Relative Frequencies of Generalized Simulated Annealing

Published Online:https://doi.org/10.1287/moor.1050.0177

References

  • Catoni O. Rough large deviation estimates for simulated annealing: Application to exponential schedules. Ann. Probability (1992) 20(3):1109–1146CrossrefGoogle Scholar
  • Chong E. K. P., Wang I.-J., Kulkarni S. R. Noise conditions for prespecified convergence rates of stochastic approximation algorithms. IEEE Trans. Inform. Theory (1999) 45(2):810–814CrossrefGoogle Scholar
  • Connors D. P., Kumar P. R. Simulated annealing type Markov chains and their order balance equations. SIAM J. Control Optim. (1989) 27(6):1440–1461CrossrefGoogle Scholar
  • Cot C., Catoni O. Piecewise constant triangular cooling schedules for generalized simulated annealing algorithms. Ann. Appl. Probability (1998) 8(2):375–396CrossrefGoogle Scholar
  • Del Moral P., Miclo L. On the convergence and applications of generalized simulated annealing. SIAM J. Control Optim. (1999) 37(4):1222–1250CrossrefGoogle Scholar
  • Durrett R.Probability: Theory and Examples (1996) 2nd ed.(Duxbury Press, Belmont, CA) Google Scholar
  • Freidlin M. I., Wentzell A. D. Random perturbations of dynamical systems. Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences] (1984) Vol. 260(Springer-Verlag, New York) . [J. Szücs, trans.]Google Scholar
  • Gelfand S. B., Mitter S. K. Simulated annealing type algorithms for multivariate optimization. Algorithmica (1991) 6(3):419–436CrossrefGoogle Scholar
  • Geman S., Geman D. Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intelligence (1984) 6:721–741CrossrefGoogle Scholar
  • Gong W.-B., Ho Y.-C., Zhai W. Stochastic comparison algorithm for discrete optimization with estimation. SIAM J. Optim. (2000) 10(2):384–404CrossrefGoogle Scholar
  • Hajek B. Cooling schedules for optimal annealing. Math. Oper. Res. (1988) 13(2):311–329LinkGoogle Scholar
  • Kirkpatrick S., Gelatt C. D., Vecchi M. P. Optimization by simulated annealing. Science220(4598):671–680Google Scholar
  • Kulkarni S. R., Horn C. S. An alternative proof for convergence of stochastic approximation algorithms. IEEE Trans. Automatic Control (1996) 41(3):419–424CrossrefGoogle Scholar
  • Metropolis N., Rosenbluth A. W., Rosenbluth M. N., Teller H., Teller E. Equation of State Calculations by Fast Computing Machines. J. Chemical Phys. (1953) 21(6):1087–1092CrossrefGoogle Scholar
  • Trouvé A. Convergence optimale pour les algorithmes de recuits généralisés. C. R. Acad. Sci. Paris Sér. I Math. (1992) 315(11):1197–1202Google Scholar
  • Tsallis C., Stariolo D. A. Generalized simulated annealing. Physica A (1996) 233(1–2):395–406CrossrefGoogle Scholar
  • Tsitsiklis J. N. Markov chains with rare transitions and simulated annealing. Math. Oper. Res. (1989) 14(1):70–90LinkGoogle Scholar
  • Wang I.-J., Chong E. K. P. A deterministic analysis of stochastic approximation with randomized directions. IEEE Trans. Automat. Control (1998) 43(12):1745–1749CrossrefGoogle Scholar
  • Wang I.-J., Chong E. K. P., Kulkarni S. R. Equivalent necessary and sufficient conditions on noise sequences for stochastic approximation algorithms. Adv. Appl. Probability (1996) 28(3):784–801CrossrefGoogle Scholar
  • Wang I.-J., Chong E. K. P., Kulkarni S. R. Weighted averaging and stochastic approximation. Math. Control Signals Systems (1997) 10(1):41–60CrossrefGoogle Scholar
  • Yan D., Mukai H. Stochastic discrete optimization. SIAM J. Control Optim. (1992) 30(3):594–612CrossrefGoogle Scholar
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