This paper is concerned with the sensitivities of function space oriented interior point approximations in parameter dependent optimization problems. For an abstract setting that covers control constrained optimal control problems, the convergence of interior point sensitivities to the sensitivities of the optimal solution is shown. Error bounds for Lq norms are derived and illustrated with num...

A class of nonlinear elliptic optimal control problems with mixed control-state constraints arising, e.g., in Lavrentiev-type regularized state constrained optimal control is considered. Based on its first order necessary optimality conditions, a semismooth Newton method is proposed and its fast local convergence in function space as well as a mesh-independence principle for appropriate discret...

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.

The logistic loss is strictly convex and does not attain its infimum; consequently the solutions of logistic regression are in general off at infinity. This work provides a convergence analysis of gradient descent applied to logistic regression under no assumptions on the problem instance. Firstly, the risk is shown to converge at a rate O(ln(t)/t). Secondly, the parameter convergence is charac...

It is well known that the likelihood sequence of the EM algorithm is nondecreasing and convergent (Dempster, Laird and Rubin (1977)), and that the limit points of the EM algorithm are stationary points of the likelihood (Wu (1982)), but the issue of the convergence of the EM sequence itself has not been completely settled. In this paper we close this gap and show that under general, simple, ver...

We show the existence of Lipschitz-in-space optimal controls for a class mean-field control problems with dynamics given by non-local continuity equation. The proof relies on vanishing viscosity method: we prove convergence same problem where diffusion term is added, small parameter. By using stochastic control, first sequence diffusion. then build optimizer original letting parameter go to zero.

Parameter optimization of water turbine regulating system (WTRS) is decisive in providing support for the power quality and stability analysis of power system. In this paper, an improved fuzzy particle swarm optimization (IFPSO) algorithm is proposed and used to solve the optimization problem for WTRS under frequency and load disturbances conditions. The novel algorithm which is based on the st...

This paper performs a thorough empirical investigation of the conditions placed on particle swarm optimization control parameters to ensure convergent behavior. At present there exists a large number of theoretically derived parameter regions that will ensure particle convergence, however, selecting which region to utilize in practice is not obvious. The empirical study is carried out over a re...

We consider stochastic processes fX(t; ) : t 2 Tg with continuous parameter t 2 T = [0;1[. These processes are so called generalized supermartingales, where the generalization comes from a modification of the right side of the supermartingale inequality. The assumptions on the process and the probability space are the same as for the classical convergence theorem of DOOB (see KOPP [2]). The aim...

Following the development of a parameter convergence analysis procedure for output-feedback nonlinear systems, we shift our attention to strict-feedback nonlinear systems in this paper. We develop an analytic procedure which allows us, given a speciic nonlinear system and a speciic reference signal, to determine a priori whether or not the parameter estimates will converge to their true values,...