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We consider the minimization of a convex objective function subject to the set of minima of another convex function, under the assumption that both functions are twice continuously differentiable. We approach this optimization problem from a continuous perspective by means of a second-order dynamical system with Hessian-driven damping and a penalty term corresponding to the constrained function. By constructing appropriate energy functionals, we prove weak convergence of the trajectories generated by this differential equation to a minimizer of the optimization problem as well as convergence for the objective function values along the trajectories. The performed investigations rely on Lyapunov analysis in combination with the continuous version of the Opial Lemma. In case the objective function is strongly convex, we can even show strong convergence of the trajectories.

We introduce a penalty term-based splitting algorithm with inertial effects designed for solving monotone inclusion problems involving the sum of maximally monotone operators and the convex normal cone to the (nonempty) set of zeros of a monotone and Lipschitz continuous operator. We show weak ergodic convergence of the generated sequence of iterates to a solution of the monotone inclusion problem, provided a condition expressed via the Fitzpatrick function of the operator describing the underlying set of the normal cone is verified. Under strong monotonicity assumptions we can even show strong nonergodic convergence of the iterates. This approach constitutes the starting point for investigating from a similar perspective monotone inclusion problems involving linear compositions of parallel-sum operators and, further, for the minimization of a complexly structured convex objective function subject to the set of minima of another convex and differentiable function., arXiv admin note: text overlap with arXiv:1306.0352

We investigate the asymptotic convergence of the trajectories generated by the second-order dynamical system , where are the convex and smooth functions defined on a real Hilbert space , and is a function of time which controls the penalty term. We show weak convergence of the trajectories to a minimizer of the function over the (nonempty) set of minima of as well as convergence for the objective function values along the trajectories, provided a condition expressed via the Fenchel conjugate of is fulfilled. When the function is assumed to be strongly convex, we can even show strong convergence of the trajectories. The results can be seen as the second-order counterparts of the ones given by Attouch and Czarnecki (Journal of Differential Equations 248(6), 1315–1344, 2010) for first-order dynamical systems associated to the constrained variational inequalities. At the same time we give a positive answer to an open problem posed by Attouch and Czarnecki in a recent preprint.

In this paper, we analyse the convergence rate of the sequence of objective function values of a primal-dual proximal-point algorithm recently introduced in the literature for solving a primal convex optimization problem having as objective the sum of linearly composed infimal convolutions, nonsmooth and smooth convex functions and its Fenchel-type dual one. The theoretical part is illustrated by numerical experiments in image processing.

Considering a vector optimization problem to which properly efficient solutions are defined by using convex cone-monotone scalarization functions, we attach to it, by means of perturbation theory, new vector duals. When the primal problem, the scalarization function and the perturbation function are particularized, different dual vector problems are obtained, some of them are already known in the literature. Weak and strong duality statements are delivered in each case.

We give a lower semicontinuous type regularity condition and a closedness type one which turn out to be necessary and sufficient for the fulfilment of two different formulae involving the e-subdifferential of a perturbation function, respectively. These regularity conditions prove to be sufficient also for having formulae for the classical subdifferential of a perturbation function. Some recently published results concerning e-subdifferentials are rediscovered as special cases.

We give two generalized Moreau–Rockafellar-type results for the sum of a convex function with a composition of convex functions in separated locally convex spaces. Then we equivalently characterize the stable strong duality for composed convex optimization problems through two new regularity conditions, which also guarantee two formulae of the subdifferential of the mentioned sum of functions. We also treat some special cases, rediscovering older results in the literature. A discussion on the topological assumptions for the vector function used in the composition closes this article.

We give a sufficient condition, weaker than the others known so far, that guarantees that the sum of two maximal monotone operators on a reflexive Banach space is maximal monotone. Then we give a w...

In the first part of this study we introduced six different multiobjective dual problems to a general multiobjective optimization problem, for which we presented weak as well as strong duality assertions. Afterwards, we derived some inclusion results for the image sets of three of these problems. The aim of this second part is to complete our investigations by studying the relations between all six multiobjective dual problems. Moreover, conditions under which the dual problems are equivalent are given. The results are illustrated by some examples. A general scheme containing the relations between the six multiobjective duals and other duals mentioned in the literature is derived.