  We propose a continuous-time second-order optimization algorithm for solving
unconstrained convex optimization problems with bounded Hessian. We show that
this alternative algorithm has a comparable convergence rate to that of the
continuous-time Newton-Raphson method, however structurally, it is amenable to
a more efficient distributed implementation. We present a distributed
implementation of our proposed optimization algorithm and prove its convergence
via Lyapunov analysis. A numerical example demonstrates our results.
