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Comparison of bundle and classical column generation

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Abstract

When a column generation approach is applied to decomposable mixed integer programming problems, it is standard to formulate and solve the master problem as a linear program. Seen in the dual space, this results in the algorithm known in the nonlinear programming community as the cutting-plane algorithm of Kelley and Cheney-Goldstein. However, more stable methods with better theoretical convergence rates are known and have been used as alternatives to this standard. One of them is the bundle method; our aim is to illustrate its differences with Kelley’s method. In the process we review alternative stabilization techniques used in column generation, comparing them from both primal and dual points of view. Numerical comparisons are presented for five applications: cutting stock (which includes bin packing), vertex coloring, capacitated vehicle routing, multi-item lot sizing, and traveling salesman. We also give a sketchy comparison with the volume algorithm.

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References

  1. Anstreicher, K., Wolsey, L.A.: On Dual Solutions in Subgradient Optimization. Unpublished manuscript, CORE, Louvain-la-Neuve (1993)

  2. Augerat, P.: VRP problem instances (1995) http://www.branchandcut.org/VRP/data/

  3. Barahona F. and Anbil R. (2000). The volume algorithm: Producing primal solutions with a subgradient method. Math. Program. 87(3): 385–399

    Article  MATH  MathSciNet  Google Scholar 

  4. Beasley, J.E.: Or-library: distributing test problems by electronic mail. J. Oper. Res. Soc. 41(11), 1069–1072 (1990) http://mscmga.ms.ic.ac.uk/jeb/orlib/binpackinfo.html

    Google Scholar 

  5. Ben-Amor, H., Desrosiers, J.: A proximal-like algorithm for column generation stabilization. Technical report G-2003-43, Les Cahiers du Gerad, Montréal (2003)

  6. Benameur, W., Neto, J.: Acceleration of cutting plane and column generation algorithms: application to network design (to appear, 2006)

  7. Cheney E. and Goldstein A. (1959). Newton’s method for convex programming and Tchebycheff approximations. Numer. Math. 1: 253–268

    Article  MATH  MathSciNet  Google Scholar 

  8. COmputational INfrastructure for Operations Research: http://www.coin-or.org

  9. Dash Optimization: Xpress-MP: User guide and reference manual, release 12 (2001) http://www.dashoptimization.com

  10. Degraeve Z. and Peeters M. (2003). Optimal integer solutions to industrial cutting stock problems: part 2: benchmark results. INFORMS J. Comput. 15(1–3): 58–81

    Article  MathSciNet  Google Scholar 

  11. du Merle O., Villeneuve D., Desrosiers J. and Hansen P. (1999). Stabilized column generation. Disc. Math. 194(1–3): 229–237

    Article  MATH  MathSciNet  Google Scholar 

  12. Ermol’ev Y.M. (1966). Methods of solution of nonlinear extremal problems. Kibernetica 2(4): 1–17

    MathSciNet  Google Scholar 

  13. Feillet D., Dejax P., Gendreau M. and Gueguen C. (2004). An exact algorithm for the elementary shortest path problem with resource constraints: application to some vehicle routing problems. Networks 44(3): 216–229

    Article  MATH  MathSciNet  Google Scholar 

  14. Frangioni A. (2003). Generalized bundle methods. SIAM J. Optim. 13(1): 117–156

    Article  MathSciNet  Google Scholar 

  15. Gau T. and Wäscher G. (1995). CUTGEN1: a problem generator for the standard one-dimensional cutting stock problem. Eur. J. Oper. Res. 84: 572–579

    Article  MATH  Google Scholar 

  16. Geoffrion A.M. (1974). Lagrangean relaxation for integer programming. Math. Program. Study 2: 82–114

    MathSciNet  Google Scholar 

  17. Goffin J.-L., Haurie A. and Vial J.-Ph. (1992). Decomposition and nondifferentiable optimization with the projective algorithm. Manage. Sci. 38(2): 284–302

    MATH  Google Scholar 

  18. Goffin J.-L., Luo Z.-Q. and Ye Y. (1996). Complexity analysis of an interior cutting plane for convex feasibility problems. SIAM J. Optim. 6(3): 638–652

