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MSO: a framework for bound-constrained black-box global optimization algorithms. (English) Zbl 1394.90466
Summary: This paper addresses a class of algorithms for solving bound-constrained black-box global optimization problems. These algorithms partition the objective function domain over multiple scales in search for the global optimum. For such algorithms, we provide a generic procedure and refer to as multi-scale optimization (MSO). Furthermore, we propose a theoretical methodology to study the convergence of MSO algorithms based on three basic assumptions: (a) local Hölder continuity of the objective function \(f\), (b) partitions boundedness, and (c) partitions sphericity. Moreover, the worst-case finite-time performance and convergence rate of several leading MSO algorithms, namely, Lipschitzian optimization methods, multi-level coordinate search, dividing rectangles, and optimistic optimization methods have been presented.

90C26 Nonconvex programming, global optimization
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[1] Archetti, F; Betrò, B, A priori analysis of deterministic strategies for global optimization problems, Towards Global Optim., 2, 31, (1978) · Zbl 0404.90082
[2] Auer, P; Cesa-Bianchi, N; Fischer, P, Finite-time analysis of the multiarmed bandit problem, Mach. Learn., 47, 235-256, (2002) · Zbl 1012.68093
[3] Bertsekas, D.P.: Constrained Optimization and Lagrange Multiplier Methods. Athena Scientific, Belmont (1996) · Zbl 0572.90067
[4] Browne, CB; Powley, E; Whitehouse, D; Lucas, SM; Cowling, PI; Rohlfshagen, P; Tavener, S; Perez, D; Samothrakis, S; Colton, S, A survey of Monte Carlo tree search methods, IEEE Trans. Acoust. Speech Signal Process. Comput. Intell. AI Games, 4, 1-43, (2012)
[5] Cesa-Bianchi, N., Lugosi, G.: Prediction, Learning, and Games. Cambridge University Press, Cambridge (2006) · Zbl 1114.91001
[6] Chaput, JC; Szostak, JW, Evolutionary optimization of a nonbiological atp binding protein for improved folding stability, Chem. Biol., 11, 865-874, (2004)
[7] Chaslot, G., Saito, J.T., Bouzy, B., Uiterwijk, J., Van Den Herik, H.J.: Monte-carlo strategies for computer go. In: Proceedings of the 18th BeNeLux Conference on Artificial Intelligence, Namur, Belgium, pp. 83-91. Citeseer (2006) · Zbl 0404.90082
[8] Clarke, F.H.: Nonsmooth analysis and optimization. In: Proceedings of the International Congress of Mathematicians (Helsinki, 1978), pp. 847-853 (1983) · Zbl 0598.90075
[9] Cousty, J., Najman, L., Perret, B.: Constructive links between some morphological hierarchies on edge-weighted graphs. In: Mathematical Morphology and Its Applications to Signal and Image Processing, pp. 86-97. Springer (2013) · Zbl 1382.68291
[10] Csendes, T; Ratz, D, Subdivision direction selection in interval methods for global optimization, SIAM J. Numer. Anal., 34, 922-938, (1997) · Zbl 0873.65063
[11] Derbel, B., Preux, P.: Simultaneous optimistic optimization on the noiseless bbob testbed. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2010-2017 (2015). doi:10.1109/CEC.2015.7257132 · Zbl 0742.90069
[12] Evtushenko, Y; Posypkin, M, A deterministic approach to global box-constrained optimization, Optim. Lett., 7, 819-829, (2013) · Zbl 1269.90082
[13] Evtushenko, YG, Numerical methods for finding global extrema (case of a non-uniform mesh), USSR Comput. Math. Math. Phys., 11, 38-54, (1971) · Zbl 0233.90007
[14] Evtushenko, YG; Malkova, V; Stanevichyus, A, Parallel global optimization of functions of several variables, Comput. Math. Math. Phys., 49, 246-260, (2009) · Zbl 1199.65197
[15] Finkel, D; Kelley, C, Additive scaling and the direct algorithm, J .Global Optim., 36, 597-608, (2006) · Zbl 1142.90488
[16] Finkel, D.E., Kelley, C.T.: Convergence analysis of the direct algorithm. NCSU Mathematics Department, Raleigh, NC (2004) · Zbl 1296.90090
[17] Floudas, C.A., Pardalos, P.M., Adjiman, C., Esposito, W.R., Gümüs, Z.H., Harding, S.T., Klepeis, J.L., Meyer, C.A., Schweiger, C.A.: Handbook of Test Problems in Local and Global Optimization, vol. 33. Springer Science & Business Media, Berlin (2013) · Zbl 0943.90001
