The generation of experimental data for computational testing in optimization

Experimental Methods for the Analysis of Optimization Algorithms

Volumn , Issue , 2010, Pages 73-101

  Hall, Nicholas G ^a   Posner, Marc E ^b  

^a OHIO STATE UNIVERSITY   (United States)

^b The Ohio State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84880057132     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-642-02538-9_4     Document Type: Chapter

References (124)

1. Aksoy H, Bayazit M(2000) A model for daily flows of intermittent streams. Hydrological Processes 14:1725-1744.
2. Amini MM, Racer M (1994) A rigorous computational comparison of alternative solution methods for the generalized assignment problem. Management Science 40:868-890.
3. Andersen ED, Ye Y (1998) A computational study of the homogeneous algorithm for large-scale convex optimization. Computational Optimization and Applications 10:243-269.
4. Angel E, Zissimopoulos V (1998) Autocorrelation coefficient for the graph bipartitioning problem. Theoretical Computer Science 191:229-243.
5. Angel E, Zissimopoulos V (2000) On the classification of NP-complete problems in terms of their correlation coefficient. Discrete Applied Mathematics 99:261-277.
6. Arthur JL, Frendewey JO (1988) Generating travelling-salesman problems with known optimal tours. Journal of the Operational Research Society 39:153-159.
7. Atamturk A (2007) Strong formulations of robust mixed 0-1 programming. Mathematical Programming 108:235-250.
8. Bailey DD, Dalmau V, Kolaitis PG (2007) Phase transitions of PP-complete satisfiability problems. Discrete Applied Mathematics 155:1627-1639.
9. Balas E, Martin CH (1980) Pivot and complement - A heuristic for 0-1 programming. Management Science 26:86-96.
10. Balas E, Zemel E (1980) An algorithm for large zero-one knapsack problems. Operations Research 28:1130-1154.
11. Bauer HU, Herrmann M, Villmann T (1999) Neural maps and topographic vector quantization. Neural Networks 12:659-676.
12. Bayraksan G, Morton D (2007) Assessing solution quality in stochastic programs. Mathematical Programming 108:495-514.
13. Bertsimas D, Natarajan K, Teo CP (2006) Persistence in discrete optimization under demand uncertainty. Mathematical Programming 108:251-274.
14. Beyer K, Goldstein J, Ramakrishnan R (1999) When is nearest neighbour meaningful? Database Theory - ICDT '99 1540:217-235.
15. Bienstock D, Raskina O, Saniee I, Wang Q (2006) Combined network design and multiperiod pricing: Modeling, solution techniques and computation. Operations Research 54:261-276.
16. Bijmolt THA, Wedel M (1999) A comparison of multidimensional scaling methods for perceptual mapping. Journal of Marketing Research 36:277-285.
17. Brahimi N, Dauzere-Peres S, Najid NM (2006) Capacitated multi-item lot-sizing problems with time windows. Operations Research 54:951-967.
18. Cario MC, Clifford JJ, Hill RR, Yang J, Yang K, Reilly CH (2002) An investigation of the relationship between problem characteristics and algorithm performance: A case study of the GAP. IIE Transactions 34:297-312.
19. Chalmet L, Gelders L (1976) Lagrangean relaxation for a generalized assignmenttype problem. North-Holland, Amsterdam, The Netherlands.
20. Cheeseman P, Kanefsky B, Taylor WM (1991) Where the really hard problems are. In: Proceedings of IJCAI-91, Morgan Kaufmann, San Mateo, CA, pp 331-337.
21. Chen ZL, Pundoor G (2006) Order assignment and scheduling in a supply chain. Operations Research 54:555-572.
22. Cordeau JJ (2006) A branch-and-cut algorithm for the dial-a-ride problem. Operations Research 54:573-586.
23. Culberson J, Beacham A, Papp D (1995) Hiding our colors. In: Proceedings of the CP '95 Workshop on Studying and Solving Really Hard Problems, Cassis, France, pp 31-42.
