  This paper proposes two nonlinear dynamics to solve constrained distributed
optimization problem for resource allocation over a multi-agent network. In
this setup, coupling constraint refers to resource-demand balance which is
preserved at all-times. The proposed solutions can address various model
nonlinearities, for example, due to quantization and/or saturation. Further, it
allows to reach faster convergence or to robustify the solution against
impulsive noise or uncertainties. We prove convergence over weakly connected
networks using convex analysis and Lyapunov theory. Our findings show that
convergence can be reached for general sign-preserving odd nonlinearity. We
further propose delay-tolerant mechanisms to handle general bounded
heterogeneous time-varying delays over the communication network of agents
while preserving all-time feasibility. This work finds application in CPU
scheduling and coverage control among others. This paper advances the
state-of-the-art by addressing (i) possible nonlinearity on the agents/links,
meanwhile handling (ii) resource-demand feasibility at all times, (iii)
uniform-connectivity instead of all-time connectivity, and (iv) possible
heterogeneous and time-varying delays. To our best knowledge, no existing work
addresses contributions (i)-(iv) altogether. Simulations and comparative
analysis are provided to corroborate our contributions.
