******* Contributed Minisymposia *********

**CM-1  (3 speakers)

<MT>Condition Numbers and Problem Complexity in Convex Programming 
(Part I of II)

<x>There are many weaknesses in the traditional measure of problem 
complexity for
linear programming based on the bit-length L of a binary encoding of a problem,
not the least of which include the lack of extendibility of this measure to
nonlinear optimization, the inability of this measure to adequately predict the
difficulty in solving problems in practice, and the lack of connection between
L and standard condition measures for solving linear equations.  This
minisymposium will report on recent research aimed at exploring the properties
and prospects of some alternative ways of defining the "condition number" of an 
optimization problem, that are based on more intuitive and appealing notions of
"conditioning" for convex optimization, and that are relevant both to the theory
and the application of linear and nonlinear programming.

<o>Organizer: Robert M. Freund
<x>Massachusetts Institute of Technology 

<MST>The Geometry of Condition Numbers
<MA>James Renegar, Cornell University

<MST>Characterizations of the Condition Number of a Convex Program, and the
Complexity of the Ellipsoid Algorithm and Dantzig's von Neumann Algorithm
<MA>Robert M. Freund, Organizer

<MST>On the use of Condition Measures and Knowledge in the Complexity Theory of
Linear Programming
<MA>Sharon Filipowski, Iowa State University


**CM-2  (4 speakers)

<MT>Condition Numbers and Problem Complexity in Convex Programming (Part II of
II)

<x>For description, see CM-1

<o>Organizer: Robert M. Freund
<x>Massachusetts Institute of Technology

<MST>Convergence of an interior point method for linear programming in terms of
the condition number of the coefficient matrix
<MA>Stephen Vavasis, Cornell University

<MST>Condition Measures and Properties of the Central Trajectory of a Linear
Program
<MA>Manuel Nunez, Massachusetts Institute of Technology

<MST>Finite Precision Computation in Optimization:  A Complexity Analysis based
on Ill-Posedness Measures
<MA>Jorge Vera, University of Chile, Chile 

<MST>Perturbation Analysis of a Condition Number for Convex Systems
<MA>Sien Deng, Northern Illinois University


**CM-3  (3 speakers)

<MT>Optimization in Control and Design Applications (Part I of II)
   
<x>The application of optimization methods to optimal control and design 
problems
is a challenging and rewarding endeavor. The appropriate formulation of these
applications as optimization problems, the computation of derivatives, the
efficient solution of mostly very large subproblems, and the convergence      
analysis for practical optimization algorithms are issues that have to be
addressed. The speakers in this minisymposium report on new developments in
optimization methods for this class of problems and on recent applications of
optimization methods to important industrial problems.
       
<o>Organizers: Matthias Heinkenschloss, Virginia Polytechnic Institute and 
State
University; Juan Meza, Sandia National Laboratories; and Volker Schulz,      
Universitat Heidelberg, Germany 
 
<MST>Reduced Hessian SQP methods for Process Optimization: Some Recent Advances
<MA>Larry Biegler* and David Ternet, Carnegie Mellon University
 
<MST>Numerical Solution of Optimization Boundary Value Problems in Industrial
Applications
<MA>Hans Georg Bock, University of Heidelberg, Germany; and Volker Schulz,
Organizer 

<MST>Optimal Design of Micro-Optical Structures via Constrained Optimization
<MA>David Dobson, Texas A&M  University

**CM-4  (3 speakers)

<MT>Optimization in Control and Design Applications (Part II of II)
   
<x>For description, see CM-3.

<MST>Comparison of Numerical Methods for Optimal Shape Design Problems
<MA>Manfred Laumen, Universitat Trier, Germany

<MST>Optimal Control of Chemical Vapor Deposition Reactors
<MA>Juan Meza, Organizer 

<MST>Optimization Methods for the Optimal Control of Fluid Flow
<MA>Matthias Heinkenschloss, Organizer


**CM-5  (4 speakers)

<MT>Trust Region Methods for Problems with Simple Bounds

<x>This speakers in this minisymposium will present many views of trust region
algorithms for minimization problems with simple bounds and their applications.
Projection and interior point algorithms will be discussed together with
applications to optimal control. Both finite and infinite dimensional results
will be discussed.

