arXiv subjects

Thatchaphol Saranurak

Publications and source records attributed to Thatchaphol Saranurak.

At least 91 records · Page 5Linked to original sources

arXiv2020

A Simple Deterministic Algorithm for Edge Connectivity

We show a deterministic algorithm for computing edge connectivity of a simple graph with $m$ edges in $m^{1+o(1)}$ time. Although the fastest deterministic algorithm by Henzinger, Rao, and Wang [SODA'17] has a faster running time of $O(m\log^{2}m\log\log m)$, we believe that our algorithm is conceptually simpler. The key tool for this simplication is the expander decomposition. We exploit it in a very straightforward way compared to how it has been previously used in the literature.

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arXiv2020

Deterministic Distributed Expander Decomposition and Routing with Applications in Distributed Derandomization

There is a recent exciting line of work in distributed graph algorithms in the $\mathsf{CONGEST}$ model that exploit expanders. All these algorithms so far are based on two tools: expander decomposition and expander routing. An $(ε,ϕ)$-expander decomposition removes $ε$-fraction of the edges so that the remaining connected components have conductance at least $ϕ$, i.e., they are $ϕ$-expanders, and expander routing allows each vertex $v$ in a $ϕ$-expander to very quickly exchange $\text{deg}(v)$ messages with any other vertices, not just its local neighbors. In this paper, we give the first efficient deterministic distributed algorithms for both tools. We show that an $(ε,ϕ)$-expander decomposition can be deterministically computed in $\text{poly}(ε^{-1}) n^{o(1)}$ rounds for $ϕ= \text{poly}(ε) n^{-o(1)}$, and that expander routing can be performed deterministically in $\text{poly}(ϕ^{-1})n^{o(1)}$ rounds. Both results match previous bounds of randomized algorithms by [Chang and Saranurak, PODC 2019] and [Ghaffari, Kuhn, and Su, PODC 2017] up to subpolynomial factors. Consequently, we derandomize existing distributed algorithms that exploit expanders. We show that a minimum spanning tree on $n^{o(1)}$-expanders can be constructed deterministically in $n^{o(1)}$ rounds, and triangle detection and enumeration on general graphs can be solved deterministically in $O(n^{0.58})$ and $n^{2/3 + o(1)}$ rounds, respectively. We also give the first polylogarithmic-round randomized algorithm for constructing an $(ε,ϕ)$-expander decomposition in $\text{poly}(ε^{-1}, \log n)$ rounds for $ϕ= 1 / \text{poly}(ε^{-1}, \log n)$. The previous algorithm by [Chang and Saranurak, PODC 2019] needs $n^{Ω(1)}$ rounds for any $ϕ\ge 1/\text{poly}\log n$.

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arXiv2020

The Expander Hierarchy and its Applications to Dynamic Graph Algorithms

We introduce a notion for hierarchical graph clustering which we call the expander hierarchy and show a fully dynamic algorithm for maintaining such a hierarchy on a graph with $n$ vertices undergoing edge insertions and deletions using $n^{o(1)}$ update time. An expander hierarchy is a tree representation of graphs that faithfully captures the cut-flow structure and consequently our dynamic algorithm almost immediately implies several results including: (1) The first fully dynamic algorithm with $n^{o(1)}$ worst-case update time that allows querying $n^{o(1)}$-approximate conductance, $s$-$t$ maximum flows, and $s$-$t$ minimum cuts for any given $(s,t)$ in $O(\log^{1/6} n)$ time. Our results are deterministic and extend to multi-commodity cuts and flows. The key idea behind these results is a fully dynamic algorithm for maintaining a tree flow sparsifier, a notion introduced by Räcke [FOCS'02] for constructing competitive oblivious routing schemes. (2) A deterministic fully dynamic connectivity algorithm with $n^{o(1)}$ worst-case update time. This significantly simplifies the recent algorithm by Chuzhoy et al.~that uses the framework of Nanongkai et al. [FOCS'17]. (3) The first non-trivial deterministic fully dynamic treewidth decomposition algorithm on constant-degree graphs with $n^{o(1)}$ worst-case update time that maintains a treewidth decomposition of width $\text{tw}(G)\cdot n^{o(1)}$ where $\text{tw}(G)$ denotes the treewidth of the current graph. Our technique is based on a new stronger notion of the expander decomposition, called the boundary-linked expander decomposition. This decomposition is more robust against updates and better captures the clustering structure of graphs. Given that the expander decomposition has proved extremely useful in many fields, we expect that our new notion will find more future applications.

