Discrete Morse theory is a combinatorial adaptation of Morse theory developed by Robin Forman. The theory has various practical applications in diverse fields of applied mathematics and computer science, such as configuration spaces,[1] homology computation,[2][3] denoising,[4] mesh compression,[5] and topological data analysis.[6]

Notation regarding CW complexes edit

Let   be a CW complex and denote by   its set of cells. Define the incidence function   in the following way: given two cells   and   in  , let   be the degree of the attaching map from the boundary of   to  . The boundary operator is the endomorphism   of the free abelian group generated by   defined by

 

It is a defining property of boundary operators that  . In more axiomatic definitions[7] one can find the requirement that  

 

which is a consequence of the above definition of the boundary operator and the requirement that  .

Discrete Morse functions edit

A real-valued function   is a discrete Morse function if it satisfies the following two properties:

  1. For any cell  , the number of cells   in the boundary of   which satisfy   is at most one.
  2. For any cell  , the number of cells   containing   in their boundary which satisfy   is at most one.

It can be shown[8] that the cardinalities in the two conditions cannot both be one simultaneously for a fixed cell  , provided that   is a regular CW complex. In this case, each cell   can be paired with at most one exceptional cell  : either a boundary cell with larger   value, or a co-boundary cell with smaller   value. The cells which have no pairs, i.e., whose function values are strictly higher than their boundary cells and strictly lower than their co-boundary cells are called critical cells. Thus, a discrete Morse function partitions the CW complex into three distinct cell collections:  , where:

  1.   denotes the critical cells which are unpaired,
  2.   denotes cells which are paired with boundary cells, and
  3.   denotes cells which are paired with co-boundary cells.

By construction, there is a bijection of sets between  -dimensional cells in   and the  -dimensional cells in  , which can be denoted by   for each natural number  . It is an additional technical requirement that for each  , the degree of the attaching map from the boundary of   to its paired cell   is a unit in the underlying ring of  . For instance, over the integers  , the only allowed values are  . This technical requirement is guaranteed, for instance, when one assumes that   is a regular CW complex over  .

The fundamental result of discrete Morse theory establishes that the CW complex   is isomorphic on the level of homology to a new complex   consisting of only the critical cells. The paired cells in   and   describe gradient paths between adjacent critical cells which can be used to obtain the boundary operator on  . Some details of this construction are provided in the next section.

The Morse complex edit

A gradient path is a sequence of paired cells

 

satisfying   and  . The index of this gradient path is defined to be the integer

 

The division here makes sense because the incidence between paired cells must be  . Note that by construction, the values of the discrete Morse function   must decrease across  . The path   is said to connect two critical cells   if  . This relationship may be expressed as  . The multiplicity of this connection is defined to be the integer  . Finally, the Morse boundary operator on the critical cells   is defined by

 

where the sum is taken over all gradient path connections from   to  .

Basic Results edit

Many of the familiar results from continuous Morse theory apply in the discrete setting.

The Morse Inequalities edit

Let   be a Morse complex associated to the CW complex  . The number   of  -cells in   is called the  -th Morse number. Let   denote the  -th Betti number of  . Then, for any  , the following inequalities[9] hold

 , and
 

Moreover, the Euler characteristic   of   satisfies

 

Discrete Morse Homology and Homotopy Type edit

Let   be a regular CW complex with boundary operator   and a discrete Morse function  . Let   be the associated Morse complex with Morse boundary operator  . Then, there is an isomorphism[10] of homology groups

 

and similarly for the homotopy groups.

Applications edit

Discrete Morse theory finds its application in molecular shape analysis,[11] skeletonization of digital images/volumes,[12] graph reconstruction from noisy data,[13] denoising noisy point clouds[14] and analysing lithic tools in archaeology.[15]

See also edit

References edit

  1. ^ Mori, Francesca; Salvetti, Mario (2011), "(Discrete) Morse theory for Configuration spaces" (PDF), Mathematical Research Letters, 18 (1): 39–57, doi:10.4310/MRL.2011.v18.n1.a4, MR 2770581
  2. ^ Perseus: the Persistent Homology software.
  3. ^ Mischaikow, Konstantin; Nanda, Vidit (2013). "Morse Theory for Filtrations and Efficient computation of Persistent Homology". Discrete & Computational Geometry. 50 (2): 330–353. doi:10.1007/s00454-013-9529-6.
  4. ^ Bauer, Ulrich; Lange, Carsten; Wardetzky, Max (2012). "Optimal Topological Simplification of Discrete Functions on Surfaces". Discrete & Computational Geometry. 47 (2): 347–377. arXiv:1001.1269. doi:10.1007/s00454-011-9350-z.
  5. ^ Lewiner, T.; Lopes, H.; Tavares, G. (2004). "Applications of Forman's discrete Morse theory to topology visualization and mesh compression" (PDF). IEEE Transactions on Visualization and Computer Graphics. 10 (5): 499–508. doi:10.1109/TVCG.2004.18. PMID 15794132. S2CID 2185198. Archived from the original (PDF) on 2012-04-26.
  6. ^ "the Topology ToolKit". GitHub.io.
  7. ^ Mischaikow, Konstantin; Nanda, Vidit (2013). "Morse Theory for Filtrations and Efficient computation of Persistent Homology". Discrete & Computational Geometry. 50 (2): 330–353. doi:10.1007/s00454-013-9529-6.
  8. ^ Forman 1998, Lemma 2.5
  9. ^ Forman 1998, Corollaries 3.5 and 3.6
  10. ^ Forman 1998, Theorem 7.3
  11. ^ Cazals, F.; Chazal, F.; Lewiner, T. (2003). "Molecular shape analysis based upon the morse-smale complex and the connolly function". Proceedings of the nineteenth annual symposium on Computational geometry. ACM Press. pp. 351–360. doi:10.1145/777792.777845. ISBN 978-1-58113-663-0. S2CID 1570976.
  12. ^ Delgado-Friedrichs, Olaf; Robins, Vanessa; Sheppard, Adrian (March 2015). "Skeletonization and Partitioning of Digital Images Using Discrete Morse Theory". IEEE Transactions on Pattern Analysis and Machine Intelligence. 37 (3): 654–666. doi:10.1109/TPAMI.2014.2346172. hdl:1885/12873. ISSN 1939-3539. PMID 26353267. S2CID 7406197.
  13. ^ Dey, Tamal K.; Wang, Jiayuan; Wang, Yusu (2018). Speckmann, Bettina; Tóth, Csaba D. (eds.). Graph Reconstruction by Discrete Morse Theory. 34th International Symposium on Computational Geometry (SoCG 2018). Leibniz International Proceedings in Informatics (LIPIcs). Vol. 99. Dagstuhl, Germany: Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik. pp. 31:1–31:15. doi:10.4230/LIPIcs.SoCG.2018.31. ISBN 978-3-95977-066-8. S2CID 3994099.
  14. ^ Mukherjee, Soham (2021-09-01). "Denoising with discrete Morse theory". The Visual Computer. 37 (9): 2883–94. doi:10.1007/s00371-021-02255-7. S2CID 237426675.
  15. ^ Bullenkamp, Jan Philipp; Linsel, Florian; Mara, Hubert (2022), "Lithic Feature Identification in 3D based on Discrete Morse Theory", Proceedings of Eurographics Workshop on Graphics and Cultural Heritage (GCH), Delft, Netherlands: Eurographics Association, pp. 55–58, doi:10.2312/VAST/VAST10/131-138, ISBN 9783038681786, ISSN 2312-6124, S2CID 17294591, retrieved 2022-10-05