References
- Ba, S., Brenneman, W. A., Myers, W. R. ( 2015). Optimal sliced Latin hypercube designs. Technometrics 57:479–487.
- Bagirov, A., Karmitsa, N., Mäkelä, M. M. ( 2014). Introduction to Nonsmooth Optimization: Theory, Practice and Software. New York: Springer.
- Bazaraa, M. S., Sherali, H. D., Shetty, C. M. ( 2013). Nonlinear Programming: Theory and Algorithms. 3rd ed. New York: John Wiley & Sons.
- Chen, J., Qian, P. Z. G. ( 2014). Latin hypercube designs with controlled correlations and multi-dimensional stratification. Biometrika 101:319–332.
- Chen, R. B., Hsieh, D. N., Hung, Y., Wang, W. ( 2013). Optimizing Latin hypercube designs by particle swarm. Statistics and Computing 23:663–676.
- Conway, J. H., Sloane, N. J. A., Bannai, E., Leech, J., Norton, S. P., Odlyzko, A. M., Venkov, B. B. ( 1999). Sphere Packings, Lattices and Groups. 3rd ed. New York: Springer.
- Dick, J., Kuo, F. Y., Sloan, I. H. ( 2013). High-dimensional integration: The quasi-Monte Carlo way. Acta Numerica 22:133–288.
- Dorsey, R. E., Mayer, M. J. ( 1995). Genetic algorithms for estimation problems with multiple optima, nondifferentiability, and other irregular features. Journal of Business and Economic Statistics 13:53–66.
- Drezner, Z. eds. ( 1995). Facility Location: A Survey of Applications and Methods. New York: Springer.
- Fang, K. T., Hickernell, F. J., Winker, P. ( 1996). Some global optimization algorithms in statistics. In: Du, D. Z., Zhang, X. S., Cheng, K., eds. Lecture Notes in Operations Research. Beijing: World Publishing Corporation, pp. 14–24.
- Fang, K. T., Lin, D. K. J., Winker, P., Zhang, Y. ( 2000). Uniform design: Theory and application. Technometrics 42:953–963.
- Gardeux, V., Chelouah, R., Siarry, P., Glover, F. ( 2009). Unidimensional search for solving continuous high-dimensional optimization problems. In: Ninth International Conference on Intelligent Systems Design and Applications. (2009).”, 2009. ISDA’09. Washington, DC: IEEE, pp. 1096–1101.
- Gardeux, V., Chelouah, R., Siarry, P., and Glover, F. ( 2011). EM323: A line search based algorithm for solving high-dimensional continuous non-linear optimization problems. Soft Computing 15:2275–2285.
- Gensane, T. ( 2004). Dense packings of equal spheres in a cube. The Electronic Journal of Combinatorics 11(1):33–50.
- Glover, F. ( 1998). A template for scatter search and path relinking. In: Hao, J.-K., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D., eds. Artificial Evolution (Lecture Notes in Computer Science, 1363). Berlin/Heidelberg: Springer, pp. 13–54.
- Halton, J. (1964), Algorithm 247: Radical-inverse quasi-random point sequence, Communications of the ACM 7:701–702.
- He, Y., Tang, B. ( 2013). Strong orthogonal arrays and associated Latin hypercubes for computer experiments. Biometrika 100:254–260.
- Jin, R., Chen, W., Sudjianto, A. ( 2005). An efficient algorithm for constructing optimal design of computer experiments. Journal of Statistical Planning and Inference 134:268–287.
- Johnson, M. E., Moore, L. M., Ylvisaker, D. ( 1990). Minimax and maximin distance designs. Journal of Statistical Planning and Inference 26:131–148.
- Joseph, V. R., Dasgupta, T., Tuo, R., Wu, C. F. J. ( 2015). Sequential exploration of complex surfaces using minimum energy designs. Technometrics 57:64–74.
- Joseph, V. R., Hung, Y. ( 2008). Orthogonal-maximin Latin hypercube designs. Statistica Sinica 18:171–186.
- Kennedy, M. C., O’Hagan, A. ( 2000). Predicting the output from a complex computer code when fast approximations are available. Biometrika 87:1–13.
- Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. ( 1983). Optimization by simulated annealing. Science 220:671–680.
- Kolda, T. G., Lewis, R. M., Torczon, V. ( 2003). Optimization by direct search: New perspectives on some classical and modern methods. SIAM Review 45:385–482.