24. Degraeve Z, Schrage L (1997) Should I use a portable generator in an emergency? Working paper, Department of Applied Economic Sciences, Katholieke Universiteit Leuven, Belgium.
25. Demeulemeester E, Van houcke M, Herroelen W(2003) RanGen: A random network generator for activity-on-the-node networks. Journal of Scheduling 6:17-38.
26. Dupacova J, Consigli G, Wallace SW (2000) Scenarios for mutistage stochastic programs. Annals of Operations Research 100:25-53.
27. Dupacova J, Growe N, Romisch W (2003) Scenario reduction in stochastic programming: An approach using probability metrics. Mathematical Programming, 95:493-511.
28. Estivill-Castro V, Murray AT (1997) Spatial clustering for data mining with generic algorithms. Technical Report FIT-TR-97-10, Faculty of Information Management, Queensland University of Technology.
29. Fischetti M, Lodi A, Martello S, Toth P (2001) A polyhedral approach to simplified crew scheduling and vehicle scheduling problems. Management Science 47:833- 850.
30. Fisher ML (1994) Optimal solution of vehicle routing problems using minimum k-trees. Operations Research 42:626-642.
31. Freed JA (2000) Conceptual comparison of two computer models of corpuscle sectioning and of two algorithms for correction of ploidy measurements in tissue sections. Analytical and Quantitative Cytology and Histology 22:17-25.
32. Frenje L, Juhlin C (1998) Scattering of seismic waves simulated by finite difference modelling in random media: Application to the Gravberg-1 well. Sweden Tectonophysics 293:61-68.
33. Garey MR, Johnson DS (1979) Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman, San Francisco, CA.
34. Gelius LJ, Westerdahl H (1997) Seismic noise modelling. Journal of Seismic Exploration 6:351-366.
35. Ghiani G, Laporte G, Semet F (2006) The black and white traveling salesman problem. Operations Research 54:366-378.
36. Gonzalez J, Gutierrez R (1999) Direct motion estimation from a range scan sequence. Journal of Robotic Systems 16:73-80.
37. Goutte C (2000) Extraction of the relevant delays in temporal modelling. IEEE Transactions on Signal Processing 48:1787-1795.
38. Grate JW, Wise BM, Abraham MH (1999) Method for unknown vapor characterization and classification using a multivariate sorption detector. Analytical Chemistry 71:4544-4553.
39. Guignard M, Rosenwein MB (1989) An improved dual based algorithm for the generalized knapsack problem. Operations Research 37:658-663.
40. Hadjar A, Marcotte O, Soumis F (2006) A branch-and-cut algorithm for the multiple depot vehicle scheduling problem. Operations Research 54:130-149.
41. Hall NG, Posner ME (2001) Generating experimental data for computational testing with machine scheduling applications. Operations Research 49:854-865.
42. Hall NG, Posner ME (2007) Performance prediction and preselection for optimization procedures. Operations Research 55:703-716.
43. Hariri AM, Potts CN (1983) An algorithm for single machine sequencing with release dates to minimize total weighted completion time. Discrete Applied Mathematics 5:99-109.
44. Hays WL (1973) Statistics for the Social Sciences, 2nd edn. Holt, Rinehart and Winston, Inc., New York, NY.
45. Heitsch H, Romisch W (2005) Generation of multivariate scenario trees to model stochasticity in power management. In: Power Tech, IEEE Russia, pp 1-7.
46. Herique A (1999) Radio wave back-propogating in polar coordinates: A linear filter in the time-frequency angle-frequency domain. Radio Science 34:509-519.
47. Hill RR, Reilly CH (1994) Composition for multivariate random variables. In: Proceedings, 1994 Winter Simulation Conference, Institute of Electrical and Electronics Engineers, Orlando, FL, pp 332-342.
