A classical problem in matrix computations is the efficient and reliable approximation of a given matrix by a matrix of lower rank. The truncated singular value decomposition (SVD) is known to provide the best such approximation for any given fixed rank. However, the SVD is also known to be very costly to compute. Among the different approaches in the literature for computing low-rank approxima...

This work tackles the evaluation of a multigrid cycling strategy using inner flexible Krylov subspace iterations. It provides a valuable improvement to the Reitzinger and Schöberl algebraic multigrid method for systems coming from edgeelement discretizations.

We present a stochastic planner based on Markov Decision Processes (MDPs) that participates to the probabilistic planning track of the 2006 International Planning Competition. The planner transforms the PPDDL problems into factored MDPs that are then solved with a structured modified value iteration algorithm based on the safest stochastic path computation from the initial states to the goal st...

Solving Partially Observable Markov Decision Processes (POMDPs) generally is computationally intractable. In this paper, we study a special POMDP class, namely informative POMDPs, where each observation provides good albeit incomplete information about world states. We propose two ways to accelerate value iteration algorithm for such POMDPs. First, dynamic programming (DP) updates can be carrie...

Preconditioning  Krylov Subspace Methods are commonly used for solving linear system  Standard implementations are communication-bound due to required SpMV and orthogonalization in every iteration  Solution: rearrange algorithms to perform s iterations at a time without communicating (s-step methods)  SpMV in each iteration is replaced with a call to the Matrix Powers Kernel, which performs...

We propose a subspace-tracking-based space-time adaptive processing technique for airborne radar applications. By applying a modified approximated power iteration subspace tracing algorithm, the principal subspace in which the clutter-plus-interference reside is estimated. Therefore, the moving targets are detected by projecting the data on the minor subspace which is orthogonal to the principa...

Parallel solution of irregular problems require solving the graph partitioning problem. The extended eigenproblem appears as the solution of some relaxed formulations of the graph partitioning problem. In this paper, a new subspace algorithm for the solving the extended eigenproblem is presented. The structure of this subspace method allows the incorporation of multigrid preconditioners. We num...

This paper introduces a fast implementation of the power iterations method for subspace tracking, based on an approximation less restrictive than the well known projection approximation. This algorithm guarantees the orthonormality of the estimated subspace weighting matrix at each iteration, and satisfies a global and exponential convergence property. Moreover, it outperforms many subspace tra...