2013
DOI: 10.1007/s10208-013-9145-0
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Discrete Morse Theoretic Algorithms for Computing Homology of Complexes and Maps

Abstract: We provide explicit and efficient reduction algorithms based on discrete Morse theory to simplify homology computation for a very general class of complexes. A set-valued map of top-dimensional cells between such complexes is a natural discrete approximation of an underlying (and possibly unknown) continuous function, especially when the evaluation of that function is subject to measurement errors. We introduce a new Morse theoretic pre-processing framework for deriving chain maps from such set-valued maps, an… Show more

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Cited by 80 publications
(104 citation statements)
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“…For example, two algorithms which are similar but slightly different to the one presented above are described in [17,18]. We plan to discuss and compare a range of algorithms in a subsequent article [2].…”
mentioning
confidence: 98%
“…For example, two algorithms which are similar but slightly different to the one presented above are described in [17,18]. We plan to discuss and compare a range of algorithms in a subsequent article [2].…”
mentioning
confidence: 98%
“…The first approach [18], that we call the coreduction-based algorithm, is based on the construction of an acyclic matching on a simplicial complex Σ by using coreduction pairs and removals of free simplices, where a free simplex is a simplex with an empty boundary. To obtain an acyclic matching (A, w : Q → K) of Σ, the approach in [18] initializes first sets A, Q, K and map w as null.…”
Section: A Using Coreduction Sequences and Reduction Sequencesmentioning
confidence: 99%
“…At the moment the best worst case complexity analysis of the SNF algorithm gives a lower bound that is super cubical in the dimensions of the chain complex. We developed a preprocessing technique based on discrete Morse theory that provides a linear time reduction in the size of the original complex after which the SNF algorithm is applied [5,6].…”
Section: Approximation Of Mapsmentioning
confidence: 99%