Add to ORKGThis thesis falls within the theory of optimization problems. In the first part, terms such as epi- convergence, lower and upper semicontinuous function, epi-continuity and CLM set are defined. For a better understanding, the definitions of the key terms are accompanied with illustrative examples and observations of their basic properties. The following part deals with searching of (local) minimizers of random or deterministic function. Using the knowledge from the first part it is showed that under a set of assumptions it is possible to transfer this search to a sequence of random functions of specific requirements. Powered by TCPDF (www.tcpdf.org
A theoretical technique for the minimization of a function by a random search is presented. The sear...
Deterministic optimization models are usually formulated as problems of mini-mizing or maximizing a ...
A novel algorithm for the global optimization of functions (C-RTS) is presented, in which a combinat...
This thesis falls within the theory of optimization problems. In the first part, terms such as epi- ...
summary:Continuous convergence and epi-convergence of sequences of random functions are crucial assu...
We introduce CLS, for continuous local search, a class of polynomial-time checkable total functions ...
The paper presents an equivalent characterization of the epi-convergence of lower semicontinuous fun...
Coupled Local Minimizers (CLM) is a new method applicable to global optimization problems. With the ...
AbstractAn optimum random-search algorithm is considered. The convergence conditions to the greatest...
Local search has been applied successfully to a diverse collection of optimization problems. It's ap...
summary:The aim of this paper is to present some ideas how to relax the notion of the optimal soluti...
The paper presents an effective, gradient-based procedure for locating the optimum for either constr...
We consider scenario approximation of problems given by the optimization of a function over a constr...
The goal of this article is to provide a general framework for locally convergent random-search algo...
International audienceThis chapter sets up a formal framework for local search and provides a certai...
A theoretical technique for the minimization of a function by a random search is presented. The sear...
Deterministic optimization models are usually formulated as problems of mini-mizing or maximizing a ...
A novel algorithm for the global optimization of functions (C-RTS) is presented, in which a combinat...
This thesis falls within the theory of optimization problems. In the first part, terms such as epi- ...
summary:Continuous convergence and epi-convergence of sequences of random functions are crucial assu...
We introduce CLS, for continuous local search, a class of polynomial-time checkable total functions ...
The paper presents an equivalent characterization of the epi-convergence of lower semicontinuous fun...
Coupled Local Minimizers (CLM) is a new method applicable to global optimization problems. With the ...
AbstractAn optimum random-search algorithm is considered. The convergence conditions to the greatest...
Local search has been applied successfully to a diverse collection of optimization problems. It's ap...
summary:The aim of this paper is to present some ideas how to relax the notion of the optimal soluti...
The paper presents an effective, gradient-based procedure for locating the optimum for either constr...
We consider scenario approximation of problems given by the optimization of a function over a constr...
The goal of this article is to provide a general framework for locally convergent random-search algo...
International audienceThis chapter sets up a formal framework for local search and provides a certai...
A theoretical technique for the minimization of a function by a random search is presented. The sear...
Deterministic optimization models are usually formulated as problems of mini-mizing or maximizing a ...
A novel algorithm for the global optimization of functions (C-RTS) is presented, in which a combinat...