We define the minimal time function associated with a collection of sets which is motivated by the optimal time problem for nonconvex constant dynamics. We first provide various basic properties of this new function: lower semicontinuity, principle of optimality, convexity, Lipschitz continuity, among others. We also compute and estimate proximal, Fréchet and limiting subdifferentials of the new function at points inside the target set as well as at points outside the target. An application to location problems is also given.
Keywords: Convex dynamics set, minimal time function, subdifferentials, normal cones, location problems
@article{COCV_2020__26_1_A93_0,
author = {Nguyen, Luong V. and Qin, Xiaolong},
title = {The minimal time function associated with a collection of sets},
journal = {ESAIM: Control, Optimisation and Calculus of Variations},
year = {2020},
publisher = {EDP Sciences},
volume = {26},
doi = {10.1051/cocv/2020017},
mrnumber = {4175377},
zbl = {1459.49008},
language = {en},
url = {https://www.numdam.org/articles/10.1051/cocv/2020017/}
}
TY - JOUR AU - Nguyen, Luong V. AU - Qin, Xiaolong TI - The minimal time function associated with a collection of sets JO - ESAIM: Control, Optimisation and Calculus of Variations PY - 2020 VL - 26 PB - EDP Sciences UR - https://www.numdam.org/articles/10.1051/cocv/2020017/ DO - 10.1051/cocv/2020017 LA - en ID - COCV_2020__26_1_A93_0 ER -
%0 Journal Article %A Nguyen, Luong V. %A Qin, Xiaolong %T The minimal time function associated with a collection of sets %J ESAIM: Control, Optimisation and Calculus of Variations %D 2020 %V 26 %I EDP Sciences %U https://www.numdam.org/articles/10.1051/cocv/2020017/ %R 10.1051/cocv/2020017 %G en %F COCV_2020__26_1_A93_0
Nguyen, Luong V.; Qin, Xiaolong. The minimal time function associated with a collection of sets. ESAIM: Control, Optimisation and Calculus of Variations, Tome 26 (2020), article no. 93. doi: 10.1051/cocv/2020017
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This paper was supported by the National Natural Science Foundation of China under Grant No.11401152.
LVN was supported by the Research Fund for International Young Scientists from NNSFC under Grant No.11850410438 and China Postdoctoral Science Foundation under Grant No. 2017M6200421.





