Optimization Online


Reduced RLT Representations for Nonconvex Polynomial Programming Problems

Hanif D. Sherali(hanifs***at***vt.edu)
Evrim Dalkiran(dalkiran***at***vt.edu)
Leo Liberti(leoliberti***at***gmail.com)

Abstract: This paper explores equivalent, reduced size Reformulation-Linearization Technique (RLT)-based formulations for polynomial programming problems. Utilizing a basis partitioning scheme for an embedded linear equality subsystem, we show that a strict subset of RLT defining equalities imply the remaining ones. Applying this result, we derive significantly reduced RLT representations and develop certain coherent associated branching rules that assure convergence to a global optimum, along with static as well as dynamic basis selection strategies to implement the proposed procedure. In addition, we enhance the RLT relaxations with v-semidefinite cuts, which are empirically shown to further improve the relative performance of the reduced RLT method over the usual RLT approach. We present computational results for randomly generated instances to test the different proposed reduction strategies and to demonstrate the improvement in overall computational effort when such reduced RLT mechanisms are employed.

Keywords: Reformulation-Linearization Technique (RLT), reduced basis techniques, polynomial programs, global optimization, semidefinite cuts, BARON

Category 1: Global Optimization (Theory )

Citation: submitted to Journal of Global Optimization

Download: [PDF]

Entry Submitted: 11/04/2010
Entry Accepted: 11/04/2010
Entry Last Modified: 11/04/2010

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Programming Society