Since 1980, the Adomian decomposition method (ADM) has been extensively used as a simple powerful tool that applies directly to solve different kinds of nonlinear equations including functional, differential, integro-differential and algebraic equations. However, for differential-algebraic equations (DAEs) the ADM is applied only in four earlier works. There, the DAEs are first pre-processed by...

Semidiscrete decomposition (SDD) is usually presented as a storage-eecient analogue of singular value decomposition. We show, however, that SDD actually works in a completely diierent way, and is best thought of as a bump-hunting technique; it is extremely eeective at nding outlier clusters in datasets. We suggest that SDD's success in text retrieval applications such as latent semantic indexin...

Many researchers have investigated the process of decomposing transactions into smaller pieces to increase concurrency. The research typically focuses on implementing a decomposition supplied by the database application developer, with relatively little attention to what constitutes a desirable decomposition and how the developer should obtain such a decomposition. In this paper, we argue that ...

Understanding a large software product is known as a very challenging task. One way that facilitate program understanding is program slicing. Slicing is an important decomposition technique for program understanding[3]. However, traditional slicing methods tend to produce too large slices for human inspection as modern programs are often huge and static program analyses are hard to be precise e...

Optimized Schwarz methods are domain decomposition methods in which a largescale PDE problem is solved by subdividing it into smaller subdomain problems, solving the subproblems in parallel, and iterating until one obtains a global solution that is consistent across subdomain boundaries. Fast convergence can be obtained if Robin conditions are used along subdomain boundaries, provided that the ...