IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
A merit function approach to the subgradient method with averaging
Optimization Methods & Software
An inexact modified subgradient algorithm for nonconvex optimization
Computational Optimization and Applications
A Randomized Incremental Subgradient Method for Distributed Optimization in Networked Systems
SIAM Journal on Optimization
Incremental Subgradients for Constrained Convex Optimization: A Unified Framework and New Methods
SIAM Journal on Optimization
A variant of the constant step rule for approximate subgradient methods over nonlinear networks
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
An infeasible-point subgradient method using adaptive approximate projections
Computational Optimization and Applications
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We present a unified convergence framework for approximate subgradient methods that covers various stepsize rules (including both diminishing and nonvanishing stepsizes), convergence in objective values, and convergence to a neighborhood of the optimal set. We discuss ways of ensuring the boundedness of the iterates and give efficiency estimates. Our results are extended to incremental subgradient methods for minimizing a sum of convex functions, which have recently been shown to be promising for various large-scale problems, including those arising from Lagrangian relaxation.