  Motivated by recent development in networking and parallel data-processing,
we consider a distributed and localized finite-sum (or fixed-sum) allocation
technique to solve resource-constrained convex optimization problems over
multi-agent networks (MANs). Such networks include (smart) agents representing
an intelligent entity capable of communication, processing, and
decision-making. In particular, we consider problems subject to practical
nonlinear constraints on the dynamics of the agents in terms of their
communications and actuation capabilities (referred to as the node dynamics),
e.g., networks of mobile robots subject to actuator saturation and quantized
communication. The considered distributed sum-preserving optimization solution
further enables adding purposeful nonlinear constraints, for example,
sign-based nonlinearities, to reach convergence in predefined-time or robust to
impulsive noise and disturbances in faulty environments. Moreover, convergence
can be achieved under minimal network connectivity requirements among the
agents; thus, the solution is applicable over dynamic networks where the
channels come and go due to the agent's mobility and limited range. This paper
discusses how various nonlinearity constraints on the optimization problem
(e.g., collaborative allocation of resources) can be addressed for different
applications via a distributed setup (over a network).
