scholarly journals Comparison between Hirschberg’s algorithm and Needleman-Wunsch algorithm in finding optimal alignment in terms of search space and time complexity

2017 ◽  
Vol 05 (03) ◽  
pp. 19388-19394
Author(s):  
Fahad Almsned ◽  
Keyword(s):  
Time Complexity ◽  
Search Space ◽  
Optimal Alignment ◽  
Space And Time

scholarly journals Space and time complexity for infinite time Turing machines

2020 ◽  
Vol 30 (6) ◽  
pp. 1239-1255
Author(s):  
Merlin Carl
Keyword(s):  
Time Complexity ◽  
Space Complexity ◽  
Complexity Classes ◽  
Turing Machines ◽  
Infinite Time ◽  
Space And Time ◽  
Open Questions ◽  
Separation Results ◽  
Ordinal Computability

Abstract We consider notions of space by Winter [21, 22]. We answer several open questions about these notions, among them whether low space complexity implies low time complexity (it does not) and whether one of the equalities P=PSPACE, P$_{+}=$PSPACE$_{+}$ and P$_{++}=$PSPACE$_{++}$ holds for ITTMs (all three are false). We also show various separation results between space complexity classes for ITTMs. This considerably expands our earlier observations on the topic in Section 7.2.2 of Carl (2019, Ordinal Computability: An Introduction to Infinitary Machines), which appear here as Lemma $6$ up to Corollary $9$.

FUNCTIONAL PEARL Functional chart parsing of context-free grammars

2004 ◽  
Vol 14 (6) ◽  
pp. 669-680
Author(s):  
PETER LJUNGLÖF
Keyword(s):  
Data Structure ◽  
Time Complexity ◽  
Inference Rules ◽  
Global State ◽  
Bottom Up ◽  
Space And Time ◽  
Chart Parsing ◽  
The Many ◽  
Context Free ◽  
Context Free Grammars

This paper implements a simple and elegant version of bottom-up Kilbury chart parsing (Kilbury, 1985; Wirén, 1992). This is one of the many chart parsing variants, which are all based on the data structure of charts. The chart parsing process uses inference rules to add new edges to the chart, and parsing is complete when no further edges can be added. One novel aspect of this implementation is that it doesn't have to rely on a global state for the implementation of the chart. This makes the code clean, elegant and declarative, while still having the same space and time complexity as the standard imperative implementations.

scholarly journals Worst-case space and time complexity of recursive procedures

1996 ◽  
Vol 11 (2) ◽  
pp. 115-144 ◽  
Author(s):  
Johann Blieberger ◽  
Roland Lieger
Keyword(s):  
Time Complexity ◽  
Worst Case ◽  
Space And Time ◽  
Recursive Procedures

scholarly journals A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment

2017 ◽  
Author(s):  
Álvaro Rubio-Largo ◽  
Leonardo Vanneschi ◽  
Mauro Castelli ◽  
Miguel A. Vega-Rodríguez
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Time Complexity ◽  
Optimization Problem ◽  
Optimal Alignment ◽  
Multiple Sequence ◽  
Parallel Metaheuristics ◽  
Parallel Performance ◽  
Parallel Version ◽  
The Comparative Study

AbstractThe alignment among three or more nucleotides/amino-acids sequences at the same time is known as Multiple Sequence Alignment (MSA), an NP-hard optimization problem. The time complexity of finding an optimal alignment raises exponentially when the number of sequences to align increases. In this work, we deal with a multiobjective version of the MSA problem where the goal is to simultaneously optimize the accuracy and conservation of the alignment. A parallel version of the Hybrid Multiobjective Memetic Metaheuristics for Multiple Sequence Alignment is proposed. In order to evaluate the parallel performance of our proposal, we have selected a pull of datasets with different number of sequences (up to 1000 sequences) and study its parallel performance against other well-known parallel metaheuristics published in the literature, such as MSAProbs, T-Coffee, Clustal Ω, and MAFFT. The comparative study reveals that our parallel aligner is around 25 times faster than the sequential version with 32 cores, obtaining a parallel efficiency around 80%.

FROM C-CONTINUATIONS TO NEW QUADRATIC ALGORITHMS FOR AUTOMATON SYNTHESIS

2001 ◽  
Vol 11 (06) ◽  
pp. 707-735 ◽  
Author(s):  
J.-M. CHAMPARNAUD ◽  
D. ZIADI
Keyword(s):  
Time Complexity ◽  
Regular Expression ◽  
Time Algorithm ◽  
The Other ◽  
Worst Case ◽  
Partial Derivatives ◽  
Space And Time ◽  
Other Hand ◽  
Deterministic Automata ◽  
The Way

Two classical non-deterministic automata recognize the language denoted by a regular expression: the position automaton which deduces from the position sets defined by Glushkov and McNaughton–Yamada, and the equation automaton which can be computed via Mirkin's prebases or Antimirov's partial derivatives. Let |E| be the size of the expression and ‖E‖ be its alphabetic width, i.e. the number of symbol occurrences. The number of states in the equation automaton is less than or equal to the number of states in the position automaton, which is equal to ‖E‖+1. On the other hand, the worst-case time complexity of Antimirov algorithm is O(‖E‖3· |E|2), while it is only O(‖E‖·|E|) for the most efficient implementations yielding the position automaton (Brüggemann–Klein, Chang and Paige, Champarnaud et al.). We present an O(|E|2) space and time algorithm to compute the equation automaton. It is based on the notion of canonical derivative which makes it possible to efficiently handle sets of word derivatives. By the way, canonical derivatives also lead to a new O(|E|2) space and time algorithm to construct the position automaton.

scholarly journals Matching Qualitative Constraint Networks with Online Reinforcement Learning

10.29007/1g5q ◽  
2018 ◽  
Author(s):  
Malumbo Chipofya
Keyword(s):  
Reinforcement Learning ◽  
Time Complexity ◽  
Graph Matching ◽  
Search Space ◽  
Qualitative Reasoning ◽  
Spatial Knowledge ◽  
Constraint Networks ◽  
True Value ◽  
Reasoning Systems ◽  
Prior Estimate

Local Compatibility Matrices (LCMs) are mechanisms for computing heuristics for graph matching that are particularly suited for matching qualitative constraint networks enabling the transfer of qualitative spatial knowledge between qualitative reasoning systems or agents. A system of LCMs can be used during matching to compute a pre-move evaluation, which acts as a prior optimistic estimate of the value of matching a pair of nodes, and a post-move evaluation which adjusts the prior estimate in the direction of the true value upon completing the move. We present a metaheuristic method that uses reinforcement learning to improve the prior estimates based on the posterior evaluation. The learned values implicitly identify unprofitable regions of the search space. We also present data structures that allow a more compact implementation, limiting the space and time complexity of our algorithm.

scholarly journals Reducing The Space And Time Complexity By The Use of Triangular Matrices

2018 ◽  
Vol 5 (1) ◽  
pp. 1-10
Author(s):  
Zeynep Nihan BERBERLER ◽  
Murat Erşen BERBERLER
Keyword(s):  
Time Complexity ◽  
Triangular Matrices ◽  
Space And Time
Sign in / Sign up

Export Citation Format

Share Document