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arXiv2020

Fast Dynamic Cuts, Distances and Effective Resistances via Vertex Sparsifiers

We present a general framework of designing efficient dynamic approximate algorithms for optimization on undirected graphs. In particular, we develop a technique that, given any problem that admits a certain notion of vertex sparsifiers, gives data structures that maintain approximate solutions in sub-linear update and query time. We illustrate the applicability of our paradigm to the following problems. (1) A fully-dynamic algorithm that approximates all-pair maximum-flows/minimum-cuts up to a nearly logarithmic factor in $\tilde{O}(n^{2/3})$ amortized time against an oblivious adversary, and $\tilde{O}(m^{3/4})$ time against an adaptive adversary. (2) An incremental data structure that maintains $O(1)$-approximate shortest path in $n^{o(1)}$ time per operation, as well as fully dynamic approximate all-pair shortest path and transshipment in $\tilde{O}(n^{2/3+o(1)})$ amortized time per operation. (3) A fully-dynamic algorithm that approximates all-pair effective resistance up to an $(1+ε)$ factor in $\tilde{O}(n^{2/3+o(1)} ε^{-O(1)})$ amortized update time per operation. The key tool behind result (1) is the dynamic maintenance of an algorithmic construction due to Madry [FOCS' 10], which partitions a graph into a collection of simpler graph structures (known as j-trees) and approximately captures the cut-flow and metric structure of the graph. The $O(1)$-approximation guarantee of (2) is by adapting the distance oracles by [Thorup-Zwick JACM `05]. Result (3) is obtained by invoking the random-walk based spectral vertex sparsifier by [Durfee et al. STOC `19] in a hierarchical manner, while carefully keeping track of the recourse among levels in the hierarchy.

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arXiv2020

A Deterministic Algorithm for Balanced Cut with Applications to Dynamic Connectivity, Flows, and Beyond

We consider the classical Minimum Balanced Cut problem: given a graph $G$, compute a partition of its vertices into two subsets of roughly equal volume, while minimizing the number of edges connecting the subsets. We present the first {\em deterministic, almost-linear time} approximation algorithm for this problem. Specifically, our algorithm, given an $n$-vertex $m$-edge graph $G$ and any parameter $1\leq r\leq O(\log n)$, computes a $(\log m)^{r^2}$-approximation for Minimum Balanced Cut on $G$, in time $O\left ( m^{1+O(1/r)+o(1)}\cdot (\log m)^{O(r^2)}\right )$. In particular, we obtain a $(\log m)^{1/ε}$-approximation in time $m^{1+O(1/\sqrtε)}$ for any constant $ε$, and a $(\log m)^{f(m)}$-approximation in time $m^{1+o(1)}$, for any slowly growing function $m$. We obtain deterministic algorithms with similar guarantees for the Sparsest Cut and the Lowest-Conductance Cut problems. Our algorithm for the Minimum Balanced Cut problem in fact provides a stronger guarantee: it either returns a balanced cut whose value is close to a given target value, or it certifies that such a cut does not exist by exhibiting a large subgraph of $G$ that has high conductance. We use this algorithm to obtain deterministic algorithms for dynamic connectivity and minimum spanning forest, whose worst-case update time on an $n$-vertex graph is $n^{o(1)}$, thus resolving a major open problem in the area of dynamic graph algorithms. Our work also implies deterministic algorithms for a host of additional problems, whose time complexities match, up to subpolynomial in $n$ factors, those of known randomized algorithms. The implications include almost-linear time deterministic algorithms for solving Laplacian systems and for approximating maximum flows in undirected graphs.

