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20102023
most citedMultivariate Fine-Grained Complexity of Longest Common Subsequence

44 citations · 115 across the 13 of their papers we have counts for

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cs.CC2023★ 3 cited

The NFA Acceptance Hypothesis: Non-Combinatorial and Dynamic Lower Bounds

Karl Bringmann, Allan Grønlund, Marvin Künnemann +1

We pose the fine-grained hardness hypothesis that the textbook algorithm for the NFA Acceptance problem is optimal up to subpolynomial factors, even for dense NFAs and fixed alphab…

cs.CC2020★ 3 cited

Impossibility Results for Grammar-Compressed Linear Algebra

Amir Abboud, Arturs Backurs, Karl Bringmann +1

To handle vast amounts of data, it is natural and popular to compress vectors and matrices. When we compress a vector from size down to size , it certainly makes it ea…

cs.CC2018★ 44 cited

Multivariate Fine-Grained Complexity of Longest Common Subsequence

Karl Bringmann, Marvin Künnemann

We revisit the classic combinatorial pattern matching problem of finding a longest common subsequence (LCS). For strings and of length , a textbook algorithm solves LCS…

cs.CC2018★ 23 cited

Fine-Grained Complexity of Analyzing Compressed Data: Quantifying Improvements over Decompress-And-Solve

Amir Abboud, Arturs Backurs, Karl Bringmann +1

Can we analyze data without decompressing it? As our data keeps growing, understanding the time complexity of problems on compressed inputs, rather than in convenient uncompressed…

cs.CC2017

Tight Conditional Lower Bounds for Longest Common Increasing Subsequence

Lech Duraj, Marvin Künnemann, Adam Polak

We consider the canonical generalization of the well-studied Longest Increasing Subsequence problem to multiple sequences, called -LCIS: Given integer sequences $X_1,\dots,X…

cs.CC2015★ 28 cited

Quadratic Conditional Lower Bounds for String Problems and Dynamic Time Warping

Karl Bringmann, Marvin Künnemann

Classic similarity measures of strings are longest common subsequence and Levenshtein distance (i.e., the classic edit distance). A classic similarity measure of curves is dynamic…