We consider the problem of minimizing the weighted sum of a smooth function f and a convex function P of n real variables subject to m linear equality constraints. We propose a block-coordinate gradient descent method for solving this problem, with
Projected Gradient Methods for Non-negative Matrix Factorization Chih-Jen Lin Department of Computer Science Abstract Non-negative matrix factorization (NMF) can be formulated as a minimization problem with bound constraints. Although bound-constrai
