Optimization Online


Random projections for trust region subproblems

Ky Vu(vukhacky***at***gmail.com)
Pierre-Louis Poirion(kiwisensei***at***gmail.com)
Claudia D'Ambrosio(dambrosio***at***lix.polytechnique.fr)
Leo Liberti(liberti***at***lix.polytechnique.fr)

Abstract: The trust region method is an algorithm traditionally used in the field of derivative free optimization. The method works by iteratively constructing surrogate models (often linear or quadratic functions) to approximate the true objective function inside some neighborhood of a current iterate. The neighborhood is called ``trust region'' in the sense that the model is trusted to be good enough inside the neighborhood. Updated points are found by solving the corresponding trust region subproblems. In this paper, we describe an application of random projections to solving trust region subproblems approximately.

Keywords: Johnson-Lindenstrauss Lemma, quadratic programming, linear programming

Category 1: Nonlinear Optimization (Quadratic Programming )

Category 2: Linear, Cone and Semidefinite Programming (Linear Programming )


Download: [PDF]

Entry Submitted: 06/08/2017
Entry Accepted: 06/08/2017
Entry Last Modified: 06/08/2017

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 Optimization Society