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Graph-Based Local Elimination Algorithms in Discrete Optimization

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Foundations of Computational Intelligence Volume 3

Part of the book series: Studies in Computational Intelligence ((SCI,volume 203))

Abstract

The aim of this chapter is to provide a review of structural decomposition methods in discrete optimization and to give a unified framework in the form of local elimination algorithms (LEA). This chapter is organized as follows. Local elimination algorithms for discrete optimization (DO) problems (DOPs) with constraints are considered; a classification of dynamic programming computational procedures is given. We introduce Elimination Game and Elimination tree. Application of bucket elimination algorithm from constraint satisfaction (CS) to solving DOPs is done. We consider different local elimination schemes and related notions. Clustering that merges several variables into single meta-variable defines a promising approach to solve DOPs. This allows to create a quotient (condensed) graph and apply a local block elimination algorithm. In order to describe a block elimination process, we introduce Block Elimination Game. We discuss the connection of aforementioned local elimination algorithmic schemes and a way of transforming the directed acyclic graph (DAG) of computational LEA procedure to the tree decomposition.

Research supported by FWF (Austrian Science Funds) under the project P17948-N13.

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References

  1. Amestoy, P.R., Davis, T.A., Duff, I.S.: An approximate minimum degree ordering algorithm. SIAM J. on Matrix Analysis and Applications 17, 886–905 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  2. Amir, E.: Efficient approximation for triangulation of minimum treewidth. In: Proceedings of UAI (2001)

    Google Scholar 

  3. Aris, R.: The optimal design of chemical reactors. Academic Press, New York (1961)

    MATH  Google Scholar 

  4. Arnborg, S.: Efficient algorithms for combinatorial problems on graphs with bounded decomposability — A survey. BIT 25, 2–23 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  5. Arnborg, S., Corneil, D.G., Proskurowski, A.: Complexity of finding embeddings in a k-tree. SIAM J. Alg. Disc. Meth. 8(2), 277–284 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  6. Arnborg, S., Lagergren, J., Seese, D.: Easy problems for tree-decomposable graphs. J. of Algorithms 12, 308–340 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  7. Ashcraft, C.: Compressed graphs and the minimum degree algorithm. SIAM J. Sci. Comput. 16(6), 1404–1411 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  8. Ashcraft, C., Liu, J.W.H.: Robust ordering of sparse matrices using multisection. SIAM J. Matrix Anal. Appl. 19(3), 816–832 (1995)

    Article  MathSciNet  Google Scholar 

  9. Barnhart, C., Johnson, E.L., Nemhauser, G.L., Savelsbergh, M.W.P., Vance, P.H.: Branch and price: Column generation for solving huge integer programs. Operations Research 46, 316–329 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  10. Beeri, C., Fagin, R., Maier, D., Yannakakis, M.: On the desirability of acyclic database schemes. Journal ACM 30, 479–513 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  11. Beightler, C.S., Johnson, D.B.: Superposition in branching allocation problems. Journal of Mathematical Analysis and Applications 12, 65–70 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  12. Bellman, R., Dreyfus, S.: Applied Dynamic Programming. Princeton University Press, Princeton (1962)

    MATH  Google Scholar 

  13. Benders, J.F.: Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik 4, 238–252 (1962)

    Article  MATH  MathSciNet  Google Scholar 

  14. Bertele, U., Brioschi, F.: A new algorithm for the solution of the secondary optimization problem in nonserial dynamic programming. Journal of Mathematical Analysis and Applications 27, 565–574 (1969)

    Article  MATH  MathSciNet  Google Scholar 

  15. Bertele, U., Brioschi, F.: Contribution to nonserial dynamic programming. Journal of Mathematical Analysis and Applications 28, 313–325 (1969)

    Article  MATH  MathSciNet  Google Scholar 

  16. Bertele, U., Brioschi, F.: Nonserial Dynamic Programming. Academic Press, New York (1972)

    MATH  Google Scholar 

  17. Berry, A.: A wide-range efficient algorithm for minimal triangulation. In: Proceedings of SODA (1999)

    Google Scholar 

  18. Blair, J.R.S., Peyton, B.: An introduction to chordal graphs and clique trees. In: Graph theory and sparse matrix computation. Springer, New York (1993)