<o>C. T. Kelley
<x>North Carolina State University

<MST>A Trust Region Method for Parabolic Boundary Control Problems
<MA>Ekkehard W. Sachs, Universitat Trier, Germany

<MST>Trust-region interior-point SQP algorithms with applications 
         to optimal control
<MA>Luis Vicente, Rice University

<MST>Newton's Method for Large-Scale Bound Constrained Optimization
<MA>Ali Bouaricha, Argonne National Laboratory

<MST>Automatic Determination of an Initial Trust Region in Nonlinear Programming
<MA>Annick Sartenaer, Facultes Universitaires ND de la Paix, Belgium 


**CM-6  (4 speakers)

<MT>Stability, Sensitivity and Real-Time Control of Perturbed Nonlinear Control
Systems

<x>In recent years, considerable progress has been made 
in the stability and sensitivity analysis of perturbed
control problems subject to control and state constraints. 
It has been recognized for a long time that  stability properties 
of optimal solutions are crucial for developing real-time 
control algorithms on-line. The authors in this minisymposium 
will highlight some recent developments in stability and 
sensitivity analysis and make an effort to link these results 
to numerical algorithms. They discuss nonlinear programming methods 
for solving optimal control problems and design 
real-time control approximations of perturbed systems. 

<o>Organizer:  Helmut Maurer
<x>Westfalische Wilhelms-Universitt Munster, Germany

<MST>Second Order Sufficient Conditions for Nonlinear Optimal Control
Problems Subject to State Constraints
<MA>Kazimierz Malanowski, Polish Academy of Sciences, Poland

<MST>Stability Analysis and Algorithms for Control Problems with State
Constraints
<MA>William W. Hager, University of Florida

<MST>Sensitivity Analysis and Real-Time Control of Perturbed Control Problems by
Nonlinear Programming Methods
<MA>Christof Buskens, Westfalische Wilhelms-Universitat Munster, Germany; and
Helmut Maurer, Organizer 

<MST>Local Convergence and Mesh Independence of Newton's Method for Generalized
Equations
<MA>Walter Alt, Friedrich-Schiller-Universitaet Jena, Germany


**CM-7  (3 speakers)

<MT>Semidefinite Programming (Part II of II)

<x>For description, see Invited Minisymposium IM-10

<o>Organizer: Michael L. Overton
<x>New York University, Courant

<MST>Primal-dual path following algorithms for semidefinite
programming  
<MA>Renato D.C. Monteiro, Georgia Institute of Technology

<MST>Title to be determined
<MA>Masakazu Kojima, Tokyo Institute of Technology, Tokyo

<MST>Cone-LP's, Semidefinite Programs, and Eigenvalue Optimization:
Geometry and Algorithms
<MA>Gabor Pataki, University of Waterloo, Canada


**CM-8  (4 speakers)

<MT>Beyond Taylor Series Approximations: The Use of Alternative Models in
Nonlinear Programming

<x>It is standard practice in nonlinear programming to exploit first-order and
second-order information about the objective function and the constraints.  This
information is typically used to construct the Taylor series approximations 
that are required by globalized quasi-Newton methods.  When it works, this
modeling strategy is extremely effective, as evidenced by the popularity of
quasi-Newton methods and the elegant convergence theory that supports them.
      But what can be done when, as often occurs in practice, the standard
techniques do not apply?  What if the objective function is not differentiable,
or reliable derivatives are not available?  Here we examine several promising
ideas for nonstandard modeling strategies that address these issues.

<o>Organizer: Virginia Torczon
<x>College of William and Mary

<MST>Global Modeling for Optimization
<MA>Paul Frank, Boeing Information and Support Services
  
<MST>An Approach to Derivative-Free Optimization
<MA>Andrew R. Conn, Thomas J. Watson Research Center
  
<MST>Managing Approximation Models in Optimization
<MA>J.E. Dennis, Jr., Rice University

<MST>Local Quadratic Models in Stochastic Optimization
<MA>Michael W. Trosset, University of Arizona

**CM-9  (4 speakers)

<MT>Linear Algebra for Interior Point Methods
   
<x>This minisymposium will focus on computational linear algebra for 
interior point methods.  Interior point methods are now the preferred 
approach to large-scale structured linear programming problems.  Almost 
all the computational work associated with these methods is finding 
the Newton step, which requires solution of a system of linear 
equations with special structure.  These equations can be written 
in so-called ``KKT'' form and also as weighted least-squares problems, 
and they can be highly ill-conditioned.  Speakers in this 
minisymposium will discuss recent advances in the development of 
efficient and stable algorithms for determining the Newton step.

