Nonlinear programming in approximate dynamic programming bang-bang solutions, stock-management and unsmooth penalties

ICINCO 2007 - 4th International Conference on Informatics in Control, Automation and Robotics, Proceedings

Volumn ICSO, Issue , 2007, Pages 47-54

  Teytaud, Olivier ^a   Gelly, Sylvain ^a  

^a UNIVERSITÉ PARIS SACLAY   (France)

Author keywords

Evolutionary computation and control; Optimization algorithms

Indexed keywords

ACTION SELECTION; APPROXIMATE DYNAMIC PROGRAMMING; BANG-BANG; DISCRETIZATION; DISCRETIZATION METHOD; DISCRETIZATIONS; EVOLUTIONARY COMPUTATION AND CONTROL; GRADIENT BASED; HIGH-DIMENSIONAL; LEARNING TASKS; MATRIX; NON-LINEAR; OPTIMIZATION ALGORITHMS; OPTIMIZATION APPROACH; QUASI-RANDOM; STOCHASTIC DYNAMIC PROGRAMMING;

COVARIANCE MATRIX; EDUCATION; EVOLUTIONARY ALGORITHMS; LEARNING ALGORITHMS; NONLINEAR PROGRAMMING; OPTIMIZATION; ROBOTICS; SYSTEMS ENGINEERING;

DYNAMIC PROGRAMMING;

EID: 67149116633     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (39)

