OPTIMAL CONTROL PROBLEM
| dc.contributor.author | Kappar, Yerdaulet | |
| dc.date.accessioned | 2024-05-03T10:51:28Z | |
| dc.date.available | 2024-05-03T10:51:28Z | |
| dc.date.issued | 2024-04-15 | |
| dc.description.abstract | This research paper delves into optimizing the Average Value at Risk (AVaR) using Approximate Dynamic Programming (ADP) in the context of optimal control problems. The study focuses on comparing different numerical optimization methods to achieve that. The methods include Bisection, Gradient Descent, Simulated Annealing, and Conjugate Gradient. The purpose is to assess their accuracy and computational effectiveness in optimizing AVaR function within discrete time, finite horizon settings. | en_US |
| dc.identifier.citation | Kappar, Y. (2024). OPTIMAL CONTROL PROBLEM. Nazarbayev University School of Sciences and Humanities | en_US |
| dc.identifier.uri | http://nur.nu.edu.kz/handle/123456789/7618 | |
| dc.language.iso | en | en_US |
| dc.publisher | Nazarbayev University School of Sciences and Humanities | en_US |
| dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
| dc.subject | Type of access: Open Access | en_US |
| dc.subject | approximate dynamic programming | en_US |
| dc.subject | average value-at-risk | en_US |
| dc.subject | convex optimization | en_US |
| dc.subject | optimization techniques | en_US |
| dc.subject | stochastic modelling | en_US |
| dc.subject | Markov decision processes | en_US |
| dc.subject | optimal control | en_US |
| dc.title | OPTIMAL CONTROL PROBLEM | en_US |
| dc.type | Capstone Project | en_US |
| workflow.import.source | science |