In this paper, we consider the worst-case regret of Linear Thompson Sampling (LinTS) for the linear bandit problem. citet{russo2014learning} show that the Bayesian regret of LinTS is bounded above by $widetilde{mathcal{O}}(dsqrt{T})$ where $T$ is the time horizon and $d$ is the number of parameters. While this bound matches the minimax lower-bounds for this problem up to logarithmic factors, the existence of a similar worst-case regret bound is still unknown. The only known worst-case regret bound for LinTS, due to cite{agrawal2013thompson,abeille2017linear}, is $widetilde{mathcal{O}}(dsqrt{dT})$ which requires the posterior variance to be inflated by a factor of $widetilde{mathcal{O}}(sqrt{d})$. While this bound is far from the minimax optimal rate by a factor of $sqrt{d}$, in this paper we show that it is the best possible one can get, settling an open problem stated in cite{russo2018tutorial}. Specifically, we construct examples to show that, without the inflation, LinTS can incur linear regret up to time $exp(Omega(d))$. We then demonstrate that, under mild conditions, a slightly modified version of LinTS requires only an $widetilde{mathcal{O}}(1)$ inflation where the constant depends on the diversity of the optimal arm.
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