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Since the state and action space are finite, the policy iteration algorithm converges in a finite number of steps.
The simple geometry of "diagonals in disguise". The power iteration algorithm.
The iteration algorithm for implementing the adaptive SR is given.
This method applies an iteration algorithm, which is virtually equivalent to the iteration algorithm of the unmodified Newton Raphson method combined with the vector potential formulation.
The stochastic eigenvalue problem is solved by using QR iteration algorithm.
In order to solve the model, a new fast iteration algorithm is designed.
Recently, a probabilistic Subgradient Iteration algorithm was proposed for solving LMIs.
Firstly, a model-based policy iteration algorithm is introduced to obtain the optimal control law.
An adaptive inverse iteration algorithm using the interpolating multiwavelets is presented to solve structural eigenvalue problems.
By using the alternative minimization method, we design a fast iteration algorithm to solve our model.
At last, two examples are used to demonstrate the effectiveness of the proposed ADP iteration algorithm.
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