A Primer on Coordinate Descent Algorithms
It provides an accessible primer for mathematicians, statisticians, and engineers to apply these algorithms to modern data science problems, but it is incremental as it focuses on existing methods without new contributions.
The monograph introduces coordinate descent algorithms to practitioners outside optimization, highlighting their effectiveness for large-scale problems in machine learning and data science.
This monograph presents a class of algorithms called coordinate descent algorithms for mathematicians, statisticians, and engineers outside the field of optimization. This particular class of algorithms has recently gained popularity due to their effectiveness in solving large-scale optimization problems in machine learning, compressed sensing, image processing, and computational statistics. Coordinate descent algorithms solve optimization problems by successively minimizing along each coordinate or coordinate hyperplane, which is ideal for parallelized and distributed computing. Avoiding detailed technicalities and proofs, this monograph gives relevant theory and examples for practitioners to effectively apply coordinate descent to modern problems in data science and engineering.