48. Hill RR, Reilly CH (2000) The effects of coefficient correlation structure in twodimensional knapsack problems on solution procedure. Management Science 46:302-317.
49. Ho TK, Baird HS (1997) Large-scale simulation studies in image pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 19:1067- 1079.
50. Hooker JN (1994) Needed: An empirical science of algorithms. Operations Research 42:201-212.
51. Hooker JN (1995) Testing heuristics: We have it all wrong. Journal of Heuristics 1:33-42.
52. Hooshyar MA, Lam TH, Razavy M (2000) Inverse problem of the wave equation and the Schwinger approximation. Journal of the Acoustical Society of America 107:404-413.
53. Hoyland K, Wallace SW (2001) Generating scenario trees for multstage decision problems. Management Science 47:295-307.
54. Hoyland K, Kaut M, Wallace SW (2003) A heuristic for moment-matching scenario generation. Annals of Operations Research 24:169-185.
55. Iman RL, Conover WJ (1982) A distribution-free approach to inducing rank correlation among input variables. Communications in Statistics: Simulation and Computing B11:311-334.
56. John TC (1989) Tradeoff solutions in single machine production scheduling for minimizing flow time and maximum penalty. Computers & Operations Research 16:471-479.
57. Kadlec RH (2000) The inadequacy of first-order treatment wetland models. Ecological Engineering 15:105-119.
58. Kall P, Mayer J (1993) SLP-IOR: On the design of a workbench for testing SLP codes. Revista Investigacion Operacional 14:148-161.
59. Karp RM (1972) Reducibility among combinatorial problems. In: Complexity of Computer Computations, Plenum, New York, NY, pp 85-103.
60. Karyapis G, Han EH, Kumar V (1999) CHAMELEON: A hierarchical clustering algorithm using dynamic modeling. IEEE Computer 32:68-75.
61. Kaufman L, Rousseeuw PJ (1990) Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York, NY.
62. Kaut M, Wallace SW (2003) Evaluation of scenario-generation methods for stochastic programming. Working paper, Molde University College, Norway.
63. Koehler JR, Owen AB (1996) Computer experiments. In: Ghosh S, Rao C (eds) Handbook of Statistics, vol 13, Elsevier Science, New York, NY, pp 261-308.
64. Kolisch R, Sprecher A, Drexl A (1995) Characterization and generation of a general class of resource-constrained project scheduling problems. Management Science 41:1693-1703.
65. Krieger AM, Green PE (1999) A cautionary note on using internal cross validation to select the number of clusters. Psychometrika 64:341-353.
66. Laguna M, Rafael M (2001) A GRASP for coloring sparse graphs. Computational Optimization and Applications 19:165-178.
67. Larsson T, Yuan D (2004) An augmented Lagrangian algorithm for large scale multicommodity routing. Computational Optimization and Applications 27:187-215.
68. Law AM, Kelton WD(1991) Simulation Modeling and Analysis, 2nd edn. McGraw- Hill, New York, NY.
69. Linderoth J, Shapiro A, Wright S (2006) The empirical behavior of sampling methods for stochastic programming. Annals of Operations Research 142:215-241.
70. Lium AG, Crainic TG, Wallace SW (2007) Correlations in stochastic programming: A case from stochastic service network design. Revista Investigacion Operacional 24:161-179.
71. Lu Z, Wyss M, Pulpan H (1997) Details of stress directions in the Alaska subduction zone from fault plane solutions. Journal of Geophysical Research-Solid Earth 102:5385-5402.
72. Martello S, Toth P (1979) The 0-1 knapsack problem. In: Christofides N, Mingozzi A, Toth P, Sandi C (eds) Combinatorial Optimization, Wiley, New York, NY, pp 237-279.
73. Martello S, Toth P (1981) An algorithm for the generalized assignment problem. In: Brans JP (ed) Operational Research '81, North-Holland, Amsterdam, The Netherlands, pp 589-603.
