Timothy Davis, CISE
William Hager, Mathematics
Panos Pardalos, ISE

As optimization researchers tackle larger and larger problems, scale interactions play an increasingly important role. One general strategy for dealing with a large or difficult problem is to partition it into smaller ones, which are hopefully much easier to solve, and then work backwards towards the solution of original problem, using a solution from a previous level as a starting guess at the next level. This conference brings together mathematicians, engineers, and scientists working on the latest techniques for solving difficult continuous and discrete optimization problem. Application areas include circuit board and micro-chip design,task assignment in parallel computing, dynamic simulations, and sparse matrix pivoting.

  • Continuous and discrete multilevel optimization techniques
  • Sparse matrix techniques in large scale optimization
  • Applications including VLSI design, parallel computing, simulation

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  • Gregoire Allaire (Ecole Polytechnique, France)
        A Level-set Method for Shape and Topology Optimization

  • Balabhaskar Balasundaram (Texas A&M Univ.)
        Constructing Test Functions for Global Optimization Using Continuous Formulations of Optimization Problems on Graphs.

  • Francisco Barahona (IBM)
       Network Reinforcement

  • Earl Barnes (Georgia Tech)
        A Linear Programming Approach to the Maximum Clique Problem

  • Sergiy Butenko (Texas A&M Univ.)
       An Algorithm for Finding All Maximal Cliques in a Graph

  • Ken Chan (Aerospace Corp)
        A New Approach for Solving The Trust-Region Subproblem in Nonlinear Programming

  • Tony Chan (UCLA)
        Multilevel Optimization for Circuit Placement

  • Jein-Shan Chen
        An unconstrained smooth minimization reformulation of the second-order cone complementarity problem

  • Jason Cong (UCLA)
        Multilevel Optimization Opportunities in VLSICAD

  • Yu-hong  Dai (Academy of Sciences, Beijing)
        New Algorithms for A Singly Constrained Class of Quadratic Programs Subject to Lower and Upper Bounds

  • Ken Dill (UC San Francisco)
       Protein Folding: A Global Optimization Problem

  • Asen Dontchev (Math Reviews)
        Conditioning of Optimization Problems

  • Paola Festa (Naples, Italy)
        On randomized heuristics for the Max-Cut problem

  • Pando Georgiev (Brain Science Institute, RIKEN Lab., Japan)
        Optimization Algorithms for Sparse Representations and Applications

  • Fred Glover (Univ. Colorado)
        A Unified Modeling and Solution Framework for Partitioning and Related Problems

  • Jean-Louis Goffin (McGill Univ., Canada)
        Analytic centers cutting plane methods and multilevel Lagrangean relaxation

  • Jacek Gondzio (Univ. Edinburgh, Scotland)
       Parallel Interior Point Solver for Very Large Scale Optimization

  • Igor Griva (Princeton Univ.)
       Convergence analysis of primal-dual interior and exterior point methods for constrained optimization

  • Herve Kerivin (University of Minnesota)
       Design of Networks under Traffic Uncertainty

  • S. Kartik Krishnan (McMaster Univ.)
        A non-polyhedral primal active set approach for Semidefinite and Second Order Cone Programming

  • Istvan Maros (Imperial College, London)
        Recent Advances in the Sparse Simplex Method

  • John Mitchell (RPI)
       A unifying framework for several cutting plane methods for semidefinite programming

  • Walter Murray (Stanford Univ.)
        Siting Substations in an Electrical Network

  • Stephen Nash (George Mason Univ.)
        Model Problems for the Multigrid Optimization of Systems Governed by Differential Equations

  • Jiawang Nie (Univ. Calif. Berkeley)
        Polynomial systems and Semidefinite Programming

  • Carlos Oliveira (UF)
        Efficient Algorithms for Optimization of Multicast Networks on the Internet

  • Dominique Orban (Ecole Polytechnique de Montreal)
        An Interior-point L1-penalty Method for Nonlinear Optimization

  • Roman Polyak (George Mason Univ.)
        Lagrangian Transformation in Convex Optimization

  • Liqun Qi (Polytechnic Univ., Hong Kong)
        Semismooth Newton Methods for Shape-Preserving Interpolation, Option Price and Semi-Infinite Programs

  • Ni Qin (Nanjing Univ.)
        A New Method of Moving Asymptotes with Trust Region Technique for General Nonlinear Programming

  • M.G.C. Resende (Lucent)
        A Hybrid Multistart Heuristic for the Uncapacitated Facility Location Problem

  • Mario Szegedy (Rutgers Univ.)
        Local Optimization on Graphs: Deterministic, Randomized and Quantum

  • Nang Keung Sze (UCLA)
        Multilevel Algorithm for VLSI Circuit Placement

  • Paul Tseng (Univ. Washington)
        On SDP-based approximation algorithms for minimizing a nonconvex quadratic function subject to ellipsoid constraints

  • Philippe Toint (Univ. Namur, Belgium)
        Trust-regions, filter and non-monotonicity in nonlinear optimization

  • Henry Wolkowicz (Waterloo, Canada)
        Robust Algorithms for Large Sparse Semidefinite Programming (SDP)

  • Yinyu Ye (Stanford Univ.)
        Solving Very Large Scale SDPs that Arise from Ad Hoc Wireless
       Sensor Network Localization and Other Euclidean Geometry Problems

  • Ya-xiang Yuan (Acad. Sciences, Beijing)
        A Subspace Trust Region Algorithm

  • Jiawei Zhang (Stanford Univ.)
        Approximation Algorithms for Discrete Facility Location Problems

  • Yin Zhang (Rice Univ.)
        Optimization in Action: Helping Treat Cancers

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    Please direct comments on the website to: Dr. Soonchul Park