Stochastic Optimization, Stochastic Approximation and Simulated Annealing
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[1] S. Mitter,et al. Simulated annealing with noisy or imprecise energy measurements , 1989 .
[2] J. Blum. Approximation Methods which Converge with Probability one , 1954 .
[3] H. White. Some Asymptotic Results for Learning in Single Hidden-Layer Feedforward Network Models , 1989 .
[4] A new normalized stochastic approximation algorithm using a time‐shift parameter , 1995 .
[5] Fahimeh Rezayat. On the use of an SPSA-based model-free controller in quality improvement , 1995, Autom..
[6] Kurt Hornik,et al. Convergence of learning algorithms with constant learning rates , 1991, IEEE Trans. Neural Networks.
[7] James C. Spall,et al. A one-measurement form of simultaneous perturbation stochastic approximation , 1997, Autom..
[8] H. Kushner,et al. Stochastic approximation with averaging of the iterates: Optimal asymptotic rate of convergence for , 1993 .
[9] M. A. Styblinski,et al. Experiments in nonconvex optimization: Stochastic approximation with function smoothing and simulated annealing , 1990, Neural Networks.
[10] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Michael L. Lightstone,et al. A new efficient approach for the removal of impulse noise from highly corrupted images , 1996, IEEE Trans. Image Process..
[12] George Ch. Pflug,et al. Optimization of Stochastic Models , 1996 .
[13] P. Glynn,et al. Stochastic Optimization by Simulation: Convergence Proofs for the GI/G/1 Queue in Steady-State , 1994 .
[14] D. C. Chin,et al. Comparative study of stochastic algorithms for system optimization based on gradient approximations , 1997, IEEE Trans. Syst. Man Cybern. Part B.
[15] C. Z. Wei. Multivariate Adaptive Stochastic Approximation , 1987 .
[16] S. Evans,et al. On the almost sure convergence of a general stochastic approximation procedure , 1986, Bulletin of the Australian Mathematical Society.
[17] G. Pflug. Stochastic minimization with constant step-size: asymptotic laws , 1986 .
[18] Terrence L. Fine,et al. Online Steepest Descent Yields Weights with Nonnormal Limiting Distribution , 1996, Neural Computation.
[19] S. Mitter,et al. Metropolis-type annealing algorithms for global optimization in R d , 1993 .
[20] Boris Polyak,et al. Acceleration of stochastic approximation by averaging , 1992 .
[21] Michael C. Fu,et al. Conditional Monte Carlo , 1997 .
[22] H. Kushner,et al. Stochastic approximation with averaging and feedback: rapidly convergent "on-line" algorithms , 1995, IEEE Trans. Autom. Control..
[23] Gang George Yin,et al. Budget-Dependent Convergence Rate of Stochastic Approximation , 1995, SIAM J. Optim..
[24] R. Suri,et al. Perturbation analysis: the state of the art and research issues explained via the GI/G/1 queue , 1989, Proc. IEEE.
[25] V. Fabian. Simulated annealing simulated , 1997 .
[26] J. Sacks. Asymptotic Distribution of Stochastic Approximation Procedures , 1958 .
[27] J. Blum. Multidimensional Stochastic Approximation Methods , 1954 .
[28] Yutaka Maeda,et al. A learning rule of neural networks via simultaneous perturbation and its hardware implementation , 1995, Neural Networks.
[29] H. Kushner,et al. Asymptotic Properties of Stochastic Approximations with Constant Coefficients. , 1981 .
[30] R. Vidal. Applied simulated annealing , 1993 .
[31] J. H. Venter. An extension of the Robbins-Monro procedure , 1967 .
[32] James C. Spall,et al. A neural network controller for systems with unmodeled dynamics with applications to wastewater treatment , 1997, IEEE Trans. Syst. Man Cybern. Part B.
[33] V. Fabian. On Asymptotic Normality in Stochastic Approximation , 1968 .
[34] D. L. Hanson,et al. Almost Sure Convergence for the Robbins-Monro Process , 1976 .
[35] S. Brooks,et al. Optimization Using Simulated Annealing , 1995 .
[36] Bernard Delyon,et al. Accelerated Stochastic Approximation , 1993, SIAM J. Optim..
[37] John N. Tsitsiklis,et al. Analysis of Temporal-Diffference Learning with Function Approximation , 1996, NIPS.
[38] G. Yin,et al. Asymptotic optimal rate of convergence for an adaptive estimation procedure , 1992 .
[39] Lennart Ljung,et al. Analysis of recursive stochastic algorithms , 1977 .
[40] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[41] D. Ruppert. A Newton-Raphson Version of the Multivariate Robbins-Monro Procedure , 1985 .
[42] G. Yin,et al. Passive stochastic approximation with constant step size and window width , 1996, IEEE Trans. Autom. Control..
[43] Payman Sadegh,et al. Constrained optimization via stochastic approximation with a simultaneous perturbation gradient approximation , 1997, Autom..
[44] H. Kesten. Accelerated Stochastic Approximation , 1958 .
[45] J. Kiefer,et al. Stochastic Estimation of the Maximum of a Regression Function , 1952 .
[46] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[47] J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation , 1992 .