Deterministic Distance Approximation in MPC via Improved Hitting Sets

2026-06-02

AI Summary

This work addresses the absence of efficient deterministic algorithms for constructing sparse spanners and approximating shortest paths in the Massively Parallel Computation (MPC) and Congested Clique models. The authors introduce a general derandomization technique based on deterministic hitting sets that transforms classical randomized sampling procedures into deterministic ones. Leveraging this approach, they achieve the first sub-logarithmic-round deterministic distance approximation algorithms in these parallel models. Specifically, they construct spanners with O(k) stretch in a constant number of rounds, solve O(1)-approximate all-pairs shortest paths (APSP) in O(log log log n) rounds in the Congested Clique, and obtain (1+ε)-approximate single-source shortest paths (SSSP) in polylog log n rounds in the near-linear MPC model.

Abstract

In this paper, we provide the first deterministic algorithms with sublogarithmic round complexity for spanners and approximate shortest paths in various MPC models. Moreover, we significantly improve upon the state of the art in the deterministic Congested Clique. In particular, we obtain the following four results on undirected graphs: 1. In both linear MPC and Congested Clique, we obtain an $O(k)$ stretch-spanner of a weighted graph of size $O(n^{1+1/k})$ in $O(1)$ rounds, for some parameter $k\ge 0$. For $k=O(\log{n})$, this leads to an $O(\log n)$ approximation of APSP in constant rounds in both models. 2. In sublinear MPC, we obtain an $O(k^{1+\varepsilon})$-stretch spanner of a weighted graph of size $O(n^{1+1/k})$ in $O(\log k)$ rounds, for any fixed constant $\varepsilon>0$. 3. In Congested Clique, we obtain $O(1)$-approximate APSP for weighted graphs in $O(\log \log \log n)$ rounds. 4. In near-linear MPC, we obtain $(1+\varepsilon)$-approximate single-source shortest paths and $O(1)$-approximate all-pairs shortest paths for unweighted graphs in $\textsf{poly}\log \log n$ rounds. Our algorithm only requires a single near-linear memory machine, where the rest can have sublinear memory. Our deterministic algorithms obtain similar guarantees to the state of the art randomized algorithms without incurring additional factors in the round complexity. To obtain these results, we inspect the randomized algorithms and isolate a randomized sampling routine. Then we derandomize these sampling routines by using a deterministic hitting set. Hereto, we develop a versatile deterministic hitting set algorithm, which we hope will have further derandomization applications.

Problem

• Deterministic Algorithms
• Distance Approximation
• Spanners
• Massively Parallel Computation
• Congested Clique

Innovation

• deterministic algorithms
• hitting sets
• MPC
• spanners
• approximate shortest paths