DOCSLIB.ORG

The TOMLAB Optimization Environment in Matlab1

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The TOMLAB Optimization Environment in Matlab1 AMO - Advanced Modeling and Optimization Volume 1, Number 1, 1999 The TOMLAB Optimization 1 Environment in Matlab 2 Kenneth Holmstrom Center for Mathematical Mo deling, Department of Mathematics and Physics Malardalen University, P.O. Box 883, SE-721 23 Vasteras, Sweden Abstract TOMLAB is a general purp ose, op en and integrated Matlab developmentenvironment 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 typ es of solvers; at the same time having full access to the p ower of Matlab . The design principle is: de ne 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 1.0 , a Matlab to olb ox for linear and discrete optimization. More than 65 di erent algorithms and graphical utilities are implemented. It is p ossible to call solvers in the Matlab Optimization Toolbox and general-purp ose solvers implemented in For- tran or C using a MEX- le interface. Currently, MEX- le interfaces have b een develop ed for MINOS, NPSOL, NPOPT, NLSSOL, LPOPT, QPOPT and LSSOL. A problem is solved by a direct call to a solver or bya general multi-solver driver routine, or interactively, using a graphical user interface (GUI) or a menu system. If analytical derivatives are not available, automatic di erentiation is easy using an interface to ADMAT/ADMIT TB. Furthermore, ve typ es of numerical di erentiation metho ds are included in NLPLIB TB . A large set of standard test problems are included, as well as example and demonstration les. New user-de ned problems are easily added. Furthermore, using MEX- le interfaces, problems in the CUTE test problem data base and problems de ned in the AMPL mo deling language can b e solved. TOMLAB currently solves small and medium size dense problems, where the available memory is the limit. TOMLAB is free for academic purp oses and down- loadable at our home page. Keywords: Mathematical Programming, Nonlinear Programming, Op erations Research, Matlab, CUTE, AMPL, Graphical User Interface, Software Engineering, Mathematical Software, Optimiza- tion, Algorithms, Nonlinear Least Squares. AMS Sub ject Classi cation: 90C99, 90C30, 90C99 1 Financed bytheMalardalen University Research Board, pro ject Applied Optimization and Modeling (TOM). 2 E-mail: [email protected]; URL: http://www.ima.mdh.se/tom. 47 The TOMLAB Optimization Environmentin Matlab 48 1 Intro duction Many scientists and engineers are using Matlab as a mo deling and analysis to ol, but for the solution of optimization problems, the supp ort is weak. That was one strong motivation for starting the de- velopment of TOMLAB [32,31]; a general-purp ose, op en and integrated developmentenvironment in Matlab for research and teaching in optimization. Another motivation was to provide p owerful to ols for the research on the hard-to-solve applied problems our research group are working on, by systematic development of a large set of usable optimization to ols. This pap er presents the design and contents of TOMLAB. To solve optimization problems, traditionally the user has b een forced to write a Fortran co de that calls some standard solver written as a Fortran subroutine. For nonlinear problems, the user must also write subroutines computing the ob jective function value and the vector of constraint function values. The needed derivatives are either explicitly co ded, computed by using numerical di erences or derived using automatic di erentiation techniques. In recentyears several mo deling languages are develop ed, like AIMMS [5], AMPL [21], ASCEND [49], GAMS [6, 10] and LINGO [1]. The mo deling system acts as a prepro cessor. The user describ es the details of his problem in a very verbal language; an opp osite to the concise mathematical description of the problem. The problem description le is normally mo di ed in a text editor, with help from example les solving the same typ e of problem. Much e ort is directed to the development of more user friendly interfaces. The mo del system pro cesses the input description le and calls any of the available solvers. For a solver to b e accessible in the mo deling system, sp ecial typ es of interfaces are develop ed. The mo deling language approach is suitable for many management and decision problems, but may not always b e the b est way for engineering problems, which often are nonlinear with a complicated problem description. Until recently, the supp ort for nonlinear problems in the mo deling languages has b een crude. This is now rapidly changing [16]. For p eople with a