Distributed Optimization via Energy Conservation Laws in Dilated Coordinates

Continuous-time models can reveal accelerated structures in distributed optimization, but their rates need not survive direct discretization. We introduce a second-order primal--dual flow for smooth convex distributed optimization and construct an exactly conserved energy that yields an mathcal O(t-2) rate for both the aggregate objective gap and the squared consensus error. We then prove a horizon-wise Omega(k-1) lower bound for a broad class of single-loop finite-memory primal--dual discretizations, ruling out a mathcal O(k-2) aggregate-objective guarantee within this class. Motivated by this barrier, we develop a double-loop method that combines finite-step polynomial consensus with an accelerated outer update. It uses one gradient evaluation and at most m-1 communication rounds per outer iteration, m being the number of agents, maintains exact consensus and achieves an mathcal O(k-2) aggregate-objective rate. Numerical comparisons with representative distributed methods support the theory and quantify the communication cost of acceleration.