 NSFPAR ID:
 10253471
 Date Published:
 Journal Name:
 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020, Durham, NC, USA, November 1619, 2020
 Page Range / eLocation ID:
 919 to 930
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
More Like this

We present a weighted approach to compute a maximum cardinality matching in an arbitrary bipartite graph. Our main result is a new algorithm that takes as input a weighted bipartite graph G(A cup B,E) with edge weights of 0 or 1. Let w <= n be an upper bound on the weight of any matching in G. Consider the subgraph induced by all the edges of G with a weight 0. Suppose every connected component in this subgraph has O(r) vertices and O(mr/n) edges. We present an algorithm to compute a maximum cardinality matching in G in O~(m(sqrt{w} + sqrt{r} + wr/n)) time. When all the edge weights are 1 (symmetrically when all weights are 0), our algorithm will be identical to the wellknown HopcroftKarp (HK) algorithm, which runs in O(m sqrt{n}) time. However, if we can carefully assign weights of 0 and 1 on its edges such that both w and r are sublinear in n and wr=O(n^{gamma}) for gamma < 3/2, then we can compute maximum cardinality matching in G in o(m sqrt{n}) time. Using our algorithm, we obtain a new O~(n^{4/3}/epsilon^4) time algorithm to compute an epsilonapproximate bottleneck matching of A,B subsetR^2 and an 1/(epsilon^{O(d)}}n^{1+(d1)/(2d1)}) poly log n time algorithm for computing epsilonapproximate bottleneck matching in ddimensions. All previous algorithms take Omega(n^{3/2}) time. Given any graph G(A cup B,E) that has an easily computable balanced vertex separator for every subgraph G'(V',E') of size V'^{delta}, for delta in [1/2,1), we can apply our algorithm to compute a maximum matching in O~(mn^{delta/1+delta}) time improving upon the O(m sqrt{n}) time taken by the HKAlgorithm.more » « less

Abstract In a Merlin–Arthur proof system, the proof verifier (Arthur) accepts valid proofs (from Merlin) with probability 1, and rejects invalid proofs with probability arbitrarily close to 1. The running time of such a system is defined to be the length of Merlin’s proof plus the running time of Arthur. We provide new Merlin–Arthur proof systems for some key problems in finegrained complexity. In several cases our proof systems have optimal running time. Our main results include:
Certifying that a list of
n integers has no 3SUM solution can be done in Merlin–Arthur time . Previously, Carmosino et al. [ITCS 2016] showed that the problem has a nondeterministic algorithm running in$$\tilde{O}(n)$$ $\stackrel{~}{O}\left(n\right)$ time (that is, there is a proof system with proofs of length$$\tilde{O}(n^{1.5})$$ $\stackrel{~}{O}\left({n}^{1.5}\right)$ and a deterministic verifier running in$$\tilde{O}(n^{1.5})$$ $\stackrel{~}{O}\left({n}^{1.5}\right)$ time).$$\tilde{O}(n^{1.5})$$ $\stackrel{~}{O}\left({n}^{1.5}\right)$Counting the number of
k cliques with total edge weight equal to zero in ann node graph can be done in Merlin–Arthur time (where$${\tilde{O}}(n^{\lceil k/2\rceil })$$ $\stackrel{~}{O}\left({n}^{\lceil k/2\rceil}\right)$ ). For odd$$k\ge 3$$ $k\ge 3$k , this bound can be further improved for sparse graphs: for example, counting the number of zeroweight triangles in anm edge graph can be done in Merlin–Arthur time . Previous Merlin–Arthur protocols by Williams [CCC’16] and Björklund and Kaski [PODC’16] could only count$${\tilde{O}}(m)$$ $\stackrel{~}{O}\left(m\right)$k cliques in unweighted graphs, and had worse running times for smallk .Computing the AllPairs Shortest Distances matrix for an
n node graph can be done in Merlin–Arthur time . Note this is optimal, as the matrix can have$$\tilde{O}(n^2)$$ $\stackrel{~}{O}\left({n}^{2}\right)$ nonzero entries in general. Previously, Carmosino et al. [ITCS 2016] showed that this problem has an$$\Omega (n^2)$$ $\Omega \left({n}^{2}\right)$ nondeterministic time algorithm.$$\tilde{O}(n^{2.94})$$ $\stackrel{~}{O}\left({n}^{2.94}\right)$Certifying that an
n variablek CNF is unsatisfiable can be done in Merlin–Arthur time . We also observe an algebrization barrier for the previous$$2^{n/2  n/O(k)}$$ ${2}^{n/2n/O\left(k\right)}$ time Merlin–Arthur protocol of R. Williams [CCC’16] for$$2^{n/2}\cdot \textrm{poly}(n)$$ ${2}^{n/2}\xb7\text{poly}\left(n\right)$ SAT: in particular, his protocol algebrizes, and we observe there is no algebrizing protocol for$$\#$$ $\#$k UNSAT running in time. Therefore we have to exploit nonalgebrizing properties to obtain our new protocol.$$2^{n/2}/n^{\omega (1)}$$ ${2}^{n/2}/{n}^{\omega \left(1\right)}$ Due to the centrality of these problems in finegrained complexity, our results have consequences for many other problems of interest. For example, our work implies that certifying there is no Subset Sum solution toCertifying a Quantified Boolean Formula is true can be done in Merlin–Arthur time
. Previously, the only nontrivial result known along these lines was an Arthur–Merlin–Arthur protocol (where Merlin’s proof depends on some of Arthur’s coins) running in$$2^{4n/5}\cdot \textrm{poly}(n)$$ ${2}^{4n/5}\xb7\text{poly}\left(n\right)$ time.$$2^{2n/3}\cdot \textrm{poly}(n)$$ ${2}^{2n/3}\xb7\text{poly}\left(n\right)$n integers can be done in Merlin–Arthur time , improving on the previous best protocol by Nederlof [IPL 2017] which took$$2^{n/3}\cdot \textrm{poly}(n)$$ ${2}^{n/3}\xb7\text{poly}\left(n\right)$ time.$$2^{0.49991n}\cdot \textrm{poly}(n)$$ ${2}^{0.49991n}\xb7\text{poly}\left(n\right)$ 
null (Ed.)We present an m^{4/3+o(1)} log W time algorithm for solving the minimum cost flow problem in graphs with unit capacity, where W is the maximum absolute value of any edge weight. For sparse graphs, this improves over the best known running time for this problem and, by wellknown reductions, also implies improved running times for the shortest path problem with negative weights, minimum cost bipartite bmatching when b_1 = O(m), and recovers the running time of the currently fastest algorithm for maximum flow in graphs with unit capacities (LiuSidford, 2020). Our algorithm relies on developing an interior point method–based framework which acts on the space of circulations in the underlying graph. From the combinatorial point of view, this framework can be viewed as iteratively improving the cost of a suboptimal solution by pushing flow around circulations. These circulations are derived by computing a regularized version of the standard Newton step, which is partially inspired by previous work on the unitcapacity maximum flow problem (LiuSidford, 2019), and subsequently refined based on the very recent progress on this problem (LiuSidford, 2020). The resulting step problem can then be computed efficiently using the recent work on l_pnorm minimizing flows (KyngPengSachdevaWang, 2019). We obtain our faster algorithm by combining this new step primitive with a customized preconditioning method, which aims to ensure that the graph on which these circulations are computed has sufficiently large conductance.more » « less