mathematical background, mo deling languages often seems to b e a very tedious way to de ne an optimization problem. There has b een several attempts to nd languages more suitable than Fortran or C/C++ to describ e mathematical problems, like the compact and p owerful APL language [39,50]. Nowadays, languages like Matlab has a rapid growth of users. Matlab was originally develop ed [45] as a prepro cessor to the standard Fortran subroutine libraries in numerical linear algebra, LINPACK [12] and EISPACK [53][22], much the same idea as the mo deling languages discussed ab ove. Matlab of to dayisanadvanced and p owerful to ol, with graphics, animation and advanced menu design p ossibilities integrated with the mathematics. The Matlab language has made the developmentoftoolboxes p ossible, whichserves as a direct extension to the language itself. Using Matlab as an environment for solving optimization problems o ers much more p ossibilities for analysis than just the pure solution of the problem. The concept of TOMLAB is to integrate all di erent systems, getting access to the b est of all worlds. Furthermore TOMLAB provides well-implemented solution algorithms for most common optimization problems. TOMLAB should be seen as a complement to existing mo del languages, for the user needing more p ower and exibilitythangiven by a mo deling system. _ Much e ort has b een put on advanced design utilizing the p ower and features of MatlabThe design principle is: de ne your problem once, optimize using any suitable solver. This is p ossible using general gateway and interface routines, global variables, string evaluations and structure arrays. When solving advanced applied problems, the design principle is of sp ecial imp ortance, as it might be a huge task to rewrite the function, constraint and derivative sp eci cations. A stack strategy for the global variables makes recursive calls p ossible, in e.g. separable nonlinear least squares algorithms. One structure holds all information ab out the problem and one holds the results. For the optimization algorithm develop er and the applied researcher in need of optimization to ols it is very easy to compare di erentsolvers or do test runs on thousands of problems. Atool is provided to automate the tedious work of making computational result tables from test runs, directly making LaTeX tables for inclusion in pap ers. TOMLAB should b e seen as a prop osal for The TOMLAB Optimization Environmentin Matlab 49 a standard for optimization in Matlab. In Sweden TOMLAB has b een used for several years in optimization courses. It is distributed free of charge for academic purp oses. During the rst months since the rst ocial release of TOMLAB, Decemb er 11, 1998, ab out 800 researchers and research groups havedownloaded the software. All users have b een requested to answer what applications they have in mind for TOMLAB,anditis clear that most elds of applied mathematics are represented. So there is an obvious need for go o d optimization to ols in the scienti c community. This pap er is organized as follows. In Section 2 we discuss the design of TOMLAB. Overviews of the two to olb oxes that are part of TOMLAB are given in Section 3 (NLPLIB TB [29]) and Section 4 (OPERA TB [29]). The graphical user interface (GUI) is further discussed in [15, 14]. The MEX- le interfaces to several solvers are discussed in Section 5, and the interface to the Matlab Optimization To olb ox [9] is discussed in Section 6. Currently TOMLAB may run problems de ned in CUTE [8] (Section 7) and AMPL [21] (Section 8). Finally, we end with some conclusions in Section 10. 2 The Design of TOMLAB As the scop e of TOMLAB is large and broad, there is a clear need of a well-designed system. It is also necessary to use the p ower of the Matlab language, to make the system exible and easy to use and maintain. Wehave used the concept of structure arrays and made use of b oth the abilityin Matlab to execute Matlab co de de ned as string expressions and to execute functions sp eci ed by a string. To b e able to live up to the basic design principle of TOMLAB, de ne your problem once, optimize using any suitable solver,very manydi erentinterface routines, as well as several interface layers was needed. It is a dicult task to provide the necessary information for a particular solver, esp ecially when the problem class is just a subset of the class of problems the particular solver is able to handle. Tosolve this design problem in a general and easily expandable way, we use one structure array that holds all information ab out the problem, all input for the solution pro cess.