<o>Organizer: Stephen A. Vavasis
<x>Cornell University

<MST>Stable Solution of Weighted Least Squares for Near-Degenerate
Linear Programming Problems
<MA>Patricia Hough*, Cornell University and S. Vavasis, Organizer 

<MST>Preconditioners and the iterative solution of the linearized
KKT-systems in linear programming
<MA>Florian Jarre, Universitat Wurzburg, Germany and Roland Freund, 
AT&T Bell Laboratories

<MST>Solution of KKT Systems within OSL's Barrier Algorithm
<MA>Michael Saunders, Stanford University; and J. Tomlin

<MST>Finite Precision Effects in Interior-Point Methods
<MA>Stephen Wright, Argonne National Laboratory
                                                       
  

**CM-13  (4 speakers)

<MT>Use of Iterative Methods in Optimization and Nonlinear Equations 
(Part I of II)

<x>Most algorithms for optimization problems and nonlinear equations require 
the solution of linear systems of equations. In many applications the 
linear systems are very large while in others the coefficient matrix 
is not explicitly available. These difficulties suggest the use of 
iterative solvers.
      The goal of this minisymposium is to present new ideas concerning the use
 of iterative methods in several algorithmic frameworks for solving optimization
problems and nonlinear equations.  The presentations given here will address
practical as well as theoretical issues concerning this topic. Some of these
issues are the use of Krylov subspace methods, preconditioning, limited memory
Quasi-Newton updates, and inexactness.

<o>Organizers: Amr S. El-Bakry, Alexandria University, Egypt; and Luis N,
Vicente,  Rice University

<MST>Truncated-Newton Methods for Large-Scale Optimization
<MA>Steven G. Nash,  George Mason University

<MST>Preconditioning of Elliptic Variational Inequalities
<MA>Tony Choi, North Carolina State University

<MST>Newton-Krylov Methods
<MA>Homer F. Walker, Utah State University 

<MST>Title to be determined 
<MA>Amr S. El-Bakry, Organizer

**CM-14  (4 speakers)

<MT>Use of Iterative Methods in Optimization and Nonlinear Equations (Part II
of II)

<x>For description, see CM-13

<o>Organizers: Amr S. El-Bakry, Alexandria University, Egypt; and
Luis N. Vicente, Rice University

<MST>Inexact Ideas
<MA>Trond Steihaug, University of Bergen, Norway

<MST>Solving Large Scale Systems of Nonlinear Equations with Hybrid 
Krylov-Secant Methods
<MA>Hector Manuel Klie and Marcelo Rame, Rice University; and Mary F. Wheeler,
University of Texas

<MST>A QMR-Based Inexact Interior-Point Algorithm for Solving Linear Programs
<MA>Roland W. Freund, AT&T Bell Laboratories and Florian Jarre, Universitat of
Wurzburg, Germany       
                                                 
<MST>A Truncated Newton Interior-Point Algorithm for the Solution of Linear and
Monotone Linear Complementarity Network Flow Problems
<MA>Luis Portugal and Joaquim Judice, Universidade de Coimbra, Portugal 


**CM-17  (4 speakers)

<MT>Parameter Estimation and Optimum Experimental Design

<x>Mathematical models for real-life processes typically contain parameters
that have to be determined by experiment. We examine two issues related to this
process: The design of experiments that produce high-quality data for parameter
estimation ("optimum experimental design") and the task of actually estimating
the parameters from given experimental data ("parameter estimation"). The talks
describe recent developments in numerical methods for the treatment of complex
nonlinear models, especially DAE and PDE boundary value problems. Topics 
include reduced Gauss-Newton and SQP-type methods, stepsize strategies, 
exploitation of structures, parallel algorithms. Applications from 
chemical engineering, mechanical engineering and environmental physics 
will also be discussed.

<o>Organizers: Johannes P. Schloeder, University of Heidelberg, Germany; 
Stephen J. Wright, Argonne National Laboratory

<MST>Feasible Point Trust-Region Methods for Equality Constrained Least Squares
Problems and Application to Parameter Estimation in Nonlinear Models with
Singularities
<MA>Hubert Schwetlick and Stefan Schleiff, Technische Universitaet Dresden, 
Germany

<MST>Global Optimization of Functionals Constrained by Differential
Equations: Bayesian Search on Approximants
<MA>Prasana Venkatesh, University of Minnesota

<MST>Optimum Experimental Design for Nonlinear Dynamic Processes: Methods,
Algorithms, Applications in Robotics and Chemical Kinetics
<MA>Klaus-Dieter Hilf, University of Heidelberg, Germany

<MST>Efficient Numerical Methods for Parameter Estimation in Nonlinear 2D
Transport Reaction Processes
<MA>Matthias Ziesse, University of Heidelberg, Germany

