This paper focuses on convex constrained optimization problems, where the solution is subject to a convex inequality constraint. In particular, we aim at challenging problems for which both projection into the constrained domain and a linear optimization under the inequality constraint are time-consuming, which render both projected gradient methods and conditional gradient methods (a.k.a. the ...

Nonnegative matrix factorization (NMF) can be formulated as a minimization problem with bound constraints. Although bound-constrained optimization has been studied extensively in both theory and practice, so far no study has formally applied its techniques to NMF. In this letter, we propose two projected gradient methods for NMF, both of which exhibit strong optimization properties. We discuss ...

When solving the general smooth nonlinear optimization problem involving equality and/or inequality constraints, an approximate first-order critical point of accuracy ǫ can be obtained by a second-order method using cubic regularization in at most O(ǫ) problem-functions evaluations, the same order bound as in the unconstrained case. This result is obtained by first showing that the same result ...

This paper introduces an efficient modified derivative-free method for bound constrained optimization problems. It is based on the coordinate search method. During the running of the algorithm, it incorporates the progressive obtained local information into the current iteration. Actually, after we find two different suitable descent directions, we introduce the composite expansion step. By doi...

We analyze a trust region version of Newton’s method for bound-constrained problems. Our approach relies on the geometry of the feasible set, not on the particular representation in terms of constraints. The convergence theory holds for linearly constrained problems and yields global and superlinear convergence without assuming either strict complementarity or linear independence of the active ...