Load more

Recommended publications
  • Derivative-Free Optimization: a Review of Algorithms and Comparison of Software Implementations
    J Glob Optim (2013) 56:1247–1293 DOI 10.1007/s10898-012-9951-y Derivative-free optimization: a review of algorithms and comparison of software implementations Luis Miguel Rios · Nikolaos V. Sahinidis Received: 20 December 2011 / Accepted: 23 June 2012 / Published online: 12 July 2012 © Springer Science+Business Media, LLC. 2012 Abstract This paper addresses the solution of bound-constrained optimization problems using algorithms that require only the availability of objective function values but no deriv- ative information. We refer to these algorithms as derivative-free algorithms. Fueled by a growing number of applications in science and engineering, the development of derivative- free optimization algorithms has long been studied, and it has found renewed interest in recent time. Along with many derivative-free algorithms, many software implementations have also appeared. The paper presents a review of derivative-free algorithms, followed by a systematic comparison of 22 related implementations using a test set of 502 problems. The test bed includes convex and nonconvex problems, smooth as well as nonsmooth prob- lems. The algorithms were tested under the same conditions and ranked under several crite- ria, including their ability to find near-global solutions for nonconvex problems, improve a given starting point, and refine a near-optimal solution. A total of 112,448 problem instances were solved. We find that the ability of all these solvers to obtain good solutions dimin- ishes with increasing problem size. For the problems used in this study, TOMLAB/MULTI- MIN, TOMLAB/GLCCLUSTER, MCS and TOMLAB/LGO are better, on average, than other derivative-free solvers in terms of solution quality within 2,500 function evaluations.
    [Show full text]
  • TOMLAB –Unique Features for Optimization in MATLAB
    TOMLAB –Unique Features for Optimization in MATLAB Bad Honnef, Germany October 15, 2004 Kenneth Holmström Tomlab Optimization AB Västerås, Sweden [email protected] ´ Professor in Optimization Department of Mathematics and Physics Mälardalen University, Sweden http://tomlab.biz Outline of the talk • The TOMLAB Optimization Environment – Background and history – Technology available • Optimization in TOMLAB • Tests on customer supplied large-scale optimization examples • Customer cases, embedded solutions, consultant work • Business perspective • Box-bounded global non-convex optimization • Summary http://tomlab.biz Background • MATLAB – a high-level language for mathematical calculations, distributed by MathWorks Inc. • Matlab can be extended by Toolboxes that adds to the features in the software, e.g.: finance, statistics, control, and optimization • Why develop the TOMLAB Optimization Environment? – A uniform approach to optimization didn’t exist in MATLAB – Good optimization solvers were missing – Large-scale optimization was non-existent in MATLAB – Other toolboxes needed robust and fast optimization – Technical advantages from the MATLAB languages • Fast algorithm development and modeling of applied optimization problems • Many built in functions (ODE, linear algebra, …) • GUIdevelopmentfast • Interfaceable with C, Fortran, Java code http://tomlab.biz History of Tomlab • President and founder: Professor Kenneth Holmström • The company founded 1986, Development started 1989 • Two toolboxes NLPLIB och OPERA by 1995 • Integrated format for optimization 1996 • TOMLAB introduced, ISMP97 in Lausanne 1997 • TOMLAB v1.0 distributed for free until summer 1999 • TOMLAB v2.0 first commercial version fall 1999 • TOMLAB starting sales from web site March 2000 • TOMLAB v3.0 expanded with external solvers /SOL spring 2001 • Dash Optimization Ltd’s XpressMP added in TOMLAB fall 2001 • Tomlab Optimization Inc.
