-

 

 

 




Optimization Online





 

A New Use of Douglas-Rachford Splitting and ADMM for Identifying Infeasible, Unbounded, and Pathological Conic Programs

Yanli Liu (yanli***at***math.ucla.edu)
Ernest Ryu (eryu***at***math.ucla.edu)
Wotao Yin (wotaoyin***at***math.ucla.edu)

Abstract: In this paper, we present a method for identifying infeasible, unbounded, and pathological conic programs based on Douglas-Rachford splitting, or equivalently ADMM. When an optimization program is infeasible, unbounded, or pathological, the iterates of Douglas-Rachford splitting diverge.Somewhat surprisingly, such divergent iterates still provide useful information, which our method uses for identification. In addition, for strongly infeasible problems the method produces a separating hyperplane and informs the user on how to minimally modify the given problem to achieve strong feasibility. Asa first-order method, the proposed algorithm relies on simple subroutines, and therefore is simple to implement and has low per-iteration cost.

Keywords: Douglas-Rachford Splitting, infeasible, unbounded, pathological, conic programs

Category 1: Linear, Cone and Semidefinite Programming

Category 2: Convex and Nonsmooth Optimization

Category 3: Nonlinear Optimization

Citation: Liu, Yanli, Ernest K. Ryu, and Wotao Yin. "A New Use of Douglas-Rachford Splitting and ADMM for Identifying Infeasible, Unbounded, and Pathological Conic Programs." arXiv preprint arXiv:1706.02374 (2017).

Download: [PDF]

Entry Submitted: 05/24/2017
Entry Accepted: 06/01/2017
Entry Last Modified: 10/15/2017

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