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arXiv2019

Pinning Down the Strong Wilber 1 Bound for Binary Search Trees

The dynamic optimality conjecture, postulating the existence of an $O(1)$-competitive online algorithm for binary search trees (BSTs), is among the most fundamental open problems in dynamic data structures. Despite extensive work and some notable progress, including, for example, the Tango Trees (Demaine et al., FOCS 2004), that give the best currently known $O(\log \log n)$-competitive algorithm, the conjecture remains widely open. One of the main hurdles towards settling the conjecture is that we currently do not have approximation algorithms achieving better than an $O(\log \log n)$-approximation, even in the offline setting. All known non-trivial algorithms for BST's so far rely on comparing the algorithm's cost with the so-called Wilber's first bound (WB-1). Therefore, establishing the worst-case relationship between this bound and the optimal solution cost appears crucial for further progress, and it is an interesting open question in its own right. Our contribution is two-fold. First, we show that the gap between the WB-1 bound and the optimal solution value can be as large as $Ω(\log \log n/ \log \log \log n)$; in fact, the gap holds even for several stronger variants of the bound. Second, we provide a simple algorithm, that, given an integer $D>0$, obtains an $O(D)$-approximation in time $\exp\left(O\left (n^{1/2^{Ω(D)}}\log n\right )\right )$. In particular, this gives a constant-factor approximation sub-exponential time algorithm. Moreover, we obtain a simpler and cleaner efficient $O(\log \log n)$-approximation algorithm that can be used in an online setting. Finally, we suggest a new bound, that we call {\em Guillotine Bound}, that is stronger than WB, while maintaining its algorithm-friendly nature, that we hope will lead to better algorithms. All our results use the geometric interpretation of the problem, leading to cleaner and simpler analysis.

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arXiv2019

Computing and Testing Small Connectivity in Near-Linear Time and Queries via Fast Local Cut Algorithms

Consider the following "local" cut-detection problem in a directed graph: We are given a seed vertex $x$ and need to remove at most $k$ edges so that at most $ν$ edges can be reached from $x$ (a "local" cut) or output $\bot$ to indicate that no such cut exists. If we are given query access to the input graph, then this problem can in principle be solved without reading the whole graph and with query complexity depending on $k$ and $ν$. In this paper we consider a slack variant of this problem where, when such a cut exists, we can output a cut with up to $O(kν)$ edges reachable from $x$. We present a simple randomized algorithm spending $O(k^2ν)$ time and $O(kν)$ queries for the above variant, improving in particular a previous time bound of $O(k^{O(k)}ν)$ by Chechik et al. [SODA '17]. We also extend our algorithm to handle an approximate variant. We demonstrate that these local algorithms are versatile primitives for designing substantially improved algorithms for classic graph problems by providing the following three applications. (Throughout, $\tilde O(T)$ hides $\operatorname{polylog}(T)$.) (1) A randomized algorithm for the classic $k$-vertex connectivity problem that takes near-linear time when $k=O(\operatorname{polylog}(n))$, namely $\tilde O(m+nk^3)$ time in undirected graphs. For directed graphs our $\tilde O(mk^2)$-time algorithm is near-linear when $k=O(\operatorname{polylog}(n))$. Our techniques also yield an improved approximation scheme. (2) Property testing algorithms for $k$-edge and -vertex connectivity with query complexities that are near-linear in $k$, exponentially improving the state-of-the-art. This resolves two open problems, one by Goldreich and Ron [STOC '97] and one by Orenstein and Ron [Theor. Comput Sci. '11]. (3) A faster algorithm for computing the maximal $k$-edge connected subgraphs, improving prior work of Chechik et al. [SODA '17].

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arXiv2019

Deterministic Graph Cuts in Subquadratic Time: Sparse, Balanced, and k-Vertex

We study deterministic algorithms for computing graph cuts, with focus on two fundamental problems: balanced sparse cut and $k$-vertex connectivity for small $k$ ($k=O(\polylog n)$). Both problems can be solved in near-linear time with randomized algorithms, but their previous deterministic counterparts take at least quadratic time. In this paper, we break this bound for both problems. Interestingly, achieving this for one problem crucially relies on doing so for the other. In particular, via a divide-and-conquer argument, a variant of the cut-matching game by [Khandekar et al.`07], and the local vertex connectivity algorithm of [Nanongkai et al. STOC'19], we give a subquadratic time algorithm for $k$-vertex connectivity using a subquadratic time algorithm for computing balanced sparse cuts on sparse graphs. To achieve the latter, we improve the previously best $mn$ bound for approximating balanced sparse cut for the whole range of $m$. This starts from (1) breaking the $n^3$ barrier on dense graphs to $n^{ω+ o(1)}$ (where $ω< 2.372$) using the the PageRank matrix, but without explicitly sweeping to find sparse cuts; to (2) getting the $\tilde O(m^{1.58})$ bound by combining the $J$-trees by [Madry FOCS `10] with the $n^{ω+ o(1)}$ bound above, and finally; to (3) getting the $m^{1.5 + o(1)}$ bound by recursively invoking the second bound in conjunction with expander-based graph sparsification. Interestingly, our final $m^{1.5 + o(1)}$ bound lands at a natural stopping point in the sense that polynomially breaking it would lead to a breakthrough for the dynamic connectivity problem.

