Sparse and switching infinite horizon optimal controls with mixed-norm penalizations
ESAIM: Control, Optimisation and Calculus of Variations, Tome 26 (2020), article no. 61.

A class of infinite horizon optimal control problems involving mixed quasi-norms of L$$-type cost functionals for the controls is discussed. These functionals enhance sparsity and switching properties of the optimal controls. The existence of optimal controls and their structural properties are analyzed on the basis of first order optimality conditions. A dynamic programming approach is used for numerical realization.

DOI : 10.1051/cocv/2019038
Classification : 93D15, 93B52, 93C05, 93C20
Mots-clés : Optimal control, infinite horizon control, sparse controls, switching controls, optimality conditions, dynamic programming
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     author = {Kalise, Dante and Kunisch, Karl and Rao, Zhiping},
     title = {Sparse and switching infinite horizon optimal controls with mixed-norm penalizations},
     journal = {ESAIM: Control, Optimisation and Calculus of Variations},
     publisher = {EDP-Sciences},
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     url = {http://www.numdam.org/articles/10.1051/cocv/2019038/}
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Kalise, Dante; Kunisch, Karl; Rao, Zhiping. Sparse and switching infinite horizon optimal controls with mixed-norm penalizations. ESAIM: Control, Optimisation and Calculus of Variations, Tome 26 (2020), article no. 61. doi : 10.1051/cocv/2019038. http://www.numdam.org/articles/10.1051/cocv/2019038/

[1] G. Albi, M. Fornasier and D. Kalise, A Boltzmann approach to mean-field sparse feedback control. IFAC-PapersOnLine 50 (2017) 2898–2903.

[2] A. Alla, M. Falcone and D. Kalise, An efficient policy iteration algorithm for the solution of dynamic programming equations. SIAM J. Sci. Comput. 37 (2015) A181–A200. | DOI | MR | Zbl

[3] W. Alt and C. Schneider, Linear-quadratic control problems with L 1 -control cost. Optim. Control Appl. Methods 36 (2015) 512–534. | DOI | MR | Zbl

[4] S.M. Aseev and V.M. Veliov, Another View of the Maximum Principle for Infinite-Horizon Optimal Control Problems in Economics. Researchreport 2017, ORCOS, Vienna University of Technology (2017). | MR

[5] J.P. Aubin and A. Cellina, Differential Inclusions: Set-Valued Maps and Viability Theory. Fundamental Principles of Mathematical Sciences. Vol. 264 of Grundlehren der Mathematischen Wissenschaften. Springer-Verlag, Berlin (1984). | DOI | MR | Zbl

[6] M. Bardi and I. Capuzzo-Dolcetta, Optimal Control and Viscosity Solutions of Hamilton-Jacobi-Bellman Equations. Systems & Control: Foundations & Applications. Birkhäuser Boston, Inc., Boston, MA (1997). | MR | Zbl

[7] F. Bach, R. Jenatton, J. Mairal and G. Obozinski, Structured sparsity through convex optimization. Stat. Sci. 27 (2012) 450–468. | DOI | MR | Zbl

[8] E. Casas, C. Clason and K. Kunisch, Approximation of elliptic control problems in measure spaces with sparse solutions. SIAM J. Control Optim. 50 (2012) 1735–1752. | DOI | MR | Zbl

[9] P. Cannarsa and C. Sinestrari, Semiconcave Functions Hamilton-Jacobi Equations, and Optimal Control. In Vol. 58 of Progress in Nonlinear Differential Equations and their Applications. Birkhäuser Boston, Inc., Boston, MA (2004). | MR | Zbl

[10] I. Cioranescu, Geometry of Banach Spaces, Duality Mappings and Nonlinear Problems. Vol. 62 of Mathematics and Its Applications. Kluwer Academic Publishers (1990). | MR | Zbl

[11] C. Clason, K. Ito and K. Kunisch, A convex analysis approach to optimal controls with switching structure for partial differential equations. ESAIM: COCV 22 (2016) 581–609. | Numdam | MR | Zbl