We study local symmetry breaking problems in the Congest model, focusing on ruling set problems, which generalize the fundamental Maximal Independent Set (MIS) problem. The time (round) complexity of MIS (and ruling sets) have attracted much attention in the Local model. Indeed, recent results (Barenboim et al., FOCS 2012, Ghaffari SODA 2016) for the MIS problem have tried to break the longstanding O(log n)round "barrier" achieved by Luby's algorithm, but these yield o(log n)round complexity only when the maximum degree Delta is somewhat small relative to n. More importantly, these results apply only in the Local model. In fact, the best known time bound in the Congest model is still O(log n) (via Luby's algorithm) even for moderately small Delta (i.e., for Delta = Omega(log n) and Delta = o(n)). Furthermore, message complexity has been largely ignored in the context of local symmetry breaking. Luby's algorithm takes O(m) messages on medge graphs and this is the best known bound with respect to messages. Our work is motivated by the following central question: can we break the Theta(log n) time complexity barrier and the Theta(m) message complexity barrier in the Congest model for MIS or closelyrelated symmetry breaking problems? This paper presents progress towards this question for the distributed ruling set problem in the Congest model. A betaruling set is an independent set such that every node in the graph is at most beta hops from a node in the independent set. We present the following results:  Time Complexity: We show that we can break the O(log n) "barrier" for 2 and 3ruling sets. We compute 3ruling sets in O(log n/log log n) rounds with high probability (whp). More generally we show that 2ruling sets can be computed in O(log Delta (log n)^(1/2 + epsilon) + log n/log log n) rounds for any epsilon > 0, which is o(log n) for a wide range of Delta values (e.g., Delta = 2^(log n)^(1/2epsilon)). These are the first 2 and 3ruling set algorithms to improve over the O(log n)round complexity of Luby's algorithm in the Congest model.  Message Complexity: We show an Omega(n^2) lower bound on the message complexity of computing an MIS (i.e., 1ruling set) which holds also for randomized algorithms and present a contrast to this by showing a randomized algorithm for 2ruling sets that, whp, uses only O(n log^2 n) messages and runs in O(Delta log n) rounds. This is the first messageefficient algorithm known for ruling sets, which has message complexity nearly linear in n (which is optimal up to a polylogarithmic factor).more » « less

null (Ed.)We present an $m^{4/3}+o(1) \log W$ time algorithm for solving the minimum cost flow problem in graphs with unit capacity, where W is the maximum absolute value of any edge weight. For sparse graphs, this improves over the best known running time for this problem and, by wellknown reductions, also implies improved running times for the shortest path problem with negative weights, minimum cost bipartite $b$matching when $\b\_1 = O(m)$, and recovers the running time of the currently fastest algorithm for maximum flow in graphs with unit capacities (LiuSidford, 2020). Our algorithm relies on developing an interior point method–based framework which acts on the space of circulations in the underlying graph. From the combinatorial point of view, this framework can be viewed as iteratively improving the cost of a suboptimal solution by pushing flow around circulations. These circulations are derived by computing a regularized version of the standard Newton step, which is partially inspired by previous work on the unitcapacity maximum flow problem (LiuSidford, 2019), and subsequently refined based on the very re cent progress on this problem (LiuSidford, 2020). The resulting step problem can then be computed efficiently using the recent work on $l_p$norm minimizing flows (KyngPengSachdeva Wang, 2019). We obtain our faster algorithm by combining this new step primitive with a customized preconditioning method, which aims to ensure that the graph on which these circulations are computed has sufficiently large conductance.more » « less