74. Martello S, Toth P (1988) A new algorithm for the 0-1 knapsack problem. Management Science 34:633-644.
75. Martello S, Toth P (1997) Upper bounds and algorithms for hard 0-1 knapsack problems. Operations Research 45:768-778.
76. McGeoch CC (1996) Towards an experimental method for algorithm simulation. INFORMS Journal on Computing 8:1-15.
77. McIntosh SW, Charbonneau P, Brown JC (2000) Preconditioning the differential emission measure (T-e) inverse problem. Astrophysics Journal 529:1115-1130.
78. Miller DL (1995) A matching based exact algorithm for capacitated vehicle routing problems. ORSA Journal on Computing 7:1-9.
79. Munizaga MA, Heydecker BG, Ortuzar JD (2000) Representation of heteroskedasticity in discrete choice models. Transportation Research B - Methodology 34:219-240.
80. Ng R, Han J (1994) Efficient and effective clustering methods for spatial data mining. In: Proceedings of International Conference on Very Large Data Bases, Santiago, Chile, pp 144-155.
81. Ow P (1985) Focused scheduling in proportionate flowshops. Management Science 31:852-869.
82. Palmer CR, Faloutsos C (2000) Density biased sampling: An improved method for data mining and clustering. SIGMOD Record 29:82-92.
83. Pan Y, Shi L (2007) On the equivalence of the max-min transportation lower bound and the time-indexed lower bound for single-machine scheduling problems. Mathematical Programming 110:543-559.
84. Patterson JH (1984) A comparison of exact procedures for solving the multipleconstrained resource project scheduling problem. Management Science 30:854- 867.
85. Pearson WR, Robins G, Zhang TT (1999) Generalized neighbor-joining: more reliable phylogenetic tree reconstruction. Molecular Biology and Evolution 16:806- 816.
86. Pei Y, Zaiane O (2006) A synthetic data generator for clustering and outlier analysis. In: Technical report TR06-15, University of Alberta, Edmonton, Alberta.
87. Pennanen T (2005) Epi-convergent discretizations of multistage stochastic programs. Mathematics of Operations Research 30:245-256.
88. Pilcher MG, Rardin RL (1992) Partial polyhedral description and generation of discrete optimization problems with known optima. Naval Research Logistics 39:839-858.
89. Potts CN, Van Wassenhove LN (1985) A Lagrangean based branch and bound algorithm for single machine sequencing with precedence constraints to minimize total weighted completion time. Management Science 31:1300-1311.
90. Potts CN, Van Wassenhove LN (1988) Algorithms for scheduling a single machine to minimize the weighted number of late jobs. Management Science 34:843-858.
91. Potts CN, Van Wassenhove LN (1992) Single machine scheduling to minimize total late work. Operations Research 40:586-595.
92. Qin G, Jing BY (2000) Asymptotic properties for estimation of partial linear models with censored data. Journal of Statistical Planning and Inference 84:95-110.
93. Racer M, Amini MM(1994) A robust heuristic for the generalized assignment problem. Annals of Operations Research 50:487-503.
94. Rardin RL, Uzsoy R (2001) Experimental evaluation of heuristic optimization algorithms: A tutorial. Journal of Heuristics 7:261-304.
95. Ray KS, Ghoshal J (2000) Neuro-genetic approach to multidimensional fuzzy reasoning for pattern classification. Fuzzy Sets and Systems 112:449-483.
96. Reiter JP (2002) Satisfying disclosure restrictions with synthetic data sets. Journal of Official Statistics 18:531-543.
97. Romeijn HE, Morales DR (2001a) Generating experimental data for the generalized assignment problem. Operations Research 49:866-878.
98. Romeijn HE, Morales DR (2001b) A probabilistic analysis of the multi-period single-sourcing problem. Discrete Applied Mathematics 112:301-328.
99. Ross GT, Soland RM (1975) A branch and bound algorithm for the generalized assignment problem. Mathematical Programming 8:91-103.
