On High-order Model Regularization for Multiobjective Optimization

A p-order regularization method for finding weak stationary points of multiobjective optimization problems with constraints is introduced. Under Holder conditions on the derivatives of the objective functions, complexity results are obtained that generalize properties recently proved for scalar optimization. Article Download View On High-order Model Regularization for Multiobjective Optimization

Strict Fejér Monotonicity by Superiorization of Feasibility-Seeking Projection Methods

We consider the superiorization methodology, which can be thought of as lying between feasibility-seeking and constrained minimization. It is not quite trying to solve the full fledged constrained minimization problem; rather, the task is to find a feasible point which is superior (with respect to the objective function value) to one returned by a feasibility-seeking … Read more

Projected subgradient minimization versus superiorization

The projected subgradient method for constrained minimization repeatedly interlaces subgradient steps for the objective function with projections onto the feasible region, which is the intersection of closed and convex constraints sets, to regain feasibility. The latter poses a computational difficulty and, therefore, the projected subgradient method is applicable only when the feasible region is “simple … Read more

An Inexact Proximal Method for Quasiconvex Minimization

In this paper we propose an inexact proximal point method to solve constrained minimization problems with locally Lipschitz quasiconvex objective functions. Assuming that the function is also bounded from below, lower semicontinuous and using proximal distances, we show that the sequence generated for the method converges to a stationary point of the problem. Citation July … Read more

An interior-point method for MPECs based on strictly feasible relaxations

An interior-point method for solving mathematical programs with equilibrium constraints (MPECs) is proposed. At each iteration of the algorithm, a single primal-dual step is computed from each subproblem of a sequence. Each subproblem is defined as a relaxation of the MPEC with a nonempty strictly feasible region. In contrast to previous approaches, the proposed relaxation … Read more