The Langevin Equation as a Global Minimization Algorithm
- 181 Downloads
During the past two years a great deal of attention has been given to simulated annealing as a global minimization algorithm in combinatorial optimization problems , image processing problems , and other problems . The first rigorous result concerning the convergence of the annealing algorithm was obtained in . In , the annealing algorithm was treated as a special case of non-stationary Markov chains, and some optimal convergence estimates and an ergodic theorem were established. Optimal estimates for the annealing algorithm have recently been obtained by nice intuitive arguments in .
Unable to display preview. Download preview PDF.
- 1.Dixon, L.C.W., and G.P. Szegö (eds.): Towards Global Optimization 2, North-Holland, (1978).Google Scholar
- 3.Geman, S. and C.R. Huang: “Diffusions for Global Optimization”, preprint, 1984.Google Scholar
- 5.Gidas, B.: “Global Minimization via the Langevin Equation” in preparation.Google Scholar
- 6.Grenander, U.: Tutorial in Pattern Theory, Brown University, (1983).Google Scholar
- 7.Hajek, B.: “Cooling Schedules for Optimal Annealing”, preprint, 1985.Google Scholar
- 8.Helffer, B. and I. Sjöstrand: “Puits Multiples en Mecanique Semi-Classique IV, Etude du Complexe de Witten”, preprint, 1984.Google Scholar
- 9.Hinton, G., T. Sejnowski, and D. Ackley: “Boltzmann Machine: Constraint Satisfaction Networks that Learn”., preprint, 1984.Google Scholar
- 10.Kan, A., C. Boender, and G. Timmer: “A Stochastic Approach to Global Optimization”, preprint, 1984.Google Scholar
- 13.Parisi, G.: “Prolegomena To any Further Computer Evaluation of the QCD Mass Spectrum”, in Progress in Gauge Field Theory Cargese (1983).Google Scholar