@Article{IJNAM-10-3,
author = {},
title = {An Algorithm for Finding Nonnegative Minimal Norm Solutions of Linear Systems},
journal = {International Journal of Numerical Analysis and Modeling},
year = {2013},
volume = {10},
number = {3},
pages = {745--755},
abstract = {<p style="text-align: justify;">A system of linear equations $Ax = b$, in $n$ unknowns and $m$ equations which has a
nonnegative solution is considered. Among all its solutions, the one which has the least norm
is sought when $\mathbb{R}^n$ is equipped with a strictly convex norm. We present a globally convergent,
iterative algorithm for computing this solution. This algorithm takes into account the special
structure of the problem. Each iteration cycle of the algorithm involves the solution of a similar
quadratic problem with a modified objective function. Duality conditions for optimality are
studied. Feasibility and global convergence of the algorithm are proved. As a special case we
implemented and tested the algorithm for the $\ell^p$-norm, where $1 &lt; p &lt; ∞$. Numerical results are
included.</p>},
issn = {2617-8710},
doi = {https://doi.org/2013-IJNAM-593},
url = {https://global-sci.com/article/83429/an-algorithm-for-finding-nonnegative-minimal-norm-solutions-of-linear-systems}
}