    Google Scholar 

  19. Bodlaender, H.L. (ed.): WG 2003. LNCS, vol. 2880. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  20. Bodlaender, H.L.: Treewidth: Algorithmic techniques and results. In: Privara, I., Ružička, P. (eds.) MFCS 1997. LNCS, vol. 1295. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  21. Bodlaender, H., Koster, A.M.C.A.: Combinatorial optimization on graphs of bounded treewidth. Computer Journal 51, 255–269 (2008)

    Article  MathSciNet  Google Scholar 

  22. Burkard, R.E., Hamacher, H.W., Tind, J.: On General Decomposition Schemes in Mathematical Programming. Mathematical Programming Studies 24: Festschrift on the occasion of the 70 th birthday of George B. Dantzig, 238–252 (1985)

    Google Scholar 

  23. Cook, S.A.: The complexity of theorem-proving procedures. In: Proc. 3rd Ann. ACM Symp. on Theory of Computing Machinery, New York (1971)

    Google Scholar 

  24. Cook, W., Seymour, P.D.: Tour merging via branch-decomposition. INFORMS Journal on Computing 15, 233–248 (2003)

    Article  MathSciNet  Google Scholar 

  25. Courcelle, B.: The monadic second-order logic of graphs I: Recognizable sets of finite graphs. Information and Computation 85, 12–75 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  26. Crama, Y., Hansen, P., Jaumard, B.: The basic algorithm for pseudo-boolean programming revisited. Discrete Applied Mathematics 29, 171–185 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  27. Dantzig, G.B.: Programming of interdependent activities II: Mathematical model. Econometrica 17, 200–211 (1949)

    Article  MathSciNet  Google Scholar 

  28. Dantzig, G.B.: Time-staged methods in linear programming. Comments and early history. In: Dantzig, G.B., et al. (eds.) Large-Scale Linear Programming, IIASA, Laxenburg, Austria, pp. 3–16 (1981)

    Google Scholar 

  29. Dantzig, G.B.: Solving staircase linear programs by a nested block-angular method. Technical Report 73-1. Stanford Univ., Dept. of Operations Research, Stanford (1973)

    Google Scholar 

  30. Dasgupta, S., Papadimitriou, C.H., Vazirani, U.V.: Algorithms. McGraw-Hill, New York (2006)

    Google Scholar 

  31. Dechter, R.: Constraint networks. In: Encyclopedia of Artificial Intelligence, 2nd edn. Wiley, New York (1992)

    Google Scholar 

  32. Dechter, R.: Bucket elimination: A unifying framework for reasoning. Artificial Intelligence 113, 41–85 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  33. Dechter, R., El Fattah, Y.: Topological parameters for time-space tradeoff. Artificial Intelligence 125, 93–118 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  34. Dechter, R.: Constraint processing. Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

  35. Dechter, R., Pearl, J.: Tree clustering for constraint networks. Artificial Intelligence 38, 353–366 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  36. Dolgui, A., Soldek, J., Zaikin, O. (eds.): Supply chain optimisation: product/process design, facilities location and flow control. Series: Applied Optimization, vol. 94, XVI. Springer, Heidelberg (2005)

    Google Scholar 

  37. Esogbue, A.O., Marks, B.: Non-serial dynamic programming – A survey. Operational Research Quarterly 25, 253–265 (1974)

    Article  MATH  Google Scholar 

  38. Fernandez-Baca, D.: Nonserial dynamic programming formulations of satisfiability. Information Processing Letters 27, 323–326 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  39. YuYu, F.: On solving discrete programming problems of special form. Economics and Mathematical Methods 1, 262–270 (1965) (Russian)

    Google Scholar 

  40. Floudas, C.A.: Nonlinear and mixed-integer optimization: fundamentals and applications. Oxford University Press, Oxford (1995)

    MATH  Google Scholar 

  41. Fomin, F., Kratsch, D., Todinca, I.: Exact (exponential) algorithms for treewidth and minimum fill-in. In: Díaz, J., Karhumäki, J., Lepistö, A., Sannella, D. (eds.) ICALP 2004. LNCS, vol. 3142, pp. 568–580. Springer, Heidelberg (2004)