**CM-19  (3 speakers)

<MT>Global Optimization Methods for Molecular Conformation and Protein Folding
(Part I of II)

<x>One of the most significant and challenging problems in molecular biophysics
and biochemistry is that of computing the native 3-dimensional conformation
(folded state) of a globular protein given its amino acid sequence, possibly in
the presence of additional agents (e.g., drugs). The papers in this 
minisymposium will discuss algorithms for computing the global minima 
of a class of associated nonconvex energy functions. Progress toward 
the solution techniques for globally minimizing nonconvex energy 
functions associated with the protein folding problem
will facilitate the design, synthesis and utilization of pharmaceutical 
products, as well as the utilization of new protein materials with 
specific advantageous properties.

<o>Organizer: Panos Pardalos
<x>University of Florida

<MST>Algorithms for Energy Optimization of Complex Molecular Systems
<MA>John E. Straub, Boston University

<MST>Combining Hierarchical Analysis and Effective Energy Methods for
Protein Free-Energy Global Minimization
<MA>David Shalloway, Cornell University 

<MST>Computing the Dependence of Molecular Structure on Residue Sequence by
Global Minimization
<MA>J.B. Rosen, UCSD; A.T. Phillips, US Naval Academy; Ken Dill, UCSF

**CM-20  (3 speakers)

<MT>Global Optimization Methods for Molecular Conformation and Protein Folding
(Part II of II)

<x>For description, see CM-19

<MST>Computational Study of a Linear Time Algorithm for Potential Field
Evaluation in N-Body Simulations
<MA>Guoliang Xue, The University Of Vermont
     
<MST>Exploring Conformational Space with a Lattice Model for Protein
Folding using Tabu Search
<MA>P.M. Pardalos, Organizer; and and X. Liu, University of Florida

<MST>Smoothing Transform and Continuation for Global Optimization
<MA>Jorge More and Zhijun Wu, Argonne National Laboratory

**CM-21  (4 speakers)

<MT>Algorithms for Multilevel Programming

<x>In a multi-level programming problem, successive subsets of the variables 
are constrained to be optimal solutions of other mathematical programs,
parameterized by the preceding blocks of variables.  Bilevel problems arise in
economics and mechanics, while general multilevel problems are encountered in
multiobjective optimization and distributed engineering design optimization.
      The engineering community's interest in large-scale multilevel programming
coupled with the ad hoc nature of many of their approaches indicates the need 
for further work in this field by the nonlinear programmers.
      The focus of this minisymposium is practical algorithms for solving
multilevel programs.  This session will include discussion of convergence 
theory as well as practical issues encountered in the implementation of 
algorithms.

<o>Organizer: Robert Michael Lewis
<x>NASA Langley Research Center

<MST>A Two-Level Approach to Computing Worst Case Optimum Designs
<MA>J.R. Jagannatha Rao and K. Badhrinath, University of Houston 

<MST>Solving Nonlinear Bilevel Programs Using Trust Regions and an Exact
Penalty Function
<MA>Paul Calamai and Lori M. Case, University of Waterloo; and Andrew R. Conn,
T.J. Watson Research Center

<MST>General Nonlinear Multilevel Optimization for MDO
<MA>Natalia Alexandrov, NASA Langley Research Center

<MST>Applications of a Bilevel Algorithm to Systems Governed by PDE
<MA>Robert Michael Lewis, Organizer


**CM-22  (4 speakers)

<MT>Computational Mixed Integer Programming 

<x>The potential for solving large-scale applied problems has always been a
driving force behind the study of combinatorial optimization. In this
minisymposium, several computational methodologies for solving important
applications of mixed integer programming will be described. The applications
addressed include airline fleet scheduling, survivability of telecommunication
networks, and machine learning and statistical classification. The 
methodologies
utilized include an interior-point cutting plane algorithm; a branch-and-cut
algorithm incorporating heuristics, preprocessing, and approximation 
of nonlinear constraints; a cutting plane algorithm based on the concept 
of analytic center; 
and a column generation approach coupled with branch-and-cut. The talks
demonstrate the diversity of methods utilized to solve various classes of
problems, and emphasize the increasing integration of nonlinear and discrete
techniques within a common framework to solve real-world problems. 

