91 research outputs found
Why is it hard to beat for Longest Common Weakly Increasing Subsequence?
The Longest Common Weakly Increasing Subsequence problem (LCWIS) is a variant
of the classic Longest Common Subsequence problem (LCS). Both problems can be
solved with simple quadratic time algorithms. A recent line of research led to
a number of matching conditional lower bounds for LCS and other related
problems. However, the status of LCWIS remained open.
In this paper we show that LCWIS cannot be solved in strongly subquadratic
time unless the Strong Exponential Time Hypothesis (SETH) is false.
The ideas which we developed can also be used to obtain a lower bound based
on a safer assumption of NC-SETH, i.e. a version of SETH which talks about NC
circuits instead of less expressive CNF formulas
Bounds on the Number of Longest Common Subsequences
This paper performs the analysis necessary to bound the running time of
known, efficient algorithms for generating all longest common subsequences.
That is, we bound the running time as a function of input size for algorithms
with time essentially proportional to the output size. This paper considers
both the case of computing all distinct LCSs and the case of computing all LCS
embeddings. Also included is an analysis of how much better the efficient
algorithms are than the standard method of generating LCS embeddings. A full
analysis is carried out with running times measured as a function of the total
number of input characters, and much of the analysis is also provided for cases
in which the two input sequences are of the same specified length or of two
independently specified lengths.Comment: 13 pages. Corrected typos, corrected operation of hyperlinks,
improved presentatio
Dynamic Set Intersection
Consider the problem of maintaining a family of dynamic sets subject to
insertions, deletions, and set-intersection reporting queries: given , report every member of in any order. We show that in the word
RAM model, where is the word size, given a cap on the maximum size of
any set, we can support set intersection queries in
expected time, and updates in expected time. Using this algorithm
we can list all triangles of a graph in
expected time, where and
is the arboricity of . This improves a 30-year old triangle enumeration
algorithm of Chiba and Nishizeki running in time.
We provide an incremental data structure on that supports intersection
{\em witness} queries, where we only need to find {\em one} .
Both queries and insertions take O\paren{\sqrt \frac{N}{w/\log^2 w}} expected
time, where . Finally, we provide time/space tradeoffs for
the fully dynamic set intersection reporting problem. Using words of space,
each update costs expected time, each reporting query
costs expected time where
is the size of the output, and each witness query costs expected time.Comment: Accepted to WADS 201
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