It is well known that the gradient-projection algorithm GPA is very useful in solving constrained convex minimization problems. In this paper, we combine a general iterative method with the gradient-projection algorithm to propose a hybrid gradient-projection algorithm and prove that the sequence generated by the hybrid gradient-projection algorithm converges in norm to a minimizer of constrain...

In this paper, we focus on solving a distributed convex aggregative optimization problem in network, where each agent has its own cost function which depends not only decision variables but also the aggregated of all agents' variables. The variable is constrained within feasible set. order to minimize sum functions when knows local function, propose Frank–Wolfe algorithm based gradient tracking...