[18] Fowkes, JM; Gould, NI; Farmer, CL, A branch and bound algorithm for the global optimization of Hessian Lipschitz continuous functions, J. Global Optim., 56, 1791-1815, (2013) · Zbl 1296.90090
[19] Gablonsky, J.: An implementation of the direct algorithm. Centre for Research in Scientific Computing, North Carolina State University, Tech. Rep. CRSC-TR98-29 (1998) · Zbl 1296.90090
[20] Gablonsky, J.: Modifications of the direct algorithm. Ph.D. thesis, North Carolina State University, Raleigh, North Carolina (2001) · Zbl 1039.90049
[21] Gablonsky, JM; Kelley, CT, A locally-biased form of the direct algorithm, J. Global Optim., 21, 27-37, (2001) · Zbl 1039.90049
[22] Hansen, N., Auger, A., Finck, S., Ros, R.: Real-parameter black-box optimization benchmarking 2012: Experimental setup. Tech. rep., INRIA (2012). http://coco.gforge.inria.fr/bbob2012-downloads
[23] Hansen, N., Finck, S., Ros, R., Auger, A.: Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions. Tech. Rep. RR-6829, INRIA (2009). http://hal.inria.fr/inria-00362633/en/ · Zbl 1297.90130
[24] Hansen, P; Jaumard, B; Lu, SH, On the number of iterations of piyavskii’s global optimization algorithm, Math. Oper. Res., 16, 334-350, (1991) · Zbl 0742.90069
[25] Horst, R; Tuy, H, On the convergence of global methods in multiextremal optimization, J. Optim. Theory Appl., 54, 253-271, (1987) · Zbl 0595.90079
[26] Hu, J., Wang, Y., Zhou, E., Fu, M.C., Marcus, S.I.: A survey of some model-based methods for global optimization. In: Optimization, Control, and Applications of Stochastic Systems, pp. 157-179. Springer (2012) · Zbl 1374.90311
[27] Huyer, W; Neumaier, A, Global optimization by multilevel coordinate search, J. Global Optim., 14, 331-355, (1999) · Zbl 0956.90045
[28] Ivanov, V, On optimal algorithms of minimization in the class of functions with the Lipschitz condition, Inf. Process., 71, 1324-1327, (1972)
[29] Jones, DR; Perttunen, CD; Stuckman, BE, Lipschitzian optimization without the Lipschitz constant, J. Optim. Theory Appl., 79, 157-181, (1993) · Zbl 0796.49032
[30] Kvasov, DE; Pizzuti, C; Sergeyev, YD, Local tuning and partition strategies for diagonal go methods, Numer. Math., 94, 93-106, (2003) · Zbl 1056.65059
[31] Kvasov, DE; Sergeyev, YD, Lipschitz gradients for global optimization in a one-point-based partitioning scheme, J. Comput. Appl. Math., 236, 4042-4054, (2012) · Zbl 1246.65091
[32] Kvasov, DE; Sergeyev, YD, Deterministic approaches for solving practical black-box global optimization problems, Adv. Eng. Softw., 80, 58-66, (2015)
[33] Lai, TL; Robbins, H, Asymptotically efficient adaptive allocation rules, Adv. Appl. Math., 6, 4-22, (1985) · Zbl 0568.62074
[34] Laurence, A., Wolsey, G.L.N.: Integer and Combinatorial Optimization. Wiley, New York (1988) · Zbl 0652.90067
[35] Liu, Q; Cheng, W, A modified direct algorithm with bilevel partition, J. Global Optim., 60, 483-499, (2014) · Zbl 1303.90083
[36] Mayne, D; Polak, E, Outer approximation algorithm for nondifferentiable optimization problems, J. Optim. Theory Appl., 42, 19-30, (1984) · Zbl 0505.90068
[37] Mladineo, FH, An algorithm for finding the global maximum of a multimodal, multivariate function, Math. Prog., 34, 188-200, (1986) · Zbl 0598.90075
[38] Munos, R.: Optimistic optimization of a deterministic function without the knowledge of its smoothness. In: Advances in Neural Information Processing Systems, vol. 24, pp. 783-791. Curran Associates, Inc. (2011). http://papers.nips.cc/paper/4304-optimistic-optimization-of-a-deterministic-function-without-the-knowledge-of-its-smoothness.pdf
[39] Paulavičius, R; Sergeyev, YD; Kvasov, DE; Žilinskas, J, Globally-biased DISIMPL algorithm for expensive global optimization, J. Global Optim., 59, 545-567, (2014) · Zbl 1297.90130
[40] Paulavičius, R., Žilinskas, J.: Simplicial Global Optimization. Springer, New York (2014) · Zbl 1401.90017
[41] Pintér, J.: Globally convergent methods for \(n\)-dimensional multiextremal optimization. Optimization 17(2), 187-202 (1986) · Zbl 0595.90071
