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Combinatorial optimal control of semilinear elliptic PDEs

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Abstract

Optimal control problems (OCPs) containing both integrality and partial differential equation (PDE) constraints are very challenging in practice. The most wide-spread solution approach is to first discretize the problem, which results in huge and typically nonconvex mixed-integer optimization problems that can be solved to proven optimality only in very small dimensions. In this paper, we propose a novel outer approximation approach to efficiently solve such OCPs in the case of certain semilinear elliptic PDEs with static integer controls over arbitrary combinatorial structures, where we assume the nonlinear part of the PDE to be non-decreasing and convex. The basic idea is to decompose the OCP into an integer linear programming (ILP) master problem and a subproblem for calculating linear cutting planes. These cutting planes rely on the pointwise concavity or submodularity of the PDE solution with respect to the control variables. The decomposition allows us to use standard solution techniques for ILPs as well as for PDEs. We further benefit from reoptimization strategies for the PDE solution due to the iterative structure of the algorithm. Experimental results show that the new approach is capable of solving the combinatorial OCP of a semilinear Poisson equation with up to 180 binary controls to global optimality within a 5 h time limit. In the case of the screened Poisson equation, which yields semi-infinite integer linear programs, problems with as many as 1400 binary controls are solved.

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References

  1. Alibert, J.-J., Raymond, J.-P.: Boundary control of semilinear elliptic equations with discontinuous leading coefficients and unbounded controls. Numer. Funct. Anal. Optim. 18, 235–250 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  2. Avraam, M., Shah, N., Pantelides, C.: Modelling and optimisation of general hybrid systems in the continuous time domain. Comput. Chem. Eng. 22(Supplement 1), S221–S228 (1998)

    Article  Google Scholar 

  3. Balakrishna, S., Biegler, L.T.: A unified approach for the simultaneous synthesis of reaction, energy, and separation systems. Ind. Eng. Chem. Res. 32, 1372–1382 (1993)

    Article  Google Scholar 

  4. Bansal, V., Sakizlis, V., Ross, R., Perkins, J.D., Pistikopoulos, E.N.: New algorithms for mixed-integer dynamic optimization. Comput. Chem. Eng. 27, 647–668 (2003)

    Article  Google Scholar 

  5. Baumann, F., Berckey, S., Buchheim, C.: Exact algorithms for combinatorial optimization problems with submodular objective functions. In: Jünger, M., Reinelt, G. (eds.) Facets of Combinatorial Optimization-Festschrift for Martin Grötschel, pp. 271–294. Springer, Berlin (2013)

    Chapter  Google Scholar 

  6. Belotti, P., Kirches, C., Leyffer, S., Linderoth, J., Luedtke, J., Mahajan, A.: Mixed-integer nonlinear optimization. Acta Numer. 22, 1–131 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. Boehme, T.J., Schori, M., Frank, B., Schultalbers, M., Lampe B.: Solution of a hybrid optimal control problem for parallel hybrid vehicles subject to thermal constraints. In: 2013 IEEE 52nd Annual Conference on Decision and Control (CDC), IEEE, pp. 2220–2226 (2013)

  8. Bonami, P., Biegler, L., Conn, A., Cornuéjols, G., Grossmann, I., Laird, C., Lee, J., Lodi, A., Margot, F., Sawaya, N., Wächter, A.: An algorithmic framework for convex mixed integer nonlinear programs. Discrete Optim. 5, 186–204 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  9. Casas, E.: Control of an elliptic problem with pointwise state constraints. SIAM J. Control Optim. 24, 1309–1318 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  10. Casas, E.: Boundary control of semilinear elliptic equations with pointwise state constraints. SIAM J. Control Optim. 31, 993–1006 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  11. Chandra, R.: Partial differential equations constrained combinatorial optimization on an adiabatic quantum computer. Master’s thesis, Purdue University (2013)

  12. De Santis, M., Di Pillo, G., Lucidi, S.: An active set feasible method for large-scale minimization problems with bound constraints. Comput. Optim. Appl. 53, 395–423 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  13. Deckelnick, K., Hinze, M.: Convergence of a finite element approximation to a state constrained elliptic control problem. SIAM J. Numer. Anal. 45, 1937–1953 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  14. Dimitriadis, V.D., Pistikopoulos, E.N.: Flexibility analysis of dynamic systems. Ind. Eng. Chem. Res. 34, 4451–4462 (1995)

    Article  Google Scholar 

  15. Duran, M.A., Grossmann, I.E.: An outer-approximation algorithm for a class of mixed-integer nonlinear programs. Math. Program. 36, 307–339 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  16. Edmonds, J.: Submodular functions, matroids, and certain polyhedra. In: Jünger, M., Reinelt, G., Rinaldi, G. (eds.) Combinatorial Optimization—Eureka, You Shrink!, vol. 2570 of LNCS, pp. 11–26. Springer (2003)

  17. Fügenschuh, A., Geissler, B., Martin, A., Morsi, A.: The transport PDE and mixed-integer linear programming. In: Barnhart, C., Clausen, U., Lauther, U., Möhring, R.H. (eds.) Models and Algorithms for Optimization in Logistics, no. 09261 in Dagstuhl Seminar Proceedings, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany (2009)

