Definition
Simulated annealing is a stochastic computational technique derived from statistical mechanics for finding near globally-minimum-cost solutions to large optimization problems [1].
Background
Many computer vision problems require the minimization of an application dependent objective function in a high-dimensional state space subject to conflicting constraints. Finding the global minimum can be an NP-complete problem since the objective function tends to have many local minima. A procedure for solving hard optimization problems should sample values of the objective function in such a way as to have a high probability of finding a near-optimal solution and should also lend itself to efficient implementation. A method which meets these criteria was introduced by Kirkpatrick et al. [2] and independently by ÄŒerny [3] in the early 1980s. They introduced the concepts of...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Davis L (1987) Genetic algorithms and simulated annealing. Morgan Kaufmann, San Francisco
Kirkpatrick S, Jr CG, Vecchi M (1983) Optimization by simulated annealing. Science 220(4598):671–680
Černý V (1985) Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J Optim Theory Appl 45(1):41–51
Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equations of state calculations by fast computing machines. J Chem Phys 21(6): 1087–1092
Geman S, Geman D (1984) Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6:721–741
Hastings W (1970) Monte carlo sampling methods using markov chains and their applications. Biometrika 57(1): 97–109
Hajek B (1988) Cooling schedules for optimal annealing. Math Oper Res 13(2):311–329
Royer G (1989) A remark on simulated annealing of diffusion processes. SIAM J Control Optim 27(6):1403–1408
Aarts E, Korst J (1989) Simulated annealing and Boltzmann machines: a stochastical approach to combinatorial optimization and neural computing. Wiley, New York
Gidas B (1995) 7: Metropolis-type Monte Carlo simulation algorithms and simulated annealing. In: Topics in contemporary probability and its applications. CRC, Boca Raton, pp 159–232
Szu H, Hartley R (1987) Fast simulated annealing. Phys Lett A 122:157–162
Tsallis C, Stariolo D (1996) Generalized simulated annealing. Physica A 233:395–406
Rose K (1998) Deterministic annealing for clustering, compression, classification, regression, and related optimization problems. Proc IEEE 86(11): 2210–2239
Moral PD (2004) Feynman-Kac formulae. Genealogical and interacting particle systems with applications. Springer, New York
Gall J, Rosenhahn B, Seidel HP (2008) An Introduction to Interacting Simulated Annealing. In: Human motion – understanding, modeling, capture and animation. Springer, Dordrecht, pp 319–343
Robert C, Casella G (2002) Monte Carlo statistical methods. Springer, New York
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Gall, J. (2014). Simulated Annealing. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_680
Download citation
DOI: https://doi.org/10.1007/978-0-387-31439-6_680
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30771-8
Online ISBN: 978-0-387-31439-6
eBook Packages: Computer ScienceReference Module Computer Science and Engineering