Abstract
The nonnegative rank of a matrix is the smallest inner dimension when writing it as a product of two nonnegative matrices. Such nonnegative matrix factorizations have numerous applications in machine learning and data mining, where one usually allows inexact factorizations. The exact counterpart is related to the so-called extension complexity of a polytope, an important parameter in combinatorial optimization. We implemented different algorithms for computing lower bounds on the nonnegative rank of a matrix. In this extended abstract we focus on results that relate our best algorithms' performance to the size of the matrix.
Original language | English |
---|---|
Pages | 41-44 |
Number of pages | 4 |
Publication status | Published - 1 Jan 2019 |
Event | 17th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, CTW 2019 - U-Parkhotel, Enschede, Netherlands Duration: 1 Jul 2019 → 3 Jul 2019 Conference number: 17 http://wwwhome.math.utwente.nl/~ctw/ |
Workshop
Workshop | 17th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, CTW 2019 |
---|---|
Abbreviated title | CTW 2019 |
Country/Territory | Netherlands |
City | Enschede |
Period | 1/07/19 → 3/07/19 |
Internet address |