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On Perspective Functions and Vanishing Constraints in Mixed-Integer Nonlinear Optimal Control

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Facets of Combinatorial Optimization

Abstract

Logical implications appear in a number of important mixed-integer nonlinear optimal control problems (MIOCPs). Mathematical optimization offers a variety of different formulations that are equivalent for boolean variables, but result in different relaxations. In this article we give an overview over a variety of different modeling approaches, including outer versus inner convexification, generalized disjunctive programming, and vanishing constraints. In addition to the tightness of the respective relaxations, we also address the issue of constraint qualification and the behavior of computational methods for some formulations. As a benchmark, we formulate a truck cruise control problem with logical implications resulting from gear-choice specific constraints. We provide this benchmark problem in AMPL format along with different realistic scenarios. Computational results for this benchmark are used to investigate feasibility gaps, integer feasibility gaps, quality of local solutions, and well-behavedness of the presented reformulations of the benchmark problem. Vanishing constraints give the most satisfactory results.

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References

  1. Abichandani, P., Benson, H., Kam, M.: Multi-vehicle path coordination under communication constraints. In: American Control Conference, pp. 650–656 (2008)

    Google Scholar 

  2. Achtziger, W., Kanzow, C.: Mathematical programs with vanishing constraints: optimality conditions and constraint qualifications. Math. Program., Ser. A 114, 69–99 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  3. Anitescu, M., Tseng, P., Wright, S.: Elastic-mode algorithms for mathematical programs with equilibrium constraints: global convergence and stationarity properties. Math. Program., Ser. A 110, 337–371 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  4. Balas, E.: Disjunctive programming and a hierarchy of relaxations for discrete optimization problems. SIAM J. Algebr. Discrete Methods 6, 466–486 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bär, V.: Ein Kollokationsverfahren zur numerischen Lösung allgemeiner Mehrpunktrandwert aufgaben mit Schalt- und Sprungbedingungen mit Anwendungen in der optimalen Steuerung und der Parameteridentifizierung. Diploma thesis, Rheinische Friedrich-Wilhelms-Universität zu Bonn (1983)

    Google Scholar 

  6. Barton, P.: The modelling and simulation of combined discrete/continuous processes. Ph.D. thesis, Department of Chemical Engineering, Imperial College of Science, Technology and Medicine, London (1992)

    Google Scholar 

  7. Baumrucker, B., Biegler, L.: MPEC strategies for optimization of a class of hybrid dynamic systems. J. Process Control 19(8), 1248–1256 (2009)

    Article  Google Scholar 

  8. Belotti, P., Kirches, C., Leyffer, S., Linderoth, J., Luedtke, J., Mahajan, A.: Mixed-integer nonlinear optimization. In: Iserles, A. (ed.) Acta Numerica 2013, vol. 22. Cambridge University Press, Cambridge (2013). www.optimization-online.org/DB_HTML/2012/12/3698.html

  9. Betts, J.: Practical Methods for Optimal Control Using Nonlinear Programming. SIAM, Philadelphia (2001)

    MATH  Google Scholar 

  10. Biegler, L.: Solution of dynamic optimization problems by successive quadratic programming and orthogonal collocation. Comput. Chem. Eng. 8, 243–248 (1984)

    Article  Google Scholar 

  11. Biegler, L.: Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes. Series on Optimization. SIAM, Philadelphia (2010)

    Book  MATH  Google Scholar 

  12. Bock, H., Longman, R.: Computation of optimal controls on disjoint control sets for minimum energy subway operation. Adv. Astronaut. Sci. 50, 949–972 (1985)

    Google Scholar 

  13. Bock, H., Plitt, K.: A Multiple Shooting algorithm for direct solution of optimal control problems. In: Proceedings of the 9th IFAC World Congress, pp. 242–247. Pergamon Press, Budapest (1984)

    Google Scholar 

  14. Burgschweiger, J., Gnädig, B., Steinbach, M.: Nonlinear programming techniques for operative planning in large drinking water networks. Open Appl. Math. J. 3, 1–16 (2009)

    Article  MathSciNet  Google Scholar 

  15. Ceria, S., Soares, J.: Convex programming for disjunctive optimization. Math. Program. 86, 595–614 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  16. Facchinei, F., Jiang, H., Qi, L.: A smoothing method for mathematical programs with equilibrium constraints. Math. Program. 85, 107–134 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  17. Fang, H., Leyffer, S., Munson, T.: A pivoting algorithm for linear programs with complementarity constraints. Optim. Methods Softw. 87, 89–114 (2012)

    Article  MathSciNet  Google Scholar 

  18. Ferris, M., Kanzow, C.: Complementarity and related problems: a survey (1998)

    Google Scholar 

  19. Fischer, A.: A special Newton-type optimization method. Optimization 24, 269–284 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  20. Fletcher, R., de la Maza, E.S.: Nonlinear programming and nonsmooth optimization by successive linear programming. Math. Program. 43(3), 235–256 (1989)

