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A simplex based algorithm to solve separated continuous linear programs

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Abstract

We consider the separated continuous linear programming problem with linear data. We characterize the form of its optimal solution, and present an algorithm which solves it in a finite number of steps, using an analog of the simplex method, in the space of bounded measurable functions.

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References

  1. Anderson, E.J.: A Continuous Model for Job-Shop Scheduling. Ph. D. Thesis. University of Cambridge, Cambridge (1978)

    Google Scholar 

  2. Anderson, E.J.: A new continuous model for job-shop scheduling. Int. J. Syst. Sci. 12, 1469–1475 (1981)

    Article  MATH  Google Scholar 

  3. Anderson, E.J., Nash, P.: Linear Programming in Infinite Dimensional Spaces. Wiley-Interscience, Chichester (1987)

    MATH  Google Scholar 

  4. Anderson, E.J., Philpott, A.B.: A continuous time network simplex algorithm. Networks 19, 395–425 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  5. Anderson, E.J., Philpott, A.B.: Erratum, a continuous time network simplex algorithm. Networks 19, 823–827 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  6. Anderson, E.J., Pullan, M.C.: Purification for separated continuous linear programs. Math. Methods Oper. Res. 43, 9–33 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  7. Anstreicher, K.M.: Generation of feasible descent directions in continuous time linear programming. Technical Report, SOL 83–18, Department of Operations Research, Stanford University, Stanford (1983)

  8. Barvinok, A.: A Course in Convexity American Mathematical Society, Providence (2002)

  9. Bellman, R.: Bottleneck problems and dynamic programming. Proc. Natl. Acad. Sci. 39, 947–951 (1953)

    Article  MATH  Google Scholar 

  10. Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)

    Google Scholar 

  11. Dantzig, G.B.: Linear Programming and Extensions. Princeton University Press, Princeton (1963)

    MATH  Google Scholar 

  12. Dorfman, R., Samuelson, P.A., Solow, R.M.: Linear Programming and Economic Analysis. MacGraw Hill, New York (Dover edn, 1987) (1958)

  13. Fleischer, L., Sethuraman, J.: Efficient algorithms for separated continuous linear programs: the multicommodity flow problem with holding costs and extensions. Math. Oper. Res. 30, 916–938 (2005)

    Article  MathSciNet  Google Scholar 

  14. Fleischer, L., Skutella, M.: Quickest flows over time. SIAM J. Comput. 36, 1600–1630 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  15. Fukuda, K., Terlaky, T.: Criss cross methods, a fresh view on pivot algorithms. Math. Program. 79, 369–395 (1997)

    MathSciNet  Google Scholar 

  16. Grinold, R.C.: Symmetric duality for continuous linear programs. SIAM J. Appl. Math. 18, 32–51 (1970)

    Article  MathSciNet  Google Scholar 

  17. Hajek, B., Ogier, R.G.: Optimal dynamic routing in communications networks with continuous traffic. Networks 14, 457–487 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  18. Ito, C., Kelley, T., Sachs, E.W.: Inexact primal dual interior point iteration for linear program in function spaces. Comput. Optim. Appl. 4, 189–202 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  19. Koopmans, T.C. (ed.) in Cooperation with Alchien, A., Dantzig, G.B., Georgescu-Roegen, N., Samuelson, P.A., Tucker, A.W. Activity Analysis of Production and Allocation. Wiley/Chapman and Hall, New York/London (1951)

  20. Leontief, W.W.: The Structure of the American Economy, 1919–1929 Harvard University Press, Cambridge (new, enlarged edition, Oxford University Press, New York, 1951) (1941)

  21. Luo, X., Bertsimas, D.: A new algorithm for state constrained separated continuous linear programs. SIAM J. Control Optim. 37, 177–210 (1998)

    Article  MathSciNet  Google Scholar 

  22. Nazarathy, Y., Weiss, G.: Near optimal control of queueing networks over a finite time horizon Annals of Operations Research (to appear) (2007)

  23. Perold, A.F.: Fundamentals of a continuous time simplex method. Technical Report, SOL 78-26, Department of Operations Research, Stanford University, Stanford (1978)

  24. Perold, A.F.: Extreme points and basic feasible solutions in continuous time linear programming. SIAM J. Control Optim. 19, 52–63 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  25. Philpott, A.B., Craddock, M.: An adaptive discretization algorithm for a class of continuous network programs. Networks 26, 1–11 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  26. Pullan, M.C.: An algorithm for a class of continuous linear programs. SIAM J. Control Optim. 31, 1558–1577 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  27. Pullan, M.C.: Forms of optimal solutions for separated continuous linear programs. SIAM J. Control Optim. 33, 1952–1977 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  28. Pullan, M.C.: A duality theory for separated continuous linear programs. SIAM J. Control Optim. 34, 931–965 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  29. Pullan, M.C.: Existence and duality theory for separated continuous linear programs. Math. Model. Syst. 3, 219–245 (1997)

    MATH  MathSciNet  Google Scholar 

  30. Pullan, M.C.: Convergence of a general class of algorithms for separated continuous linear programs. SIAM J. Optim. 10, 722–731 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  31. Pullan, M.C.: An extended algorithm for separated continuous linear programs. Math. Program. 93, 415–451 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  32. Shapiro, A.: On duality theory of conic linear problems. In: Goberna, M.A., Lopez , M.A.(eds) Semi-Infinite Programming, Chap. 7., pp. 135–165. Kluwer, Netherlands (2001)

    Google Scholar 

  33. Vanderbei, R.J: Linear Programming, Foundations and Extensions. Kluwer, Boston, 2nd edn. 2001 (1997)

  34. Weiss, G.: On optimal draining of fluid re-entrant lines. In: Kelly, F.P., Williams, R.(eds) Stochastic Networks, IMA Volumes in Mathematics and its Applications, pp. 93–105. Springer, New York (1995)

    Google Scholar 

  35. Weiss, G.: Scheduling and control of manufacturing systems—a fluid approach Proceedings of the 37 Allerton Conference, pp. 577–586, 21–24 September, 1999, Monticello (1999)

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Correspondence to Gideon Weiss.

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Research supported in part by US-Israel BSF grant 9400196, by German-Israel GIF grant I-564-246/06/97 and by Israel Science Foundation Grants 249/02 and 454/05.

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Weiss, G. A simplex based algorithm to solve separated continuous linear programs. Math. Program. 115, 151–198 (2008). https://doi.org/10.1007/s10107-008-0217-x

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