A new decomposition is derived for any complex square matrix. This decomposition is based on the canonical angles between the column space of this matrix and the column space of its conjugate transpose. Some applications of this factorization are given; in particular some matrix partial orderings and the relationship between the canonical angles and various classes of matrices are studied.

A partial fraction decomposition of the Fermi function resulting in a finite sum over simple poles is proposed. This allows for efficient calculations involving the Fermi function in various contexts of electronic structure or electron transport theories. The proposed decomposition converges in a well-defined region faster than exponential and is thus superior to the standard Matsubara expansion.

This work presents some space decomposition algorithms for a convex minimization problem. The algorithms has linear rate of convergence and the rate of convergence depends only on four constants. The space decomposition could be a multigrid or domain decomposition method. We explain the detailed procedure to implement our algorithms for a two-level overlapping domain decomposition method and es...

This paper proposes a decomposition approach for uncertainty analysis of systems governed by partial differential equations (PDEs). The system is split into local components using domain decomposition. Our domain-decomposed uncertainty quantification (DDUQ) approach performs uncertainty analysis independently on each local component in an “offline” phase, and then assembles global uncertainty a...

⎯ The decomposition of volatile organic compounds (VOCs) was investigated using a flow-type plasma-driven catalysis (PDC) system and a cycled system. In the flow-type PDC reactor a trade-off relation was observed between the formation of nitrogen oxides and the decomposition of VOCs. Complete decomposition of VOC to CO2 was achieved with the cycled system without forming CO, aerosol, and any ni...

In this paper, we present a fast implementation of the Singular Value Thresholding (SVT) algorithm for matrix completion. A rank-revealing randomized singular value decomposition (RSVD) algorithm is used to adaptively carry out partial singular value decomposition (SVD) to fast approximate the SVT operator given a desired, fixed precision. We extend the RSVD algorithm to a recycling rank reveal...