Data-Driven Optimal Control: An Inverse Optimization Model and Algorithm

In Inverse Optimization (IO), it is hypothesized that experts, when making decisions, implicitly engage in solving an optimization problem. If we can reconstruct this optimization problem using the decision data of the expert, then the behavior of the expert can be emulated. In this thesis, a novel inverse optimization model, Kernel Inverse...

Learning for Control: An Inverse Optimization Approach

Data driven learning has become a common practice in many decision making applications. Machine learning techniques are the dominant methods employed but have limitations with respect to constraints satisfaction. A learning method is presented to learn the mapping from an input space to an action space, which is particularly suitable when the...