scholarly journals Mirror descent algorithm on the indefinite control horizon

2021 ◽  
Vol 2052 (1) ◽  
pp. 012039
Author(s):  
D N Shiyan ◽  
A V Kolnogorov
Keyword(s):  
Optimal Control ◽  
Data Processing ◽  
Random Environment ◽  
Optimal Strategy ◽  
Control Algorithm ◽  
Batch Processing ◽  
Significant Gain ◽  
Descent Algorithm ◽  
Mirror Descent ◽  
Classical Statement

Abstract We consider the problem of optimal control in a random environment in a minimax setting as applied to data processing. It is assumed that the random environment provides two methods of data processing, the effectiveness of which is not known in advance. The goal of the control in this case is to find the optimal strategy for the application of processing methods and to minimize losses. To solve this problem, the mirror descent algorithm is used, including its modifications for batch processing. The use of algorithms for batch processing allows us to get a significant gain in speed due to the parallel processing of batches. In the classical statement, the search for the optimal strategy is considered on a fixed control horizon but this article considers an indefinite control horizon. With an indefinite horizon, the control algorithm cannot use information about the value of the horizon when searching for an optimal strategy. Using numerical modeling, the operation of the mirror descent algorithm and its modifications on an indefinite control horizon is studied and obtained results are presented.

scholarly journals Two-armed bandit problem and batch version of the mirror descent algorithm

2021 ◽  
Vol 13 (2) ◽  
pp. 9-39
Author(s):  
Александр Валерианович Колногоров ◽  
Alexander Kolnogorov ◽  
Александр Викторович Назин ◽  
Alexander Nazin ◽  
Дмитрий Николаевич Шиян ◽  
...  
Keyword(s):  
Data Processing ◽  
A Priori ◽  
Control Performance ◽  
Bandit Problem ◽  
Minimax Risk ◽  
Descent Algorithm ◽  
Alternative Processing ◽  
Mirror Descent ◽  
Processing Data ◽  
Parallel Data

We consider the minimax setup for the two-armed bandit problem as applied to data processing if there are two alternative processing methods with different a priori unknown efficiencies. One should determine the most efficient method and provide its predominant application. To this end, we use the mirror descent algorithm (MDA). It is well-known that corresponding minimax risk has the order of $N^{1/2$ with $N$ being the number of processed data and this bound is unimprovable in order. We propose a batch version of the MDA which allows processing data by packets that is especially important if parallel data processing can be provided. In this case, the processing time is determined by the number of  batches rather than by the total number of data. Unexpectedly, it turned out that the batch version behaves unlike the ordinary one even if the number of packets is large. Moreover, the batch version provides significantly smaller value of the minimax risk, i.e., it considerably improves a control performance. We explain this result by considering another batch modification of the MDA which behavior is close to behavior of the ordinary version and minimax risk is close as well. Our estimates use invariant descriptions of the algorithms based on Gaussian approximations of incomes in batches of data in the domain of ``close'' distributions and are obtained by Monte-Carlo simulations.

An Economic Model Predictive Control Approach for Wind Power Smoothing and Tower Load Mitigation

2018 ◽  
Author(s):  
Mohamed M. Alhneaish ◽  
Mohamed L. Shaltout ◽  
Sayed M. Metwalli
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Wind Power ◽  
Predictive Control ◽  
Economic Model ◽  
Control Algorithm ◽  
Fatigue Load ◽  
Economic Model Predictive Control ◽  
Control Framework

An economic model predictive control framework is presented in this study for an integrated wind turbine and flywheel energy storage system. The control objective is to smooth wind power output and mitigate tower fatigue load. The optimal control problem within the model predictive control framework has been formulated as a convex optimal control problem with linear dynamics and convex constraints that can be solved globally. The performance of the proposed control algorithm is compared to that of a standard wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is studied. The simulation results, with various wind scenarios, showed the ability of the proposed control algorithm to achieve the aforementioned objectives in terms of smoothing output power and mitigating tower fatigue load at the cost of a minimal reduction of the wind energy harvested.

Bayes Performance of Batch Data Mining Based on Functional Dependencies

2019 ◽  
Vol 33 (03) ◽  
pp. 1959011
Author(s):  
Haixu Xi ◽  
Feiyue Ye ◽  
Sheng He ◽  
Yijun Liu ◽  
Hongfen Jiang
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Video Processing ◽  
Mining Area ◽  
Batch Processing ◽  
Video Data ◽  
Batch Processes ◽  
Functional Dependencies ◽  
Workflow System ◽  
Traffic Video

Batch processes and phenomena in traffic video data processing, such as traffic video image processing and intelligent transportation, are commonly used. The application of batch processing can increase the efficiency of resource conservation. However, owing to limited research on traffic video data processing conditions, batch processing activities in this area remain minimally examined. By employing database functional dependency mining, we developed in this study a workflow system. Meanwhile, the Bayesian network is a focus area of data mining. It provides an intuitive means for users to comply with causality expression approaches. Moreover, graph theory is also used in data mining area. In this study, the proposed approach depends on relational database functions to remove redundant attributes, reduce interference, and select a property order. The restoration of selective hidden naive Bayesian (SHNB) affects this property order when it is used only once. With consideration of the hidden naive Bayes (HNB) influence, rather than using one pair of HNB, it is introduced twice. We additionally designed and implemented mining dependencies from a batch traffic video processing log for data execution algorithms.

Application of Optimal Control Algorithm to Inertia Friction Welding Process

2013 ◽  
Vol 21 (3) ◽  
pp. 891-898 ◽  
Author(s):  
Johannes Lohe ◽  
Marc Lotz ◽  
Mark Cannon ◽  
Basil Kouvaritakis
Keyword(s):  
Optimal Control ◽  
Friction Welding ◽  
Control Algorithm ◽  
Welding Process ◽  
Inertia Friction Welding

scholarly journals Fully efficient time-parallelized quantum optimal control algorithm

2016 ◽  
Vol 93 (4) ◽  
Author(s):  
M. K. Riahi ◽  
J. Salomon ◽  
S. J. Glaser ◽  
D. Sugny
Keyword(s):  
Optimal Control ◽  
Control Algorithm ◽  
Quantum Optimal Control

Globalization of Pantoja’s Optimal Control Algorithm

2002 ◽  
pp. 125-130
Author(s):  
Bruce Christianson ◽  
Michael Bartholomew-Biggs
Keyword(s):  
Optimal Control ◽  
Control Algorithm
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