Optimization Online


A barrier-based smoothing proximal point algorithm for NCPs over closed convex cones

Chek Beng Chua (cbchua***at***ntu.edu.sg)
Zhen Li (lizhen***at***ntu.edu.sg)

Abstract: We present a new barrier-based method of constructing smoothing approximations for the Euclidean projector onto closed convex cones. These smoothing approximations are used in a smoothing proximal point algorithm to solve monotone nonlinear complementarity problems (NCPs) over a convex cones via the normal map equation. The smoothing approximations allow for the solution of the smoothed normal map equations with Newton's method, and do not require additional analytical properties of the Euclidean projector. The use of proximal terms in the algorithm adds stability to the solution of the smoothed normal map equation, and avoids numerical issues due to ill-conditioning at iterates near the boundary of the cones. We prove a sufficient condition on the barrier used that guarantees the convergence of the algorithm to a solution of the NCP. The sufficient condition is satisfied by all logarithmically homogeneous barriers. Preliminary numerical tests on semidefinite programming problems (SDPs) shows that our algorithm is comparable with the Newton-CG augmented Lagrangian algorithm (SDPNAL) proposed in [X.~Y. Zhao, D. Sun, and K.-C.Toh, SIAM J. Optim. \textbf{20} (2010), 1737--1765].

Keywords: nonlinear complementarity problem, smoothing approximation, proximal point algorithm, normal map equation.

Category 1: Complementarity and Variational Inequalities

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Citation: Research report, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, May 2012.

Download: [PDF]

Entry Submitted: 04/30/2012
Entry Accepted: 05/01/2012
Entry Last Modified: 11/30/2012

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