Efficient Stochastic Optimization Algorithms with Specific Bioinformatics Applications

Abstract

Large scale stochastic optimization is at the core of machine learning and plays an important role in solving optimization problems in bioinformatics. Most of the existing algorithms are based on stochastic gradient descent (SGD), a conceptually simple algorithm that works reasonably well in practice. However, it also suffers from a slow convergence rate and requires manual tuning for best performance. This research focuses on developing stochastic proximal algorithms and improving them further for specific applications in bioinformatics, e.g. drug repurposing. The goal is to 1) make the per-iteration complexity efficient and 2) benefit from the structure of the missing data and external information available. Upon successfully being conducted, it greatly benefits the field of optimization, machine learning and bioinformatics.