<o>Organizer: Eva K. Lee
<x>Columbia University

<MST>Using an Interior Point Algorithm in a Cutting Plane Method for Solving
Integer Programming Problems
<MA>John E. Mitchell, Rensselaer Polytechnic Institute 

<MST>Linear and Nonlinear Mixed Integer Models for Machine Learning and
Statistical Classification
<MA>Richard J. Gallagher, Columbia-Presbyterian Medical Center; Eva K. Lee,
Organizer; and Dave Patterson, University of Montana

<MST>Survivability in Telecommunication Network
<MA>Robert Sarkissian, Universite de Geneve, Switzerland

<MST>Solving Fleet Scheduling Problems using Column Generation Techniques
<MA>Karla L. Hoffman, Peter Ball, George Mason University


**CM-23  (4 speakers)

<MT>Optimization and Optimal Control of Incompressible Flows

<x>The speakers will address various analytical and computational aspects of
optimization and optimal control problems for unsteady, viscous incompressible
flows. These problems have a wide range of engineering and technological 
applications. In recent years, great progress has been made in the area of
optimization and optimal control of incompressible flows which makes it 
possible to apply optimization and optimal control techniques to practical 
and complicated engineering designs and sophisticated real-time control 
systems. The topics covered in this minisymposium include: the study of 
the sensitivity derivatives of discretized shape optimization problems; 
modeling, analysis and computational techniques for boundary optimal 
control; optimization and optimal control of vortex dominated flows; 
and the long time behaviors and numerical approximations
of solutions of piecewise (in time) optimal control problems.

<o>Organizer: L.S. Hou
<x>York University

<MST>Discretized Sensitivities Are Not Derivatives
<MA>John Burkardt, Virginia Polytechnical Institute & State University

<MST>Analysis and computation for control of time dependent flows
<MA>Max D. Gunzburger, Iowa State University

<MST>Control of Vortex Dominated Flows
<MA>Thomas P. Svobodny, Wright State University

<MST>Dynamics and Approximations of Velocity Tracking for the Piecewise
Controlled Navier-Stokes Equations                      
<MA>Yin Yan, Virginia Polytechnical Institute & State University


**CM-25  (4 speakers)

<MT>Recession Methods in Nonlinear Analysis and Optimization (Part I of II) 

<x>This minisymposium is aimed at bringing together experts working in different
areas of nonlinear analysis and optimization who use the ``recession method"
approach to solve problems. Just like the boundedness of a convex set can be
described by its recession cone, in the ``recession method" approach, one
attempts to get some information on the solution of the problem by imposing 
conditions at ``infinity". As the speakers of this minisymposium will
demonstrate, such a method has been particularly useful in the existence, error
bound, and convergence analysis of solutions of optimization problems,
variational inequalities, piecewise affine equations, and convex inequality
systems. 

<o>Organizers: M. Seetharama Gowda, University of Maryland; and Michel Thera,
Universite de Limoges, France 

Chair: M. Seetharama Gowda

<MST>Viscosity Methods in Recession Analysis
<MA>Hedy Attouch, Universite Montpellier 2, France

<MST>A Study of Global Error Bounds of Convex Systems via their 
Related Recession Functions
<MA>Sien Deng, Northern Illinois University

<MST>Minimizing and Stationary Sequences of Optimization problems
<MA>Jong-Shi Pang, Johns Hopkins University

<MST>Some Properties of the Recession Function of a Piecewise Affine Function
<MA>M. Seetharama Gowda, Organizer


**CM-26  (4 speakers)

<MT>Recession Methods in Nonlinear Analysis and Optimization (Part II of II)

<x>For description, CM-25_

<o>Organizers: M. Seetharama Gowda, University of Maryland; and Michel Thera,
Universite de Limoges, France

Chair: Michel Thera

<MST>Non-Coercive Optimization Problems
<MA>Alfred Auslender, Universite Paris I; and Laboratoire d'Econometrie de
l'Ecole Polytechnique, France
                                       
<MST>Energies with Respect to a Measure and Low Dimensional Structures
<MA>Giuseppe Buttazzo, Universita di Pisa, Italy                               

<MST>Recession Cone, Hemivariational Inequality Approach to Nonconvex Unilateral
Problems and its Applications in Robotics
<MA>Daniel Goeleven, Universite de Limoges, France
              
<MST>Recession Methods in Noncoercive Variational Inequalities with 
Applications to Nonlinear Analysis and Optimization
<MA>Michel Thera, Organizer
                          

**CM-27  (4 speakers)

<MT>Periodic and Chaotic Solutions of Optimal Control Models

<x>During the last decade the following class of problems has been
studied in several fields of applied mathematics. Consider
deterministic, autonomous optimal control problems with infinite
time horizon. Under which conditions the long-run behavior of the
optimal solution is periodic or chaotic? The question of
endogenously generated limit cycles and chaotic attractors is of
importance in various fields of mathematic economics (like optimal
growth theory), operations research (dynamic programming applied
for instance to marketing, production or finance) and engineering.
Although several mechanisms generating complex behavior have
already been identified more work has to be done to gain insights
on the compatibility of optimality and complexity.