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arXiv2019

Sensitive Distance and Reachability Oracles for Large Batch Updates

In the sensitive distance oracle problem, there are three phases. We first preprocess a given directed graph $G$ with $n$ nodes and integer weights from $[-W,W]$. Second, given a single batch of $f$ edge insertions and deletions, we update the data structure. Third, given a query pair of nodes $(u,v)$, return the distance from $u$ to $v$. In the easier problem called sensitive reachability oracle problem, we only ask if there exists a directed path from $u$ to $v$. Our first result is a sensitive distance oracle with $\tilde{O}(Wn^{ω+(3-ω)μ})$ preprocessing time, $\tilde{O}(Wn^{2-μ}f^{2}+Wnf^ω)$ update time, and $\tilde{O}(Wn^{2-μ}f+Wnf^{2})$ query time where the parameter $μ\in[0,1]$ can be chosen. The data-structure requires $O(Wn^{2+μ} \log n)$ bits of memory. This is the first algorithm that can handle $f\ge\log n$ updates. Previous results (e.g. [Demetrescu et al. SICOMP'08; Bernstein and Karger SODA'08 and FOCS'09; Duan and Pettie SODA'09; Grandoni and Williams FOCS'12]) can handle at most 2 updates. When $3\le f\le\log n$, the only non-trivial algorithm was by [Weimann and Yuster FOCS'10]. When $W=\tilde{O}(1)$, our algorithm simultaneously improves their preprocessing time, update time, and query time. In particular, when $f=ω(1)$, their update and query time is $Ω(n^{2-o(1)})$, while our update and query time are truly subquadratic in $n$, i.e., ours is faster by a polynomial factor of $n$. To highlight the technique, ours is the first graph algorithm that exploits the kernel basis decomposition of polynomial matrices by [Jeannerod and Villard J.Comp'05; Zhou, Labahn and Storjohann J.Comp'15] developed in the symbolic computation community. [...]

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arXiv2019

Computing and Testing Small Vertex Connectivity in Near-Linear Time and Queries

We present a new, simple, algorithm for the local vertex connectivity problem (LocalVC) introduced by Nanongkai~et~al. [STOC'19]. Roughly, given an undirected unweighted graph $G$, a seed vertex $x$, a target volume $ν$, and a target separator size $k$, the goal of LocalVC is to remove $k$ vertices `near' $x$ (in terms of $ν$) to disconnect the graph in `local time', which depends only on parameters $ν$ and $k$. In this paper, we present a simple randomized algorithm with running time $O(νk^2)$ and correctness probability $2/3$. Plugging our new localVC algorithm in the generic framework of Nanongkai~et~al. immediately lead to a randomized $\tilde O(m+nk^3)$-time algorithm for the classic $k$-vertex connectivity problem on undirected graphs. ($\tilde O(T)$ hides $\text{polylog}(T)$.) This is the first near-linear time algorithm for any $4\leq k \leq \text{polylog} n$. Previous fastest algorithm for small $k$ takes $\tilde O(m+n^{4/3}k^{7/3})$ time [Nanongkai~et~al., STOC'19]. This work is inspired by the algorithm of Chechik~et~al. [SODA'17] for computing the maximal $k$-edge connected subgraphs. Forster and Yang [arXiv'19] has independently developed local algorithms similar to ours, and showed that they lead to an $\tilde O(k^3/ε)$ bound for testing $k$-edge and -vertex connectivity, resolving two long-standing open problems in property testing since the work of Goldreich and Ron [STOC'97] and Orenstein and Ron [Theor. Comput. Sci.'11]. Inspired by this, we use local approximation algorithms to obtain bounds that are near-linear in $k$, namely $\tilde O(k/ε)$ and $\tilde O(k/ε^2)$ for the bounded and unbounded degree cases, respectively. For testing $k$-edge connectivity for simple graphs, the bound can be improved to $\tilde O(\min(k/ε, 1/ε^2))$.