  18. Fujishige, S.: Submodular Functions and Optimization. Annals of Discrete Mathematics. Elsevier, Amsterdam (1991)

    MATH  Google Scholar 

  19. Geissler, B., Kolb, O., Lang, J., Leugering, G., Martin, A., Morsi, A.: Mixed integer linear models for the optimization of dynamical transport networks. Math. Methods Oper. Res. 73, 339–362 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  20. Gerdts, M.: A variable time transformation method for mixed-integer optimal control problems. Optim. Control Appl. Methods 27, 169–182 (2006)

    Article  MathSciNet  Google Scholar 

  21. Grisvard, P.: Elliptic Problems in Nonsmooth Domains. Classics in Applied Mathematics. SIAM, Philadelphia (1985)

    MATH  Google Scholar 

  22. Haller-Dintelmann, R., Meyer, C., Rehberg, J., Schiela, A.: Hölder continuity and optimal control for nonsmooth elliptic problems. Appl. Math. Optim. 60, 397–428 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  23. Hante, F.M., Sager, S.: Relaxation methods for mixed-integer optimal control of partial differential equations. Comput. Optim. Appl. 55, 197–225 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  24. Hintermüller, M., Kunisch, K.: PDE-constrained optimization subject to pointwise constraints on the control, the state, and its derivative. SIAM J. Optim. 20, 1133–1156 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  25. Incropera, F., Witt, D.D.: Fundamentals of Heat and Mass Transfer. Wiley, Chichester (1985)

    Google Scholar 

  26. Kinderlehrer, D., Stampacchia, G.: An Introduction to Variational Inequalities and Their Applications, vol. 31. SIAM, Philadelphia (2000)

    Book  MATH  Google Scholar 

  27. Kirches, C., Lenders, F.: Approximation properties and tight bounds for constrained mixed-integer optimal control. Technical report, Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University (2016)

  28. Kirches, C., Sager, S., Bock, H.G., Schlöder, J.P.: Time-optimal control of automobile test drives with gear shifts. Optim. Control Appl. Methods 31, 137–153 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  29. Lovász, L.: Submodular functions and convexity. In: Bachem, A., Korte, B., Grötschel, M. (eds.) Mathematical Programming: The State of the Art (Bonn, 1982), pp. 235–257. Springer, Berlin (1983)

    Chapter  Google Scholar 

  30. Meyer, C.: Error estimates for the finite-element approximation of an elliptic control problem with pointwise state and control constraints. Control Cybern. 37, 51–85 (2008)

    MathSciNet  MATH  Google Scholar 

  31. Meyer, C., Prüfert, U., Tröltzsch, F.: On two numerical methods for state-constrained elliptic control problems. Optim. Methods Softw. 22, 871–899 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  32. Mohideen, M.J., Perkins, J.D., Pistikopoulos, E.N.: Optimal design of dynamic systems under uncertainty. AIChE J. 42, 2251–2272 (1996)

    Article  Google Scholar 

  33. Quesada, I., Grossmann, I.: An LP/NLP based branched and bound algorithm for convex MINLP optimization problems. Comput. Chem. Eng. 16, 937–947 (1992)

    Article  Google Scholar 

  34. Sager, S., Bock, H., Diehl, M.: The integer approximation error in mixed-integer optimal control. Math. Program. 133, 1–23 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  35. Sager, S., Jung, M., Kirches, C.: Combinatorial integral approximation. Math. Methods Oper. Res. 73, 363–380 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  36. Schiela, A., Wollner, W.: Barrier methods for optimal control problems with convex nonlinear gradient state constraints. SIAM J. Optim. 21, 269–286 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  37. Till, J., Engell, S., Panek, S., Stursberg, O.: Applied hybrid system optimization: an empirical investigation of complexity. Control Eng. Practice 12, 1291–1303 (2004)

    Article  Google Scholar 

  38. Tröltzsch, F.: Optimal Control of Partial Differential Equations: Theory, Methods, and Applications. Graduate studies in Mathematics. American Mathematical Society, Providence (2010)

    Book  MATH  Google Scholar 

  39. von Stryk, O., Glocker, M.: Decomposition of mixed-integer optimal control problems using branch and bound and sparse direct collocation. In: Engell, S., Kowalewski, S., Zaytoon, J. (eds.) Proceedings of ADPM 2000—The 4th International Conference on Automation of Mixed Processes: Hybrid Dynamic Systems, Dortmund, pp. 99–104, Sept. 2000

  40. Zhang, P., Romero, D., Beck, J., Amon, C.: Solving wind farm layout optimization with mixed integer programming and constraint programming. In: Gomes, C., Sellmann, M. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, vol. 7874 of Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, pp. 284–299 (2013)

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Buchheim, C., Kuhlmann, R. & Meyer, C. Combinatorial optimal control of semilinear elliptic PDEs. Comput Optim Appl 70, 641–675 (2018). https://doi.org/10.1007/s10589-018-9993-2

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