Pricing for sparsity in the revised simplex method
Primal interior-point method for large sparse minimax optimization
In this paper, we propose a primal interior-point method for large sparse minimax optimization. After a short introduction, the complete algorithm is introduced and important implementation details are given. We prove that this algorithm is globally convergent under standard mild assumptions. Thus the large sparse nonconvex minimax optimization problems can be solved successfully. The results of extensive computational experiments given in this paper confirm efficiency and robustness of the proposed...
Primena srednjih pruga na linearno programiranje
Problème de la bipartition minimale d'un graphe
Problèmes de partitionnement : exploration arborescente ou méthode de troncatures ?
Projection method with level control in convex minimization
We study a projection method with level control for nonsmoooth convex minimization problems. We introduce a changeable level parameter to level control. The level estimates the minimal value of the objective function and is updated in each iteration. We analyse the convergence and estimate the efficiency of this method.
Projection method with residual selection for linear feasibility problems
We propose a new projection method for linear feasibility problems. The method is based on the so called residual selection model. We present numerical results for some test problems.
Projective algorithms for solving complementarity problems.
Proper orthogonal decomposition for optimality systems
Proper orthogonal decomposition (POD) is a powerful technique for model reduction of non-linear systems. It is based on a Galerkin type discretization with basis elements created from the dynamical system itself. In the context of optimal control this approach may suffer from the fact that the basis elements are computed from a reference trajectory containing features which are quite different from those of the optimally controlled trajectory. A method is proposed which avoids this problem of unmodelled...
Properties of projection and penalty methods for discretized elliptic control problems
In this paper, properties of projection and penalty methods are studied in connection with control problems and their discretizations. In particular, the convergence of an interior-exterior penalty method applied to simple state constraints as well as the contraction behavior of projection mappings are analyzed. In this study, the focus is on the application of these methods to discretized control problem.
Prox-regularization and solution of ill-posed elliptic variational inequalities
In this paper new methods for solving elliptic variational inequalities with weakly coercive operators are considered. The use of the iterative prox-regularization coupled with a successive discretization of the variational inequality by means of a finite element method ensures well-posedness of the auxiliary problems and strong convergence of their approximate solutions to a solution of the original problem. In particular, regularization on the kernel of the differential operator and regularization...
Quadratically constrained least squares and quadratic problems.
Quasi-Newton gradient method with analytical determination of the direction and length of step
Quasi-Newton methods without projections for linearly constrained minimization
Quasi-Newton methods without projections for unconstrained minimization
Quasi-Newton type of diagonal updating for the L-BFGS method.
Quasi-Newton-Verfahren vom Rang-Eins-Typ zur Lösung unrestringierter Minimierungsprobleme. Teil 1. Verfahren und grundlegende Eigenschaften.
Quasi-Newton-Verfahren vom Rang-Eins-Typ zur Lösung unrestringierter Minimierungsprobleme. Teil 2. «-Schritt-quadratische Konvergenz für Restart-Varianten.
Quasi-norm error bounds for the finite element approximation of some degenerate quasilinear elliptic equations and variational inequalities
Quelques expériences numériques sur la programmation non linéaire en nombres entiers