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arXiv2019

Improved Distributed Expander Decomposition and Nearly Optimal Triangle Enumeration

An $(ε,ϕ)$-expander decomposition of a graph $G=(V,E)$ is a clustering of the vertices $V=V_{1}\cup\cdots\cup V_{x}$ such that (1) each cluster $V_{i}$ induces subgraph with conductance at least $ϕ$, and (2) the number of inter-cluster edges is at most $ε|E|$. In this paper, we give an improved distributed expander decomposition. Specifically, we construct an $(ε,ϕ)$-expander decomposition with $ϕ=(ε/\log n)^{2^{O(k)}}$ in $O(n^{2/k}\cdot\text{poly}(1/ϕ,\log n))$ rounds for any $ε\in(0,1)$ and positive integer $k$. For example, a $(0.01,1/\text{poly}\log n)$-expander decomposition can be computed in $O(n^γ)$ rounds, for any arbitrarily small constant $γ>0$. Previously, the algorithm by Chang, Pettie, and Zhang can construct a $(1/6,1/\text{poly}\log n)$-expander decomposition using $\tilde{O}(n^{1-δ})$ rounds for any $δ>0$, with a caveat that the algorithm is allowed to throw away a set of edges into an extra part which forms a subgraph with arboricity at most $n^δ$. Our algorithm does not have this caveat. By slightly modifying the distributed algorithm for routing on expanders by Ghaffari, Kuhn and Su [PODC'17], we obtain a triangle enumeration algorithm using $\tilde{O}(n^{1/3})$ rounds. This matches the lower bound by Izumi and Le Gall [PODC'17] and Pandurangan, Robinson and Scquizzato [SPAA'18] of $\tildeΩ(n^{1/3})$ which holds even in the CONGESTED CLIQUE model. This provides the first non-trivial example for a distributed problem that has essentially the same complexity (up to a polylogarithmic factor) in both CONGEST and CONGESTED CLIQUE. The key technique in our proof is the first distributed approximation algorithm for finding a low conductance cut that is as balanced as possible. Previous distributed sparse cut algorithms do not have this nearly most balanced guarantee.

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arXiv2019

Dynamic Matrix Inverse: Improved Algorithms and Matching Conditional Lower Bounds

The dynamic matrix inverse problem is to maintain the inverse of a matrix undergoing element and column updates. It is the main subroutine behind the best algorithms for many dynamic problems whose complexity is not yet well-understood, such as maintaining the largest eigenvalue, rank and determinant of a matrix and maintaining reachability, distances, maximum matching size, and $k$-paths/cycles in a graph. Understanding the complexity of dynamic matrix inverse is a key to understand these problems. In this paper, we present (i) improved algorithms for dynamic matrix inverse and their extensions to some incremental/look-ahead variants, and (ii) variants of the Online Matrix-Vector conjecture [Henzinger et al. STOC'15] that, if true, imply that these algorithms are tight. Our algorithms automatically lead to faster dynamic algorithms for the aforementioned problems, some of which are also tight under our conjectures, e.g. reachability and maximum matching size (closing the gaps for these two problems was in fact asked by Abboud and V. Williams [FOCS'14]). Prior best bounds for most of these problems date back to more than a decade ago [Sankowski FOCS'04, COCOON'05, SODA'07; Kavitha FSTTCS'08; Mucha and Sankowski Algorithmica'10; Bosek et al. FOCS'14]. Our improvements stem mostly from the ability to use fast matrix multiplication ``one more time'', to maintain a certain transformation matrix which could be maintained only combinatorially previously (i.e. without fast matrix multiplication). Oddly, unlike other dynamic problems where this approach, once successful, could be repeated several times (``bootstrapping''), our conjectures imply that this is not the case for dynamic matrix inverse and some related problems. However, when a small additional ``look-ahead'' information is provided we can perform such repetition to drive the bounds down further.

