Do you know the difference between an optimist and a pessimist? The former believes we
live in the best possible world, and the latter is afraid that the former might be right.… In that …

We present a framework for analyzing convergence and local rates of convergence of a
class of descent algorithms, assuming the objective function is weakly convex. The …

We study convergence rates of the classic proximal bundle method for a variety of
nonsmooth convex optimization problems. We show that, without any modification, this …

We consider the problem of minimizing the difference of two nonsmooth convex functions
over a simple convex set. To deal with this class of nonsmooth and nonconvex optimization …

Probability constraints are a popular modelling mechanism in applications. They help to
model feasible decisions when the latter are taken prior to observing uncertainty and both …

An optimization algorithm for nonsmooth nonconvex constrained optimization problems with
upper-objective functions is proposed and analyzed. Upper-is a weakly concave property …

Proximal bundle methods are among the most successful approaches for convex and
nonconvex optimization problems in linear spaces and it is natural to extend these methods …

An adaptive regularization algorithm using inexact function and derivatives evaluations is
proposed for the solution of composite nonsmooth nonconvex optimization. It is shown that …

We contribute improvements to a Lagrangian dual solution approach applied to large-scale
optimization problems whose objective functions are convex, continuously differentiable and …

This work considers nonsmooth and nonconvex optimization problems whose objective and
constraint functions are defined by difference-of-convex (DC) functions. We consider an …