103 research outputs found
Optimal Bounds for the -cut Problem
In the -cut problem, we want to find the smallest set of edges whose
deletion breaks a given (multi)graph into connected components. Algorithms
of Karger & Stein and Thorup showed how to find such a minimum -cut in time
approximately . The best lower bounds come from conjectures about
the solvability of the -clique problem, and show that solving -cut is
likely to require time . Recent results of Gupta, Lee & Li have
given special-purpose algorithms that solve the problem in time , and ones that have better performance for special classes of graphs
(e.g., for small integer weights).
In this work, we resolve the problem for general graphs, by showing that the
Contraction Algorithm of Karger outputs any fixed -cut of weight with probability , where
denotes the minimum -cut size. This also gives an extremal bound of
on the number of minimum -cuts and an algorithm to compute a
minimum -cut in similar runtime. Both are tight up to lower-order factors,
with the algorithmic lower bound assuming hardness of max-weight -clique.
The first main ingredient in our result is a fine-grained analysis of how the
graph shrinks -- and how the average degree evolves -- in the Karger process.
The second ingredient is an extremal bound on the number of cuts of size less
than , using the Sunflower lemma.Comment: Final version of arXiv:1911.09165 with new and more general proof
Matrix probing and its conditioning
When a matrix A with n columns is known to be well approximated by a linear
combination of basis matrices B_1,..., B_p, we can apply A to a random vector
and solve a linear system to recover this linear combination. The same
technique can be used to recover an approximation to A^-1. A basic question is
whether this linear system is invertible and well-conditioned. In this paper,
we show that if the Gram matrix of the B_j's is sufficiently well-conditioned
and each B_j has a high numerical rank, then n {proportional} p log^2 n will
ensure that the linear system is well-conditioned with high probability. Our
main application is probing linear operators with smooth pseudodifferential
symbols such as the wave equation Hessian in seismic imaging. We demonstrate
numerically that matrix probing can also produce good preconditioners for
inverting elliptic operators in variable media
Practical Minimum Cut Algorithms
The minimum cut problem for an undirected edge-weighted graph asks us to
divide its set of nodes into two blocks while minimizing the weight sum of the
cut edges. Here, we introduce a linear-time algorithm to compute near-minimum
cuts. Our algorithm is based on cluster contraction using label propagation and
Padberg and Rinaldi's contraction heuristics [SIAM Review, 1991]. We give both
sequential and shared-memory parallel implementations of our algorithm.
Extensive experiments on both real-world and generated instances show that our
algorithm finds the optimal cut on nearly all instances significantly faster
than other state-of-the-art algorithms while our error rate is lower than that
of other heuristic algorithms. In addition, our parallel algorithm shows good
scalability
Learning and Testing Variable Partitions
Let be a multivariate function from a product set to an
Abelian group . A -partition of with cost is a partition of
the set of variables into non-empty subsets such that is -close to
for some with
respect to a given error metric. We study algorithms for agnostically learning
partitions and testing -partitionability over various groups and error
metrics given query access to . In particular we show that
Given a function that has a -partition of cost , a partition
of cost can be learned in time
for any .
In contrast, for and learning a partition of cost is NP-hard.
When is real-valued and the error metric is the 2-norm, a
2-partition of cost can be learned in time
.
When is -valued and the error metric is Hamming
weight, -partitionability is testable with one-sided error and
non-adaptive queries. We also show that even
two-sided testers require queries when .
This work was motivated by reinforcement learning control tasks in which the
set of control variables can be partitioned. The partitioning reduces the task
into multiple lower-dimensional ones that are relatively easier to learn. Our
second algorithm empirically increases the scores attained over previous
heuristic partitioning methods applied in this context.Comment: Innovations in Theoretical Computer Science (ITCS) 202
Fast and Deterministic Approximations for k-Cut
In an undirected graph, a k-cut is a set of edges whose removal breaks the graph into at least k connected components. The minimum weight k-cut can be computed in n^O(k) time, but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Goldschmidt and Hochbaum, 1994]. For poly(m,n,k)-time algorithms, the best possible approximation factor is essentially 2 under the small set expansion hypothesis [Manurangsi, 2017]. Saran and Vazirani [1995] showed that a (2 - 2/k)-approximately minimum weight k-cut can be computed via O(k) minimum cuts, which implies a O~(km) randomized running time via the nearly linear time randomized min-cut algorithm of Karger [2000]. Nagamochi and Kamidoi [2007] showed that a (2 - 2/k)-approximately minimum weight k-cut can be computed deterministically in O(mn + n^2 log n) time. These results prompt two basic questions. The first concerns the role of randomization. Is there a deterministic algorithm for 2-approximate k-cuts matching the randomized running time of O~(km)? The second question qualitatively compares minimum cut to 2-approximate minimum k-cut. Can 2-approximate k-cuts be computed as fast as the minimum cut - in O~(m) randomized time?
We give a deterministic approximation algorithm that computes (2 + eps)-minimum k-cuts in O(m log^3 n / eps^2) time, via a (1 + eps)-approximation for an LP relaxation of k-cut
Computing the Girth of a Planar Graph in Linear Time
The girth of a graph is the minimum weight of all simple cycles of the graph.
We study the problem of determining the girth of an n-node unweighted
undirected planar graph. The first non-trivial algorithm for the problem, given
by Djidjev, runs in O(n^{5/4} log n) time. Chalermsook, Fakcharoenphol, and
Nanongkai reduced the running time to O(n log^2 n). Weimann and Yuster further
reduced the running time to O(n log n). In this paper, we solve the problem in
O(n) time.Comment: 20 pages, 7 figures, accepted to SIAM Journal on Computin
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