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Solving feasibility problems is a central task in mathematics and the applied sciences. One particularly successful method is the Douglas--Rachford algorithm. In this paper, we provide many new conditions sufficient for finite convergence. Numerou s examples illustrate our results. [ABSTRACT FROM AUTHOR]

Motivated by an obstacle problem for a membrane subject to cohesion forces, constrained minimization problems involving a nonconvex and nondifferentiable objective functional representing the total potential energy are considered. The associated first-order optimality system leads to a hemivaxiational inequality, which can also be interpreted as a special complementarity problem in function space. Besides an analytical investigation of first-order optimality, a primal-dual active set solver is introduced. It is associated to a limit case of a semismooth Newton method for a regularized version of the underlying problem class. For the numerical algorithms studied in this paper, global as well as local convergence properties are derived and verified numerically. [ABSTRACT FROM AUTHOR]

Gould and Robinson [SIAM J. Optim., 20 (2010), pp. 2023-2048] proved global convergence of a second derivative SQP method for minimizing the exact ℓ1-merit function for a fixed value of the penalty parameter. This result required the properties of a so-called Cauchy step, which was itself computed from a so-called predictor step. In addition, they allowed for the additional computation of a variety of (optional) accelerator steps that were intended to improve the efficiency of the algorithm. The main purpose of this paper is to prove that a nonmonotone variant of the algorithm is quadratically convergent for two specific realizations of the accelerator step; this is verified with preliminary numerical results on the Hock and Schittkowski test set. Once fast local convergence is established, we consider two specific aspects of the algorithm that are important for an efficient implementation. First, we discuss a strategy for defining the positive-definite matrix Bk used in computing the predictor step that is based on a limited-memory BFGS update. Second, we provide a simple strategy for updating the penalty parameter based on approximately minimizing the ℓ1-penalty function over a sequence of increasing values of the penalty parameter. [ABSTRACT FROM AUTHOR]

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 closed pointed convex cone. This algorithm is based on the well-known scalarization approach by Pascoletti and Serafini and adaptively controls the scalarization parameters using new sensitivity results. The computed image points give a nearly equidistant approximation of the whole Pareto surface. The effectiveness of this new method is demonstrated with various test problems and an applied problem from medicine. [ABSTRACT FROM AUTHOR]