    [Show full text]
  • Theory and Experimentation
    Acta Astronautica 122 (2016) 114–136 Contents lists available at ScienceDirect Acta Astronautica journal homepage: www.elsevier.com/locate/actaastro Suboptimal LQR-based spacecraft full motion control: Theory and experimentation Leone Guarnaccia b,1, Riccardo Bevilacqua a,n, Stefano P. Pastorelli b,2 a Department of Mechanical and Aerospace Engineering, University of Florida 308 MAE-A building, P.O. box 116250, Gainesville, FL 32611-6250, United States b Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy article info abstract Article history: This work introduces a real time suboptimal control algorithm for six-degree-of-freedom Received 19 January 2015 spacecraft maneuvering based on a State-Dependent-Algebraic-Riccati-Equation (SDARE) Received in revised form approach and real-time linearization of the equations of motion. The control strategy is 18 November 2015 sub-optimal since the gains of the linear quadratic regulator (LQR) are re-computed at Accepted 18 January 2016 each sample time. The cost function of the proposed controller has been compared with Available online 2 February 2016 the one obtained via a general purpose optimal control software, showing, on average, an increase in control effort of approximately 15%, compensated by real-time implement- ability. Lastly, the paper presents experimental tests on a hardware-in-the-loop six- Keywords: Spacecraft degree-of-freedom spacecraft simulator, designed for testing new guidance, navigation, Optimal control and control algorithms for nano-satellites in a one-g laboratory environment. The tests Linear quadratic regulator show the real-time feasibility of the proposed approach. Six-degree-of-freedom & 2016 The Authors.
    [Show full text]
  • Research and Development of an Open Source System for Algebraic Modeling Languages
    Vilnius University Institute of Data Science and Digital Technologies LITHUANIA INFORMATICS (N009) RESEARCH AND DEVELOPMENT OF AN OPEN SOURCE SYSTEM FOR ALGEBRAIC MODELING LANGUAGES Vaidas Juseviˇcius October 2020 Technical Report DMSTI-DS-N009-20-05 VU Institute of Data Science and Digital Technologies, Akademijos str. 4, Vilnius LT-08412, Lithuania www.mii.lt Abstract In this work, we perform an extensive theoretical and experimental analysis of the char- acteristics of five of the most prominent algebraic modeling languages (AMPL, AIMMS, GAMS, JuMP, Pyomo), and modeling systems supporting them. In our theoretical comparison, we evaluate how the features of the reviewed languages match with the requirements for modern AMLs, while in the experimental analysis we use a purpose-built test model li- brary to perform extensive benchmarks of the various AMLs. We then determine the best performing AMLs by comparing the time needed to create model instances for spe- cific type of optimization problems and analyze the impact that the presolve procedures performed by various AMLs have on the actual problem-solving times. Lastly, we pro- vide insights on which AMLs performed best and features that we deem important in the current landscape of mathematical optimization. Keywords: Algebraic modeling languages, Optimization, AMPL, GAMS, JuMP, Py- omo DMSTI-DS-N009-20-05 2 Contents 1 Introduction ................................................................................................. 4 2 Algebraic Modeling Languages .........................................................................
    [Show full text]
  • Open Source Tools for Optimization in Python
    Open Source Tools for Optimization in Python Ted Ralphs Sage Days Workshop IMA, Minneapolis, MN, 21 August 2017 T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Outline 1 Introduction 2 COIN-OR 3 Modeling Software 4 Python-based Modeling Tools PuLP/DipPy CyLP yaposib Pyomo T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Outline 1 Introduction 2 COIN-OR 3 Modeling Software 4 Python-based Modeling Tools PuLP/DipPy CyLP yaposib Pyomo T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Caveats and Motivation Caveats I have no idea about the background of the audience. The talk may be either too basic or too advanced. Why am I here? I’m not a Sage developer or user (yet!). I’m hoping this will be a chance to get more involved in Sage development. Please ask lots of questions so as to guide me in what to dive into! T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Mathematical Optimization Mathematical optimization provides a formal language for describing and analyzing optimization problems. Elements of the model: Decision variables Constraints Objective Function Parameters and Data The general form of a mathematical optimization problem is: min or max f (x) (1) 8 9 < ≤ = s.t. gi(x) = bi (2) : ≥ ; x 2 X (3) where X ⊆ Rn might be a discrete set. T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Types of Mathematical Optimization Problems The type of a mathematical optimization problem is determined primarily by The form of the objective and the constraints.
