Over the last two decades, it has been observed that using the gradient vector as a search
direction in large-scale optimization may lead to efficient algorithms. The effectiveness relies …

This work surveys essential properties of the so-called copositive matrices, the study of
which has been spread over more than fifty-five years. Special emphasis is given to …

In this paper we analyze several new methods for solving nonconvex optimization problems
with the objective function consisting of a sum of two terms: one is nonconvex and smooth …

In this paper we propose a variant of the random coordinate descent method for solving
linearly constrained convex optimization problems with composite objective functions. If the …

The purpose of this paper is to develop new efficient approaches based on DC (Difference
of Convex functions) programming and DCA (DC Algorithm) to perform clustering via …

In this paper, we consider the tensor generalized eigenvalue complementarity problem
(TGEiCP), which is an interesting generalization of matrix eigenvalue complementarity …

Copositive optimization is a quickly expanding scientific research domain with wide-spread
applications ranging from global nonconvex problems in engineering to NP-hard …

This work concerns the spectral analysis of connected graphs from a non-traditional point of
view. Instead of the usual eigenvalues of the adjacency matrix AG of a graph G under …

We study several variants of a nonsmooth Newton-type algorithm for solving an eigenvalue
problem of the form K ∋ x ⊥ (Ax-λ Bx) ∈ K^+. Such an eigenvalue problem arises in …

In this paper, we study the Eigenvalue Complementarity Problem (EiCP) when its matrix A
belongs to the class S (G)={A=[aij]: aij= aji≠ 0 iff ij∈ E}, where G=(V, E) is a connected …