Traditionally, computer security seeks to ensure a system's integrity against attackers by creating clear boundaries between the system and the outside world ( Bishop, 2002).
In this paper, we show that by employing an away-step update, similar rates can be generalized to arbitrary polytopes with strong empirical performance.
Bayesian optimization (BO) is a popular way to tackle optimization problems with expensive objective function evaluations, but has mostly been applied to unconstrained problems.
Our analysis relies on a novel combination of four ideas applied to the primal-dual gap function: smoothing, acceleration, homotopy, and coordinate descent with non-uniform sampling.