Search for: [Abstract = "The paper addresses the useful properties of ballstep subgradient methods for convex optimization that use level controls for estimating the optimal value. Augmented with simple averaging schemes, they asymptotically find objective and constraint subgradients involved in optimality conditions. When applied to Lagrangian relaxation of convex programs, theyfind both primal and dual solutions, and have practicable stopping criteria. Up till now, similar results have only been known for proximal bundle methods, and for subgradient methods with divergent series stepsizes, whose convergence can be słow. Encouraging numerical results arepresented for large\-scale nonlinear multicommodity network flow problems."]

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