License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CCC.2021.13
URN: urn:nbn:de:0030-drops-142879
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14287/
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Franks, W. Cole ; Reichenbach, Philipp

Barriers for Recent Methods in Geodesic Optimization

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LIPIcs-CCC-2021-13.pdf (1 MB)

Abstract

We study a class of optimization problems including matrix scaling, matrix balancing, multidimensional array scaling, operator scaling, and tensor scaling that arise frequently in theory and in practice. Some of these problems, such as matrix and array scaling, are convex in the Euclidean sense, but others such as operator scaling and tensor scaling are geodesically convex on a different Riemannian manifold. Trust region methods, which include box-constrained Newton’s method, are known to produce high precision solutions very quickly for matrix scaling and matrix balancing (Cohen et. al., FOCS 2017, Allen-Zhu et. al. FOCS 2017), and result in polynomial time algorithms for some geodesically convex problems like operator scaling (Garg et. al. STOC 2018, Bürgisser et. al. FOCS 2019). One is led to ask whether these guarantees also hold for multidimensional array scaling and tensor scaling.
We show that this is not the case by exhibiting instances with exponential diameter bound: we construct polynomial-size instances of 3-dimensional array scaling and 3-tensor scaling whose approximate solutions all have doubly exponential condition number. Moreover, we study convex-geometric notions of complexity known as margin and gap, which are used to bound the running times of all existing optimization algorithms for such problems. We show that margin and gap are exponentially small for several problems including array scaling, tensor scaling and polynomial scaling. Our results suggest that it is impossible to prove polynomial running time bounds for tensor scaling based on diameter bounds alone. Therefore, our work motivates the search for analogues of more sophisticated algorithms, such as interior point methods, for geodesically convex optimization that do not rely on polynomial diameter bounds.

BibTeX - Entry

@InProceedings{franks_et_al:LIPIcs.CCC.2021.13,
  author =	{Franks, W. Cole and Reichenbach, Philipp},
  title =	{{Barriers for Recent Methods in Geodesic Optimization}},
  booktitle =	{36th Computational Complexity Conference (CCC 2021)},
  pages =	{13:1--13:54},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-193-1},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{200},
  editor =	{Kabanets, Valentine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/14287},
  URN =		{urn:nbn:de:0030-drops-142879},
  doi =		{10.4230/LIPIcs.CCC.2021.13},
  annote =	{Keywords: Geodesically Convex Optimization, Weight Margin, Moment Polytope, Diameter Bounds, Tensor Scaling, Matrix Scaling}
}

Keywords: Geodesically Convex Optimization, Weight Margin, Moment Polytope, Diameter Bounds, Tensor Scaling, Matrix Scaling
Collection: 36th Computational Complexity Conference (CCC 2021)
Issue Date: 2021
Date of publication: 08.07.2021

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