[42] Pintér, J.: Global Optimization in Action: Continuous and Lipschitz Optimization: Algorithms, Implementations and Applications, vol. 6. Springer Science & Business Media, Berlin (1995) · Zbl 0842.90110
[43] Piyavskii, S, An algorithm for finding the absolute extremum of a function, USSR Comput. Math. Math. Phys., 12, 57-67, (1972) · Zbl 0249.65046
[44] Pošík, P.: Bbob-benchmarking the direct global optimization algorithm. In: GECCO ’09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, pp. 2315-2320. ACM, New York, NY, USA (2009). doi:10.1145/1570256.1570323
[45] Pošík, P; Huyer, W; Pál, L, A comparison of global search algorithms for continuous black box optimization, Evolut. Comput., 20, 509-541, (2012)
[46] Preux, P., Munos, R., Valko, M.: Bandits attack function optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2245-2252 (2014)
[47] Ratz, D; Csendes, T, On the selection of subdivision directions in interval branch-and-bound methods for global optimization, J. Global Optim., 7, 183-207, (1995) · Zbl 0841.90116
[48] The Morgridge Institute for Research, I.M.: Bound constrained optimization. http://www.neos-guide.org/content/bound-constrained-optimization
[49] Robbins, H; etal., Some aspects of the sequential design of experiments, Bull. Am. Math. Soc., 58, 527-535, (1952) · Zbl 0049.37009
[50] Roslund, J; Shir, OM; Bäck, T; Rabitz, H, Accelerated optimization and automated discovery with covariance matrix adaptation for experimental quantum control, Phys. Rev. A, 80, 043-415, (2009)
[51] Sergeyev, YD, A one-dimensional deterministic global minimization algorithm, Comput. Math. Math. Phys., 35, 705-717, (1995)
[52] Sergeyev, YD, On convergence of divide the best global optimization algorithms, Optimization, 44, 303-325, (1998) · Zbl 0986.90058
[53] Sergeyev, YD; Kvasov, DE, Global search based on efficient diagonal partitions and a set of Lipschitz constants, SIAM J. Optim., 16, 910-937, (2006) · Zbl 1097.65068
[54] Sergeyev, YD; Kvasov, DE, A deterministic global optimization using smooth diagonal auxiliary functions, Commun. Nonlinear Scie. Numer. Simul., 21, 99-111, (2015) · Zbl 1329.90112
[55] Sergeyev, YD; Strongin, RG, A global minimization algorithm with parallel iterations, USSR Comput. Math. Math. Phys., 29, 7-15, (1990) · Zbl 0702.65064
[56] Shubert, BO, A sequential method seeking the global maximum of a function, SIAM J. Numer. Anal., 9, 379-388, (1972) · Zbl 0251.65052
[57] Srinivas, N., Krause, A., Kakade, S.M., Seeger, M.: Gaussian process optimization in the bandit setting: no regret and experimental design. In: 27th International Conference on Machine Learning (2010) · Zbl 1365.94131
[58] Stover, C., Weisstein, E.W.: Hölder condition. MathWorld-A Wolfram Web Resource. http://mathworld.wolfram.com/HoelderCondition.html
[59] Strongin, R.G.: Numerical methods in multi-extremal problems (information-statistical algorithms) (1978) · Zbl 0458.65062
[60] Strongin, RG, On the convergence of an algorithm for finding a global extremum, Eng. Cybernet., 11, 549-555, (1973)
[61] Strongin, R.G., Sergeyev, Y.: Global Optimization and Non-Convex Constraints: Sequential and Parallel Algorithms. Kluwer Academic Publishers, Dordrecht (2000) · Zbl 0987.90068
[62] Strongin, RG; Sergeyev, YD, Global multidimensional optimization on parallel computer, Parallel Comput., 18, 1259-1273, (1992) · Zbl 0766.65052
[63] Sukharev, AG, Optimal strategies of the search for an extremum, USSR Comput. Math. Math. Phys., 11, 119-137, (1971) · Zbl 0255.90059
[64] Thompson, W.R.: On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, pp. 285-294 (1933) · JFM 59.1159.03
[65] Torn, A., Zilinskas, A.: Global Optimization. Springer, New York (1989) · Zbl 0752.90075
[66] Valko, M., Carpentier, A., Munos, R.: Stochastic simultaneous optimistic optimization. In: Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 19-27 (2013) · Zbl 0986.90058
[67] Wang, Z., Shakibi, B., Jin, L., de Freitas, N.: Bayesian multi-scale optimistic optimization. In: Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS 2014), pp. 1005-1014 (2014) · Zbl 0505.90068
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