  Although the field of distributed optimization is well-developed, relevant
literature focused on the application of distributed optimization to
multi-robot problems is limited. This survey constitutes the second part of a
two-part series on distributed optimization applied to multi-robot problems. In
this paper, we survey three main classes of distributed optimization algorithms
-- distributed first-order methods, distributed sequential convex programming
methods, and alternating direction method of multipliers (ADMM) methods --
focusing on fully-distributed methods that do not require coordination or
computation by a central computer. We describe the fundamental structure of
each category and note important variations around this structure, designed to
address its associated drawbacks. Further, we provide practical implications of
noteworthy assumptions made by distributed optimization algorithms, noting the
classes of robotics problems suitable for these algorithms. Moreover, we
identify important open research challenges in distributed optimization,
specifically for robotics problem.
