Adjoint variable method for the study of combined active and passive magnetic shielding.
Algorithms for Bound Constrained Quadratic Programming Problems.
An algorithm for indefinite quadratic programming based on a partial Cholesky factorization
An Enumerative Algorithm for Non-Linear Multi-Level Integer Programming Problem
An implementation of recursive quadratic programming variable metric methods for linearly constrained nonlinear minimax approximation
An infinite horizon predictive control algorithm based on multivariable input-output models
In this paper an infinite horizon predictive control algorithm, for which closed loop stability is guaranteed, is developed in the framework of multivariable linear input-output models. The original infinite dimensional optimisation problem is transformed into a finite dimensional one with a penalty term. In the unconstrained case the stabilising control law, using a numerically reliable SVD decomposition, is derived as an analytical formula, calculated off-line. Considering constraints needs solving...
An interior point algorithm for convex quadratic programming with strict equilibrium constraints
We describe an interior point algorithm for convex quadratic problem with a strict complementarity constraints. We show that under some assumptions the approach requires a total of number of iterations, where is the input size of the problem. The algorithm generates a sequence of problems, each of which is approximately solved by Newton’s method.
An interior point algorithm for convex quadratic programming with strict equilibrium constraints
We describe an interior point algorithm for convex quadratic problem with a strict complementarity constraints. We show that under some assumptions the approach requires a total of number of iterations, where L is the input size of the problem. The algorithm generates a sequence of problems, each of which is approximately solved by Newton's method.
An iterative algorithm of solution for quadratic minimization problem in Hilbert spaces.
An optimal algorithm with Barzilai-Borwein steplength and superrelaxation for QPQC problem
We propose a modification of MPGP algorithm for solving minimizing problem of strictly convex quadratic function subject to separable spherical constraints. This active set based algorithm explores the faces by the conjugate gradients and changes the active sets and active variables by the gradient projection with the Barzilai-Borwein steplength. We show how to use the algorithm for the solution of separable and equality constraints. The power of our modification is demonstrated on the solution...
An unconstrained optimization technique for nonsmooth nonlinear complementarity problems.
Application de l'optimisation en nombres entiers à un problème d'«arrondissage»
Augmented Lagrangian method for recourse problem of two-stage stochastic linear programming
In this paper, the augmented Lagrangian method is investigated for solving recourse problems and obtaining their normal solution in solving two-stage stochastic linear programming problems. The objective function of stochastic linear programming problem is piecewise linear and non-differentiable. Therefore, to use a smooth optimization methods, the objective function is approximated by a differentiable and piecewise quadratic function. Using quadratic approximation, it is required to obtain the...
Bilinear programming
Characterizations of the Solution Sets of Generalized Convex Minimization Problems
2000 Mathematics Subject Classification: 90C26, 90C20, 49J52, 47H05, 47J20.In this paper we obtain some simple characterizations of the solution sets of a pseudoconvex program and a variational inequality. Similar characterizations of the solution set of a quasiconvex quadratic program are derived. Applications of these characterizations are given.
Combinatorial algorithms for computing column space bases that have sparse inverses.
Combined trust region methods for nonlinear least squares
Comparaison de deux méthodes de résolution d'un problème combinatoire quadratique
Computing the Stackelberg/Nash equilibria using the extraproximal method: Convergence analysis and implementation details for Markov chains games
In this paper we present the extraproximal method for computing the Stackelberg/Nash equilibria in a class of ergodic controlled finite Markov chains games. We exemplify the original game formulation in terms of coupled nonlinear programming problems implementing the Lagrange principle. In addition, Tikhonov's regularization method is employed to ensure the convergence of the cost-functions to a Stackelberg/Nash equilibrium point. Then, we transform the problem into a system of equations in the...
Conjugate gradient algorithms for conic functions
The paper contains a description and an analysis of two modifications of the conjugate gradient method for unconstrained minimization which find a minimum of the conic function after a finite number of steps. Moreover, further extension of the conjugate gradient method is given which is based on a more general class of the model functions.