  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.
