4,415 research outputs found
Hierarchical interpolative factorization for elliptic operators: differential equations
This paper introduces the hierarchical interpolative factorization for
elliptic partial differential equations (HIF-DE) in two (2D) and three
dimensions (3D). This factorization takes the form of an approximate
generalized LU/LDL decomposition that facilitates the efficient inversion of
the discretized operator. HIF-DE is based on the multifrontal method but uses
skeletonization on the separator fronts to sparsify the dense frontal matrices
and thus reduce the cost. We conjecture that this strategy yields linear
complexity in 2D and quasilinear complexity in 3D. Estimated linear complexity
in 3D can be achieved by skeletonizing the compressed fronts themselves, which
amounts geometrically to a recursive dimensional reduction scheme. Numerical
experiments support our claims and further demonstrate the performance of our
algorithm as a fast direct solver and preconditioner. MATLAB codes are freely
available.Comment: 37 pages, 13 figures, 12 tables; to appear, Comm. Pure Appl. Math.
arXiv admin note: substantial text overlap with arXiv:1307.266
Hierarchical interpolative factorization for elliptic operators: integral equations
This paper introduces the hierarchical interpolative factorization for
integral equations (HIF-IE) associated with elliptic problems in two and three
dimensions. This factorization takes the form of an approximate generalized LU
decomposition that permits the efficient application of the discretized
operator and its inverse. HIF-IE is based on the recursive skeletonization
algorithm but incorporates a novel combination of two key features: (1) a
matrix factorization framework for sparsifying structured dense matrices and
(2) a recursive dimensional reduction strategy to decrease the cost. Thus,
higher-dimensional problems are effectively mapped to one dimension, and we
conjecture that constructing, applying, and inverting the factorization all
have linear or quasilinear complexity. Numerical experiments support this claim
and further demonstrate the performance of our algorithm as a generalized fast
multipole method, direct solver, and preconditioner. HIF-IE is compatible with
geometric adaptivity and can handle both boundary and volume problems. MATLAB
codes are freely available.Comment: 39 pages, 14 figures, 13 tables; to appear, Comm. Pure Appl. Mat
A fast semi-direct least squares algorithm for hierarchically block separable matrices
We present a fast algorithm for linear least squares problems governed by
hierarchically block separable (HBS) matrices. Such matrices are generally
dense but data-sparse and can describe many important operators including those
derived from asymptotically smooth radial kernels that are not too oscillatory.
The algorithm is based on a recursive skeletonization procedure that exposes
this sparsity and solves the dense least squares problem as a larger,
equality-constrained, sparse one. It relies on a sparse QR factorization
coupled with iterative weighted least squares methods. In essence, our scheme
consists of a direct component, comprised of matrix compression and
factorization, followed by an iterative component to enforce certain equality
constraints. At most two iterations are typically required for problems that
are not too ill-conditioned. For an HBS matrix with
having bounded off-diagonal block rank, the algorithm has optimal complexity. If the rank increases with the spatial dimension as is
common for operators that are singular at the origin, then this becomes
in 1D, in 2D, and
in 3D. We illustrate the performance of the method on
both over- and underdetermined systems in a variety of settings, with an
emphasis on radial basis function approximation and efficient updating and
downdating.Comment: 24 pages, 8 figures, 6 tables; to appear in SIAM J. Matrix Anal. App
Orthogonal Trace-Sum Maximization: Applications, Local Algorithms, and Global Optimality
This paper studies a problem of maximizing the sum of traces of matrix
quadratic forms on a product of Stiefel manifolds. This orthogonal trace-sum
maximization (OTSM) problem generalizes many interesting problems such as
generalized canonical correlation analysis (CCA), Procrustes analysis, and
cryo-electron microscopy of the Nobel prize fame. For these applications
finding global solutions is highly desirable but has been out of reach for a
long time. For example, generalizations of CCA do not possess obvious global
solutions unlike their classical counterpart to which a global solution is
readily obtained through singular value decomposition; it is also not clear how
to test global optimality. We provide a simple method to certify global
optimality of a given local solution. This method only requires testing the
sign of the smallest eigenvalue of a symmetric matrix, and does not rely on a
particular algorithm as long as it converges to a stationary point. Our
certificate result relies on a semidefinite programming (SDP) relaxation of
OTSM, but avoids solving an SDP of lifted dimensions. Surprisingly, a popular
algorithm for generalized CCA and Procrustes analysis may generate oscillating
iterates. We propose a simple modification of this standard algorithm and prove
that it reliably converges. Our notion of convergence is stronger than
conventional objective value convergence or subsequence convergence.The
convergence result utilizes the Kurdyka-Lojasiewicz property of the problem.Comment: 22 pages, 1 figur
Improving the Quality of the Documentation System in a Health Care Environment
An effective documentation system in a managed care organization is complicated yet important in today\u27s business environment. Being too busy taking care of patients, health care professionals often fall behind in paperwork and quality care provided. An action research model developed by Cummings and Worley (2001) was utilized to assess the organization\u27s status. Data collection methods included survey questionnaires, group interviews, and secondary data. A collaborative team approach designed to reach a consensus decision was used in the evaluation, interpretation, and validation of the information collected. Three intervention methods relating to training and education, teamwork, and staff knowledge and skills were proposed with one of these recommended for implementation. This action research will bring the organization to greater success in the health care environment
Butterfly Factorization
The paper introduces the butterfly factorization as a data-sparse
approximation for the matrices that satisfy a complementary low-rank property.
The factorization can be constructed efficiently if either fast algorithms for
applying the matrix and its adjoint are available or the entries of the matrix
can be sampled individually. For an matrix, the resulting
factorization is a product of sparse matrices, each with
non-zero entries. Hence, it can be applied rapidly in operations.
Numerical results are provided to demonstrate the effectiveness of the
butterfly factorization and its construction algorithms
Particle trajectory computer program for icing analysis of axisymmetric bodies
General aviation aircraft and helicopters exposed to an icing environment can accumulate ice resulting in a sharp increase in drag and reduction of maximum lift causing hazardous flight conditions. NASA Lewis Research Center (LeRC) is conducting a program to examine, with the aid of high-speed computer facilities, how the trajectories of particles contribute to the ice accumulation on airfoils and engine inlets. This study, as part of the NASA/LeRC research program, develops a computer program for the calculation of icing particle trajectories and impingement limits relative to axisymmetric bodies in the leeward-windward symmetry plane. The methodology employed in the current particle trajectory calculation is to integrate the governing equations of particle motion in a flow field computed by the Douglas axisymmetric potential flow program. The three-degrees-of-freedom (horizontal, vertical, and pitch) motion of the particle is considered. The particle is assumed to be acted upon by aerodynamic lift and drag forces, gravitational forces, and for nonspherical particles, aerodynamic moments. The particle momentum equation is integrated to determine the particle trajectory. Derivation of the governing equations and the method of their solution are described in Section 2.0. General features, as well as input/output instructions for the particle trajectory computer program, are described in Section 3.0. The details of the computer program are described in Section 4.0. Examples of the calculation of particle trajectories demonstrating application of the trajectory program to given axisymmetric inlet test cases are presented in Section 5.0. For the examples presented, the particles are treated as spherical water droplets. In Section 6.0, limitations of the program relative to excessive computer time and recommendations in this regard are discussed
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