A global method for some class of optimization and control problems.
A linear acceleration row action method for projecting onto subspaces.
A modified filter SQP method as a tool for optimal control of nonlinear systems with spatio-temporal dynamics
Our aim is to adapt Fletcher's filter approach to solve optimal control problems for systems described by nonlinear Partial Differential Equations (PDEs) with state constraints. To this end, we propose a number of modifications of the filter approach, which are well suited for our purposes. Then, we discuss possible ways of cooperation between the filter method and a PDE solver, and one of them is selected and tested.
A necessary and sufficient condition for static output feedback stabilizability of linear discrete-time systems
Necessary and sufficient conditions for a discrete-time system to be stabilizable via static output feedback are established. The conditions include a Riccati equation. An iterative as well as non-iterative LMI based algorithm with guaranteed cost for the computation of output stabilizing feedback gains is proposed and introduces the novel LMI approach to compute the stabilizing output feedback gain matrix. The results provide the discrete- time counterpart to the results by Kučera and De Souza.
A new nonmonotone adaptive trust region algorithm
We propose a new and efficient nonmonotone adaptive trust region algorithm to solve unconstrained optimization problems. This algorithm incorporates two novelties: it benefits from a radius dependent shrinkage parameter for adjusting the trust region radius that avoids undesirable directions and exploits a new strategy to prevent sudden increments of objective function values in nonmonotone trust region techniques. Global convergence of this algorithm is investigated under some mild conditions....
A property of divided differences with multiple nodes.
Abschätzungen bei Variationsmethoden mit Hilfe von Dualitätssätzen. II.
Abschätzungen bei Variationsmethoden mit Hilfe von Dualitätssätzen. I.
An application of conjugate duality for numerical solution of continuous convex optimal control problems
An SQP method for mathematical programs with complementarity constraints with strong convergence properties
We propose an SQP algorithm for mathematical programs with complementarity constraints which solves at each iteration a quadratic program with linear complementarity constraints. We demonstrate how strongly M-stationary solutions of this quadratic program can be obtained by an active set method without using enumeration techniques. We show that all limit points of the sequence of iterates generated by our SQP method are at least M-stationary.
An SQP trust region method for solving the discrete-time linear quadratic control problem
In this paper, a sequential quadratic programming method combined with a trust region globalization strategy is analyzed and studied for solving a certain nonlinear constrained optimization problem with matrix variables. The optimization problem is derived from the infinite-horizon linear quadratic control problem for discrete-time systems when a complete set of state variables is not available. Moreover, a parametrization approach is introduced that does not require starting a feasible solution...
Brève communication. Méthode de l'état adjoint par «relaxation»
Convergence of convex-concave saddle functions : applications to convex programming and mechanics
Convergence of the Lagrange-Newton method for optimal control problems
Convergence results for two Lagrange-Newton-type methods of solving optimal control problems are presented. It is shown how the methods can be applied to a class of optimal control problems for nonlinear ODEs, subject to mixed control-state constraints. The first method reduces to an SQP algorithm. It does not require any information on the structure of the optimal solution. The other one is the shooting method, where information on the structure of the optimal solution is exploited. In each case,...
Dentable functions and radially uniform quasi-convexity.
Deterministic global optimization using interval constraint propagation techniques
The purpose of this article is to show the great interest of the use of propagation (or pruning) techniques, inside classical interval Branch-and-Bound algorithms. Therefore, a propagation technique based on the construction of the calculus tree is entirely explained and some properties are presented without the need of any formalism (excepted interval analysis). This approach is then validated on a real example: the optimal design of an electrical rotating machine.
Deterministic global optimization using interval constraint propagation techniques
The purpose of this article is to show the great interest of the use of propagation (or pruning) techniques, inside classical interval Branch-and-Bound algorithms. Therefore, a propagation technique based on the construction of the calculus tree is entirely explained and some properties are presented without the need of any formalism (excepted interval analysis). This approach is then validated on a real example: the optimal design of an electrical rotating machine.
Extra multistep BFGS updates in quasi-Newton methods.
Generalized convexity and optimization problems
Global convergence of a modified SQP method for mathematical programs with inequalities and equalities constraints.