summary:Continuous convergence and epi-convergence of sequences of random functions are crucial assumptions if mathematical programming problems are approximated on the basis of estimates or via sampling. The paper investigates “almost surely” and “in probability” versions of these convergence notions in more detail. Part I of the paper presents definitions and theoretical results and Part II is focused on sufficient conditions which apply to many models for statistical estimation and stochastic optimization
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
Stochastic Convergence, Second Edition covers the theoretical aspects of random power series dealing...
A new concept of (normalized) convergence of random variables is introduced. This convergence is pr...
summary:Continuous convergence and epi-convergence of sequences of random functions are crucial assu...
summary:Part II of the paper aims at providing conditions which may serve as a bridge between existi...
Shapiro and Xu [18] investigated uniform large deviation of a class of HÄolder continuous random fun...
In Stochastic Programming, the aim is often the optimization of a criterion function that can be wri...
In stochastic programming, statistics, or econometrics, the aim is in general the optimization of a ...
In the paper we study sequences of random functions which are defined by some interpolation procedur...
Convergence analysis for optimization problems with chance constraints concerns impact of variation ...
This thesis falls within the theory of optimization problems. In the first part, terms such as epi- ...
AbstractIn this paper we shall deal with statistical estimates in stochastic programming problems. T...
The ever increasing complexity of the systems to be modeled and analyzed, taxes the existing mathema...
Abstract. The paper extends certain stochastic convergence of sequences of Rk-valued random variable...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
Stochastic Convergence, Second Edition covers the theoretical aspects of random power series dealing...
A new concept of (normalized) convergence of random variables is introduced. This convergence is pr...
summary:Continuous convergence and epi-convergence of sequences of random functions are crucial assu...
summary:Part II of the paper aims at providing conditions which may serve as a bridge between existi...
Shapiro and Xu [18] investigated uniform large deviation of a class of HÄolder continuous random fun...
In Stochastic Programming, the aim is often the optimization of a criterion function that can be wri...
In stochastic programming, statistics, or econometrics, the aim is in general the optimization of a ...
In the paper we study sequences of random functions which are defined by some interpolation procedur...
Convergence analysis for optimization problems with chance constraints concerns impact of variation ...
This thesis falls within the theory of optimization problems. In the first part, terms such as epi- ...
AbstractIn this paper we shall deal with statistical estimates in stochastic programming problems. T...
The ever increasing complexity of the systems to be modeled and analyzed, taxes the existing mathema...
Abstract. The paper extends certain stochastic convergence of sequences of Rk-valued random variable...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
Stochastic Convergence, Second Edition covers the theoretical aspects of random power series dealing...
A new concept of (normalized) convergence of random variables is introduced. This convergence is pr...