scholarly journals Comparison of function approximation, heuristic, and derivative-based methods for automatic calibration of computationally expensive groundwater bioremediation models

2005 ◽  
Vol 41 (11) ◽  
Author(s):  
Pradeep Mugunthan ◽  
Christine A. Shoemaker ◽  
Rommel G. Regis
Keyword(s):  
Function Approximation ◽  
Automatic Calibration ◽  
Computationally Expensive ◽  
Approximation Heuristic

scholarly journals Scalable Initial State Interdiction for Factored MDPs

2018 ◽  
Author(s):  
Swetasudha Panda ◽  
Yevgeniy Vorobeychik
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Optimal Policy ◽  
Function Approximation ◽  
Stackelberg Game ◽  
Game Model ◽  
Initial State ◽  
Novel Approach ◽  
Linear Function Approximation ◽  
Computationally Expensive

We propose a novel Stackelberg game model of MDP interdiction in which the defender modifies the initial state of the planner, who then responds by computing an optimal policy starting with that state. We first develop a novel approach for MDP interdiction in factored state space that allows the defender to modify the initial state. The resulting approach can be computationally expensive for large factored MDPs. To address this, we develop several interdiction algorithms that leverage variations of reinforcement learning using both linear and non-linear function approximation. Finally, we extend the interdiction framework to consider a Bayesian interdiction problem in which the interdictor is uncertain about some of the planner's initial state features. Extensive experiments demonstrate the effectiveness of our approaches.

Approximation of Computationally Expensive and Noisy Functions for Constrained Nonlinear Optimization

1987 ◽  
Vol 109 (4) ◽  
pp. 528-532 ◽  
Author(s):  
J. W. Free ◽  
A. R. Parkinson ◽  
G. R. Bryce ◽  
R. J. Balling
Keyword(s):  
Nonlinear Programming ◽  
Approximation Method ◽  
Function Approximation ◽  
Experimental Designs ◽  
Test Problems ◽  
Constrained Nonlinear Optimization ◽  
Noisy Functions ◽  
Computationally Expensive ◽  
And Function ◽  
Programming Algorithms

The use of statistical experimental designs is explored as a method of approximating computationally expensive and noisy functions. The advantages of experimental designs and function approximation for use in optimization are discussed. Several test problems are reported showing the approximation method to be competitive with the most efficient optimization algorithms when no noise is present. When noise is introduced, the approximation method is more efficient and solves more problems than conventional nonlinear programming algorithms.

On strategy for determining displacement solely from perturbed phase of self mixing interferometry

2020 ◽  
Author(s):  
Junaid Khan
Keyword(s):  
Coupling Factor ◽  
Moving Target ◽  
Linear Phase ◽  
Spectral Filtering ◽  
Signal Shape ◽  
Phase Equation ◽  
Sensing Applications ◽  
Computationally Expensive ◽  
Fixed Point Methods ◽  
Factor C

While self mixing interferometry(SMI) has proven to be suitable for displacement measurement and other sensing applications,its characteristic self mixing signal shape is strongly governed by the non-linear phase equation which forms relation between perturbed and unperturbed phase of self mixing laser.Therefore, while it is desirable for robust estimation of displacement of moving target, the algorithms to achieve this must have an objective strategy which can be achieved by understanding the characteristic of extracting knowledge of perturbed phase from unperturbed phase. Therefore, it has been proved and shown that such strategy must not involve sole methods where perturbed phase is continuous function of unperturbed phase (e.g:Taylor series or fixed point methods) or through successive displacements (e.g: variations of Gauss Seidal method). Subset of this strategy is to perform spectral filtering of perturbed phase followed by perturbative or homotopic deformation. A less computationally expensive approach of this strategy is adopted to achieve displacement with mean error of 62.2nm covering all feedback regimes, when coupling factor 'C' is unknown.<br>

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