THE OPTIMIZATION OF SATELLITE RECONNAISSANCE BY THE APPLICATION OF DYNAMIC PROGRAMMING TECHNIQUES

1963 ◽  
Author(s):  
J. A. Hunt
Keyword(s):  
Dynamic Programming ◽  
Programming Techniques

scholarly journals A Constrained Markovian Diffusion Model for Controlling the Pollution Accumulation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1466
Author(s):  
Beatris Adriana Escobedo-Trujillo ◽  
José Daniel López-Barrientos ◽  
Javier Garrido-Meléndez
Keyword(s):  
Dynamic Programming ◽  
Dirichlet Problem ◽  
Stochastic Control ◽  
Finite Time ◽  
Time Horizon ◽  
Closed Loop ◽  
Programming Techniques ◽  
Pollution Accumulation ◽  
Finite Time Horizon ◽  
The Cost

This work presents a study of a finite-time horizon stochastic control problem with restrictions on both the reward and the cost functions. To this end, it uses standard dynamic programming techniques, and an extension of the classic Lagrange multipliers approach. The coefficients considered here are supposed to be unbounded, and the obtained strategies are of non-stationary closed-loop type. The driving thread of the paper is a sequence of examples on a pollution accumulation model, which is used for the purpose of showing three algorithms for the purpose of replicating the results. There, the reader can find a result on the interchangeability of limits in a Dirichlet problem.

Dynamic Programming Techniques for Feedback Control*

2011 ◽  
Vol 44 (1) ◽  
pp. 6857-6862
Author(s):  
Jorge Estrela da Silva ◽  
João Borges de Sousa
Keyword(s):  
Dynamic Programming ◽  
Feedback Control ◽  
Programming Techniques

scholarly journals Performance comparison of approximate dynamic programming techniques for dynamic stochastic scheduling

2021 ◽  
Vol 11 (2) ◽  
pp. 178-185
Author(s):  
Yasin Göçgün
Keyword(s):  
Dynamic Programming ◽  
Approximate Dynamic Programming ◽  
Relevant Literature ◽  
Stochastic Scheduling ◽  
Performance Comparison ◽  
Direct Search ◽  
Basis Functions ◽  
Scheduling Problems ◽  
Programming Techniques ◽  
Dynamic Stochastic

This paper focuses on the performance comparison of several approximate dynamic programming (ADP) techniques. In particular, we evaluate three ADP techniques through a class of dynamic stochastic scheduling problems: Lagrangian-based ADP, linear programming-based ADP, and direct search-based ADP. We uniquely implement the direct search-based ADP through basis functions that differ from those used in the relevant literature. The class of scheduling problems has the property that jobs arriving dynamically and stochastically must be scheduled to days in advance. Numerical results reveal that the direct search-based ADP outperforms others in the majority of problem sets generated.

Considerations in Applying Dynamic Programming Filters to the Smoothing of Noisy Data

1994 ◽  
Vol 116 (4) ◽  
pp. 528-531 ◽  
Author(s):  
Antony J. Hodgson
Keyword(s):  
Dynamic Programming ◽  
Global Minimum ◽  
Cross Validation ◽  
Noisy Data ◽  
Optimization Criterion ◽  
Smoothing Parameter ◽  
Generalized Cross Validation ◽  
Programming Techniques ◽  
Multiple Minima ◽  
Robust To Noise

Dynamic programming techniques are useful in smoothing and differentiating noisy data signals according to an optimization criterion and the results are generally quite robust to noise spectra different from that assumed in the construction of the filter. If the noise properties are sufficiently different, however, the generalized cross-validation function used in the optimization can exhibit either multiple minima or no minima other than that corresponding to an insignificant amount of smoothing; in these cases, the smoothing parameter desired by the user typically does not lie at the global minimum of the generalized cross-validation function, but at some other point on the curve which can be identified heuristically. I present two cases to demonstrate this phenomenon and describe what measures one can take to ensure that the desired smoothing parameter is obtained.

Sign in / Sign up

Export Citation Format

Share Document