To Narrow if Possible Upgrade These Skills Knowledge to Add Ability Level Gap Impt Skill Level Gap Impt Knowledge Level Gap Impt Near Vision 59 6 65 Management Economics Speech of Financial 74 20 77 and 79 31 89 46 4 62 Recognition Resources Accounting Time English 70 11 87 70 17 83 Management Language Monitoring 70 7 70 LEVEL and IMPT (IMPORTANCE) refer to the Target Financial Analysts. GAP refers to level difference between Operations Research Analysts and Financial Analysts. ASK ANALYSIS Ability Level Comparison - Abilities with importance scores over 50 Operations Research Description Analysts Financial Analysts Importance Written Comprehension 64 62 78 Oral Comprehension 64 59 75 Oral Expression 64 60 75 Written Expression 62 59 75 Deductive Reasoning 62 62 72 Inductive Reasoning 60 51 68 Near Vision 53 59 65 TORQ Analysis Page 1 of 13. Copyright 2009. Workforce Associates, Inc. Operations Research Analysts Financial Analysts Speech Clarity 46 46 65 Problem Sensitivity 57 55 62 Number Facility 66 55 62 Speech Recognition 42 46
    [Show full text]
  • INTERIOR-POINT LINEAR PROGRAMMING SOLVERS We Present an Overview of Available Software for Solving Linear Programming Problems U
    INTERIOR-POINT LINEAR PROGRAMMING SOLVERS HANDE Y. BENSON Abstract. We present an overview of available software for solving linear pro- gramming problems using interior-point methods. Some of the codes discussed include primal and dual simplex solvers as well, but we focus the discussion on the implementation of the interior-point solver. For each solver, we present types of problems solved, available distribution modes, input formats and mod- eling languages, as well as algorithmic details, including problem formulation, use of higher corrections, presolve techniques, ordering heuristics for symbolic Cholesky factorization, and the specifics of numerical factorization. We present an overview of available software for solving linear programming problems using interior-point methods. We consider both open-source and pro- prietary commercial codes, including BPMPD ([37], [36]), CLP [16], FortMP [15], GIPALS32 [45], GLPK [34], HOPDM ([24], [28]), IBM ILOG CPLEX [30], LINDO [31], LIPSOL [49], LOQO [48], Microsoft Solver Foundation [38], MOSEK [2], PCx [13], SAS/OR [44], and Xpress Optimizer [43]. Some of the codes discussed include primal and dual simplex solvers as well, but we focus the discussion on the implementation of the interior-point solver. For each solver, we present types of problems solved, available distribution modes, input formats and modeling languages, as well as algorithmic details, including problem formulation, use of higher corrections, presolve techniques, ordering heuristics for symbolic Cholesky factorization, and the specifics of numerical factorization. Many of the solvers allow user-control over some algorithmic details such as the type of presolve techniques applied to the problem and the choice of ordering heuristic. As the following discussion will show, the solvers tend to all have similar charac- teristics, and performance differences are generally due to subtle changes in the presolve phase, the implementation of the linear algebra routines, and other special considerations, such as scaling, to promote numerical stability.
    [Show full text]
  • The TOMLAB Optimization Environment in Matlab1
    AMO - Advanced Modeling and Optimization Volume 1, Number 1, 1999 The TOMLAB Optimization Environment in Matlab Kenneth Holmstrom Center for Mathematical Mo deling Department of Mathematics and Physics Malardalen University PO Box SE Vasteras Sweden Abstract TOMLAB is a general purp ose op en and integrated Matlab development environment for research and teaching in optimization on Unix and PC systems One motivation for TOMLAB is to simplify research on practical optimization problems giving easy access to all types of solvers at the same time having full access to the p ower of Matlab The design principle is dene your problem once optimize using any suitable solver In this pap er we discuss the design and contents of TOMLAB as well as some applications where TOMLAB has b een successfully applied TOMLAB is based on NLPLIB TB a Matlab to olb ox for nonlinear programming and pa rameter estimation and OPERA TB a Matlab to olb ox for linear and discrete optimization More than dierent algorithms and graphical utilities are implemented It is p ossible to call solvers in the Matlab Optimization Toolb ox and generalpurp ose solvers implemented in For tran or C using a MEXle interface Currently MEXle interfaces have b een developed for MINOS NPSOL NPOPT NLSSOL LPOPT QPOPT and LSSOL A problem is solved by a direct call to a solver or by a general multisolver driver routine or interactively using a graphical user interface GUI or a menu system If analytical derivatives are not available automatic dierentiation is easy using an interface
    [Show full text]
  • MINLP Solver Software
    MINLP Solver Software Michael R. Bussieck and Stefan Vigerske GAMS Development Corp., 1217 Potomac St, NW Washington, DC 20007, USA [email protected], [email protected] March 10, 2014 Abstract In this article we give a brief overview of the start-of-the-art in software for the solution of mixed integer nonlinear programs (MINLP). We establish several groupings with respect to various features and give concise individual descriptions for each solver. The provided information may guide the selection of a best solver for a particular MINLP problem. Keywords: mixed integer nonlinear programming, solver, software, MINLP, MIQCP 1 Introduction The general form of an MINLP is minimize f(x; y) subject to g(x; y) ≤ 0 (P) x 2 X y 2 Y integer n+s n+s m The function f : R ! R is a possibly nonlinear objective function and g : R ! R a possibly nonlinear constraint function. Most algorithms require the functions f and g to be continuous and differentiable, but some may even allow for singular discontinuities. The variables x and y are the decision variables, where y is required to be integer valued. The n s sets X ⊆ R and Y ⊆ R are bounding-box-type restrictions on the variables. Additionally to integer requirements on variables, other kinds of discrete constraints are commonly used. These are, e.g., special-ordered-set constraints (only one (SOS type 1) or two consecutive (SOS type 2) variables in an (ordered) set are allowed to be nonzero) [8], semicontinuous variables (the variable is allowed to take either the value zero or a value above some bound), semiinteger variables (like semicontinuous variables, but with an additional integer restric- tion), and indicator variables (a binary variable indicates whether a certain set of constraints has to be enforced).
    [Show full text]