  Distributed Constraint Optimization Problems (DCOPs) have been widely used to
coordinate interactions (i.e. constraints) in cooperative multi-agent systems.
The traditional DCOP model assumes that variables owned by the agents can take
only discrete values and constraints' cost functions are defined for every
possible value assignment of a set of variables. While this formulation is
often reasonable, there are many applications where the variables are
continuous decision variables and constraints are in functional form. To
overcome this limitation, Functional DCOP (F-DCOP) model is proposed that is
able to model problems with continuous variables. The existing F-DCOPs
algorithms experience huge computation and communication overhead. This paper
applies continuous non-linear optimization methods on Cooperative Constraint
Approximation (CoCoA) algorithm. We empirically show that our algorithm is able
to provide high-quality solutions at the expense of smaller communication cost
and execution time compared to the existing F-DCOP algorithms.
