Imperial College London
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Dr Dante Kalise

Faculty of Natural Sciences, Department of Mathematics

Reader in Computational Optimisation and Control
 
 
 
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Contact

 

d.kalise-balza Website CV

 
 
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Location

 

742Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kalise:2023:10.1137/22M1498401,
author = {Kalise, D and Saluzzi, L and Sergey, D},
doi = {10.1137/22M1498401},
journal = {SIAM Journal on Scientific Computing},
pages = {A2153--A2184},
title = {Data-driven tensor train gradient cross approximation for Hamilton-Jacobi-Bellman equations},
url = {http://dx.doi.org/10.1137/22M1498401},
volume = {45},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A gradient-enhanced functional tensor train cross approximation method for the resolution of the Hamilton–Jacobi–Bellman (HJB) equations associated with optimal feedback control of nonlinear dynamics is presented. The procedure uses samples of both the solution of the HJB equation and its gradient to obtain a tensor train approximation of the value function. The collection of the data for the algorithm is based on two possible techniques: Pontryagin Maximum Principle and State-Dependent Riccati Equations. Several numerical tests are presented in low and high dimension showing the effectiveness of the proposed method and its robustness with respect to inexact data evaluations, provided by the gradient information. The resulting tensor train approximation paves the way towards fast synthesis of the control signal in real-time applications.
AU  - Kalise,D
AU  - Saluzzi,L
AU  - Sergey,D
DO  - 10.1137/22M1498401
EP  - 2184
PY  - 2023///
SN  - 1064-8275
SP  - 2153
TI  - Data-driven tensor train gradient cross approximation for Hamilton-Jacobi-Bellman equations
T2  - SIAM Journal on Scientific Computing
UR  - http://dx.doi.org/10.1137/22M1498401
UR  - http://hdl.handle.net/10044/1/103494
VL  - 45
ER  -
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