    Article  MATH  MathSciNet  Google Scholar 

  19. Goffin J.-L. and Vial J.-Ph. (2002). Convex nondifferentiable optimization: a survey focused on the analytic center cutting plane method. Optim. Methods Softw. 17(5): 805–867

    Article  MATH  MathSciNet  Google Scholar 

  20. Held M. and Karp R. (1970). The traveling salesman problem and minimum spanning trees. Oper. Res. 18: 1138–1162

    MATH  MathSciNet  Google Scholar 

  21. Held M. and Karp R. (1971). The traveling salesman problem and minimum spanning trees: part II. Math. Program. 1(1): 6–25

    Article  MATH  MathSciNet  Google Scholar 

  22. Hiriart-Urruty, J.-B., Lemaréchal, C.: Convex Analysis and Minimization Algorithms. Springer, Berlin Heidelberg New York (1993, Two volumes)

  23. Hiriart-Urruty J.-B. and Lemaréchal C. (2001). Fundamentals of Convex Analysis. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  24. Kelley J.E. (1960). The cutting plane method for solving convex programs. J. Soc. Indust. Appl. Math. 8: 703–712

    Article  MathSciNet  Google Scholar 

  25. Kim S., Chang K.N. and Lee J.Y. (1995). A descent method with linear programming subproblems for nondifferentiable convex optimization. Math. Program. 71(1): 17–28

    Article  MathSciNet  Google Scholar 

  26. Kiwiel K.C. (1989). A dual method for certain positive semidefinite quadratic programming problems. SIAM J. Sci. Stat. Comput. 10(1): 175–186

    Article  MATH  MathSciNet  Google Scholar 

  27. Kiwiel K.C. (1983). An aggregate subgradient method for nonsmooth convex minimization. Math. Program. 27: 320–341

    Article  MATH  MathSciNet  Google Scholar 

  28. Kiwiel K.C. (1985). Methods of Descent for Nondifferentiable Optimization. Lecture Notes in Mathematics 1133. Springer, Berlin Heidelberg New York

    Google Scholar 

  29. Kiwiel K.C. (1994). A Cholesky dual method for proximal piecewise linear programming. Numer. Math. 68: 325–340

    Article  MATH  MathSciNet  Google Scholar 

  30. Kiwiel, K.C.: An inexact bundle approach to cutting stock problems. Technical report, Systems Research Institute, Warsaw. Submitted to INFORMS J. Comput. (2004)

  31. Kiwiel K.C. (2006). A proximal bundle method with approximate subgradient linearizations. SIAM J. Optim. 16(4): 1007–1023

    Article  MATH  MathSciNet  Google Scholar 

  32. Kiwiel, K.C., Lemaréchal, C.: An inexact conic bundle variant suited to column generation (in preparation)

  33. Sagastizábal C., Bahiense L. and Maculan N. (2002). The volume algorithm revisited: relation with bundle methods. Math. Program. 94(1): 41–69

    Article  MATH  MathSciNet  Google Scholar 

  34. Larsson T., Patriksson M. and Strömberg A.B. (1999). Ergodic, primal convergence in dual subgradient schemes for convex programming. Math. Program. 86(2): 283–312

    Article  MATH  MathSciNet  Google Scholar 

  35. Lemaréchal, C.: An algorithm for minimizing convex functions. In: Rosenfeld, J.L. (ed.) Information Processing ’74, pp. 552–556. North Holland, Amsterdam (1974)

  36. Lemaréchal, C.: Nonsmooth optimization and descent methods. Research Report 78-4, IIASA (1978)

  37. Lemaréchal, C.: Lagrangian relaxation. In: Jünger, M., Naddef, D. (eds.) Computational Combinatorial Optimization, pp. 112–156. Springer, Berlin Heidelberg New York (2001)

  38. Lemaréchal, C.: The omnipresence of Lagrange. 4OR 1(1), 7,25 (2003)

  39. Lemaréchal C., Nemirovskii A.S. and Nesterov Yu.E. (1995). New variants of bundle methods. Math. Program. 69: 111–148

    Article  Google Scholar 

  40. Lemaréchal, C., Pellegrino, F., Renaud, A., Sagastizábal, C.: Bundle methods applied to the unit-commitment problem. In: Dolež al, J., Fidler, J. (eds.) System Modelling and Optimization, pp. 395–402. Chapman and Hall, London (1996)

