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• W2149192560 abstract "We exhibit useful properties of ballstep subgradient methods for convex optimization using 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, they find both primal and dual solutions, and have practicable stopping criteria. Up until now, similar results have only been known for proximal bundle methods, and for subgradient methods with divergent series stepsizes, whose convergence can be slow. Encouraging numerical results are presented for large-scale nonlinear multicommodity network flow problems." @default.
• W2149192560 created "2016-06-24" @default.
• W2149192560 creator A5014187442 @default.
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• W2149192560 date "2007-08-01" @default.
• W2149192560 modified "2023-09-23" @default.
• W2149192560 title "Lagrangian Relaxation via Ballstep Subgradient Methods" @default.
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• W2149192560 doi "https://doi.org/10.1287/moor.1070.0261" @default.
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