362 research outputs found
Subsequence Automata with Default Transitions
Let be a string of length with characters from an alphabet of size
. The \emph{subsequence automaton} of (often called the
\emph{directed acyclic subsequence graph}) is the minimal deterministic finite
automaton accepting all subsequences of . A straightforward construction
shows that the size (number of states and transitions) of the subsequence
automaton is and that this bound is asymptotically optimal.
In this paper, we consider subsequence automata with \emph{default
transitions}, that is, special transitions to be taken only if none of the
regular transitions match the current character, and which do not consume the
current character. We show that with default transitions, much smaller
subsequence automata are possible, and provide a full trade-off between the
size of the automaton and the \emph{delay}, i.e., the maximum number of
consecutive default transitions followed before consuming a character.
Specifically, given any integer parameter , , we
present a subsequence automaton with default transitions of size
and delay . Hence, with we
obtain an automaton of size and delay . On
the other extreme, with , we obtain an automaton of size and delay , thus matching the bound for the standard subsequence
automaton construction. Finally, we generalize the result to multiple strings.
The key component of our result is a novel hierarchical automata construction
of independent interest.Comment: Corrected typo
Combinatorial Algorithms for Subsequence Matching: A Survey
In this paper we provide an overview of a series of recent results regarding
algorithms for searching for subsequences in words or for the analysis of the
sets of subsequences occurring in a word.Comment: This is a revised version of the paper with the same title which
appeared in the Proceedings of NCMA 2022, EPTCS 367, 2022, pp. 11-27 (DOI:
10.4204/EPTCS.367.2). The revision consists in citing a series of relevant
references which were not covered in the initial version, and commenting on
how they relate to the results we survey. arXiv admin note: text overlap with
arXiv:2206.1389
Longest Common Subsequence with Gap Constraints
We consider the longest common subsequence problem in the context of
subsequences with gap constraints. In particular, following Day et al. 2022, we
consider the setting when the distance (i. e., the gap) between two consecutive
symbols of the subsequence has to be between a lower and an upper bound (which
may depend on the position of those symbols in the subsequence or on the
symbols bordering the gap) as well as the case where the entire subsequence is
found in a bounded range (defined by a single upper bound), considered by
Kosche et al. 2022. In all these cases, we present effcient algorithms for
determining the length of the longest common constrained subsequence between
two given strings
Longest common parameterized subsequences with fixed common substring
In this paper we consider the problem of the longest common parameterized subsequence with fixed common substring (STR-IC-LCPS). in particular, we show that STR-IC-LCPS is NP-complete. We describe an approach to solve STR-IC-LCPS. This approach is based on an explicit reduction from the problem to the satisfiability problem
Regular expression constrained sequence alignment revisited
International audienceImposing constraints in the form of a finite automaton or a regular expression is an effective way to incorporate additional a priori knowledge into sequence alignment procedures. With this motivation, the Regular Expression Constrained Sequence Alignment Problem was introduced, which proposed an O(n^2t^4) time and O(n^2t^2) space algorithm for solving it, where n is the length of the input strings and t is the number of states in the input non-deterministic automaton. A faster O(n^2t^3) time algorithm for the same problem was subsequently proposed. In this article, we further speed up the algorithms for Regular Language Constrained Sequence Alignment by reducing their worst case time complexity bound to O(n^2t^3/log t). This is done by establishing an optimal bound on the size of Straight-Line Programs solving the maxima computation subproblem of the basic dynamic programming algorithm. We also study another solution based on a Steiner Tree computation. While it does not improve worst case, our simulations show that both approaches are efficient in practice, especially when the input automata are dense
Source Coding for Quasiarithmetic Penalties
Huffman coding finds a prefix code that minimizes mean codeword length for a
given probability distribution over a finite number of items. Campbell
generalized the Huffman problem to a family of problems in which the goal is to
minimize not mean codeword length but rather a generalized mean known as a
quasiarithmetic or quasilinear mean. Such generalized means have a number of
diverse applications, including applications in queueing. Several
quasiarithmetic-mean problems have novel simple redundancy bounds in terms of a
generalized entropy. A related property involves the existence of optimal
codes: For ``well-behaved'' cost functions, optimal codes always exist for
(possibly infinite-alphabet) sources having finite generalized entropy. Solving
finite instances of such problems is done by generalizing an algorithm for
finding length-limited binary codes to a new algorithm for finding optimal
binary codes for any quasiarithmetic mean with a convex cost function. This
algorithm can be performed using quadratic time and linear space, and can be
extended to other penalty functions, some of which are solvable with similar
space and time complexity, and others of which are solvable with slightly
greater complexity. This reduces the computational complexity of a problem
involving minimum delay in a queue, allows combinations of previously
considered problems to be optimized, and greatly expands the space of problems
solvable in quadratic time and linear space. The algorithm can be extended for
purposes such as breaking ties among possibly different optimal codes, as with
bottom-merge Huffman coding.Comment: 22 pages, 3 figures, submitted to IEEE Trans. Inform. Theory, revised
per suggestions of reader
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