Introduction to Online Convex Optimization
It addresses the problem of handling complex, dynamic environments for practitioners in fields like machine learning and systems design, though it appears to be an introductory overview rather than presenting new incremental research.
The paper tackles the challenge of optimizing in complex environments where comprehensive theoretical models are infeasible, advocating for a robust approach that learns from experience as the problem unfolds, which has led to significant successes in practical applications.
This manuscript portrays optimization as a process. In many practical applications the environment is so complex that it is infeasible to lay out a comprehensive theoretical model and use classical algorithmic theory and mathematical optimization. It is necessary as well as beneficial to take a robust approach, by applying an optimization method that learns as one goes along, learning from experience as more aspects of the problem are observed. This view of optimization as a process has become prominent in varied fields and has led to some spectacular success in modeling and systems that are now part of our daily lives.