  Bayesian Optimization is an effective method for searching the global maxima
of an objective function especially if the function is unknown. The process
comprises of using a surrogate function and choosing an acquisition function
followed by optimizing the acquisition function to find the next sampling
point. This paper analyzes different acquistion functions like Maximum
Probability of Improvement and Expected Improvement and various optimizers like
L-BFGS and TNC to optimize the acquisitions functions for finding the next
sampling point. Along with the analysis of time taken, the paper also shows the
importance of position of initial samples chosen.
