Edit Distance Cannot Be Computed in Strongly Subquadratic Time (unless SETH is false)
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Abstract (arXiv)
The edit distance (a.k.a. the Levenshtein distance) between two strings is defined as the minimum number of insertions, deletions or substitutions of symbols needed to transform one string into another. The problem of computing the edit distance between two strings is a classical computational task, with a well-known algorithm based on dynamic programming. Unfortunately, all known algorithms for this problem run in nearly quadratic time. In this paper we provide evidence that the near-quadratic running time bounds known for the problem of computing edit distance might be tight. Specifically, we show that, if the edit distance can be computed in time O(n2-δ) for some constant δ>0, then the satisfiability of conjunctive normal form formulas with N variables and M clauses can be solved in time MO(1) 2(1-ε)N for a constant ε>0. The latter result would violate the Strong Exponential Time Hypothesis, which postulates that such algorithms do not exist.
Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2014.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 5; 2024: 2; 2025: 4; 2026: 1.
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- 2022On The Computational Complexity of Self-Attentioncited by 137
- 2025CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agentscited by 32
- 2022Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessingcited by 29
- 2021Neural Distance Embeddings for Biological Sequencescited by 17
- 2024Limits of Transformer Language Models on Learning to Compose Algorithmscited by 13
- 2023string2string: A Modern Python Library for String-to-String Algorithmscited by 9
- 2024Fundamental Limitations on Subquadratic Alternatives to Transformerscited by 9
- 2023The complexity of non-stationary reinforcement learningcited by 7
- 2025LLM-Guided Search for Deletion-Correcting Codescited by 4
- 2025Subquadratic Algorithms and Hardness for Attention with Any Temperaturecited by 4
- 2025The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architecturescited by 3
- 2022Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storagecited by 2
6 more
- 2021On Complexity of 1-Center in Various Metricscited by 1
- 2023On the Fine-Grained Hardness of Inverting Generative Modelscited by 1
- 2023DoDo-Code: an Efficient Levenshtein Distance Embedding-based Code for 4-ary IDS Channelcited by 1
- 2021FlexiTerm: A more efficient implementation of flexible multi-word term recognitioncited by 0
- 2023On the k-Hamming and k-Edit Distancescited by 0
- 2026On the Computational Hardness of Transformerscited by 0
- 2026On the Computational Hardness of Transformers11 Mar 2026
- 2025CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents4 Nov 2025
- 2025The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures6 Oct 2025
- 2025Subquadratic Algorithms and Hardness for Attention with Any Temperature20 May 2025
- 2025LLM-Guided Search for Deletion-Correcting Codes1 Apr 2025
- 2024Fundamental Limitations on Subquadratic Alternatives to Transformers5 Oct 2024
- 2024Limits of Transformer Language Models on Learning to Compose Algorithms8 Feb 2024
- 2023DoDo-Code: an Efficient Levenshtein Distance Embedding-based Code for 4-ary IDS Channel20 Dec 2023
- 2023On the Fine-Grained Hardness of Inverting Generative Models11 Sep 2023
- 2023The complexity of non-stationary reinforcement learning13 Jul 2023
- 2023On the k-Hamming and k-Edit Distances15 Jun 2023
- 2023string2string: A Modern Python Library for String-to-String Algorithms27 Apr 2023
6 more
- 2022Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing25 Nov 2022
- 2022On The Computational Complexity of Self-Attention11 Sep 2022
- 2022Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage11 Jul 2022
- 2021On Complexity of 1-Center in Various Metrics6 Dec 2021
- 2021FlexiTerm: A more efficient implementation of flexible multi-word term recognition13 Oct 2021
- 2021Neural Distance Embeddings for Biological Sequences20 Sep 2021
- 2022On The Computational Complexity of Self-Attentioncited by 137
- 2022Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessingcited by 29
- 2021Neural Distance Embeddings for Biological Sequencescited by 17
- 2024Limits of Transformer Language Models on Learning to Compose Algorithmscited by 13
- 2023string2string: A Modern Python Library for String-to-String Algorithmscited by 9
- 2023The complexity of non-stationary reinforcement learningcited by 7
- 2025LLM-Guided Search for Deletion-Correcting Codescited by 4
- 2025Subquadratic Algorithms and Hardness for Attention with Any Temperaturecited by 4
- 2025The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architecturescited by 3
- 2022Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storagecited by 2
- 2021On Complexity of 1-Center in Various Metricscited by 1
- 2023On the Fine-Grained Hardness of Inverting Generative Modelscited by 1
12 most cited of 16 All 16 (JSON)
- 2026On the Computational Hardness of Transformers11 Mar 2026
- 2025The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures6 Oct 2025
- 2025Subquadratic Algorithms and Hardness for Attention with Any Temperature20 May 2025
- 2025LLM-Guided Search for Deletion-Correcting Codes1 Apr 2025
- 2024Limits of Transformer Language Models on Learning to Compose Algorithms8 Feb 2024
- 2023DoDo-Code: an Efficient Levenshtein Distance Embedding-based Code for 4-ary IDS Channel20 Dec 2023
- 2023On the Fine-Grained Hardness of Inverting Generative Models11 Sep 2023
- 2023The complexity of non-stationary reinforcement learning13 Jul 2023
- 2023On the k-Hamming and k-Edit Distances15 Jun 2023
- 2023string2string: A Modern Python Library for String-to-String Algorithms27 Apr 2023
- 2022Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing25 Nov 2022
- 2022On The Computational Complexity of Self-Attention11 Sep 2022
12 newest of 16 All 16 (JSON)