    [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]
  • Insight MFR By
    Manufacturers, Publishers and Suppliers by Product Category 11/6/2017 10/100 Hubs & Switches ASCEND COMMUNICATIONS CIS SECURE COMPUTING INC DIGIUM GEAR HEAD 1 TRIPPLITE ASUS Cisco Press D‐LINK SYSTEMS GEFEN 1VISION SOFTWARE ATEN TECHNOLOGY CISCO SYSTEMS DUALCOMM TECHNOLOGY, INC. GEIST 3COM ATLAS SOUND CLEAR CUBE DYCONN GEOVISION INC. 4XEM CORP. ATLONA CLEARSOUNDS DYNEX PRODUCTS GIGAFAST 8E6 TECHNOLOGIES ATTO TECHNOLOGY CNET TECHNOLOGY EATON GIGAMON SYSTEMS LLC AAXEON TECHNOLOGIES LLC. AUDIOCODES, INC. CODE GREEN NETWORKS E‐CORPORATEGIFTS.COM, INC. GLOBAL MARKETING ACCELL AUDIOVOX CODI INC EDGECORE GOLDENRAM ACCELLION AVAYA COMMAND COMMUNICATIONS EDITSHARE LLC GREAT BAY SOFTWARE INC. ACER AMERICA AVENVIEW CORP COMMUNICATION DEVICES INC. EMC GRIFFIN TECHNOLOGY ACTI CORPORATION AVOCENT COMNET ENDACE USA H3C Technology ADAPTEC AVOCENT‐EMERSON COMPELLENT ENGENIUS HALL RESEARCH ADC KENTROX AVTECH CORPORATION COMPREHENSIVE CABLE ENTERASYS NETWORKS HAVIS SHIELD ADC TELECOMMUNICATIONS AXIOM MEMORY COMPU‐CALL, INC EPIPHAN SYSTEMS HAWKING TECHNOLOGY ADDERTECHNOLOGY AXIS COMMUNICATIONS COMPUTER LAB EQUINOX SYSTEMS HERITAGE TRAVELWARE ADD‐ON COMPUTER PERIPHERALS AZIO CORPORATION COMPUTERLINKS ETHERNET DIRECT HEWLETT PACKARD ENTERPRISE ADDON STORE B & B ELECTRONICS COMTROL ETHERWAN HIKVISION DIGITAL TECHNOLOGY CO. LT ADESSO BELDEN CONNECTGEAR EVANS CONSOLES HITACHI ADTRAN BELKIN COMPONENTS CONNECTPRO EVGA.COM HITACHI DATA SYSTEMS ADVANTECH AUTOMATION CORP. BIDUL & CO CONSTANT TECHNOLOGIES INC Exablaze HOO TOO INC AEROHIVE NETWORKS BLACK BOX COOL GEAR EXACQ TECHNOLOGIES INC HP AJA VIDEO SYSTEMS BLACKMAGIC DESIGN USA CP TECHNOLOGIES EXFO INC HP INC ALCATEL BLADE NETWORK TECHNOLOGIES CPS EXTREME NETWORKS HUAWEI ALCATEL LUCENT BLONDER TONGUE LABORATORIES CREATIVE LABS EXTRON HUAWEI SYMANTEC TECHNOLOGIES ALLIED TELESIS BLUE COAT SYSTEMS CRESTRON ELECTRONICS F5 NETWORKS IBM ALLOY COMPUTER PRODUCTS LLC BOSCH SECURITY CTC UNION TECHNOLOGIES CO FELLOWES ICOMTECH INC ALTINEX, INC.
