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Wen-Horng Sheu

Publications and source records attributed to Wen-Horng Sheu.

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Fixed-Threshold Peeling in Sublinear MPC: Round-Approximation Tradeoffs and Applications

A number of fundamental graph problems admit simple algorithms based on iterative peeling: repeatedly remove all vertices whose current degree is below a fixed threshold. This paradigm underlies algorithms for density-dependent edge orientation, density-dependent coloring, densest subgraph, and $k$-core decomposition. In this paper, we study these problems in the sub-linear MPC model and achieve the following round-approximation tradeoffs. For density-dependent edge orientation, given any integer $t > 0$, we compute an orientation with maximum out-degree at most $(2+ε)(t+1)α(G)$ in $O(\lg^{1/(t+2)} n \cdot \operatorname{poly}(\lg \lg n))$ rounds, where $α(G)$ denotes the minimum possible maximum out-degree of an orientation of $G$. In the $\operatorname{poly}(\lg\lg n)$-round regime, this gives an $O(\lg\lg n/\lg\lg\lg n)$-approximation, improving the approximation factor of the recent work by Ghaffari and Grunau [PODC 2025]. We obtain a similar improvement for density-dependent coloring. For densest subgraph, we obtain a $(4+ε)$-approximation in $\widetilde O(\lg^{1/3} n)$ MPC rounds and a $(6+ε)$-approximation in $\widetilde O(\lg^{1/4} n)$ MPC rounds. This improves the $\widetilde O(\sqrt{\lg n})$ round complexity of Ghaffari, Lattanzi, and Mitrović [ICML 2019] with a slightly larger approximation factor. This is the first $O(1)$-approximate algorithm for densest subgraph to break the $Θ(\sqrt{\lg n})$ round-complexity barrier in the sub-linear MPC model. For $k$-core decomposition, given any integer $t > 0$, we compute approximate coreness values within a factor of $(2+ε)(t+1)$ in $O(\lg^{1/(t+2)} n \cdot \operatorname{poly}(\lg \lg n))$ MPC rounds for any integer $t > 0$. This improves the $\widetilde O(\sqrt{\lg n})$ round complexity of Ghaffari, Lattanzi, and Mitrović [ICML 2019], again giving a round-approximation tradeoff.

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Dynamic Construction of the Lovász Local Lemma

This paper proves that a wide class of local search algorithms extend as is to the fully dynamic setting with an adaptive adversary, achieving an amortized $\tilde{O}(1)$ number of local-search steps per update. A breakthrough by Moser (2009) introduced the witness-tree and entropy compression techniques for analyzing local resampling processes for the Lovász Local Lemma. These methods have since been generalized and expanded to analyze a wide variety of local search algorithms that can efficiently find solutions to many important local constraint satisfaction problems. These algorithms either extend a partial valid assignment and backtrack by unassigning variables when constraints become violated, or they iteratively fix violated constraints by resampling their variables. These local resampling or backtracking procedures are incredibly flexible, practical, and simple to specify and implement. Yet, they can be shown to be extremely efficient on static instances, typically performing only (sub)-linear number of fixing steps. The main technical challenge lies in proving conditions that guarantee such rapid convergence. This paper extends these convergence results to fully dynamic settings, where an adaptive adversary may add or remove constraints. We prove that applying the same simple local search procedures to fix old or newly introduced violations leads to a total number of resampling steps near-linear in the number of adversarial updates. Our result is very general and yields several immediate corollaries. For example, letting $Δ$ denote the maximum degree, for a constant $ε$ and $Δ= \text{poly}(\log n)$, we can maintain a $(1+ε) Δ$-edge coloring in $\text{poly}(\log n)$ amortized update time against an adaptive adversary. The prior work for this regime has exponential running time in $\sqrt{\log n}$ [Christiansen, SODA '26].

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A framework for boosting matching approximation: parallel, distributed, and dynamic

This work designs a framework for boosting the approximation guarantee of maximum matching algorithms. As input, the framework receives a parameter $ε> 0$ and an oracle access to a $Θ(1)$-approximate maximum matching algorithm $\mathcal{A}$. Then, by invoking $\mathcal{A}$ for $\text{poly}(1/ε)$ many times, the framework outputs a $1+ε$ approximation of a maximum matching. Our approach yields several improvements in terms of the number of invocations to $\mathcal{A}$: (1) In MPC and CONGEST, our framework invokes $\mathcal{A}$ for $O(1/ε^7 \cdot \log(1/ε))$ times, substantially improving on $O(1/ε^{39})$ invocations following from [Fischer et al., STOC'22] and [Mitrovic et al., arXiv:2412.19057]. (2) In both online and offline fully dynamic settings, our framework yields an improvement in the dependence on $1/ε$ from exponential [Assadi et al., SODA25 and Liu, FOCS24] to polynomial.

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Faster MPC Algorithms for Approximate Allocation in Uniformly Sparse Graphs

We study the allocation problem in the Massively Parallel Computation (MPC) model. This problem is a special case of $b$-matching, in which the input is a bipartite graph with capacities greater than $1$ in only one part of the bipartition. We give a $(1+ε)$ approximate algorithm for the problem, which runs in $\tilde{O}(\sqrt{\log λ})$ MPC rounds, using sublinear space per machine and $\tilde{O}(λn)$ total space, where $λ$ is the arboricity of the input graph. Our result is obtained by providing a new analysis of a LOCAL algorithm by Agrawal, Zadimoghaddam, and Mirrokni [ICML 2018], which improves its round complexity from $O(\log n)$ to $O(\log λ)$. Prior to our work, no $o(\log n)$ round algorithm for constant-approximate allocation was known in either LOCAL or sublinear space MPC models for graphs with low arboricity.

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Faster Semi-streaming Matchings via Alternating Trees

We design a deterministic algorithm for the $(1+ε)$-approximate maximum matching problem. Our primary result demonstrates that this problem can be solved in $O(ε^{-6})$ semi-streaming passes, improving upon the $O(ε^{-19})$ pass-complexity algorithm by [Fischer, Mitrović, and Uitto, STOC'22]. This contributes substantially toward resolving Open question 2 from [Assadi, SOSA'24]. Leveraging the framework introduced in [FMU'22], our algorithm achieves an analogous round complexity speed-up for computing a $(1+ε)$-approximate maximum matching in both the Massively Parallel Computation (MPC) and CONGEST models. The data structures maintained by our algorithm are formulated using blossom notation and represented through alternating trees. This approach enables a simplified correctness analysis by treating specific components as if operating on bipartite graphs, effectively circumventing certain technical intricacies present in prior work.

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