  Recently, bandit optimization has received significant attention in
real-world safety-critical systems that involve repeated interactions with
humans. While there exist various algorithms with performance guarantees in the
literature, practical implementation of the algorithms has not received as much
attention. This work presents a comprehensive study on the computational
aspects of safe bandit algorithms, specifically safe linear bandits, by
introducing a framework that leverages convex programming tools to create
computationally efficient policies. In particular, we first characterize the
properties of the optimal policy for safe linear bandit problem and then
propose an end-to-end pipeline of safe linear bandit algorithms that only
involves solving convex problems. We also numerically evaluate the performance
of our proposed methods.