    Article  MATH  Google Scholar 

  21. Fletcher, R., Leyffer, S.: Solving mathematical programs with complementarity constraints as nonlinear programs. Optim. Methods Softw. 19(1), 15–40 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  22. Frangioni, A., Gentile, C.: Perspective cuts for a class of convex 0–1 mixed integer programs. Math. Program., Ser. A 106, 225–236 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  23. Fügenschuh, A., Herty, M., Klar, A., Martin, A.: Combinatorial and continuous models for the optimization of traffic flows on networks. SIAM J. Optim. 16(4), 1155–1176 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  24. Fukushima, M., Qi, L. (eds.): Reformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods. Kluwer Academic, Dordrecht (1999)

    MATH  Google Scholar 

  25. Gerdts, M.: Solving mixed-integer optimal control problems by Branch&Bound: a case study from automobile test-driving with gear shift. Optim. Control Appl. Methods 26, 1–18 (2005)

    Article  MathSciNet  Google Scholar 

  26. Gerdts, M.: A variable time transformation method for mixed-integer optimal control problems. Optim. Control Appl. Methods 27(3), 169–182 (2006)

    Article  MathSciNet  Google Scholar 

  27. Gerdts, M., Sager, S.: Mixed-integer DAE optimal control problems: necessary conditions and bounds. In: Biegler, L., Campbell, S., Mehrmann, V. (eds.) Control and Optimization with Differential-Algebraic Constraints, pp. 189–212. SIAM, Philadelphia (2012)

    Chapter  Google Scholar 

  28. Gill, P., Murray, W., Saunders, M.: SNOPT: an SQP algorithm for large-scale constrained optimization. SIAM J. Optim. 12, 979–1006 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  29. Göttlich, S., Herty, M., Kirchner, C., Klar, A.: Optimal control for continuous supply network models. Netw. Heterog. Media 1(4), 675–688 (2007)

    Google Scholar 

  30. Gräber, M., Kirches, C., Bock, H., Schlöder, J., Tegethoff, W., Köhler, J.: Determining the optimum cyclic operation of adsorption chillers by a direct method for periodic optimal control. Int. J. Refrig. 34(4), 902–913 (2011)

    Article  Google Scholar 

  31. Grossmann, I.: Review of nonlinear mixed-integer and disjunctive programming techniques. Optim. Eng. 3, 227–252 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  32. Gugat, M., Herty, M., Klar, A., Leugering, G.: Optimal control for traffic flow networks. J. Optim. Theory Appl. 126(3), 589–616 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  33. Günlük, O., Linderoth, J.: Perspective reformulations of mixed integer nonlinear programs with indicator variables. Math. Program. 124(1–2), 183–205 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  34. Hante, F., Sager, S.: Relaxation methods for mixed-integer optimal control of partial differential equations. Comput. Optim. Appl. 55(1), 197–225 (2013)

    Article  MathSciNet  Google Scholar 

  35. Hellström, E., Ivarsson, M., Aslund, J., Nielsen, L.: Look-ahead control for heavy trucks to minimize trip time and fuel consumption. Control Eng. Pract. 17, 245–254 (2009)

    Article  Google Scholar 

  36. Hoheisel, T.: Mathematical programs with vanishing constraints. Ph.D. thesis, Julius-Maximilians-Universität Würzburg (2009)

    Google Scholar 

  37. Jung, M.N., Reinelt, G., Sager, S.: The Lagrangian relaxation for the combinatorial integral approximation problem. Optim. Methods Softw. (2012, submitted). www.optimization-online.org/DB_HTML/2012/02/3354.html

  38. Kawajiri, Y., Biegler, L.: A nonlinear programming superstructure for optimal dynamic operations of simulated moving bed processes. Ind. Eng. Chem. Res. 45(25), 8503–8513 (2006)

    Article  Google Scholar 

  39. Kirches, C.: Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Advances in Numerical Mathematics. Springer Vieweg, Wiesbaden (2011)

    Book  Google Scholar 

  40. Kirches, C., Sager, S., Bock, H., Schlöder, J.: Time-optimal control of automobile test drives with gear shifts. Optim. Control Appl. Methods 31(2), 137–153 (2010)

    Article  MATH  Google Scholar 

  41. Kirches, C., Bock, H., Schlöder, J., Sager, S.: Block structured quadratic programming for the direct multiple shooting method for optimal control. Optim. Methods Softw. 26(2), 239–257 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  42. Kirches, C., Bock, H., Schlöder, J., Sager, S.: A factorization with update procedures for a KKT matrix arising in direct optimal control. Math. Program. Comput. 3(4), 319–348 (2011)

    Article  MathSciNet  Google Scholar 

  43. Kirches, C., Wirsching, L., Bock, H., Schlöder, J.: Efficient direct multiple shooting for nonlinear model predictive control on long horizons. J. Process Control 22(3), 540–550 (2012)

    Article  Google Scholar 

  44. Leineweber, D., Bauer, I., Schäfer, A., Bock, H., Schlöder, J.: An efficient multiple shooting based reduced SQP strategy for large-scale dynamic process optimization (Parts I and II). Comput. Chem. Eng. 27, 157–174 (2003)

