177 research outputs found
Symmetry-based matrix factorization
AbstractWe present a method for factoring a given matrix M into a short product of sparse matrices, provided that M has a suitable “symmetry”. This sparse factorization represents a fast algorithm for the matrix–vector multiplication with M. The factorization method consists of two essential steps. First, a combinatorial search is used to compute a suitable symmetry of M in the form of a pair of group representations. Second, the group representations are decomposed stepwise, which yields factorized decomposition matrices and determines a sparse factorization of M. The focus of this article is the first step, finding the symmetries. All algorithms described have been implemented in the library AREP. We present examples for automatically generated sparse factorizations—and hence fast algorithms—for a class of matrices corresponding to digital signal processing transforms including the discrete Fourier, cosine, Hartley, and Haar transforms
Causal Fourier Analysis on Directed Acyclic Graphs and Posets
We present a novel form of Fourier analysis, and associated signal processing
concepts, for signals (or data) indexed by edge-weighted directed acyclic
graphs (DAGs). This means that our Fourier basis yields an eigendecomposition
of a suitable notion of shift and convolution operators that we define. DAGs
are the common model to capture causal relationships between data values and in
this case our proposed Fourier analysis relates data with its causes under a
linearity assumption that we define. The definition of the Fourier transform
requires the transitive closure of the weighted DAG for which several forms are
possible depending on the interpretation of the edge weights. Examples include
level of influence, distance, or pollution distribution. Our framework is
different from prior GSP: it is specific to DAGs and leverages, and extends,
the classical theory of Moebius inversion from combinatorics. For a
prototypical application we consider DAGs modeling dynamic networks in which
edges change over time. Specifically, we model the spread of an infection on
such a DAG obtained from real-world contact tracing data and learn the
infection signal from samples assuming sparsity in the Fourier domain.Comment: 13 pages, 11 figure
Fast M\"obius and Zeta Transforms
M\"obius inversion of functions on partially ordered sets (posets)
is a classical tool in combinatorics. For finite posets it
consists of two, mutually inverse, linear transformations called zeta and
M\"obius transform, respectively. In this paper we provide novel fast
algorithms for both that require time and space, where and is the width (length of longest antichain) of
, compared to for a direct computation. Our approach
assumes that is given as directed acyclic graph (DAG)
. The algorithms are then constructed using a chain
decomposition for a one time cost of , where is the number of
edges in the DAG's transitive reduction. We show benchmarks with
implementations of all algorithms including parallelized versions. The results
show that our algorithms enable M\"obius inversion on posets with millions of
nodes in seconds if the defining DAGs are sufficiently sparse.Comment: 16 pages, 7 figures, submitted for revie
Distributed Optimization With Local Domains: Applications in MPC and Network Flows
In this paper we consider a network with nodes, where each node has
exclusive access to a local cost function. Our contribution is a
communication-efficient distributed algorithm that finds a vector
minimizing the sum of all the functions. We make the additional assumption that
the functions have intersecting local domains, i.e., each function depends only
on some components of the variable. Consequently, each node is interested in
knowing only some components of , not the entire vector. This allows
for improvement in communication-efficiency. We apply our algorithm to model
predictive control (MPC) and to network flow problems and show, through
experiments on large networks, that our proposed algorithm requires less
communications to converge than prior algorithms.Comment: Submitted to IEEE Trans. Aut. Contro
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