Summary
Many large nonlinear optimization problems are based upon discretizations of underlying continuous functions. Optimization-based multigrid methods are designed to solve such discretized problems efficiently by taking explicit advantage of the family of discretizations. The methods are generalizations of more traditional multigrid methods for solving partial differential equations. The goal of this paper is to clarify the factors that affect the performance of an optimization-based multigrid method. There are five main factors involved: (1) global convergence, (2) local convergence, (3) role of the underlying optimization method, (4) role of the multigrid recursion, and (5) properties of the optimization model. We discuss all five of these issues, and illustrate our analysis with computational examples. Optimization-based multigrid methods are an intermediate tool between general-purpose optimization software and customized software. Because discretized optimization problems arise in so many practical settings we think that they could become a valuable tool for engineering design.
This research was supported by the National Aeronautics and Space Administration under NASA Grant NCC-1-02029, and by the National Science Foundation under grant DMS-0215444.
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References
Achi Brandt. Multi-level adaptive solutions to boundary-value problems. Mathematics of Computation, 31:333–390, 1977.
William L. Briggs. A Multigrid Tutorial. Society for Industrial and Applied Mathematics, 1987.
Dan Feng and Thomas H. Pulliam. Aerodynamic design optimization via reduced Hessian SQP with solution refining. Technical Report 95-24, Research Institute for Advanced Computer Science (RIACS), NASA Ames Research Center, 1995.
Philip E. Gill, Walter Murray, and Margaret H. Wright. Practical Optimization. Academic Press, London, 1981.
W. Hackbusch. Multi-grid Methods and Applications, volume 4 of Springer series in Computational Mathematics. Springer-Verlag, Berlin, 1985.
Tamara G. Kolda, Robert Michael Lewis, and Virginia Torczon. Optimization by direct search: New perspectives on some classical and modern methods. SIAM Review, 45(3):385–482, 2003.
G. Kuruvila, S. Ta’asan, and M. D. Salas. Airfoil design and optimization by the one-shot method. AIAA paper 95-0478, 1995.
Robert Michael Lewis and Stephen G. Nash. Practical aspects of multiscale optimization methods for vlsicad. In Jason Cong and Joseph R. Shinnerl, editors, Multiscale Optimization and VLSI/CAD, pages 265–291. Kluwer Academic Publishers, Boston, 2002.
Robert Michael Lewis and Stephen G. Nash. Model problems for the multigrid optimization of systems governed by differential equations. SIAM Journal on Scientific Computing, 2005. to appear.
Stephen F. McCormick. Multilevel Adaptive Methods for Partial Differential Equations. Society for Industrial and Applied Mathematics, 1989.
Stephen G. Nash. A multigrid approach to discretized optimization problems. Journal of Computational and Applied Mathematics, 14:99–116, 2000.
Stephen G. Nash and Jorge Nocedal. A numerical study of the limited memory BFGS method and the truncated-Newton method for large scale optimization. SIAM Journal on Optimization, 1:358–372, 1991.
Stephen G. Nash and Ariela Sofer. Linear and Nonlinear Programming. McGraw-Hill, New York, 1996.
A. Sartenaer S. Gratton and Ph.L. Toint. Recursive trust-region methods for multilevel nonlinear optimization (part I): global convergence and complexity. Technical Report 04/06, CERFACS, av. G. Coriolis, Toulouse, France, 2004.
Shlomo Ta’asan. “One Shot” methods for optimal control of distributed parameter systems I: Finite dimensional control. Technical Report 91-2, Institute for Computer Applications in Science and Engineering, January 1991.
Xue-Cheng Tai and Paul Tseng. Convergence rate analysis of an asynchronous space decomposition method for convex optimization. Technical report, Department of Mathematics, University of Washington, Seattle WA, 1998.
Jianping Zhu and Yung Ming Chen. Parameter estimation for multiphase reservoir models on hypercubes. Impact of Computing in Science and Engineering, 4:97–123, 1992.
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Lewis, R.M., Nash, S.G. (2006). Factors Affecting the Performance of Optimization-based Multigrid Methods. In: Hager, W.W., Huang, SJ., Pardalos, P.M., Prokopyev, O.A. (eds) Multiscale Optimization Methods and Applications. Nonconvex Optimization and Its Applications, vol 82. Springer, Boston, MA. https://doi.org/10.1007/0-387-29550-X_6
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DOI: https://doi.org/10.1007/0-387-29550-X_6
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