Abstract
A Fokker–Planck control strategy for collective motion is investigated. This strategy is formulated as the minimisation of an expectation objective with a bilinear optimal control problem governed by the Fokker–Planck equation modelling the evolution of the probability density function of the stochastic motion. Theoretical results on existence and regularity of optimal controls are provided. The resulting optimality system is discretized using an alternate-direction implicit Chang–Cooper scheme that guarantees conservativeness, positivity, \(L^1\) stability, and second-order accuracy of the forward solution. A projected non-linear conjugate gradient scheme is used to solve the optimality system. Results of numerical experiments validate the theoretical accuracy estimates and demonstrate the efficiency of the proposed control framework.
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Acknowledgements
The authors would like to gratefully acknowledge the comments by the referees which helped to improve this paper. S. Roy would like to thank A. S. Vasudeva Murthy and Praveen Chandrashekar for several fruitful discussions during the initial phases of this work. This work was supported in part by the European Union under Grant Agreement No. 304617 Marie Curie Research Training Network “Multi-ITN STRIKE—Novel Methods in Computational Finance” and the BMBF project “ROENOBIO”. S. Roy was also supported by the DAAD Passage to India Program and the AIRBUS Group Corporate Foundation Chair in Mathematics of Complex Systems established in TIFR/ICTS, Bangalore.
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Appendix: Derivation of the numerical adjoint
Appendix: Derivation of the numerical adjoint
We derive the numerical scheme for the adjoint equation (14) using the discretize-before-optimize approach. The starting point of this derivation is the Lagrangian
In order to obtain the discrete version of the adjoint equation, we need to consider a discrete version of the Lagrange function with the ADI-CC scheme for the time–space derivatives of f. Since the ADI-CC scheme has an intermediate time step \(t^{m+\frac{1}{2}}\), we define the Lagrangian on the following double grid
The discrete Lagrangian is given by
We write the fluxes of the FP equation (13) in the following compact form
where
Similarly, we have
Therefore, we obtain
Rearranging the summation on the right-hand side of (87) to collect the terms \(f_{i,j}^{m+\frac{1}{2}}\) with same space index and using discrete flux zero (39), we have
In a similar way, we have
and
For our convenience, using (88)–(91) in (83), rearranging the time indices and collecting the terms \(f_{i,j}^{m+\frac{1}{2}}\) and \(f^{m+1}_{i,j}\), we obtain the Lagrange function in a different form as follows
When the control cost A(u) is given by (C1), taking derivative with respect to \(f^{m+1}\), we obtain the following first integration step for the adjoint equation
Taking derivative with respect to \(f_{i,j}^{m+\frac{1}{2}}\), we obtain the following second integration step for the adjoint equation
along with the terminal condition
When the control cost A(u) is given by (C2), taking derivative with respect to \(f^{m+1}\), we obtain the following first integration step for the adjoint equation
Taking derivative with respect to \(f_{i,j}^{m+\frac{1}{2}}\), we obtain the following second integration step for the adjoint equation
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Roy, S., Annunziato, M., Borzì, A. et al. A Fokker–Planck approach to control collective motion. Comput Optim Appl 69, 423–459 (2018). https://doi.org/10.1007/s10589-017-9944-3
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DOI: https://doi.org/10.1007/s10589-017-9944-3
Keywords
- Fokker–Planck equation
- Alternate direction method
- Chang–Cooper scheme
- Projected gradient method
- Control constrained PDE optimization