- Inexact Dynamic Bundle Methods Krzysztof C. Kiwiel(kiwielibspan.waw.pl) Abstract: We give a proximal bundle method for minimizing a convex function $f$ over $\mathbb{R}_+^n$. It requires evaluating $f$ and its subgradients with a possibly unknown accuracy $\epsilon\ge0$, and maintains a set of free variables $I$ to simplify its prox subproblems. The method asymptotically finds points that are $\epsilon$-optimal. In Lagrangian relaxation of convex programs, it allows for $\epsilon$-accurate solutions of Lagrangian subproblems, and finds $\epsilon$-optimal primal solutions. For programs with exponentially many constraints, it adopts a relax-and-cut approach where the set $I$ is extended only if a separation oracle finds a sufficiently violated constraint. In a simplified version, each iteration involves solving an unconstrained prox subproblem. For semidefinite programming problems, we extend the spectral bundle method to the case of weaker conditions on its optimization and separation oracles, and on the original primal problem. Keywords: convex programming, proximal bundle methods, Lagrangian relaxation, Semidefinite programming Category 1: Convex and Nonsmooth Optimization (Convex Optimization ) Category 2: Convex and Nonsmooth Optimization (Nonsmooth Optimization ) Citation: Krzysztof C. Kiwiel, "Inexact Dynamic Bundle Methods, Tech. report, Systems Research Institute, Warsaw, December 2010 Download: [PDF]Entry Submitted: 12/16/2010Entry Accepted: 12/16/2010Entry Last Modified: 12/16/2010Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Programming Society.