  This article introduces the concepts around Online Bandit Linear Optimization
and explores an efficient setup called SCRiBLe (Self-Concordant Regularization
in Bandit Learning) created by Abernethy et. al.\cite{abernethy}. The SCRiBLe
setup and algorithm yield a $O(\sqrt{T})$ regret bound and polynomial run time
complexity bound on the dimension of the input space. In this article we build
up to the bandit linear optimization case and study SCRiBLe.