    Google Scholar 

  42. Fourer, R.: Staircase matrices and systems. SIAM Review 26(1), 1–70 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  43. Freuder, E.: Constraint solving techniques. In: Tyngu, E., Mayoh, B., Penjaen, J. (eds.) Constraint Programming of series F: Computer and System Sciences. NATO ASI Series, pp. 51–74 (1992)

    Google Scholar 

  44. Fulkerson, D.R., Gross, O.A.: Incidence matrices and interval graphs. Pacific J. of Mathematics 15, 835–855 (1965)

    MATH  MathSciNet  Google Scholar 

  45. George, J.A., Liu, J.W.H.: Computer Solution of Large Sparse Positive Definite Systems. Prentice-Hall Inc., Englewood Cliffs (1981)

    MATH  Google Scholar 

  46. Gogate, V., Dechter, R.: A complete anytime algorithm for treewidth. In: Proceedings of UAI (2004)

    Google Scholar 

  47. Gottlob, G., Leone, N., Scarcello, F.: A comparison of structural CSP decomposition methods. Artificial Intelligence 124, 243–282 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  48. Gottlob, G., Szeider, S.: Fixed-parameter algorithms for artificial intelligence, constraint satisfaction and database problems. The Computer Journal 51, 303–325 (2008)

    Article  Google Scholar 

  49. Gyssens, M., Jeavons, P.G., Cohen, D.A.: Decomposing constraint satisfaction problems using database techniques. Artificial Intelligence 66, 57–89 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  50. Gu, J., Purdom, P.W., Franco, J., Wah, B.W.: Algorithms for the satisfiability (SAT) problem: A survey. Satisfiability Problem Theory and Applications (1997)

    Google Scholar 

  51. Harary, F., Norman, R.Z., Cartwright, D.: Structural Models: An Introduction to the Theory of Directed Graphs. John Wiley & Sons, Chichester (1965)

    MATH  Google Scholar 

  52. Hammer, P.L., Rudeanu, S.: Boolean Methods in Operations Research and Related Areas. Springer, Heidelberg (1968)

    MATH  Google Scholar 

  53. Heggernes, P., Eisenstat, S.C., Kumfert, G., Pothen, A.: The Computational Complexity of the Minimum Degree Algorithm. Techn. report UCRL-ID-148375. Lawrence Livermore National Laboratory (2001), http://www.llnl.gov/tid/lof/documents/pdf/241278.pdf

  54. Hendrickson, B., Rothberg, E.: Improving the run time and quality of nested dissection ordering. SIAM J. Sci. Comput. 20(2), 468–489 (1998)

    Article  MathSciNet  Google Scholar 

  55. Hicks, I.V., Koster, A.M.C.A., Kolotoglu, E.: Branch and tree decomposition techniques for discrete optimization. In: Tutorials in Operations Research. INFORMS, New Orleans (2005), http://ie.tamu.edu/People/faculty/Hicks/bwtw.pdf

  56. Hliněný, P., Oum, S., Seese, D., Gottlob, G.: Width parameters beyond tree-width and their applications. The Computer Journal 51, 326–362 (2008)

    Article  Google Scholar 

  57. Ho, J.K., Loute, E.: A set of staircase linear programming test problems. Mathematical Programming 20, 245–250 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  58. Hooker, J.N.: Logic-based Methods for Optimization: Combining Optimization and Constraint Satisfaction. John Wiley & Sons, Chichester (2000)

    MATH  Google Scholar 

  59. Hooker, J.N.: Logic, optimization and constraint programming. INFORMS Journal on Computing 14, 295–321 (2002)

    Article  MathSciNet  Google Scholar 

  60. Jeavons, P.G., Gyssens, M., Cohen, D.A.: Decomposing constraint satisfaction problems using database techniques. Artificial Intelligence 66, 57–89 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  61. Jégou, P., Ndiaye, S.N., Terrioux, C.: Computing and exploiting tree-decompositions for (Max-)CSP. In: van Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 777–781. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  62. Jensen, F.V., Lauritzen, S.L., Olesen, K.G.: Bayesian updating in causal probabilistic networks by local computations. Computat. Statist. Quart. 4, 269–282 (1990)

