Assess learners' ability to model large-scale communication-network resource-allocation problems as convex optimization formulations (e.g., power control, rate allocation, interference constraints); derive Lagrangian duals and KKT conditions; design and analyze scalable distributed solution methods (dual decomposition, ADMM, primal–dual algorithms); prove convergence and complexity bounds; and adapt algorithms for practical constraints such as asynchrony, limited signaling, and approximation. Tasks should include deriving dual problems, specifying distributed update rules and message exchanges, analyzing convergence rates and optimality gaps, and interpreting trade-offs between efficiency, scalability, and robustness.