A Guide to Stochastic Optimisation for Large-Scale Inverse Problems
It addresses the need for efficient solutions to inverse problems in fields like imaging, but is incremental as it synthesizes existing research rather than introducing new methods.
This survey tackles the problem of applying stochastic optimization algorithms to large-scale inverse problems, particularly in imaging, by reviewing state-of-the-art methods and comparing theoretical results with practical performance.
Stochastic optimisation algorithms are the de facto standard for machine learning with large amounts of data. Handling only a subset of available data in each optimisation step dramatically reduces the per-iteration computational costs, while still ensuring significant progress towards the solution. Driven by the need to solve large-scale optimisation problems as efficiently as possible, the last decade has witnessed an explosion of research in this area. Leveraging the parallels between machine learning and inverse problems has allowed harnessing the power of this research wave for solving inverse problems. In this survey, we provide a comprehensive account of the state-of-the-art in stochastic optimisation from the viewpoint of variational regularisation for inverse problems where the solution is modelled as minimising an objective function. We present algorithms with diverse modalities of problem randomisation and discuss the roles of variance reduction, acceleration, higher-order methods, and other algorithmic modifications, and compare theoretical results with practical behaviour. We focus on the potential and the challenges for stochastic optimisation that are unique to variational regularisation for inverse imaging problems and are not commonly encountered in machine learning. We conclude the survey with illustrative examples from imaging on linear inverse problems to examine the advantages and disadvantages that this new generation of algorithms bring to the field of inverse problems.