3 research outputs found
A sparse decomposition of low rank symmetric positive semi-definite matrices
Suppose that is symmetric positive
semidefinite with rank . Our goal is to decompose into
rank-one matrices where the modes
are required to be as sparse as possible. In contrast to eigen decomposition,
these sparse modes are not required to be orthogonal. Such a problem arises in
random field parametrization where is the covariance function and is
intractable to solve in general. In this paper, we partition the indices from 1
to into several patches and propose to quantify the sparseness of a vector
by the number of patches on which it is nonzero, which is called patch-wise
sparseness. Our aim is to find the decomposition which minimizes the total
patch-wise sparseness of the decomposed modes. We propose a
domain-decomposition type method, called intrinsic sparse mode decomposition
(ISMD), which follows the "local-modes-construction + patching-up" procedure.
The key step in the ISMD is to construct local pieces of the intrinsic sparse
modes by a joint diagonalization problem. Thereafter a pivoted Cholesky
decomposition is utilized to glue these local pieces together. Optimal sparse
decomposition, consistency with different domain decomposition and robustness
to small perturbation are proved under the so called regular-sparse assumption
(see Definition 1.2). We provide simulation results to show the efficiency and
robustness of the ISMD. We also compare the ISMD to other existing methods,
e.g., eigen decomposition, pivoted Cholesky decomposition and convex relaxation
of sparse principal component analysis [25] and [40]
Sparse operator compression of higher-order elliptic operators with rough coefficients
We introduce the sparse operator compression to compress a self-adjoint
higher-order elliptic operator with rough coefficients and various boundary
conditions. The operator compression is achieved by using localized basis
functions, which are energy-minimizing functions on local patches. On a regular
mesh with mesh size , the localized basis functions have supports of
diameter and give optimal compression rate of the solution
operator. We show that by using localized basis functions with supports of
diameter , our method achieves the optimal compression rate of
the solution operator. From the perspective of the generalized finite element
method to solve elliptic equations, the localized basis functions have the
optimal convergence rate for a th-order elliptic problem in the
energy norm. From the perspective of the sparse PCA, our results show that a
large set of Mat\'{e}rn covariance functions can be approximated by a rank-
operator with a localized basis and with the optimal accuracy