<o>Organizer: Gustav Feichtinger
<x>Vienna University of Technology, Austria

<MST>Periodic and chaotic solutions of non-concave optimal
control problems
<MA>Gustav Feichtinger, Organizer

<MST>Numerical solution and sensitivity analysis of periodic
control problems
<MA>Helmut Maurer, University of Muenster, Germany

<MST>Energy optimal periodic solutions and feedback control for
subway trains with non-connected control sets
<MA>Johannes Schloeder, University Heidelberg, Germany

<MST>The value of an optimal investment with periodic discount rate
<MA>Malte Sieveking, Johann Wolfgang Goethe University, Germany


**CM-29

Optimization in Computational Fluid Dynamics
     
Organizers: Georg Bock and Dave Young
     
Optimization boundary value problems in CFD are one of the most challenging 
problems in the field of numerical PDEs. Even the forward problem alone is 
very difficult and nourishes a large community of numerical mathematicians. 
Optimization problems in CFD are highly nonlinear and after discretization 
lead to a large scale finite constrained optimization problem with special 
structures that have to be exploited. Speakers in the minisymposium will 
emphasize several important issues: the impact of optimization in CFD 
applications, structure exploiting optimization methods that incorporate 
state of the art approaches both from the optimization and discretization 
fields, and questions of their optimal combination to minimize the 
complexity of the solution approach.
     
SPEAKER 1 (full name)
Volker H. Schulz,  H. Georg Bock, Organizer, and Th. Dreyer 
Interdisciplinary Center of Scientific Computing
University of Heidelberg
     
Title of Presentation (Proposed [ ]  Confirmed [ X])
Reduced SQP multigrid methods for shape optimization of turbine blades
     
SPEAKERS 2
Omar Ghattas, Beichang He,
Carnegie Mellon University, Pittsburgh, PA
     
James F. Antaki, Greg Burgreen,
University of Pittsburgh Medical Center, Pittsburgh, PA

Title of Presentation (Proposed [ ]  Confirmed [X ]) 
Shape Optimization of Viscous Incompressible Flows
     
SPEAKER 3
Max Gunzburger
Department of Mathematics
Iowa State University
e-mail
GUNZBURGER@VTCC1.CC.VT.EDU
     
Title of Presentation (Proposed [ ]  Confirmed [X ])
Shape optimization in fluid flows
     
SPEAKER 4 (full name)
William Huffman
David Young, Organizer
Robin Melvin
Michael Bieterman
Boeing Information & Support Services 
Seattle, WA
     
Craig Hilmes
Forrester Johnson
Boeing Commercial Airplane Group
Seattle, WA
e-mail
     
Title of Presentation (Proposed [ ]  Confirmed [X] 
Constrained Design Optimization in TRANAIR


** CM-30 (4 speakers)

<MT>Multidisciplinary Design Optimization: Methods and Applications

<x>Multidisciplinary Design Optimization (MDO) is a methodology for the design
of complex, coupled engineering systems, such as aircraft, the behavior of which
is  governed by interacting physical phenomena.  MDO's aim is to improve and
speed up the design process, and to lower the cost of complex systems.  MDO is
recent to applied mathematics, but has been in the development stage for a number
of years in engineering. Due to the complexity and size of the problems, MDO is
only now beginning to show its potential and it would benefit from closer
interaction among the practitioners of engineering and applied mathematics.  The
aim of the minisymposium is to bring these two areas together and share the
recent advances.

<o>Organizer: Natalia M. Alexandrov
<x>NASA Langley Research Center

<MST>On Numerical Issues in Optimal Design
<MA>John A. Burns, Virginia Polytechnic Institute and State University

<MST>Design of Automotive Valve Trains with Implicit Filtering
<MA>Carl T. Kelley, North Carolina State University

<MST>Aeroservoelastic Shape Optimization: Approximation Concepts
and Analysis Tools for MDO
<MA>Eli Livne, University of Washington

<MST>Bayesian--Validated Computer--Simulation Surrogates for Optimization and
Control
<MA>Anthony T. Patera, Massachusetts Institute of Technology