New classes of generalized invex monotonicity.
New complexity analysis of a full Nesterov- Todd step infeasible interior-point algorithm for symmetric optimization
A full Nesterov-Todd step infeasible interior-point algorithm is proposed for solving linear programming problems over symmetric cones by using the Euclidean Jordan algebra. Using a new approach, we also provide a search direction and show that the iteration bound coincides with the best known bound for infeasible interior-point methods.
New Farkas-type constraint qualifications in convex infinite programming
This paper provides KKT and saddle point optimality conditions, duality theorems and stability theorems for consistent convex optimization problems posed in locally convex topological vector spaces. The feasible sets of these optimization problems are formed by those elements of a given closed convex set which satisfy a (possibly infinite) convex system. Moreover, all the involved functions are assumed to be convex, lower semicontinuous and proper (but not necessarily real-valued). The key result...
Newton-Kantorovich method and its global convergence.
Non-compact lamination convex hulls
Nonlinear multiple hybrid procedures for solving some constrained nonlinear optimization problems
We introduce a new formulation of multiple hybrid procedures which consist in a combination of k arbitrary approximate solutions. The connection between this method and other vector sequence transformations is studied. This connection is also exploited for solving some constrained nonlinear optimization problems. A convergence acceleration result is established and numerical examples are given.
Nonlinear Successive Over-Relaxation.
Non-monotoneous parallel iteration for solving convex feasibility problems
The method of projections onto convex sets to find a point in the intersection of a finite number of closed convex sets in an Euclidean space, sometimes leads to slow convergence of the constructed sequence. Such slow convergence depends both on the choice of the starting point and on the monotoneous behaviour of the usual algorithms. As there is normally no indication of how to choose the starting point in order to avoid slow convergence, we present in this paper a non-monotoneous parallel algorithm...
Note on the paper: interior proximal method for variational inequalities on non-polyhedral sets
In this paper we clarify that the interior proximal method developed in [6] (vol. 27 of this journal) for solving variational inequalities with monotone operators converges under essentially weaker conditions concerning the functions describing the "feasible" set as well as the operator of the variational inequality.
Notwendige und hinreichende Bedingungen für (strenge) Konvexität, Pseudokonvexität und (strenge) Quasikonvexität einer quadratischen Funktion bezüglich einer konvexen Menge
Numerical behavior of the method of projection onto an acute cone with level control in convex minimization
We present the numerical behavior of a projection method for convex minimization problems which was studied by Cegielski [1]. The method is a modification of the Polyak subgradient projection method [6] and of variable target value subgradient method of Kim, Ahn and Cho [2]. In each iteration of the method an obtuse cone is constructed. The obtuse cone is generated by a linearly independent system of subgradients. The next approximation of a solution is the projection onto a translated acute cone...
Numerical considerations of a hybrid proximal projection algorithm for solving variational inequalities
In this paper, some ideas for the numerical realization of the hybrid proximal projection algorithm from Solodov and Svaiter [22] are presented. An example is given which shows that this hybrid algorithm does not generate a Fejér-monotone sequence. Further, a strategy is suggested for the computation of inexact solutions of the auxiliary problems with a certain tolerance. For that purpose, ε-subdifferentials of the auxiliary functions and the bundle trust region method from Schramm and Zowe [20]...
Numerical study of discretizations of multistage stochastic programs
This paper presents a numerical study of a deterministic discretization procedure for multistage stochastic programs where the underlying stochastic process has a continuous probability distribution. The discretization procedure is based on quasi-Monte Carlo techniques originally developed for numerical multivariate integration. The solutions of the discretized problems are evaluated by statistical bounds obtained from random sample average approximations and out-of-sample simulations. In the numerical...