Stochastic optimization

Stochastic optimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involves random objective functions or random constraints. Stochastic optimization methods also include methods with random iterates. Some stochastic optimization methods use random iterates to solve stochastic problems, combining both meanings of stochastic optimization.[1] Stochastic optimization methods generalize deterministic methods for deterministic problems.

Methods for stochastic functions

Partly random input data arise in such areas as real-time estimation and control, simulation-based optimization where Monte Carlo simulations are run as estimates of an actual system,[2] [3] and problems where there is experimental (random) error in the measurements of the criterion. In such cases, knowledge that the function values are contaminated by random "noise" leads naturally to algorithms that use statistical inference tools to estimate the "true" values of the function and/or make statistically optimal decisions about the next steps. Methods of this class include:

Randomized search methods

On the other hand, even when the data set consists of precise measurements, some methods introduce randomness into the search-process to accelerate progress.[7] Such randomness can also make the method less sensitive to modeling errors. Further, the injected randomness may enable the method to escape a local optimum and eventually to approach a global optimum. Indeed, this randomization principle is known to be a simple and effective way to obtain algorithms with almost certain good performance uniformly across many data sets, for many sorts of problems. Stochastic optimization methods of this kind include:

In contrast, some authors have argued that randomization can only improve a deterministic algorithm if the deterministic algorithm was poorly designed in the first place.[19] Fred W. Glover [20] argues that reliance on random elements may prevent the development of more intelligent and better deterministic components. The way in which results of stochastic optimization algorithms are usually presented (e.g., presenting only the average, or even the best, out of N runs without any mention of the spread), may also result in a positive bias towards randomness.

See also


  1. Spall, J. C. (2003). Introduction to Stochastic Search and Optimization. Wiley. ISBN 978-0-471-33052-3.
  2. Fu, M. C. (2002). "Optimization for Simulation: Theory vs. Practice". INFORMS Journal on Computing. 14 (3): 192–227. doi:10.1287/ijoc.
  3. M.C. Campi and S. Garatti. The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs. SIAM J. on Optimization, 19, no.3: 12111230, 2008.
  4. Robbins, H.; Monro, S. (1951). "A Stochastic Approximation Method". Annals of Mathematical Statistics. 22 (3): 400–407. doi:10.1214/aoms/1177729586.
  5. J. Kiefer; J. Wolfowitz (1952). "Stochastic Estimation of the Maximum of a Regression Function". Annals of Mathematical Statistics. 23 (3): 462–466. doi:10.1214/aoms/1177729392.
  6. Spall, J. C. (1992). "Multivariate Stochastic Approximation Using a Simultaneous Perturbation Gradient Approximation". IEEE Transactions on Automatic Control. 37 (3): 332–341. CiteSeerX doi:10.1109/9.119632.
  7. Holger H. Hoos and Thomas Stützle, Stochastic Local Search: Foundations and Applications, Morgan Kaufmann / Elsevier, 2004.
  8. S. Kirkpatrick; C. D. Gelatt; M. P. Vecchi (1983). "Optimization by Simulated Annealing". Science. 220 (4598): 671–680. Bibcode:1983Sci...220..671K. CiteSeerX doi:10.1126/science.220.4598.671. PMID 17813860.
  9. D.H. Wolpert; S.R. Bieniawski; D.G. Rajnarayan (2011). C.R. Rao; V. Govindaraju (ed.). "Probability Collectives in Optimization". Cite journal requires |journal= (help)
  10. Battiti, Roberto; Gianpietro Tecchiolli (1994). "The reactive tabu search" (PDF). ORSA Journal on Computing. 6 (2): 126–140. doi:10.1287/ijoc.6.2.126.
  11. Battiti, Roberto; Mauro Brunato; Franco Mascia (2008). Reactive Search and Intelligent Optimization. Springer Verlag. ISBN 978-0-387-09623-0.
  12. Rubinstein, R. Y.; Kroese, D. P. (2004). The Cross-Entropy Method. Springer-Verlag. ISBN 978-0-387-21240-1.
  13. Zhigljavsky, A. A. (1991). Theory of Global Random Search. Kluwer Academic. ISBN 978-0-7923-1122-5.
  14. Kagan E. and Ben-Gal I. (2014). "A Group-Testing Algorithm with Online Informational Learning" (PDF). IIE Transactions, 46:2, 164-184. Cite journal requires |journal= (help)
  15. W. Wenzel; K. Hamacher (1999). "Stochastic tunneling approach for global optimization of complex potential energy landscapes". Phys. Rev. Lett. 82 (15): 3003. arXiv:physics/9903008. Bibcode:1999PhRvL..82.3003W. doi:10.1103/PhysRevLett.82.3003.
  16. E. Marinari; G. Parisi (1992). "Simulated tempering: A new monte carlo scheme". Europhys. Lett. 19 (6): 451–458. arXiv:hep-lat/9205018. Bibcode:1992EL.....19..451M. doi:10.1209/0295-5075/19/6/002.
  17. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley. ISBN 978-0-201-15767-3. Archived from the original on 2006-07-19.
  18. Tavridovich, S. A. (2017). "COOMA: an object-oriented stochastic optimization algorithm". International Journal of Advanced Studies. 7 (2): 26–47. doi:10.12731/2227-930x-2017-2-26-47.
  20. Glover, F. (2007). "Tabu search—uncharted domains". Annals of Operations Research. 149: 89–98. CiteSeerX doi:10.1007/s10479-006-0113-9.

Further reading

This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.