-

 

 

 




Optimization Online





 

Clustering-Based Preconditioning for Stochastic Programs

Victor M Zavala (vzavala***at***mcs.anl.gov)

Abstract: We present a clustering-based preconditioning strategy for KKT systems arising in stochastic programming within an interior-point framework. The key idea is to perform adaptive clustering of scenarios (inside-the-solver) based on their influence on the problem as opposed to cluster scenarios based on problem data alone, as is done in existing (outside-thesolver) approaches. We derive spectral and error properties for the preconditioner and demonstrate that scenario compression rates of up to 87% can be obtained, leading to dramatic computational savings. In addition, we demonstrate that the proposed preconditioner can avoid scalability issues of Schur decomposition in problems with large first-stage dimensionality.

Keywords: interior-point, stochastic, large-scale, clustering

Category 1: Stochastic Programming

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Category 3: Other Topics (Other )

Citation:

Download: [PDF]

Entry Submitted: 11/05/2012
Entry Accepted: 11/05/2012
Entry Last Modified: 12/16/2014

Modify/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
Mathematical Optimization Society