    [Show full text]
  • Computing in Operations Research Using Julia
    Computing in Operations Research using Julia Miles Lubin and Iain Dunning MIT Operations Research Center INFORMS 2013 { October 7, 2013 1 / 25 High-level, high-performance, open-source dynamic language for technical computing. Keep productivity of dynamic languages without giving up speed. Familiar syntax Python+PyPy+SciPy+NumPy integrated completely. Latest concepts in programming languages. 2 / 25 Claim: \close-to-C" speeds Within a factor of 2 Performs well on microbenchmarks, but how about real computational problems in OR? Can we stop writing solvers in C++? 3 / 25 Technical advancements in Julia: Fast code generation (JIT via LLVM). Excellent connections to C libraries - BLAS/LAPACK/... Metaprogramming. Optional typing, multiple dispatch. 4 / 25 Write generic code, compile efficient type-specific code C: (fast) int f() { int x = 1, y = 2; return x+y; } Julia: (No type annotations) Python: (slow) function f() def f(): x = 1; y = 2 x = 1; y = 2 return x + y return x+y end 5 / 25 Requires type inference by compiler Difficult to add onto exiting languages Available in MATLAB { limited scope PyPy for Python { incompatible with many libraries Julia designed from the ground up to support type inference efficiently 6 / 25 Simplex algorithm \Bread and butter" of operations research Computationally very challenging to implement efficiently1 Matlab implementations too slow to be used in practice High-quality open-source codes exist in C/C++ Can Julia compete? 1Bixby, Robert E. "Solving Real-World Linear Programs: A Decade and More of Progress", Operations Research, Vol. 50, pp. 3{15, 2002. 7 / 25 Implemented benchmark operations in Julia, C++, MATLAB, Python.
    [Show full text]
  • The TOMLAB NLPLIB Toolbox for Nonlinear Programming 1
    AMO - Advanced Modeling and Optimization Volume 1, Number 1, 1999 The TOMLAB NLPLIB Toolbox 1 for Nonlinear Programming 2 3 Kenneth Holmstrom and Mattias Bjorkman Center for Mathematical Mo deling, Department of Mathematics and Physics Malardalen University, P.O. Box 883, SE-721 23 Vasteras, Sweden Abstract The pap er presents the to olb ox NLPLIB TB 1.0 (NonLinear Programming LIBrary); a set of Matlab solvers, test problems, graphical and computational utilities for unconstrained and constrained optimization, quadratic programming, unconstrained and constrained nonlinear least squares, b ox-b ounded global optimization, global mixed-integer nonlinear programming, and exp onential sum mo del tting. NLPLIB TB, like the to olb ox OPERA TB for linear and discrete optimization, is a part of TOMLAB ; an environment in Matlab for research and teaching in optimization. TOMLAB currently solves small and medium size dense problems. Presently, NLPLIB TB implements more than 25 solver algorithms, and it is p ossible to call solvers in the Matlab Optimization Toolbox. MEX- le interfaces are prepared for seven Fortran and C solvers, and others are easily added using the same typ e of interface routines. Currently, MEX- le interfaces havebeendevelop ed for MINOS, NPSOL, NPOPT, NLSSOL, LPOPT, QPOPT and LSSOL. There are four ways to solve a problem: by a direct call to the solver routine or a call to amulti-solver driver routine, or interactively, using the Graphical User Interface (GUI) or a menu system. The GUI may also be used as a prepro cessor to generate Matlab co de for stand-alone runs. If analytical derivatives are not available, automatic di erentiation is easy using an interface to ADMAT/ADMIT TB.
    [Show full text]
  • Real-Time Trajectory Planning to Enable Safe and Performant Automated Vehicles Operating in Unknown Dynamic Environments
    Real-time Trajectory Planning to Enable Safe and Performant Automated Vehicles Operating in Unknown Dynamic Environments by Huckleberry Febbo A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Mechanical Engineering) at The University of Michigan 2019 Doctoral Committee: Associate Research Scientist Tulga Ersal, Co-Chair Professor Jeffrey L. Stein, Co-Chair Professor Brent Gillespie Professor Ilya Kolmanovsky Huckleberry Febbo [email protected] ORCID iD: 0000-0002-0268-3672 c Huckleberry Febbo 2019 All Rights Reserved For my mother and father ii ACKNOWLEDGEMENTS I want to thank my advisors, Prof. Jeffrey L. Stein and Dr. Tulga Ersal for their strong guidance during my graduate studies. Each of you has played pivotal and complementary roles in my life that have greatly enhanced my ability to communicate. I want to thank Prof. Peter Ifju for telling me to go to graduate school. Your confidence in me has pushed me farther than I knew I could go. I want to thank Prof. Elizabeth Hildinger for improving my ability to write well and providing me with the potential to assert myself on paper. I want to thank my friends, family, classmates, and colleges for their continual support. In particular, I would like to thank John Guittar and Srdjan Cvjeticanin for providing me feedback on my articulation of research questions and ideas. I will always look back upon the 2018 Summer that we spent together in the Rackham Reading Room with fondness. Rackham . oh Rackham . I want to thank Virgil Febbo for helping me through my last month in Michigan.
