To apply dynamic programming to network optimization, you must first identify the state of the network, the decision variables, the objective function, and the constraints. The state of the ...

This is the code repository for Dynamic Neural Network Programming with PyTorch [Video], published by Packt. It contains all the supporting project files necessary to work through the video course ...

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 ...

aiming to improve the overall energy efficiency of the mobile network. The proposed algorithm applies Dynamic Programming along with Certainty Equivalent Control to find an optimal control policy by ...

Column generation is a decomposition technique that solves a linear program by generating only a subset of the variables that are relevant for the optimal solution. The original problem is ...

His research interests include adaptive dynamic programming, neural networks-based control ... She was a Visiting Scholar with the Department of Electrical, Computer, and Biomedical Engineering, The ...

Subsequently, a real-time cooperative voltage regulation strategy based on approximate dynamic programming is proposed, which employs segmented linear functions to process the value function to reduce ...

Many algorithmic problems on graphs can be solved efficiently for graphs of bounded treewidth or bounded clique-width by using dynamic programming on a tree decomposition ... tables and refutation ...