    MathSciNet  Google Scholar 

  63. Kask, K., Dechter, R., Larrosa, J., Dechter, A.: Unifying cluster-tree decompositions for reasoning in graphical models. Artificial Intelligence 160, 165–193 (2005)

    Article  MathSciNet  Google Scholar 

  64. Kjaerulff, U.: Triangulation of graphs – algorithms giving small total state space. Techn.report. Aalborg, Denmark (1990)

    Google Scholar 

  65. Koster, A.M.C.A., van Hoesel, C.P.M., Kolen, A.W.J.: Solving frequency assignment problems via tree-decomposition. In: Broersma, H.J., et al. (eds.) 6th Twente workshop on graphs and combinatorial optimization. Univ. of Twente, Enschede, Netherlands (1999)

    Google Scholar 

  66. Lauritzen, S.L., Spiegelhalter, D.J.: Local computation with probabilities on graphical structures and their application to expert systems. J. Roy. Statist. Soc. Ser. B 50, 157–224 (1988)

    Google Scholar 

  67. Liu, J.W.H.: The role of elimination trees in sparse factorization. SIAM Journal on Matrix Analysis and Applications 11, 134–172 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  68. Martelli, A., Montanari, U.: Nonserial Dynamic Programming: On the Optimal Strategy of Variable Elimination for the Rectangular Lattice. Journal of Mathematical Analysis and Applications 40, 226–242 (1972)

    Article  MATH  MathSciNet  Google Scholar 

  69. Mitten, L.G., Nemhauser, G.L.: Multistage optimization. Chemical Engineering Progress 54, 52–60 (1963)

    Google Scholar 

  70. Karypis, G., Kumar, V.: MeTiS - a software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices. Version 4, University of Minnesota (1998), http://www-users.cs.umn.edu/~karypis/metis

  71. Mitten, L.G., Nemhauser, G.L.: Multistage optimization. Chemical Engineering Progress 54, 52–60 (1963)

    Google Scholar 

  72. Neapolitan, R.E.: Probabilistic Reasoning in Expert Systems. Wiley, New York (1990)

    Google Scholar 

  73. Nemhauser, G.L., Wolsey, L.A.: Integer and Combinatorial Optimization. John Wiley & Sons, Chichester (1988)

    MATH  Google Scholar 

  74. Nemhauser, G.L.: The age of optimization: solving large-scale real-world problems. Operations Research 42, 5–13 (1994)

    Article  MATH  Google Scholar 

  75. Nowak, I.: Lagrangian decomposition of block-separable mixed-integer all-quadratic programs. Mathematical Programming 102, 295–312 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  76. Neumaier, A., Shcherbina, O.: Nonserial dynamic programming and local decomposition algorithms in discrete programming (submitted, 2008), http://www.optimization-online.org/DB_HTML/2006/03/1351.html

  77. Pang, W., Goodwin, S.D.: A new synthesis algorithm for solving CSPs. In: Proc. of the 2nd Int. Workshop on Constraint-Based Reasoning. Key West (1996)

    Google Scholar 

  78. Pardalos, P.M., Du, D.Z. (eds.): Handbook of combinatorial optimization, vol. 1, 2, and 3. Kluwer Academic Publishers, Dordrecht (1998)

    Google Scholar 

  79. Pardalos, P.M., Wolkowicz, H. (eds.): Novel approaches to hard discrete optimization. Fields Institute, American Mathematical Society (2003)

    MATH  Google Scholar 

  80. Parter, S.: The use of linear graphs in Gauss elimination. SIAM Review 3, 119–130 (1961)

    Article  MATH  MathSciNet  Google Scholar 

  81. Pearl, J.: Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Mateo (1988)

    Google Scholar 

  82. Ralphs, T.K., Galati, M.V.: Decomposition in integer linear programming. In: Karlof, J. (ed.) Integer Programming: Theory and Practice (2005)

    Google Scholar 

  83. Robertson, N., Seymour, P.D.: Graph minors. II. Algorithmic aspects of tree width. J. of Algorithms 7, 309–322 (1986)

