A proximal bundle method with approximate subgradient linearizations

被引:69
作者
Kiwiel, KC [1 ]
机构
[1] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland
关键词
nondifferentiable optimization; convex programming; proximal bundle methods; approximate subgradients; Lagrangian relaxation;
D O I
10.1137/040603929
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We give a proximal bundle method for minimizing a convex function f over a closed convex set. It only requires evaluating f and its subgradients with an accuracy epsilon > 0, which is fixed but possibly unknown. It asymptotically finds points that are epsilon-optimal. When applied to Lagrangian relaxation, it allows for epsilon-accurate solutions of Lagrangian subproblems and finds epsilon-optimal solutions of convex programs.
引用
收藏
页码:1007 / 1023
页数:17
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