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ADP-Based Fault-Tolerant Control with Stability Guarantee for Nonlinear Systems. [PDF]

Entropy (Basel)
Liu L, Lv J, Lin H, Zhan R, Wu L.
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Automated production cutting optimization for minimizing material waste of pipelines in prefabricated MEP systems based on integer programming. [PDF]

Sci Rep
Fan X, Yang L, Zhao X.
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Dynamic programming with Hermite approximation [PDF]

Mathematical Methods of Operations Research, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yongyang Cai, Kenneth L. Judd
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Approximating networks, dynamic programming and stochastic approximation

Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000
Approximate solution of a general N-stage stochastic optimal control problem is considered. It is known that discretizing uniformly the state components in applying dynamic programming may lead this procedure to incur the "curse of dimensionality". Approximating networks, i.e., linear combinations of parametrized basis functions provided with density ...
BAGLIETTO, MARCO, C. Cervellera, T. Parisini, SANGUINETI, MARCELLO, ZOPPOLI, RICCARDO   +4 more
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Feature discovery in approximate dynamic programming

2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, 2009
Feature discovery aims at finding the best representation of data. This is a very important topic in machine learning, and in reinforcement learning in particular. Based on our recent work on feature discovery in the context of reinforcement learning to discover a good, if not the best, representation of states, we report here on the use of the same ...
Preux, Philippe, Girgin, Sertan, Loth, Manuel   +2 more
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Approximate dynamic programming with Gaussian processes

2008 American Control Conference, 2008
In general, it is difficult to determine an optimal closed-loop policy in nonlinear control problems with continuous-valued state and control domains. Hence, approximations are often inevitable. The standard method of discretizing states and controls suffers from the curse of dimensionality and strongly depends on the chosen temporal sampling rate.
Marc Peter Deisenroth, Jan Peters 0001, Carl E. Rasmussen   +2 more
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Approximations of Dynamic Programs, II

Mathematics of Operations Research, 1978
This paper extends a procedure for approximating dynamic programs due to Fox (Fox, B. L. 1971. Finite-state approximations to denumerable-state dynamic programs. J. Math. Anal. Appl. 34 665–670.). Here, the monotone contraction operator model of Denardo (Denardo, E. V. 1967. Contraction mappings in the theory underlying dynamic programming.
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Approximate counting by dynamic programming

Proceedings of the thirty-fifth annual ACM symposium on Theory of computing, 2003
We give efficient algorithms to sample uniformly, and count approximately, the solutions to a zero-one knapsack problem. The algorithm is based on using dynamic programming to provide a deterministic relative approximation. Then "dart throwing" techniques are used to give arbitrary approximation ratios.
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Perspectives of approximate dynamic programming

Annals of Operations Research, 2012
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Bounds for the approximation of dynamic programs

Zeitschrift für Operations Research, 1986
Summary: We consider a general finite stage dynamic programming model. Bounds are derived for the approximation of the minimum expected total cost and of the optimal policy. The theory is applied to an inventory model to give bounds for ''good'' order policies.
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