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A Modeling Interface to NonLinear Programming Solvers  An instance: xMPS, the extended MPS format
, 2000
"... We present a ModelerOptimizer Interface (MOI) for general closed form NonLinear Programs (NLP), which can be used to to transfer NLPs in a clear and simple manner between optimization components in a distributed environment. We demonstrate how this interface allows rst order derivative informat ..."
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We present a ModelerOptimizer Interface (MOI) for general closed form NonLinear Programs (NLP), which can be used to to transfer NLPs in a clear and simple manner between optimization components in a distributed environment. We demonstrate how this interface allows rst order derivative information to be easily calculated on the optimizer's side, using automatic dierentiation, hence removing the bottleneck of communicating derivative information between the modeler and the optimizer. We also show how this interface directly corresponds to a le format for NLPs, the extended MPS format (xMPS). This format directly extends the standard MPS le format for linear and mixed integer programs to include NLPs and permits a standardized way of transferring benchmark problems. The format spares the modeler the tedious task of calculating derivative information with minimal extra work required by the optimizer and thus increases eciency. This work was originally done at Maximal Sof...
SymbolicAlgebraic Computations in a Modeling Language for Mathematical Programming
 In Symbolic Algebraic Methods and Verification
, 2001
"... ..."
The NLPLIB Graphical User Interface
, 1998
"... This paper presents a Graphical User Interface (GUI) for nonlinear programming in MATLAB. The GUI gives easy access to all features in the NLPLIB (NonLinear Programming LIBrary) Toolbox; a set of MATLAB solvers, test problems, graphical and computational utilities for unconstrained and constrained o ..."
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This paper presents a Graphical User Interface (GUI) for nonlinear programming in MATLAB. The GUI gives easy access to all features in the NLPLIB (NonLinear Programming LIBrary) Toolbox; a set of MATLAB solvers, test problems, graphical and computational utilities for unconstrained and constrained optimization, quadratic programming, unconstrained and constrained nonlinear least squares and exponential sum øtting. NLPLIB TB, like the operations research toolbox OPERA TB, 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 20 internal solvers. From NLPLIB TB it is possible to call solvers in the MATLAB Optimization Toolbox and generalpurpose solvers implemented in Fortran or C. Fortran and C solvers are called from MATLAB using a MEXøle interface. A problem is solved either by direct call to a parameter driven driver routine, or interactively, usi...
StAMPL: A filtrationoriented modeling tool for stochastic programming, tech
, 2006
"... This research investigates how to create a modeling tool specifically for stochastic programming problems with recourse. By taking advantage of the special structure these problems have, we produce a modeling language whose syntax is less redundant, more modular, and more expressive than the notatio ..."
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This research investigates how to create a modeling tool specifically for stochastic programming problems with recourse. By taking advantage of the special structure these problems have, we produce a modeling language whose syntax is less redundant, more modular, and more expressive than the notation commonly associated with stochastic programming. We then implement a system that can convert models written using this syntax to instances that can be solved using standard mechanisms. With this approach, we are able to represent models in a very clean, simple, and scalable format. 1.
The Numerical Algorithm Group, Ltd.
, 2011
"... Optimization, or Operational Research in general, nowadays plays an important role in our lives. No matter if you are a respected finance house or a student of mathematics, you have probably used some sort of optimization routines. The field itself has changed rapidly since linear programming was in ..."
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Optimization, or Operational Research in general, nowadays plays an important role in our lives. No matter if you are a respected finance house or a student of mathematics, you have probably used some sort of optimization routines. The field itself has changed rapidly since linear programming was introduced in the mid 1940s. More powerful computers allowed us to consider much more realistic and complex models using sophisticated algorithms. Whereas the input for linear programming problems is relatively simple, it is a much more delicate task in the case of general nonlinear programming. One way to tackle it is to introduce a specialised language for the problem description. In this tutorial we will focus on a particular one called AMPL which we have equipped with two of our NAG solvers, namely E04UFF and E04UGF. AMPL, A Mathematical Programming Language [3, 8], comes with several nice features packed together and it has earned considerable popularity. The language uses a common mathematical notation so it is easy to understand. Moreover, it includes an automatic differentiation package (a method to compute exact derivatives) therefore coding any derivatives can be completely avoided. This makes it ideal whenever you need to solve the problem fast. It is great for demonstrating or teaching as well as for rapid prototyping of mathematical models. Thus you can focus on optimization itself and not coding. It also unifies the interface for both setting the problems and solvers so you can test your solver on several problems or solve the same problem with several solvers without much effort. Thus a connection of AMPL and NAG software seems to be natural as it offers you the power of a modelling language together with NAG welltuned solvers. The tutorial is organised as follows. In the first two sections we introduce AMPL and a simple example written in the AMPL language. The 3rd section
The TOMLAB v2.0 Optimization Environment 1
, 2000
"... TOMLAB is a general purpose, open and integrated Matlab environment for the solution of a wide range of optimization problems, as well as for research and teaching in optimization. One motivation for TOMLAB is to simplify research on applied optimization problems, giving easy access to all types of ..."
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TOMLAB is a general purpose, open and integrated Matlab environment for the solution of a wide range of optimization problems, as well as for research and teaching in optimization. One motivation for TOMLAB is to simplify research on applied optimization problems, giving easy access to all types of solvers; at the same time having full access to the power of Matlab. This paper discusses the design and contents of TOMLAB, its performance and presents some applications where it has been successfully used. More than 65 different algorithms for linear, discrete and nonlinear optimization are implemented. It is also possible to call solvers in the Math Works Optimization Toolbox and a collection of generalpurpose Fortran and C solvers using predefined MEXfile interfaces. There are several ways to solve optimization problems in TOMLAB. Either by a direct call to a solver or using a general multisolver driver routine, or interactively, using the Graphical User Interface (GUI) or a menu system. TOMLAB v2.0 is also callcompatible with the Math Works Optimization Toolbox 2.0. A large set of standard test problems are included, as well as example and demonstration files. New userdefined problems are easily added. Furthermore, using MEXfile interfaces, problems in the CUTE test problem data base and problems defined in the AMPL modeling language can be solved. TOMLAB v1.0 solves small and medium size dense problems and is free for academic purposes. TOMLAB v2.0 also solves sparse problems, is possible to compile and has enhanced functionality. TOMLAB is running on Unix, Linux and PC systems. 1