- Lei, G. ( 2002). Adaptive random search in quasi-Monte Carlo methods for global optimization. Computers & Mathematics with Applications 43:747–754.
- Maaranen, H., Miettinen, K., Mäkelä, M. M. ( 2004). Quasi-random initial population for genetic algorithms. Computers & Mathematics with Applications47:1885–1895.
- Mckay, M. D., Beckman, R. J., Conover, W. J. ( 1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239–245.
- Morokoff, W. J., Caflisch, R. E. ( 1994). Quasi-random sequences and their discrepancies. SIAM Journal on Scientific Computing 15:1251–1279.
- Nelder, J. A., Mead, R. ( 1965). A simplex method for function minimization. The Computer Journal 7:308–313.
- Niederreiter, H. ( 1992). Random number generation and quasi-Monte Carlo methods. In: NSF-CBMS Regional Conference on Random Number Generation. Vol. 63. Philadelphia, PA: Society for Industrial and Applied Mathematics, pp. 23–46.
- Park, J. S. ( 2001). Optimal Latin-hypercube designs for computer experiments. Journal of Statistical Planning Inference 39:95–111.
- Pronzato, L., Müller, W. G. ( 2012). Design of computer experiments: Space filling and beyond. Statistics and Computing 22:681–701.
- Qian, P. Z. G. ( 2009). Nested Latin hypercube designs. Biometrika 96:957–970.
- Qian, P. Z. G. ( 2012). Sliced Latin hypercube designs. Journal of the American Statistical Association 107:393–399.
- Qian, P. Z. G., Ai, M., Wu, C. J. ( 2009). Construction of nested space-filling designs. Annals of Statistics 37:3616–3643.
- Qian, P. Z. G., Wu, C. F. J. ( 2009). Sliced space-filling designs. Biometrika 96:945–956.
- Qian, P. Z. G., Wu, H., Wu, C. F. J. ( 2008). Gaussian process models for computer experiments with qualitative and quantitative factors. Technometrics 50:383–396.
- Rennen, G., Husslage, B., Van Dam, E. R., Den Hertog, D. ( 2010). Nested maximin Latin hypercube designs. Structural and Multidisciplinary Optimization 41:371–395.
- Santer, T. J., Williams, B. J., Notz, W. I. ( 2003). The Design and Analysis of Computer Experiments. New York: Springer.
- Stinstra, E., den Hertog, D., Stehouwer, P., Vestjens, A. ( 2003). Constrained maximin designs for computer experiments. Technometrics 45:340–346.
- Tang, B. ( 1993). Orthogonal array-based Latin hypercubes. Journal of the American Statistical Association 88:1392–1397.
- Trosset, M. W. ( 1999). Approximate maximin distance designs. In: Proceedings of the Section on Physical and Engineering Sciences. London: Royal Society, pp. 223–227.
- Tuo, R., Webster, C. G., Zhang, G. ( 2016). A Multilevel Radial-Basis-Function Method for Computer Experiments. Technical Report. Chinese Academy of Sciences.
- Ugray, Z., Lasdon, L., Plummer, J., Glover, F., Kelly, J., Marti, R. ( 2007). Scatter search and local NLP solvers: A multistart framework for global optimization. INFORMS Journal on Computing 19:328–340.
- van Dam, E. R., Husslage, B., Den Hertog, D., Melissen, H. ( 2007). Maximin Latin hypercube designs in two dimensions. Operations Research 55:158–169.
- Wang, Y., Fang, K. T. ( 1994). Number-Theoretic Methods in Statistics. London: Chapman and Hall.
- Welch, W. J. ( 1982). Branch-and-bound search for experimental designs based on D optimality and other criteria. Technometrics 24:41–48.
- Xie, H., Xiong, S., Qian, P. Z., Wu, C. J. ( 2014). General sliced Latin hypercube designs. Statistica Sinica 24:1239–1256.
- Xiong, S., Qian, P. Z. G., Wu, C. F. J. ( 2013). Sequential design and analysis of high-accuracy and low-accuracy computer codes. Technometrics 55:37–46.
- Yang, J. Y., Liu, M. Q. ( 2012). Construction of orthogonal and nearly orthogonal Latin hypercube designs from orthogonal designs. Statistica Sinica 22:433–442.
- Ye, K. Q. ( 1998). Orthogonal column Latin hypercubes and their application in computer experiments. Journal of the American Statistical Association 93:1430–1439.