An Approximation Algorithm for the Matrix Tree Multiplication Problem

Authors Mahmoud Abo-Khamis , Ryan Curtin , Sungjin Im, Benjamin Moseley, Hung Ngo, Kirk Pruhs , Alireza Samadian



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Author Details

Mahmoud Abo-Khamis
  • RelationalAI, Berkeley, CA, USA
Ryan Curtin
  • RelationalAI, Atlanta, GA, USA
Sungjin Im
  • University of California, Merced, CA, USA
Benjamin Moseley
  • Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, USA
Hung Ngo
  • RelationalAI, Berkeley, CA, USA
Kirk Pruhs
  • Department of Computer Science, University of Pittsburgh, PA, USA
Alireza Samadian
  • Department of Computer Science, University of Pittsburgh, PA, USA

Acknowledgements

We want to thank David Fernandez-Baca for discussions and pointers related to Markovian phelogeny trees.

Cite AsGet BibTex

Mahmoud Abo-Khamis, Ryan Curtin, Sungjin Im, Benjamin Moseley, Hung Ngo, Kirk Pruhs, and Alireza Samadian. An Approximation Algorithm for the Matrix Tree Multiplication Problem. In 46th International Symposium on Mathematical Foundations of Computer Science (MFCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 202, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.MFCS.2021.6

Abstract

We consider the Matrix Tree Multiplication problem. This problem is a generalization of the classic Matrix Chain Multiplication problem covered in the dynamic programming chapter of many introductory algorithms textbooks. An instance of the Matrix Tree Multiplication problem consists of a rooted tree with a matrix associated with each edge. The output is, for each leaf in the tree, the product of the matrices on the chain/path from the root to that leaf. Matrix multiplications that are shared between various chains need only be computed once, potentially being shared between different root to leaf chains. Algorithms are evaluated by the number of scalar multiplications performed. Our main result is a linear time algorithm for which the number of scalar multiplications performed is at most 15 times the optimal number of scalar multiplications.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
Keywords
  • Matrix Multiplication
  • Approximation Algorithm

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References

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