    [Show full text]
  • Optimal Control for Constrained Hybrid System Computational Libraries and Applications
    FINAL REPORT: FEUP2013.LTPAIVA.01 Optimal Control for Constrained Hybrid System Computational Libraries and Applications L.T. Paiva 1 1 Department of Electrical and Computer Engineering University of Porto, Faculty of Engineering - Rua Dr. Roberto Frias, s/n, 4200–465 Porto, Portugal ) [email protected] % 22 508 1450 February 28, 2013 Abstract This final report briefly describes the work carried out under the project PTDC/EEA- CRO/116014/2009 – “Optimal Control for Constrained Hybrid System”. The aim was to build and maintain a software platform to test an illustrate the use of the conceptual tools developed during the overall project: not only in academic examples but also in case studies in the areas of robotics, medicine and exploitation of renewable resources. The grand holder developed a critical hands–on knowledge of the available optimal control solvers as well as package based on non–linear programming solvers. 1 2 Contents 1 OC & NLP Interfaces 7 1.1 Introduction . .7 1.2 AMPL . .7 1.3 ACADO – Automatic Control And Dynamic Optimization . .8 1.4 BOCOP – The optimal control solver . .9 1.5 DIDO – Automatic Control And Dynamic Optimization . 10 1.6 ICLOCS – Imperial College London Optimal Control Software . 12 1.7 TACO – Toolkit for AMPL Control Optimization . 12 1.8 Pseudospectral Methods in Optimal Control . 13 2 NLP Solvers 17 2.1 Introduction . 17 2.2 IPOPT – Interior Point OPTimizer . 17 2.3 KNITRO . 25 2.4 WORHP – WORHP Optimises Really Huge Problems . 31 2.5 Other Commercial Packages . 33 3 Project webpage 35 4 Optimal Control Toolbox 37 5 Applications 39 5.1 Car–Like .
    [Show full text]
  • Tomlab /Snopt1
    USER'S GUIDE FOR TOMLAB /SNOPT1 Kenneth Holmstr¨om2, Anders O. G¨oran3 and Marcus M. Edvall4 February 13, 2008 ∗More information available at the TOMLAB home page: http://tomopt.com. E-mail: [email protected]. yProfessor in Optimization, M¨alardalenUniversity, Department of Mathematics and Physics, P.O. Box 883, SE-721 23 V¨aster˚as, Sweden, [email protected]. zTomlab Optimization AB, V¨aster˚asTechnology Park, Trefasgatan 4, SE-721 30 V¨aster˚as,Sweden, [email protected]. xTomlab Optimization Inc., 855 Beech St #121, San Diego, CA, USA, [email protected]. 1 Contents Contents 2 1 Introduction 6 1.1 Overview..................................................6 1.2 Contents of this Manual..........................................6 1.3 Prerequisites................................................6 2 Using the Matlab Interface 7 3 TOMLAB /SNOPT Solver Reference8 3.1 MINOS...................................................9 3.1.1 Direct Solver Call.........................................9 3.1.2 Using TOMLAB.......................................... 15 3.1.3 optPar................................................ 19 3.2 LP-MINOS................................................. 22 3.2.1 Using TOMLAB.......................................... 22 3.2.2 optPar................................................ 26 3.3 QP-MINOS................................................. 28 3.3.1 Using TOMLAB.......................................... 28 3.3.2 optPar................................................ 32 3.4 LPOPT..................................................
    [Show full text]