Online Duet between Metric Embeddings and Minimum-Weight Perfect Matchings
Sujoy Bhore, Arnold Filtser, Csaba D. Tóth
Abstract
Low-distortional metric embeddings are a crucial component in the modern algorithmic toolkit. In an online metric embedding, points arrive sequentially and the goal is to embed them into a simple space irrevocably, while minimizing the distortion. Our first result is a deterministic online embedding of a general metric into Euclidean space with distortion O(log n) ⋅ min √ log Φ, √ n (or, O(d) ⋅ min √ log Φ, √ n if the metric has doubling dimension d), solving affirmatively a conjecture by Newman and Rabinovich (2020), and quadratically improving the dependence on the aspect ratio Φ from Indyk et al. (2010). Our second result is a stochastic embedding of a metric space into trees with expected distortion O(d ⋅ log Φ), generalizing previous results (Indyk et al. (2010), Bartal et al. ( 2020)).
Next, we study the problem of online minimum-weight perfect matching (MWPM). Here a sequence of 2n points s 1 , . . . s 2n in a metric space arrive in pairs, and one has to maintain a perfect matching on the first 2i points S i = s 1 , . . . s 2i . We allow recourse (as otherwise the order of arrival determines the matching). The goal is to return a perfect matching that approximates the minimum-weight perfect matching on S i , while minimizing the recourse. Online matchings are among the most studied online problems, however, there is no previous work on online MWPM. One potential reason for this is that online MWPM is drastically non-monotone, which makes online optimization highly challenging. Our third result is a randomized algorithm with competitive ratio O(d ⋅ log Φ) and recourse O(log Φ) against an oblivious adversary, this result is obtained via our new stochastic online embedding. Our fourth result is a deterministic algorithm that works against an adaptive adversary, using O(log 2 n) recourse, and maintains a matching of total weight at most O(log n) times the weight of the MST, i.e., a matching of lightness O(log n). We complement our upper bounds with a strategy for an oblivious adversary that, with recourse r, establishes a lower bound of Ω( log n r log r ) for both competitive ratio as well as lightness.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 0ae8ce9f-8e0d-4789-ab2f-bf1cdbd31fd3Cited by top-tier papers4
- How to Protect Yourself from Threatening Skeletons: Optimal Padded Decompositions for Minor-Free GraphsJonathan Conroy, Arnold FiltserSTOC 2025 · 11 citations
- Online Probabilistic Metric Embedding: A General Framework for Bypassing Inherent BoundsYair Bartal, Nova Fandina, Seeun William UmbohSODA 2020 · 5 citations
- Covering Approximate Shortest Paths with DAGsSepehr Assadi, Gary Hoppenworth, Nicole WeinSTOC 2025 · 1 citation
- Stochastic Embedding of Digraphs into DAGsArnold FiltserSODA 2026
Builds on13
- On Light Spanners, Low-treewidth Embeddings and Efficient Traversing in Minor-free GraphsVincent Cohen-Addad, Arnold Filtser, Philip N. Klein, Hung LeFOCS 2020 · 25 citations
- Online Edge Coloring Algorithms via the Nibble MethodSayan Bhattacharya, Fabrizio Grandoni, David WajcSODA 2021 · 14 citations
- Zeros of ferromagnetic 2-spin systemsHeng Guo, Jingcheng Liu, Pinyan LuSODA 2020 · 12 citations
- Clan embeddings into trees, and low treewidth graphsArnold Filtser, Hung LeSTOC 2021 · 11 citations
- Streaming Facility Location in High Dimension via Geometric HashingArtur Czumaj, Shaofeng H.-C. Jiang, Robert Krauthgamer, Pavel Veselý et al.FOCS 2022 · 11 citations
Related papers
- A Deterministic Polylogarithmic Competitive Algorithm for Matching with DelaysMarc Dufay, Roger WattenhoferSODA 2026
- Distortion-Oblivious Algorithms for Minimizing Flow TimeYossi Azar, Stefano Leonardi, Noam TouitouSODA 2022 · 13 citations
- The Min-Cost Matching with Concave Delays ProblemYossi Azar, Runtian Ren, Danny VainsteinSODA 2021 · 6 citations
- Fully Dynamic Embedding into ℓp SpacesKiarash Banihashem, Xiang Chen, MohammadTaghi Hajiaghayi, Sungchul Kim et al.ICML 2025
- Secretary Matching with Vertex Arrivals and No RejectionsMohak GoyalAAAI 2022 · 3 citations
