Chen Greif

Saddle-point linear systems arise in many applications involving problems with constraints. When the underlying matrices are large and sparse, iterative solvers are typically used. A number of Krylov subspace solvers can be used, but they do not...

We consider nonsingular saddle-point matrices whose (1,1) block is maximally rank deficient, and show that the inverse in this case has unique mathematical properties. We then develop a class of indefinite block preconditioners that rely on...

We introduce SPMR, a new family of methods for iteratively solving saddle-point systems using a minimum or quasi-minimum residual approach. No symmetry assumptions are made. The basic mechanism underlying the method is a novel simultaneous...

Talk 1: Nilima Nigam- An overview of finite element methods à la Dominik Schoetzau Finite element methods occupy a central and ubiquitous place in much of modern engineering and scientific computing, to the point where they are now treated as 'black...