arXiv subjects

Udit Agarwal

Publications and source records attributed to Udit Agarwal.

10 recordsLinked to original sources

arXiv2023

Distributed Deterministic Exact Minimum Weight Cycle and Multi Source Shortest Paths in Near Linear Rounds in CONGEST model

We present new deterministic algorithms for computing distributed weighted minimum weight cycle (MWC) in undirected and directed graphs and distributed weighted all nodes shortest cycle (ANSC) in directed graphs. Our algorithms for these problems run in $\tilde{O}(n)$ rounds in the CONGEST model on graphs with arbitrary non-negative edge weights, matching the lower bound up to polylogarithmic factors. Before our work, no near linear rounds deterministic algorithms were known for these problems. The previous best bound for solving these problems deterministically requires an initial computation of all pairs shortest paths (APSP) on the given graph, followed by post-processing of $O(n)$ rounds, and in total takes $\tilde{O}(n^{4/3})$ rounds, using deterministic APSP~\cite{AR-SPAA20}. The main component of our new $\tilde{O}(n)$ rounds algorithms is a deterministic technique for constructing a sequence of successive blocker sets. These blocker sets are then treated as source nodes to compute $h$-hop shortest paths, which can then be used to compute candidate shortest cycles whose hop length lies in a particular range. The shortest cycles can then be obtained by selecting the cycle with the minimum weight from all these candidate cycles. Additionally using the above blocker set sequence technique, we also obtain $\tilde{O}(n)$ rounds deterministic algorithm for the multi-source shortest paths problem (MSSP) for both directed and undirected graphs, given that the size of the source set is at most $\sqrt{n}$. This new result for MSSP can be a step towards obtaining a $o(n^{4/3})$ rounds algorithm for deterministic APSP. We also believe that our new blocker set sequence technique may have potential applications for other distributed algorithms.

cs.DC↗

arXiv2021

Optimizing Graph Transformer Networks with Graph-based Techniques

Graph transformer networks (GTN) are a variant of graph convolutional networks (GCN) that are targeted to heterogeneous graphs in which nodes and edges have associated type information that can be exploited to improve inference accuracy. GTNs learn important metapaths in the graph, create weighted edges for these metapaths, and use the resulting graph in a GCN. Currently, the only available implementation of GTNs uses dense matrix multiplication to find metapaths. Unfortunately, the space overhead of this approach can be large, so in practice it is used only for small graphs. In addition, the matrix-based implementation is not fine-grained enough to use random-walk based methods to optimize metapath finding. In this paper, we present a graph-based formulation and implementation of the GTN metapath finding problem. This graph-based formulation has two advantages over the matrix-based approach. First, it is more space efficient than the original GTN implementation and more compute-efficient for metapath sizes of practical interest. Second, it permits us to implement a sampling method that reduces the number of metapaths that must be enumerated, allowing the implementation to be used for larger graphs and larger metapath sizes. Experimental results show that our implementation is $6.5\times$ faster than the original GTN implementation on average for a metapath length of 4, and our sampling implementation is $155\times$ faster on average than this implementation without compromising on the accuracy of the GTN.

cs.AI↗

arXiv2020

BiPart: A Parallel and Deterministic Multilevel Hypergraph Partitioner

Hypergraph partitioning is used in many problem domains including VLSI design, linear algebra, Boolean satisfiability, and data mining. Most versions of this problem are NP-complete or NP-hard, so practical hypergraph partitioners generate approximate partitioning solutions for all but the smallest inputs. One way to speed up hypergraph partitioners is to exploit parallelism. However, existing parallel hypergraph partitioners are not deterministic, which is considered unacceptable in domains like VLSI design where the same partitions must be produced every time a given hypergraph is partitioned. In this paper, we describe BiPart, the first deterministic, parallel hypergraph partitioner. Experimental results show that BiPart outperforms state-of-the-art hypergraph partitioners in runtime and partition quality while generating partitions deterministically.