[12] C. Clason, K. Kunisch and A. Rund, Nonconvex penalization of switching control of partial differential equations. Syst. Control Lett. 106 (2017) 1–8. | DOI | MR | Zbl

[13] C. Clason, A. Rund, K. Kunisch and R. Barnard, A convex penalty for switching control of partial differential equations. Syst. Control Lett. 89 (2016) 66–73. | DOI | MR | Zbl

[14] M. Falcone, D. Kalise and A. Kroener, A semi-Lagrangian scheme for Lp-penalized minimum time problems. Proceedings of the 21st International Symposium on Mathematical Theory of Networks and Systems (2014) 1798–1803.

[15] M. Fornasier and H. Rauhut, Recovery algorithms for vector-valued data with joint sparsity constraints. SIAM J. Numer. Anal. 46 (2008) 577–613. | DOI | MR | Zbl

[16] O. Hájek, $L_1$-Optimization in linearsystems with bounded controls. J. Optim. Theory Appl. 29 (1979) 409–436. | DOI | MR | Zbl

[17] H. Halkin, Necessary conditions for optimal control problems with infinite horizons. Econometrica 42 (1974) 267–272. | DOI | MR | Zbl

[18] F. Hante, G. Leugering and T. Seidman, Modeling and analysis of modal switching in networked transport system. Appl. Math. Optim. 59 (2009) 275–292. | DOI | MR | Zbl

[19] F. Hante and S. Sager, Relaxation methods for mixed-integer optimal control of partial differential equations. Comput. Optim. Appl. 55 (2013) 197–225. | DOI | MR | Zbl

[20] R. Herzog, G. Stadler and G. Wachsmuth, Directional sparsity in optimal control of partial differential equations. SIAM J. Control Optim. 50 (2012) 943–963. | DOI | MR | Zbl

[21] K. Ito and K. Kunisch, A variational approach to sparsity optimization based on Lagrange multiplier theory. Inverse Probl. 30 (2014) 015001. | DOI | MR | Zbl

[22] D. Kalise, A. Kroener and K. Kunisch, Local minimization algorithms for dynamic programming equations. SIAM J. Sci. Comput. 38 (2016) A1587–A1615. | DOI | MR | Zbl

[23] D. Kalise, K. Kunisch and Z. Rao, Infinite horizon sparse optimal control. J. Optim. Theory Appl. 172 (2017) 481–517. | DOI | MR | Zbl

[24] M. Kowalski, Sparse regression using mixed norms. Appl. Comput. Harm. Anal. 27 (2009) 303–324. | DOI | MR | Zbl

[25] K. Pieper and B. Vexler, A priori error analysis for discretization of sparse elliptic optimal control problems in measure space. SIAM J. Control Optim. 51 (2013) 2788–2808. | DOI | MR | Zbl

[26] G. Teschke and R. Ramlau, An iterative algorithm for nonlinear inverse problems with joint sparsity constraints in vector-valued regimes and an application to color image inpainting. Inverse Probl. 23 (2007) 1851–1870. | DOI | MR | Zbl

[27] R. Vinter, Optimal Control. Springer, New York, NY, USA (2010). | DOI

[28] G. Vossen and H. Maurer, On L 1 -minimization in optimal control and applications to robotics. Optimal Control Appl. Methods 27 (2006) 301–321. | DOI | MR

[29] S. Wright, R. Nowak and M. Figueiredo, Sparse reconstruction by separable approximation. IEEE Trans. Signal Process. 57 (2009) 2479–2493. | DOI | MR | Zbl

[30] E. Yong, Systems governed by ordinary differential equations with continuous, switching and impulse controls. Appl. Math. Optim. 20 (1989) 223–235. | DOI | MR | Zbl

[31] E. Zuazua, Switching control. J. Eur. Math. Soc. 13 (2011) 85–117. | DOI | MR | Zbl

[32] P. Zhao, G. Rocha and B. Yu, The composite absolute penalties family for grouped and hierarchical variable selection. Ann. Statist. 37 (2009) 3468–3497. | DOI | MR | Zbl

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