    Article  Google Scholar 

  45. Leyffer, S.: Complementarity constraints as nonlinear equations: theory and numerical experience. In: Optimization with Multivalued Mappings: Theory, Applications, and Algorithms, pp. 169–208. Springer, Berlin (2006)

    Chapter  Google Scholar 

  46. Leyffer, S., López-Calva, G., Nocedal, J.: Interior methods for mathematical programs with complementarity constraints. SIAM J. Optim. 17(1), 52–77 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  47. Leyffer, S., Munson, T.: A globally convergent filter method for MPECs. Preprint ANL/MCS-P1457-0907, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA (2007)

    Google Scholar 

  48. Logist, F., Sager, S., Kirches, C., van Impe, J.: Efficient multiple objective optimal control of dynamic systems with integer controls. J. Process Control 20(7), 810–822 (2010)

    Article  Google Scholar 

  49. Martin, A., Möller, M., Moritz, S.: Mixed integer models for the stationary case of gas network optimization. Math. Program. 105, 563–582 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  50. Oldenburg, J., Marquardt, W.: Disjunctive modeling for optimal control of hybrid systems. Comput. Chem. Eng. 32(10), 2346–2364 (2008)

    Article  Google Scholar 

  51. Prata, A., Oldenburg, J., Kroll, A., Marquardt, W.: Integrated scheduling and dynamic optimization of grade transitions for a continuous polymerization reactor. Comput. Chem. Eng. 32, 463–476 (2008)

    Article  Google Scholar 

  52. Raghunathan, A., Biegler, L.: Mathematical programs with equilibrium constraints (MPECs) in process engineering. Comput. Chem. Eng. 27, 1381–1392 (2003)

    Article  Google Scholar 

  53. Raghunathan, A., Diaz, M., Biegler, L.: An MPEC formulation for dynamic optimization of distillation operations. Comput. Chem. Eng. 28, 2037–2052 (2004)

    Article  Google Scholar 

  54. Ralph, D., Wright, S.J.: Some properties of regularization and penalization schemes for MPECs. Optim. Methods Softw. 19, 527–556 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  55. Sager, S.: MIOCP benchmark site. mintoc.de

  56. Sager, S.: Numerical Methods for Mixed-Integer Optimal Control Problems. Der Andere Verlag, Tönning (2005)

    Google Scholar 

  57. Sager, S.: Reformulations and algorithms for the optimization of switching decisions in nonlinear optimal control. J. Process Control 19(8), 1238–1247 (2009)

    Article  Google Scholar 

  58. Sager, S.: On the integration of optimization approaches for mixed-integer nonlinear optimal control. Habilitation, University of Heidelberg (2011)

    Google Scholar 

  59. Sager, S.: A benchmark library of mixed-integer optimal control problems. In: Lee, J., Leyffer, S. (eds.) Mixed Integer Nonlinear Programming, pp. 631–670. Springer, Berlin (2012)

    Chapter  Google Scholar 

  60. Sager, S., Reinelt, G., Bock, H.: Direct methods with maximal lower bound for mixed-integer optimal control problems. Math. Program. 118(1), 109–149 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  61. Sager, S., Bock, H., Diehl, M.: The integer approximation error in mixed-integer optimal control. Math. Program., Ser. A 133(1–2), 1–23 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  62. Scholtes, S.: Convergence properties of a regularization scheme for mathematical programs with complementarity constraints. SIAM J. Optim. 11, 918–936 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  63. Scholtes, S.: Nonconvex structures in nonlinear programming. Oper. Res. 52(3), 368–383 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  64. Sherali, H.: RLT: a unified approach for discrete and continuous nonconvex optimization. Ann. Oper. Res. 149, 185–193 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  65. Sonntag, C., Stursberg, O., Engell, S.: Dynamic optimization of an industrial evaporator using graph search with embedded nonlinear programming. In: Proceedings of the 2nd IFAC Conference on Analysis and Design of Hybrid Systems (ADHS), pp. 211–216 (2006)

    Google Scholar 

  66. Stein, O., Oldenburg, J., Marquardt, W.: Continuous reformulations of discrete-continuous optimization problems. Comput. Chem. Eng. 28(10), 3672–3684 (2004)

    Article  Google Scholar 

  67. Stubbs, R., Mehrotra, S.: Generating convex polynomial inequalities for mixed 0–1 programs. J. Glob. Optim. 24, 311–332 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  68. Terwen, S., Back, M., Krebs, V.: Predictive powertrain control for heavy duty trucks. In: Proceedings of IFAC Symposium in Advances in Automotive Control, Salerno, Italy, pp. 451–457 (2004)

    Google Scholar 

  69. Wächter, A., Biegler, L.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math. Program. 106(1), 25–57 (2006)

    Article  MathSciNet  MATH  Google Scholar 

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Jung, M.N., Kirches, C., Sager, S. (2013). On Perspective Functions and Vanishing Constraints in Mixed-Integer Nonlinear Optimal Control. In: Jünger, M., Reinelt, G. (eds) Facets of Combinatorial Optimization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38189-8_16

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