cs.DC↗

arXiv2020

Faster Deterministic All Pairs Shortest Paths in Congest Model

We present a new deterministic algorithm for distributed weighted all pairs shortest paths (APSP) in both undirected and directed graphs. Our algorithm runs in $\tilde{O}(n^{4/3})$ rounds in the Congest models on graphs with arbitrary edge weights, and it improves on the previous $\tilde{O}(n^{3/2})$ bound of Agarwal et al. [ARKP18]. The main components of our new algorithm are a new faster technique for constructing blocker set deterministically and a new pipelined method for deterministically propagating distance values from source nodes to the blocker set nodes in the network. Both of these techniques have potential applications to other distributed algorithms. Our new deterministic algorithm for computing blocker set adapts the NC approximate hypergraph set cover algorithm in [BRS94] to the distributed construction of a blocker set. It follows the two-step process of first designing a randomized algorithm that uses only pairwise independence, and then derandomizes this algorithm using a sample space of linear size. This algorithm runs in almost the same number of rounds as the initial step in our APSP algorithm that computes $h$-hops shortest paths, and significantly improves on the deterministic blocker set algorithms in [ARKP18, AR19] by removing an additional $n\cdot |Q|$ term in the round bound, where Q is the blocker set. The other new component in our APSP algorithm is a deterministic pipelined approach to propagate distance values from source nodes to blocker nodes. We use a simple natural round-robin method for this step, and we show using a suitable progress measure that it achieve the $\tilde{O}(n^{4/3})$ bound on the number of rounds. It appears that the standard deterministic methods for efficiently broadcasting multiple values, and for sending or receiving messages using the routing schedule in [HPDG+19,LSP19] do not apply to this setting.

cs.DS↗

arXiv2020

A Polynomial Time Algorithm for Almost Optimal Vertex Fault Tolerant Spanners

We present the first polynomial time algorithm for the f vertex fault tolerant spanner problem, which achieves almost optimal spanner size. Our algorithm for constructing f vertex fault tolerant spanner takes $O(k\cdot n\cdot m^2 \cdot W)$ time, where W is the maximum edge weight, and constructs a spanner of size $O(n^{1+1/k}f^{1-1/k}\cdot (\log n)^{1-1/k})$. Our spanner has almost optimal size and is at most a $\log n$ factor away from the upper bound on the worst-case size. Prior to this work, no other polynomial time algorithm was known for constructing f vertex fault tolerant spanner with optimal size. Our algorithm is based on first greedily constructing a hitting set for the collection of paths of weight at most $k \cdot w(u,v)$ between the endpoints u and v of an edge (u,v) and then using this set to decide whether the edge (u,v) needs to be added to the growing spanner.

cs.DS↗

arXiv2018

A Deterministic Distributed Algorithm for Weighted All Pairs Shortest Paths Through Pipelining

We present a new pipelined approach to compute all pairs shortest paths (APSP) in a directed graph with nonnegative integer edge weights (including zero weights) in the CONGEST model in the distributed setting. Our deterministic distributed algorithm computes shortest paths of distance at most $Δ$ for all pairs of vertices in at most $2 n \sqrtΔ + 2n$ rounds, and more generally, it computes h-hop shortest paths for k sources in $2\sqrt{nkh} + n + k$ rounds. The algorithm is simple, and it has some novel features and a nontrivial analysis.It uses only the directed edges in the graph for communication. This algorithm can be used as a base within asymptotically faster algorithms that match or improve on the current best deterministic bound of $\tilde{O}(n^{3/2})$ rounds for this problem when edge weights are $O(n)$ or shortest path distances are $\tilde{O}(n^{3/2})$.

cs.DS↗

arXiv2018

New and Simplified Distributed Algorithms for Weighted All Pairs Shortest Paths

We consider the problem of computing all pairs shortest paths (APSP) and shortest paths for k sources in a weighted graph in the distributed CONGEST model. For graphs with non-negative integer edge weights (including zero weights) we build on a recent pipelined algorithm to obtain $\tilde{O}(λ^{1/4}\cdot n^{5/4})$ in graphs with non-negative integer edge-weight at most $λ$, and $\tilde{O}(n \cdot \bigtriangleup^{1/3})$ rounds for shortest path distances at most $\bigtriangleup$. Additionally, we simplify some of the procedures in the earlier APSP algorithms for non-negative edge weights in [HNS17,ARKP18]. We also present results for computing h-hop shortest paths and shortest paths from $k$ given sources. In other results, we present a randomized exact APSP algorithm for graphs with arbitrary edge weights that runs in $\tilde{O}(n^{4/3})$ rounds w.h.p. in n, which improves the previous best $\tilde{O}(n^{3/2})$ bound, which is deterministic. We also present an $\tilde{O}(n/ε^2)$-round deterministic $(1+ε)$ approximation algorithm for graphs with non-negative $poly(n)$ integer weights (including zero edge-weights), improving results in [Nanongkai14,LP15] that hold only for positive integer weights.

