Christos Thrampoulidis

In many machine learning problems, an objective is required to be optimizedwith respect to some constraints. In many cases, these constraints are unknownto us but we can search and take measurements within an exploration radius. Inthe machine learning community, we call this safe optimization. One importantpoint in safe optimization algorithms is how fast we can converge to optima andget in a neighborhood of the optimal solution. In this thesis, we introduce a novelsafe optimization algorithm that is fast. The experiments are performed througha software package we developed called ASFW which is available for download.On the application side, we demonstrate how safe optimization techniques can beapplied to a real-world problem and make an attempt to employ safe optimizationapproaches to solve a real-world problem and propose a frame work that may beapplied in various industrial settings.

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