A regularized decomposition method for minimizing a sum of polyhedral functions
Mathematical Programming: Series A and B
Proximity control in bundle methods for convex
Mathematical Programming: Series A and B
Scenarios and policy aggregation in optimization under uncertainty
Mathematics of Operations Research
Variable metric bundle methods: from conceptual to implementable forms
Mathematical Programming: Series A and B - Special issue on computational nonsmooth optimization
Decomposition methods in stochastic programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Dual Applications of Proximal Bundle Methods, Including Lagrangian Relaxation of Nonconvex Problems
SIAM Journal on Optimization
Decomposition of structured large-scale optimization problems and parallel optimization
Decomposition of structured large-scale optimization problems and parallel optimization
A Stochastic Programming Approach to Power Portfolio Optimization
Operations Research
On the choice of explicit stabilizing terms in column generation
Discrete Applied Mathematics
Energy-Efficient Sensing with the Low Power, Energy Aware Processing (LEAP) Architecture
ACM Transactions on Embedded Computing Systems (TECS)
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A specialized variant of bundle methods suitable for large-scale problems with separable objective is presented. The method is applied to the resolution of a stochastic unit-commitment problem solved by Lagrangian relaxation. The model includes hydro- as well as thermal-powered plants. Uncertainties lie in the demand, which evolves in time according to a tree of scenarios. Dual variables are preconditioned by using probabilities associated to nodes in the tree The approach is illustrated by numerical results, obtained on a model of the French production mix over a time horizon of 10 days and 1 month.