10 research outputs found
Nearly Tight Bounds for Sandpile Transience on the Grid
We use techniques from the theory of electrical networks to give nearly tight
bounds for the transience class of the Abelian sandpile model on the
two-dimensional grid up to polylogarithmic factors. The Abelian sandpile model
is a discrete process on graphs that is intimately related to the phenomenon of
self-organized criticality. In this process, vertices receive grains of sand,
and once the number of grains exceeds their degree, they topple by sending
grains to their neighbors. The transience class of a model is the maximum
number of grains that can be added to the system before it necessarily reaches
its steady-state behavior or, equivalently, a recurrent state. Through a more
refined and global analysis of electrical potentials and random walks, we give
an upper bound and an lower bound for the
transience class of the grid. Our methods naturally extend to
-sized -dimensional grids to give upper
bounds and lower bounds.Comment: 36 pages, 4 figure
Analysis and Maintenance of Graph Laplacians via Random Walks
Graph Laplacians arise in many natural and artificial contexts. They are linear systems associated with undirected graphs. They are equivalent to electric flows which is a fundamental physical concept by itself and is closely related to other physical models, e.g., the Abelian sandpile model. Many real-world problems can be modeled and solved via Laplacian linear systems, including semi-supervised learning, graph clustering, and graph embedding.
More recently, better theoretical understandings of Laplacians led to dramatic improvements across graph algorithms. The applications include dynamic connectivity problem, graph sketching, and most recently combinatorial optimization. For example, a sequence of papers improved the runtime for maximum flow and minimum cost flow in many different settings.
In this thesis, we present works that the analyze, maintain and utilize Laplacian linear systems in both static and dynamic settings by representing them as random walks. This combinatorial representation leads to better bounds for Abelian sandpile model on grids, the first data structures for dynamic vertex sparsifiers and dynamic Laplacian solvers, and network flows on planar as well as general graphs.Ph.D
Scaling Limits in Models of Statistical Mechanics
The workshop brought together researchers interested in spatial random processes and their connection to statistical mechanics. The principal subjects of interest were scaling limits and, in general, limit laws for various two-dimensional critical models, percolation, random walks in random environment, polymer models, random fields and hierarchical diffusions. The workshop fostered interactions between groups of researchers in these areas and led to interesting and fruitful exchanges of ideas
Graph Sparsification, Spectral Sketches, and Faster Resistance Computation, via Short Cycle Decompositions
We develop a framework for graph sparsification and sketching, based on a new
tool, short cycle decomposition -- a decomposition of an unweighted graph into
an edge-disjoint collection of short cycles, plus few extra edges. A simple
observation gives that every graph G on n vertices with m edges can be
decomposed in time into cycles of length at most , and at most
extra edges. We give an time algorithm for constructing a
short cycle decomposition, with cycles of length , and
extra edges. These decompositions enable us to make progress on several open
questions:
* We give an algorithm to find -approximations to effective
resistances of all edges in time , improving over
the previous best of .
This gives an algorithm to approximate the determinant of a Laplacian up to
in time.
* We show existence and efficient algorithms for constructing graphical
spectral sketches -- a distribution over sparse graphs H such that for a fixed
vector , we have w.h.p. and
. This implies the existence of
resistance-sparsifiers with about edges that preserve the
effective resistances between every pair of vertices up to
* By combining short cycle decompositions with known tools in graph
sparsification, we show the existence of nearly-linear sized degree-preserving
spectral sparsifiers, as well as significantly sparser approximations of
directed graphs. The latter is critical to recent breakthroughs on faster
algorithms for solving linear systems in directed Laplacians.
Improved algorithms for constructing short cycle decompositions will lead to
improvements for each of the above results.Comment: 80 page