Add to ORKGA very simple and efficient approach to deriving estimates of the convergence rate for the penalty methods is suggested. The approach is based on the application of results of the sensitivity theory to optimization problems. The suggested convergence analysis uses either various sufficient optimality conditions without imposing any regularity requirements on the constraints or growth conditions under weakened regularity requirements on the constraints. Copyright © 2004 by MAIK "Nauka/Interperiodica"
The purpose of this paper is two-fold. First, bounds on the rate of convergence of empirical measure...
Optimization problems arise in science, engineering, economy, etc. and we need to find the best sol...
For the equality-constrained optimization problem, we consider the case when the customary regularit...
A very simple and efficient approach to deriving estimates of the convergence rate for the penalty m...
The problem under consideration is the nonlinear optimization problem min f(x) text{ subject to } x ...
We solve a general optimization problem, where only approximation sequences are known instead of exa...
International audienceIn an optimization framework, some criteria might be more relevant than others...
This is a companion paper to "Ghost penalties in nonconvex constrained optimization: Diminishing ste...
Abstract. The convergence behaviour of a class of iterative methods for solving the constrained mini...
We consider nonconvex constrained optimization problems and propose a new approach to the convergenc...
n this paper we define two classes of algorithms for the solution of constrained problems. The first...
summary:We deal with a stochastic programming problem that can be inconsistent. To overcome the inco...
We analyze the global convergence properties of a class of penalty methods for nonlinear programming...
We develop a general approach to convergence analysis of feasible descent methods in the presence of...
Abstract. We adapt the convergence analysis of smoothing (Ref. 1) and regularization (Ref. 2) method...
The purpose of this paper is two-fold. First, bounds on the rate of convergence of empirical measure...
Optimization problems arise in science, engineering, economy, etc. and we need to find the best sol...
For the equality-constrained optimization problem, we consider the case when the customary regularit...
A very simple and efficient approach to deriving estimates of the convergence rate for the penalty m...
The problem under consideration is the nonlinear optimization problem min f(x) text{ subject to } x ...
We solve a general optimization problem, where only approximation sequences are known instead of exa...
International audienceIn an optimization framework, some criteria might be more relevant than others...
This is a companion paper to "Ghost penalties in nonconvex constrained optimization: Diminishing ste...
Abstract. The convergence behaviour of a class of iterative methods for solving the constrained mini...
We consider nonconvex constrained optimization problems and propose a new approach to the convergenc...
n this paper we define two classes of algorithms for the solution of constrained problems. The first...
summary:We deal with a stochastic programming problem that can be inconsistent. To overcome the inco...
We analyze the global convergence properties of a class of penalty methods for nonlinear programming...
We develop a general approach to convergence analysis of feasible descent methods in the presence of...
Abstract. We adapt the convergence analysis of smoothing (Ref. 1) and regularization (Ref. 2) method...
The purpose of this paper is two-fold. First, bounds on the rate of convergence of empirical measure...
Optimization problems arise in science, engineering, economy, etc. and we need to find the best sol...
For the equality-constrained optimization problem, we consider the case when the customary regularit...