    MATH  MathSciNet  Google Scholar 

  84. Rose, D., Tarjan, R., Lueker, G.: Algorithmic aspects of vertex elimination on graphs. SIAM J. on Computing 5, 266–283 (1976)

    Article  MATH  MathSciNet  Google Scholar 

  85. Rose, D.J.: A graph-theoretic study of the numerical solution of sparse positive definite systems of linear equations. In: Read, R.C. (ed.) Graph Theory and Computing, pp. 183–217. Academic Press, New York (1972)

    Google Scholar 

  86. Rosenthal, A.: Dynamic programming is optimal for nonserial optimization problems. SIAM J. Comput. 11, 47–59 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  87. Seidel, P.: A new method for solving constraint satisfaction problems. In: Proc. of the 7th IJCAI, Vancouver, Canada, pp. 338–342 (1981)

    Google Scholar 

  88. Sergienko, I.V., Shylo, V.P.: Discrete Optimization: Problems, Methods, Studies, Naukova Dumka, Kiev (2003)

    Google Scholar 

  89. Shcherbina, O.: A local algorithm for integer optimization problems. USSR Comput. Math. Phys. 20, 276–279 (1980)

    Article  MathSciNet  Google Scholar 

  90. Shcherbina, O.A.: On local algorithms of solving discrete optimization problems. Problems of Cybernetics (Moscow) 40, 171–200 (1983)

    MathSciNet  Google Scholar 

  91. Shcherbina, O.: Nonserial dynamic programming and tree decomposition in discrete optimization. In: Proc. of Int. Conference on Operations Research Operations Research 2006, Karlsruhe, September 6-8, pp. 155–160. Springer, Berlin (2006)

    Google Scholar 

  92. Shcherbina, O.A.: Tree decomposition and discrete optimization problems: A survey. Cybernetics and Systems Analysis 43, 549–562 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  93. Shcherbina, O.A.: Methodological issues of dynamic programming. Dynamich Sistemy 22, 21–36 (2007) (in Russian)

    MATH  Google Scholar 

  94. Shcherbina, O.A.: Local elimination algorithms for solving sparse discrete problems. Comput. Math. and Math. Phys. 48, 152–167 (2008)

    MathSciNet  Google Scholar 

  95. Shenoy, P.P., Shafer, G.: Propagating belief functions using local computations. IEEE Expert 1, 43–52 (1986)

    Article  Google Scholar 

  96. Shoikhet, K., Geiger, D.: A practical algorithm for finding optimal triangulation. In: Proceedings of AAAI (1997)

    Google Scholar 

  97. Urrutia, J.: Local solutions for global problems in wireless networks. J. of Discrete Algorithms 5, 395–407 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  98. Vanderbeck, F., Savelsbergh, M.: A generic view at the Dantzig-Wolfe decomposition approach in mixed integer programming. Operations Research Letters 34, 296–306 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  99. Van Roy, T.J.: Cross decomposition for mixed integer programming. Mathematical Programming 25, 46–63 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  100. Wah, B.W., Li, G.-J.: Systolic processing for dynamic programming problems. Circuits Systems Signal Process 7, 119–149 (1988)

    Article  MATH  Google Scholar 

  101. Wilde, D., Beightler, C.: Foundations of Optimization. Prentice-Hall, Englewood Cliffs (1967)

    MATH  Google Scholar 

  102. Weigel, R., Faltings, B.: Compiling constraint satisfaction problems. Artificial Intelligence 115, 257–287 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  103. Wets, R.J.B.: Programming under uncertainty: The equivalent convex program. SIAM J. Appl. Math. 14, 89–105 (1966)

    Article  MATH  MathSciNet  Google Scholar 

  104. YuI, Z.: Selected Works. Magistr, Moscow (1998) (in Russian)

    Google Scholar 

  105. YuI, Z., Losev, G.: Neighborhoods in problems of discrete mathematics. Cybern. Syst. Anal. 31, 183–189 (1995)

    Article  Google Scholar 

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Shcherbina, O. (2009). Graph-Based Local Elimination Algorithms in Discrete Optimization. In: Abraham, A., Hassanien, AE., Siarry, P., Engelbrecht, A. (eds) Foundations of Computational Intelligence Volume 3. Studies in Computational Intelligence, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01085-9_8

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