Multisplitting For Linear, Least Squares and Nonlinear Problems.

                       Rosemary Renaut
 In earlier work, presented at the 1994 Iterative Methods
 meeting, a multisplitting (MS) method of block relaxation type
 was utilised for the solution of the least squares problem,
 and nonlinear unconstrained problems. This talk will focus
 on recent developments of the general approach and represents
 joint work both with Andreas Frommer, University of Wupertal for 
 the linear problems and with
 Hans Mittelmann, Arizona State University for the nonlinear problems.
 The least squares problem can be solved using MS at the domain level.
 This amounts to a column partitioning of the underlying system matrix.
 Equivalently, it can be shown that the MS solution actually finds
 the solution of the normal equations using MS on the system matrix
 defined by the normal equations. Hence, this corresponds to the solution
 of a system of equations with SPD system matrix. According to the
 standard theory for MS methods this iteration will converge 
 if and only if the underlying split is P-regular. The theory   
 for convergence of the least squares problem shows, however, that convergence 
 is in this case guaranteed. Hence the splitting must be P-regular. This leads
 us to suppose that the MS of any SPD linear system is necessarily
 P-regular, and provides a convergent iteration. In this talk
 I will indicate the convergence theory that leads to this conclusion
 and investigate how we might seek an alternative proof using the 
 usual techniques from linear algebra.
 Block-Jacobi methods are practical for parallel implementations but
 the rate of convergence is limited. To attempt to remedy this 
 inherent problem MS can be reformulated as a two-phase process. 
 Two-stage methods, in which a splitting is applied to the already
 split method have already shown to be advantageous in some cases, see 
 Szyld et al. Here a different strategy, which originates in the 
 Parallel Variable Distribution idea of Mangasarian et al, is suggested. 
 At each iterative step there is some flexibility in the manner in which
 the updated global solution is obtained. Standard MS uses a
 convex combination of the recent local solutions with a fixed choice
 of weights. Instead the minimal solution can be found by 
 carrying out a local optimization with respect to these weights.
 Typically the number of subproblems indicated by the MS is 
 substantially less than the original size of the problem. Therefore
 this optimization can be carried out locally to one processor 
 with minimal extra cost. For the linear problems this 
 minimization can actually be formulated as a least squares problem
 with a system matrix that only needs to be factorized at the 
 very first iteration. Results demonstrating the increased efficiency will
 be presented.
 These ideas have also been applied for the solution of large-scale
 unconstrained convex minimization problems. Again,
 in order to not have the convergence order limited
 to linear we have started to explore an alternative approach. This
 combines an exact augmented Lagrangian with a truncated Newton
 iteration. Preliminary results will be presented.
 Rosemary A. Renaut, 
 Department of Mathematics
 Arizona State University,