George Mason University
CSI/Statistics Colloquium Series
Seminar Announcement
Nonlinear Rescaling in Constrained Optimization
Roman A. Polyak
George Mason University
ABSTRACT
The nonlinear rescaling principle (NRP) consists of transforming the
objective function and/or the constraints of a given constrained
optimization problem into an equivalent problem and using the
classical Lagrangian of the equivalent problem for both theoretical
analysis and numerical methods. The nonlinear transformation is
parameterized by a positive scalar parameter or by a vector of scaling
parameters one for each constraint. The nonlinear rescaling (NR)
methods consists of sequential unconstrained minimization of the
classical Lagrangian for the equivalent problem, followed by an
explicit formula for the Lagrange multipliers update.
We will focus on the primal-dual aspects of NR methods. Convergence,
rate of convergence, complexity issues as well as applications and
numerical results will be discussed.
Friday, September 10, 1999
George W. Johnson Center, Assembly Room C
Seminar at 10:45 a.m.
Refreshments at 10:30 a.m.
For the 1999 Fall Seminar Schedule, go to
http:www.science.gmu.edu/statseminars