170,132 research outputs found
A parallel Heap-Cell Method for Eikonal equations
Numerous applications of Eikonal equations prompted the development of many
efficient numerical algorithms. The Heap-Cell Method (HCM) is a recent serial
two-scale technique that has been shown to have advantages over other serial
state-of-the-art solvers for a wide range of problems. This paper presents a
parallelization of HCM for a shared memory architecture. The numerical
experiments in show that the parallel HCM exhibits good algorithmic
behavior and scales well, resulting in a very fast and practical solver.
We further explore the influence on performance and scaling of data
precision, early termination criteria, and the hardware architecture. A shorter
version of this manuscript (omitting these more detailed tests) has been
submitted to SIAM Journal on Scientific Computing in 2012.Comment: (a minor update to address the reviewers' comments) 31 pages; 15
figures; this is an expanded version of a paper accepted by SIAM Journal on
Scientific Computin
A Decomposition Theorem for Maximum Weight Bipartite Matchings
Let G be a bipartite graph with positive integer weights on the edges and
without isolated nodes. Let n, N and W be the node count, the largest edge
weight and the total weight of G. Let k(x,y) be log(x)/log(x^2/y). We present a
new decomposition theorem for maximum weight bipartite matchings and use it to
design an O(sqrt(n)W/k(n,W/N))-time algorithm for computing a maximum weight
matching of G. This algorithm bridges a long-standing gap between the best
known time complexity of computing a maximum weight matching and that of
computing a maximum cardinality matching. Given G and a maximum weight matching
of G, we can further compute the weight of a maximum weight matching of G-{u}
for all nodes u in O(W) time.Comment: The journal version will appear in SIAM Journal on Computing. The
conference version appeared in ESA 199
On Approximating Restricted Cycle Covers
A cycle cover of a graph is a set of cycles such that every vertex is part of
exactly one cycle. An L-cycle cover is a cycle cover in which the length of
every cycle is in the set L. The weight of a cycle cover of an edge-weighted
graph is the sum of the weights of its edges.
We come close to settling the complexity and approximability of computing
L-cycle covers. On the one hand, we show that for almost all L, computing
L-cycle covers of maximum weight in directed and undirected graphs is APX-hard
and NP-hard. Most of our hardness results hold even if the edge weights are
restricted to zero and one.
On the other hand, we show that the problem of computing L-cycle covers of
maximum weight can be approximated within a factor of 2 for undirected graphs
and within a factor of 8/3 in the case of directed graphs. This holds for
arbitrary sets L.Comment: To appear in SIAM Journal on Computing. Minor change
Quantum Algorithm for Triangle Finding in Sparse Graphs
This paper presents a quantum algorithm for triangle finding over sparse
graphs that improves over the previous best quantum algorithm for this task by
Buhrman et al. [SIAM Journal on Computing, 2005]. Our algorithm is based on the
recent -query algorithm given by Le Gall [FOCS 2014] for
triangle finding over dense graphs (here denotes the number of vertices in
the graph). We show in particular that triangle finding can be solved with
queries for some constant whenever the graph
has at most edges for some constant .Comment: 13 page
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