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Found 6 papers, 0 papers with code
Sparse Linear Regression with Constraints: A Flexible Entropy-based Framework
We demonstrate the efficacy and flexibility of our proposed approach in incorporating a variety of practical constraints, that are otherwise difficult to model using the existing benchmark methods.
Once upon a time step: A closed-loop approach to robust MPC design
A novel perspective on the design of robust model predictive control (MPC) methods is presented, whereby closed-loop constraint satisfaction is ensured using recursive feasibility of the MPC optimization.
Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint
The industrial drying process consumes approximately 12% of the total energy used in manufacturing, with the potential for a 40% reduction in energy usage through improved process controls and the development of new drying technologies.
Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework
The central idea underlying our framework is to quantify exploration in terms of the Shannon Entropy of the trajectories under the MDP and determine the stochastic policy that maximizes it while guaranteeing a low value of the expected cost along a trajectory.
A Clustering Approach to Edge Controller Placement in Software Defined Networks with Cost Balancing
In this work we introduce two novel deterministic annealing based clustering algorithms to address the problem of Edge Controller Placement (ECP) in wireless edge networks.
On the Persistence of Clustering Solutions and True Number of Clusters in a Dataset
Typically clustering algorithms provide clustering solutions with prespecified number of clusters.
