ANALYSIS OF NONNEGATIVE LEAST SQUARES ALGORITHMS
Abstract
Nonnegative least squares problems (NNLS) which are least squares solutions
that are constrained to take nonnegative values often arise in many applications like
image processing, data mining, etc. There have been several approaches to solve such a
problem like the active set method by Lawson and Hanson, FNNLS by Bro and Jong, the
Quasi-Newton minimization method, and Randomized projections methods. In this
thesis, we evaluated the performance properties of all these algorithms by implementing
them in MATLAB and compared the results. The results obtained showed that
Randomized projections seem to work very efficiently, producing results around 3 times
faster than Quasi-Newton method with a relative error of 3.25% for randomly generated
matrices using MATLAB.
Citation
Sathyakumar, Jason Stanley (2021). ANALYSIS OF NONNEGATIVE LEAST SQUARES ALGORITHMS. Master's thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /195814.