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arXiv2019

Distributed Edge Connectivity in Sublinear Time

We present the first sublinear-time algorithm for a distributed message-passing network sto compute its edge connectivity $λ$ exactly in the CONGEST model, as long as there are no parallel edges. Our algorithm takes $\tilde O(n^{1-1/353}D^{1/353}+n^{1-1/706})$ time to compute $λ$ and a cut of cardinality $λ$ with high probability, where $n$ and $D$ are the number of nodes and the diameter of the network, respectively, and $\tilde O$ hides polylogarithmic factors. This running time is sublinear in $n$ (i.e. $\tilde O(n^{1-ε})$) whenever $D$ is. Previous sublinear-time distributed algorithms can solve this problem either (i) exactly only when $λ=O(n^{1/8-ε})$ [Thurimella PODC'95; Pritchard, Thurimella, ACM Trans. Algorithms'11; Nanongkai, Su, DISC'14] or (ii) approximately [Ghaffari, Kuhn, DISC'13; Nanongkai, Su, DISC'14]. To achieve this we develop and combine several new techniques. First, we design the first distributed algorithm that can compute a $k$-edge connectivity certificate for any $k=O(n^{1-ε})$ in time $\tilde O(\sqrt{nk}+D)$. Second, we show that by combining the recent distributed expander decomposition technique of [Chang, Pettie, Zhang, SODA'19] with techniques from the sequential deterministic edge connectivity algorithm of [Kawarabayashi, Thorup, STOC'15], we can decompose the network into a sublinear number of clusters with small average diameter and without any mincut separating a cluster (except the `trivial' ones). Finally, by extending the tree packing technique from [Karger STOC'96], we can find the minimum cut in time proportional to the number of components. As a byproduct of this technique, we obtain an $\tilde O(n)$-time algorithm for computing exact minimum cut for weighted graphs.

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arXiv2018

Smooth heaps and a dual view of self-adjusting data structures

We present a new connection between self-adjusting binary search trees (BSTs) and heaps, two fundamental, extensively studied, and practically relevant families of data structures. Roughly speaking, we map an arbitrary heap algorithm within a natural model, to a corresponding BST algorithm with the same cost on a dual sequence of operations (i.e. the same sequence with the roles of time and key-space switched). This is the first general transformation between the two families of data structures. There is a rich theory of dynamic optimality for BSTs (i.e. the theory of competitiveness between BST algorithms). The lack of an analogous theory for heaps has been noted in the literature. Through our connection, we transfer all instance-specific lower bounds known for BSTs to a general model of heaps, initiating a theory of dynamic optimality for heaps. On the algorithmic side, we obtain a new, simple and efficient heap algorithm, which we call the smooth heap. We show the smooth heap to be the heap-counterpart of Greedy, the BST algorithm with the strongest proven and conjectured properties from the literature, widely believed to be instance-optimal. Assuming the optimality of Greedy, the smooth heap is also optimal within our model of heap algorithms. As corollaries of results known for Greedy, we obtain instance-specific upper bounds for the smooth heap, with applications in adaptive sorting. Intriguingly, the smooth heap, although derived from a non-practical BST algorithm, is simple and easy to implement (e.g. it stores no auxiliary data besides the keys and tree pointers). It can be seen as a variation on the popular pairing heap data structure, extending it with a "power-of-two-choices" type of heuristic.

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arXiv2018

Multi-finger binary search trees

We study multi-finger binary search trees (BSTs), a far-reaching extension of the classical BST model, with connections to the well-studied $k$-server problem. Finger search is a popular technique for speeding up BST operations when a query sequence has locality of reference. BSTs with multiple fingers can exploit more general regularities in the input. In this paper we consider the cost of serving a sequence of queries in an optimal (offline) BST with $k$ fingers, a powerful benchmark against which other algorithms can be measured. We show that the $k$-finger optimum can be matched by a standard dynamic BST (having a single root-finger) with an $O(\log{k})$ factor overhead. This result is tight for all $k$, improving the $O(k)$ factor implicit in earlier work. Furthermore, we describe new online BSTs that match this bound up to a $(\log{k})^{O(1)}$ factor. Previously only the "one-finger" special case was known to hold for an online BST (Iacono, Langerman, 2016; Cole et al., 2000). Splay trees, assuming their conjectured optimality (Sleator and Tarjan, 1983), would have to match our bounds for all $k$. Our online algorithms are randomized and combine techniques developed for the $k$-server problem with a multiplicative-weights scheme for learning tree metrics. To our knowledge, this is the first time when tools developed for the $k$-server problem are used in BSTs. As an application of our $k$-finger results, we show that BSTs can efficiently serve queries that are close to some recently accessed item. This is a (restricted) form of the unified property (Iacono, 2001) that was previously not known to hold for any BST algorithm, online or offline.

