The continuous and increasing interest concerning vector optimization perceptible in the
research community, where contributions dealing with the theory of duality abound lately …

This paper presents a new method for the numerical solution of nonlinear multiobjective
optimization problems with an arbitrary partial ordering in the objective space induced by a …

We consider a Newton-CG augmented Lagrangian method for solving semidefinite
programming (SDP) problems from the perspective of approximate semismooth Newton …

We develop a fast and robust algorithm for solving large-scale convex composite
optimization models with an emphasis on the \ell_1-regularized least squares regression …

In this paper, we present a majorized semismooth Newton-CG augmented Lagrangian
method, called SDPNAL++, for semidefinite programming (SDP) with partial or full …

Smoothing functions have been much studied in the solution of optimization and
complementarity problems with nonnegativity constraints. In this paper, we extend …

The nearest correlation matrix problem is to find a correlation matrix which is closest to a
given symmetric matrix in the Frobenius norm. The well-studied dual approach is to …

There exist efficient algorithms to project a point onto the intersection of a convex conic and
an affine subspace. Those conic projections are in turn the work-horse of a range of …

In this paper, we aim to prove the linear rate convergence of the alternating direction method
of multipliers (ADMM) for solving linearly constrained convex composite optimization …

For a locally optimal solution to the nonlinear semidefinite programming problem, under
Robinson's constraint qualification, the following conditions are proved to be equivalent: the …