The practical aspect of the stochastic approximation method (SA) is studied. Specifically, we investigated the efficiency depending on the coefficients that generate the step length in optimization algorithm, as well as the efficiency depending on the type and the level of the corresponding noise. Efficiency is measured by the mean values of the objective function at the final estimates of the algorithm, over the specified number of replications. This paper provides suggestions how to choose already mentioned coefficients, in order to achieve better performance of the stochastic approximation algorithm
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
htmlabstractApproximation algorithms are the prevalent solution methods in the field of stochastic p...
Issued as Annual report, and Final report, no. Project E-21-617Reports have title: Optional updatin...
A stochastic approximation (SA) algorithm with new adaptive step sizes for solving unconstrained mi...
Approximation algorithms are the prevalent solution methods in the field of stochastic programming. ...
AbstractWe propose a new adaptive algorithm with decreasing step-size for stochastic approximations....
We consider the following stochastic approximation algorithm of searching for the zero point x∗ of a...
Approximation algorithms are the prevalent solution methods in the field of stochastic programming. ...
We consider the following stochastic approximation algorithm of searching for the zero point x∗ of a...
We propose a new adaptive algorithm with decreasing step-size for stochastic approximations. The use...
In this dissertation, we propose two new types of stochastic approximation (SA) methods and study th...
A difficulty in using Simultaneous Perturbation Stochastics Approximation (SPSA) is its performance ...
This thesis is concerned with stochastic optimization methods. The pioneering work in the field is t...
This thesis provides an overview of stochastic optimization (SP) problems and looks at how the Sampl...
Stochastic optimization algorithms have been growing rapidly in popularity over the last decade or t...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
htmlabstractApproximation algorithms are the prevalent solution methods in the field of stochastic p...
Issued as Annual report, and Final report, no. Project E-21-617Reports have title: Optional updatin...
A stochastic approximation (SA) algorithm with new adaptive step sizes for solving unconstrained mi...
Approximation algorithms are the prevalent solution methods in the field of stochastic programming. ...
AbstractWe propose a new adaptive algorithm with decreasing step-size for stochastic approximations....
We consider the following stochastic approximation algorithm of searching for the zero point x∗ of a...
Approximation algorithms are the prevalent solution methods in the field of stochastic programming. ...
We consider the following stochastic approximation algorithm of searching for the zero point x∗ of a...
We propose a new adaptive algorithm with decreasing step-size for stochastic approximations. The use...
In this dissertation, we propose two new types of stochastic approximation (SA) methods and study th...
A difficulty in using Simultaneous Perturbation Stochastics Approximation (SPSA) is its performance ...
This thesis is concerned with stochastic optimization methods. The pioneering work in the field is t...
This thesis provides an overview of stochastic optimization (SP) problems and looks at how the Sampl...
Stochastic optimization algorithms have been growing rapidly in popularity over the last decade or t...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
htmlabstractApproximation algorithms are the prevalent solution methods in the field of stochastic p...
Issued as Annual report, and Final report, no. Project E-21-617Reports have title: Optional updatin...