cs.DS↗

arXiv2018

A Deterministic Distributed Algorithm for Exact Weighted All-Pairs Shortest Paths in $\tilde{O}(n^{3/2})$ Rounds

We present a deterministic distributed algorithm to compute all-pairs shortest paths(APSP) in an edge-weighted directed or undirected graph. Our algorithm runs in $\tilde{O}(n^{3/2})$ rounds in the Congest model, where $n$ is the number of nodes in the graph. This is the first $o(n^2)$ rounds deterministic distributed algorithm for the weighted APSP problem. Our algorithm is fairly simple and incorporates a deterministic distributed algorithm we develop for computing a `blocker set' \cite{King99}, which has been used earlier in sequential dynamic computation of APSP.

cs.DS↗

arXiv2017

Fine-Grained Complexity and Conditional Hardness for Sparse Graphs

We consider the fine-grained complexity of sparse graph problems that currently have $\tilde{O}(mn)$ time algorithms, where m is the number of edges and n is the number of vertices in the input graph. This class includes several important path problems on both directed and undirected graphs, including APSP, MWC (minimum weight cycle), and Eccentricities, which is the problem of computing, for each vertex in the graph, the length of a longest shortest path starting at that vertex. We introduce the notion of a sparse reduction which preserves the sparsity of graphs, and we present near linear-time sparse reductions between various pairs of graph problems in the $\tilde{O}(mn)$ class. Surprisingly, very few of the known nontrivial reductions between problems in the $\tilde{O}(mn)$ class are sparse reductions. In the directed case, our results give a partial order on a large collection of problems in the $\tilde{O}(mn)$ class (along with some equivalences). In the undirected case we give two nontrivial sparse reductions: from MWC to APSP, and from unweighted ANSC (all nodes shortest cycles) to APSP. The latter reduction also gives an improved algorithm for ANSC (for dense graphs). We propose the MWC Conjecture, a new conditional hardness conjecture that the weight of a minimum weight cycle in a directed graph cannot be computed in time polynomially smaller than mn. Our sparse reductions for directed path problems in the $\tilde{O}(mn)$ class establish that several problems in this class, including 2-SiSP (second simple shortest path), Radius, and Eccentricities, are MWCC hard. We also identify Eccentricities as a key problem in the $\tilde{O}(mn)$ class which is simultaneously MWCC-hard, SETH-hard and k-DSH-hard, where SETH is the Strong Exponential Time Hypothesis, and k-DSH is the hypothesis that a dominating set of size k cannot be computed in time polynomially smaller than n^k.

cs.DS↗

arXiv2016

Finding $k$ Simple Shortest Paths and Cycles

The problem of finding multiple simple shortest paths in a weighted directed graph $G=(V,E)$ has many applications, and is considerably more difficult than the corresponding problem when cycles are allowed in the paths. Even for a single source-sink pair, it is known that two simple shortest paths cannot be found in time polynomially smaller than $n^3$ (where $n=|V|$) unless the All-Pairs Shortest Paths problem can be solved in a similar time bound. The latter is a well-known open problem in algorithm design. We consider the all-pairs version of the problem, and we give a new algorithm to find $k$ simple shortest paths for all pairs of vertices. For $k=2$, our algorithm runs in $O(mn + n^2 \log n)$ time (where $m=|E|$), which is almost the same bound as for the single pair case, and for $k=3$ we improve earlier bounds. Our approach is based on forming suitable path extensions to find simple shortest paths; this method is different from the `detour finding' technique used in most of the prior work on simple shortest paths, replacement paths, and distance sensitivity oracles. Enumerating simple cycles is a well-studied classical problem. We present new algorithms for generating simple cycles and simple paths in $G$ in non-decreasing order of their weights; the algorithm for generating simple paths is much faster, and uses another variant of path extensions. We also give hardness results for sparse graphs, relative to the complexity of computing a minimum weight cycle in a graph, for several variants of problems related to finding $k$ simple paths and cycles.

cs.DS↗