100. Ross GT, Soland RM (1977) Modeling facility location problems as generalized assignment problems. Management Science 24:345-357.
101. Roversi P, Irwin JJ, Bricogne G (1998) Accurate charge density studies as an extension of Bayesian crystal structure determination. Acta Crystallographica 54:971-996.
102. Ruchala KJ, Olivera GH, Schloesser EA (1999) Megavoltage CT on a tomotherapy system. Physics in Medicine and Biology 44:2597-2621.
103. Rushmeier RA, Nemhauser GL (1993) Experiments with parallel branch-and-bound algorithms for the set covering problem. Operations Research Letters 13:277-285.
104. Schaffer C (1994) A conservation law for generalization performance. In: International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA, pp 259-265.
105. Schena G, Chiaruttini C (2000) A stereologically posed mass balance for calculating the distributed efficiency of particle separation systems. International Journal of Mineral Processing 59:149-162.
106. Schwindt C (1995) A new problem generator for different resource-constrained project scheduling problems with minimal and maximal time lags. WIORReport- 449, Institut fur Wirtschaftstheorie und Operations Research, University of Karlsruhe.
107. Shen L, Shen H, Cheng L (1999) New algorithms for efficient mining of association rules. Information Sciences 118:251-268.
108. Sherali HD, Smith JC (2006) A polyhedral study of the generalized vertex cover problem. Mathematical Programming 107:367-390.
109. Sherali HD, Zhu X (2007) On solving discrete two-stage stochastic programs having mixed-integer first- and second-stage variables. Mathematical Programming 105:597-616.
110. Smith-Miles KA (2008) Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys 41:61-625.
111. Smith-Miles KA, James RJW, Giffin JW, Tu Y (2009) A knowledge discovery approach to understanding relationships between scheduling problem structure and heuristic performance. In: Learning and Intelligent Optimizatio N Conference (LION 3), Trento, Italy.
112. Toth P, Vigo D (2002) Models, relaxations and exact approaches for the capacitated vehicle routing problem. Discrete Applied Mathematics 123:487-512.
113. Trick MA (1992) A linear relaxation heuristic for the generalized assignment problem. Naval Research Logistics 39:137-151.
114. Uma RN, Wein J (1998) On the relationship between combinatorial and LP-based approaches to NP-hard scheduling problems. Integer Programming and Combinatorial Optimization, Lecture Notes in Computer Science 1412:394-408.
115. Van der Wiel RJ, Sahinidis NV (1995) Heuristic bounds and test problem generation for the time-dependent traveling salesman problem. Transportation Science 29:167-183.
116. van de Velde SL (1995) Dual decomposition of a single-machine scheduling problem. Mathematical Programming 69:413-428.
117. Verweij B, Ahmed S, Kleywegt A, Nemhauser G, Shapiro A (2003) The sample average approximation method applied to stochastic routing problems: A computational study. Computational Optimization and Applications 24:289-333.
118. Wei CP, Lee YH, Hsu CM (2003) Empirical comparison of fast partitioningbased clustering algorithms for large data sets. Expert Systems with Applications 24:351-363.
119. Wilson RC, Hancock ER (2000) Bias variance analysis for controlling adaptive surface meshes. Computer Vision and Image Understanding 77:25-47.
120. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1:67-82.
121. Wu D, Golbasi H (2004) Multi-item, multi-facility supply chain planning: Models, complexities and algorithms. Computational Optimization and Applications 28:325-356.
122. Xu S, Freund RM, Sun J (2003) Solution methodologies for the smallest enclosing circle problem. Computational Optimization and Applications 25:283-292.
123. Yaman H, Karasan OE, Pinar MC (2007) Restricted robust uniform matroid maximization under interval uncertainty. Mathematical Programming 110:431-441.
124. Yuval A(2000) Neural network training for prediction of climatological time series, regularized by minimization of the generalized cross validation function. Monthly Weather Review 128:1456-1473.