Fifth SIAM Conference on Optimization

SIAM Short Course 1
Optimization: Algorithms, Software, and Environments

Sunday, May 19, 1996
Victoria Conference Centre,
Victoria, British Columbia, Canada

Organizer: Jorge J. More, Argonne National Laboratory


This tutorial will provide a practical introduction to mathematical programming languages, automatic differentiation tools, and recent advances in optimization technology. Mathematical programming languages, like AMPL and GAMS, greatly facilitate the formulation and solution of optimization problems, but many users are unaware of the power behind AMPL and GAMS. In addition, automatic differentiation tools, like ADIFOR and ADOLC, have made possible the efficient solution of problems that were not possible before. but many users feel that these tools are not applicable to their problems or too difficult to use. This tutorial will help users decide when and how to use these languages and tools. We will also provide a guided tour of the optimization technology available on the Web. Users of optimization will benefit by learning how the Web can help them solve their optimization problems.


Robert Fourer is a Professor in Northwestern University's Department of Industrial Engineering and Management Sciences. His professional interests include the study of optimization algorithms and the design of computer systems to support optimization. In collaboration with David Gay and Brian Kernighan of Bell Laboratories, he has designed a popular modeling language for optimization, and is co-author of the award-winning book AMPL: A Modeling Language for Mathematical Programming.

Jorge J. More� is a Senior Computer Scientist at Argonne National Laboratory. His research interests include the development and analysis of optimization software for large-scale problems. He has been actively promoting the use of problem solving environments and automatic differentiation tools for optimization, and is a co-author of the Optimization Software Guide (SIAM, 1993).

Stephen J. Wright is a Computer Scientist at Argonne National Laboratory. His interests include interior-point methods, and applications of optimization in control and engineering. He is the author of a forthcoming book on interior-point methods and the co-author of a forthcoming text on numerical optimization.

Who Should Attend?

Staff of academic, government, and industrial institutions interested in learning how optimization technology can help them solve practical problems. Researchers in biology, chemistry, physics, and economics that need to know optimization techniques for their work.

Recommended Background

A basic knowledge of computational linear algebra (Gaussian elimination, QR decomposition, Cholesky decomposition). Calculus for functions of several variables (Jacobians, gradients, Hessians). Working knowledge of a programming language (Fortran or C). Familiarity with the notation and techniques in the Optimization Software Guide (Part I only) (by Mor� and Wright, published by SIAM) is ample background.

Detailed Outline

Recent Developments in Algorithms and Software

Linear programming software update
Developments in simplex codes: steepest-edge becoming standard
Developments in interior point codes: the state-of-the-art
Nonlinear programming software update
Lancelot, SNOPT, other codes
Interior-point developments

Other areas: Mixed integer LP

Software information on the Internet and WWW
Home pages for existing software
NEOS Guide
Other resources and search tools
Tools on the WWW
NEOS Server

Mathematical Programming Modeling Languages

Motivation: a simple example
Matrix generators
Types of modeling languages
Algebraic modeling languages
Problem types: linear, nonlinear, integer, network
Indexing: sets, compound sets, sets of sets
Modeling environments
Implementing iterative schemes
Uses in practice
Solvers supported
Examples of large-scale applications

Environments and Automatic Differentiation

Forward and reverse mode, complexity
ADIFOR and ADOLC: advantages and disadvantages
Uses of automatic differentiation
Optimization software
ELSO: An optimization environment The NEOS Server
Computing sparse Jacobian and Hessian matrices
Sparsity patterns
Numerical results, accuracy, efficiency
Computing gradients
Partially separable functions
Forward and reverse modes
Numerical results, accuracy, efficiency
Constraint logic programming
Systems: Prolog based, object oriented



8:00 Registration
8:45-9:00 Welcome
Jorge More
9:00-10:30 Recent Developments in Algorithms and Software
Stephen Wright
10:30-11:00 Coffee
11:00-12:30 Mathematical Programming Languages
Robert Fourer


12:30-2:00 Lunch
(attendees are on their own for lunch)
2:00-3:00 Environments and Automatic Differentiation
Jorge More
3:00-3:30 Coffee
3:30-4:00 Environments and Automatic Differentiation (continued)
Jorge More
4:00-4:30 Constraint Logic Programming
Robert Fourer
4:30-5:00 Open discussion
5:00 Short Course adjourns

Registration Fees (for either Short Course)
SIAG/Opt Member* SIAM MemberNon-MemberStudent
Preregistration (before 5/6/96)$110$110$125 $40
Registration (after 5/6/96) $125 $125 $140 $55

*Member of SIAM Activity Group on Optimization.

Short Course fees include course notes and refreshment breaks. To register for either short course, the conference, or both, please fill-in and submit the preregistration form.

On-site registration will start on Saturday, May 18 at 6:00 PM at the entrance, Lobby Level of the Conference Centre.

Registration | Hotel Information | Transportation | Speaker Index | Program Overview

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MEM, 3/11/96