1. Auger, A., Jebalia, M., and Teytaud, O. (2005). Xse: quasirandom mutations for evolution strategies. In Proceedings of EA'2005, pages 12-21.
2. th International Conference on Genetic Algorithms, pages 2-9. Morgan Kaufmann.
3. nd Annual Conference on Evolutionary Programming, pages 11-22. Evolutionary Programming Society.
4. th Annual Conference on Evolutionary Programming. MIT Press.
5. Bertsekas, D. (1995). Dynamic Programming and Optimal Control, vols I and II. Athena Scientific.
6. Bertsekas, D. and Tsitsiklis, J. (1996). Neuro-dynamic programming, athena scientific.
7. Beyer, H.-G. (2001). The Theory of Evolutions Strategies. Springer, Heidelberg.
8. Beyer, H.-G., Olhofer, M., and Sendhoff, B. (2004). On the impact of systematic noise on the evolutionary optimization performance - a sphere model analysis, genetic programming and evolvable machines, vol.5, no. 4, pp. 327-360.
9. Broyden., C. G. (1970). The convergence of a class of double-rank minimization algorithms 2, the new algorithm, j. of the inst. for math, and applications, 6:222-231.
10. Byrd, R., Lu, P., Nocedal, J., and C.Zhu (1995). A limited memory algorithm for bound constrained optimization. SIAM J. Scientific Computing, vol.16, no.5.
11. Cervellera,C.andMuselli,M. 2003Adeterministiclearningapproachbasedondiscrepancy.InProceedingsofWIRN'03, pp53-60.(Pubitemid137634234)
12. Collobert,R.andBengio,S.2001Svmtorch:Supportvectormachinesforlarge- scaleregressionproblems.JournalofMachineLearningResearch,1:143-160. (Pubitemid33738779)
13. Conn, A., Scheinberg, K., and Toint, L. (1997). Recent progress in unconstrained nonlinear optimization without derivatives.
14. nd Conference on Parallel Problems Solving from Nature, pages 3-13. North Holland.
15. Fitzpatrick, J. and Grefenstette, J. (1988). Genetic algorithms in noisy environments, in machine learning: Special issue on genetic algorithms, p. langley, ed. dordrecht: Kluwer academic publishers, vol.3, pp. 101-120.
16. Fletcher, R. (1970). A new approach to variable-metric algorithms, computer journal, 13:317-322.
17. Gagné, C. (2005). Openbeagle 3.1.0-alpha.
18. Gelly, S., Ruette, S., and Teytaud, O. (2006). Comparison-based algorithms: worst-case optimality, optimality w.r.t a bayesian prior, the intraclass-variance minimization in eda, and implementations with billiards. In PPSN-BTP workshop.
19. Goldfarb, D. (1970). A family of variable-metric algorithms derived by variational means, mathematics of computation, 24:23-26.
20. Hansen, N. and Ostermeier, A. (1996). Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaption. In Proc. of the IEEE Conference on Evolutionary Computation (CEC 1996), pages 312-317. IEEE Press.
21. Hickernell, F. J. 1998 A generalized discrepancy and quadrature error bound. Mathematics of Computation, 67(221):299-322. (Pubitemid 128027435)
22. Hooke, R. and Jeeves, T. A. (1961). Direct search solution of numerical and statistical problems. Journal of the ACM, Vol.8, pp. 212-229.
23. Jin, Y. and Branke, J. (2005). Evolutionary optimization in uncertain environments, a survey, ieee transactions on evolutionary computation, vol.9, no. 3, pp. 303 317.
24. Kaupe, A. F. (1963). Algorithm 178: direct search. Commun. ACM, 6(6):313-314.
25. Keijzer, M., Merelo, J. J., Romero, G., and Schoenauer, M. (2001). Evolving objects: A general purpose evolutionary computation library. In Artificial Evolution, pages 231-244.
26. LaValle, S. M., Branicky, M. S., and Lindemann, S. R. (2004). On the relationship between classical grid search and probabilistic roadmaps. J. J. Robotic Res., 23(7-8):673-692.
27. L'Ecuyer, P. and Lemieux, C. (2002). Recent advances in randomized quasi-monte carlo methods, pages 419-474.
28. Lindemann, S. R. and LaValle, S. M. (2003). Incremental low-discrepancy lattice methods for motion planning. In Proceedings IEEE International Conference on Robotics and Automation, pages 2920-2927.
29. Niederreiter, H. (1992). Random Number Generation and Quasi-Monte Carlo Methods. SIAM.
30. Owen, A. (2003). Quasi-Monte Carlo Sampling, A Chapter on QMC for a SIGGRAPH 2003 course.
31. Sendhoff, B., Beyer, H.-G., and Olhofer, M. (2004). The influence of stochastic quality functions on evolutionary search, in recent advances in simulated evolution and learning, ser. advances in natural computation, k. tan, m. lim, x. yao, and 1. wang, eds. world scientific, pp 152-172.
32. Shanno, D. F. (1970.). Conditioning of quasi-newton methods for function minimization, mathematics of computation, 24:647-656.
33. Sloan,I.andWoźniakowski,H.1998Whenarequasi- MonteCarloalgorithmsefficientforhighdimensionalintegrals?JournalofComplexity, 14(1):1-33.(Pubitemid128421301)
34. Sutton, R. and Barto, A. (1998). Reinforcement learning: An introduction. MIT Press., Cambridge, MA.
35. Tsutsui, S. (1999). A comparative study on the effects of adding perturbations to phenotypic parameters in genetic algorithms with a robust solution searching scheme, in proceedings of the 1999 ieee system, man, and cybernetics conference smc 99, vol.3. ieee, pp. 585 591.
36. Tuffin, B. (1996). On the use of low discrepancy sequences in monte carlo methods. In Technical Report 1060, I.R.I.S.A.
37. Wasilkowski, G. and Wozniakowski, H. 1997 The exponent of discrepancy is at most 1.4778. Math. Comp, 66:1125-1132. (Pubitemid 127113250)
38. Wright, M. (1995). Direct search methods: Once scorned, now respectable. Numerical Analysis (D. F. Griffiths and G. A. Watson, eds.), Pitman Research Notes in Mathematics, pages 191-208. http://citeseer.ist.psu.edu/wright95direct. html.
39. Zhu, C., Byrd,R., P.Lu, and Nocedal, J. (1994). L-BFGS-B: a limited memory FORTRAN code for solving bound constrained optimization problems. Technical Report, EECS Department, Northwestern University.