  41. Lemaréchal C. and Sagastizábal C. (1997). Variable metric bundle methods: from conceptual to implementable forms. Math. Program. 76(3): 393–410

    Article  Google Scholar 

  42. Marsten R.E., Hogan W.W. and Blankenship J.W. (1975). The boxstep method for large-scale optimization. Oper. Res. 23(3): 389–405

    MATH  MathSciNet  Google Scholar 

  43. Mehrotra A. and Trick M.A. (1996). A column generation approach to graph coloring. INFORMS J. Comput. 8(4): 344–354

    Article  MATH  Google Scholar 

  44. du Merle O., Villeneuve D., Desrosiers J. and Hansen P. (1999). Stabilized column generation. Discrete Math. 194: 229–237

    Article  MATH  MathSciNet  Google Scholar 

  45. Nemirovskii, A.S., Yudin, D.: Informational complexity and efficient methods for the solution of convex extremal problems. Ékonomika i Mathematicheskie Metody 12, 357–369 (1976) (in Russian. English translation: Matekon 13, 3–25)

  46. Nemirovskii, A.S., Yudin, D.: Problem Complexity and Method Efficiency in Optimization. Wiley-Interscience Series in Discrete Mathematics (1983). (Original Russian: Nauka, 1979)

  47. Nesterov Yu.E. (1995). Complexity estimates of some cutting plane methods based on the analytic barrier. Math. Program. 69(1): 149–176

    Article  MathSciNet  Google Scholar 

  48. Nesterov Yu.E. and Vial J.-Ph. (1999). Homogeneous analytic center cutting plane methods for convex problems and variational inequalities. SIAM J. Optim. 9(3): 707–728

    Article  MATH  MathSciNet  Google Scholar 

  49. Polyak B.T. (1967). A general method for solving extremum problems. Soviet Math. Doklady 8: 593–597

    MATH  Google Scholar 

  50. Prim R.C. (1957). Shortest connection networks and some generalizations. Bell Syst. Technol. J. 36: 1389–1401

    Google Scholar 

  51. Rockafellar R.T. (1970). Convex Analysis. Princeton University Press, Princeton

    MATH  Google Scholar 

  52. Rockafellar R.T. (1973). A dual approach to solving nonlinear programming problems by constrained optimization. Math. Program. 5: 354–373

    Article  MATH  MathSciNet  Google Scholar 

  53. Rousseau, L.-M., Gendreau, M., Feillet, D.: Interior point stabilization for column generation. Working paper 39, Centre de Recherche sur les Transports, Univ. Montréal (2003)

  54. Shor N. (1970). Utilization of the operation of space dilatation in the minimization of convex functions. Cybernetics 6(1): 7–15

    Article  MathSciNet  Google Scholar 

  55. Shor N.Z. (1985). Minimization methods for non-differentiable functions. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  56. Thiongane B., Nagih A. and Plateau G. (2004). Adapted step size in a 0-1 biknapsack lagrangean dual solving algoritm. Ann. Oper. Res. 139(1): 353–373

    Article  MathSciNet  Google Scholar 

  57. Uzawa, H.: Iterative methods for concave programming. In: Arrow, K., Hurwicz, L., Uzawa, H. (eds.) Studies in Linear and Nonlinear Programming, pp. 154–165. Stanford University Press, Stanford (1959)

  58. Vanderbeck F. (2002). Extending Dantzig’s bound to the bounded multi-class binary knapsack problem. Math. Program. 94(1): 125–16

    Article  MATH  MathSciNet  Google Scholar 

  59. Vanderbeck, F.: Dantzig-Wolfe re-formulation or how to exploit simultaneaously original formulation and column generation re-formulation. Working paper U-03.24, University Bordeaux 1, Talence (2003)

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Correspondence to C. Lemaréchal.

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This research has been supported by Inria New Investigation Grant “Convex Optimization and Dantzig-Wolfe Decomposition”.

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Briant, O., Lemaréchal, C., Meurdesoif, P. et al. Comparison of bundle and classical column generation. Math. Program. 113, 299–344 (2008). https://doi.org/10.1007/s10107-006-0079-z

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