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arXiv2017

Distributed Exact Weighted All-Pairs Shortest Paths in $\tilde O(n^{5/4})$ Rounds

We study computing {\em all-pairs shortest paths} (APSP) on distributed networks (the CONGEST model). The goal is for every node in the (weighted) network to know the distance from every other node using communication. The problem admits $(1+o(1))$-approximation $\tilde O(n)$-time algorithms ~\cite{LenzenP-podc15,Nanongkai-STOC14}, which are matched with $\tilde Ω(n)$-time lower bounds~\cite{Nanongkai-STOC14,LenzenP_stoc13,FrischknechtHW12}\footnote{$\tilde Θ$, $\tilde O$ and $\tilde Ω$ hide polylogarithmic factors. Note that the lower bounds also hold even in the unweighted case and in the weighted case with polynomial approximation ratios.}. No $ω(n)$ lower bound or $o(m)$ upper bound were known for exact computation. In this paper, we present an $\tilde O(n^{5/4})$-time randomized (Las Vegas) algorithm for exact weighted APSP; this provides the first improvement over the naive $O(m)$-time algorithm when the network is not so sparse. Our result also holds for the case where edge weights are {\em asymmetric} (a.k.a. the directed case where communication is bidirectional). Our techniques also yield an $\tilde O(n^{3/4}k^{1/2}+n)$-time algorithm for the {\em $k$-source shortest paths} problem where we want every node to know distances from $k$ sources; this improves Elkin's recent bound~\cite{Elkin-STOC17} when $k=\tilde ω(n^{1/4})$.

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arXiv2017

Dynamic Minimum Spanning Forest with Subpolynomial Worst-case Update Time

We present a Las Vegas algorithm for dynamically maintaining a minimum spanning forest of an $n$-node graph undergoing edge insertions and deletions. Our algorithm guarantees an $O(n^{o(1)})$ worst-case update time with high probability. This significantly improves the two recent Las Vegas algorithms by Wulff-Nilsen [STOC'17] with update time $O(n^{0.5-ε})$ for some constant $ε>0$ and, independently, by Nanongkai and Saranurak [STOC'17] with update time $O(n^{0.494})$ (the latter works only for maintaining a spanning forest). Our result is obtained by identifying the common framework that both two previous algorithms rely on, and then improve and combine the ideas from both works. There are two main algorithmic components of the framework that are newly improved and critical for obtaining our result. First, we improve the update time from $O(n^{0.5-ε})$ in Wulff-Nilsen [STOC'17] to $O(n^{o(1)})$ for decrementally removing all low-conductance cuts in an expander undergoing edge deletions. Second, by revisiting the "contraction technique" by Henzinger and King [1997] and Holm et al. [STOC'98], we show a new approach for maintaining a minimum spanning forest in connected graphs with very few (at most $(1+o(1))n$) edges. This significantly improves the previous approach in [Wulff-Nilsen STOC'17] and [Nanongkai and Saranurak STOC'17] which is based on Frederickson's 2-dimensional topology tree and illustrates a new application to this old technique.

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arXiv2017

Dynamic Spanning Forest with Worst-Case Update Time: Adaptive, Las Vegas, and $O(n^{1/2-ε})$-Time

We present two algorithms for dynamically maintaining a spanning forest of a graph undergoing edge insertions and deletions. Our algorithms guarantee {\em worst-case update time} and work against an adaptive adversary, meaning that an edge update can depend on previous outputs of the algorithms. We provide the first polynomial improvement over the long-standing $O(\sqrt{n})$ bound of [Frederickson STOC'83, Eppstein, Galil, Italiano and Nissenzweig FOCS'92] for such type of algorithms. The previously best improvement was $O(\sqrt{n (\log\log n)^2/\log n})$ [Kejlberg-Rasmussen, Kopelowitz, Pettie and Thorup ESA'16]. We note however that these bounds were obtained by deterministic algorithms while our algorithms are randomized. Our first algorithm is Monte Carlo and guarantees an $O(n^{0.4+o(1)})$ worst-case update time, where the $o(1)$ term hides the $O(\sqrt{\log\log n/\log n})$ factor. Our second algorithm is Las Vegas and guarantees an $O(n^{0.49306})$ worst-case update time with high probability. Algorithms with better update time either needed to assume that the adversary is oblivious (e.g. [Kapron, King and Mountjoy SODA'13]) or can only guarantee an amortized update time. Our second result answers an open problem by Kapron et al. To the best of our knowledge, our algorithms are among a few non-trivial randomized dynamic algorithms that work against adaptive adversaries.

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