42 research outputs found
PyDEC: Software and Algorithms for Discretization of Exterior Calculus
This paper describes the algorithms, features and implementation of PyDEC, a
Python library for computations related to the discretization of exterior
calculus. PyDEC facilitates inquiry into both physical problems on manifolds as
well as purely topological problems on abstract complexes. We describe
efficient algorithms for constructing the operators and objects that arise in
discrete exterior calculus, lowest order finite element exterior calculus and
in related topological problems. Our algorithms are formulated in terms of
high-level matrix operations which extend to arbitrary dimension. As a result,
our implementations map well to the facilities of numerical libraries such as
NumPy and SciPy. The availability of such libraries makes Python suitable for
prototyping numerical methods. We demonstrate how PyDEC is used to solve
physical and topological problems through several concise examples.Comment: Revised as per referee reports. Added information on scalability,
removed redundant text, emphasized the role of matrix based algorithms,
shortened length of pape
Numerical Experiments for Darcy Flow on a Surface Using Mixed Exterior Calculus Methods
There are very few results on mixed finite element methods on surfaces. A
theory for the study of such methods was given recently by Holst and Stern,
using a variational crimes framework in the context of finite element exterior
calculus. However, we are not aware of any numerical experiments where mixed
finite elements derived from discretizations of exterior calculus are used for
a surface domain. This short note shows results of our preliminary experiments
using mixed methods for Darcy flow (hence scalar Poisson's equation in mixed
form) on surfaces. We demonstrate two numerical methods. One is derived from
the primal-dual Discrete Exterior Calculus and the other from lowest order
finite element exterior calculus. The programming was done in the language
Python, using the PyDEC package which makes the code very short and easy to
read. The qualitative convergence studies seem to be promising.Comment: 14 pages, 11 figure
Equivalence Theorems in Numerical Analysis : Integration, Differentiation and Interpolation
We show that if a numerical method is posed as a sequence of operators acting
on data and depending on a parameter, typically a measure of the size of
discretization, then consistency, convergence and stability can be related by a
Lax-Richtmyer type equivalence theorem -- a consistent method is convergent if
and only if it is stable. We define consistency as convergence on a dense
subspace and stability as discrete well-posedness. In some applications
convergence is harder to prove than consistency or stability since convergence
requires knowledge of the solution. An equivalence theorem can be useful in
such settings. We give concrete instances of equivalence theorems for
polynomial interpolation, numerical differentiation, numerical integration
using quadrature rules and Monte Carlo integration.Comment: 18 page
Least Squares Ranking on Graphs
Given a set of alternatives to be ranked, and some pairwise comparison data,
ranking is a least squares computation on a graph. The vertices are the
alternatives, and the edge values comprise the comparison data. The basic idea
is very simple and old: come up with values on vertices such that their
differences match the given edge data. Since an exact match will usually be
impossible, one settles for matching in a least squares sense. This formulation
was first described by Leake in 1976 for rankingfootball teams and appears as
an example in Professor Gilbert Strang's classic linear algebra textbook. If
one is willing to look into the residual a little further, then the problem
really comes alive, as shown effectively by the remarkable recent paper of
Jiang et al. With or without this twist, the humble least squares problem on
graphs has far-reaching connections with many current areas ofresearch. These
connections are to theoretical computer science (spectral graph theory, and
multilevel methods for graph Laplacian systems); numerical analysis (algebraic
multigrid, and finite element exterior calculus); other mathematics (Hodge
decomposition, and random clique complexes); and applications (arbitrage, and
ranking of sports teams). Not all of these connections are explored in this
paper, but many are. The underlying ideas are easy to explain, requiring only
the four fundamental subspaces from elementary linear algebra. One of our aims
is to explain these basic ideas and connections, to get researchers in many
fields interested in this topic. Another aim is to use our numerical
experiments for guidance on selecting methods and exposing the need for further
development.Comment: Added missing references, comparison of linear solvers overhauled,
conclusion section added, some new figures adde