- Optimization over Sparse Symmetric Sets via a Nonmonotone Projected Gradient Method Zhaosong Lu (zhaosongsfu.ca) Abstract: We consider the problem of minimizing a Lipschitz differentiable function over a class of sparse symmetric sets that has wide applications in engineering and science. For this problem, it is known that any accumulation point of the classical projected gradient (PG) method with a constant stepsize $1/L$ satisfies the $L$-stationarity optimality condition that was introduced in [3]. In this paper we introduce a new optimality condition that is stronger than the $L$-stationarity optimality condition. We also propose a nonmonotone projected gradient (NPG) method for this problem by incorporating some support-changing and coordintate-swapping strategies into a projected gradient method with variable stepsizes. It is shown that any accumulation point of NPG satisfies the new optimality condition and moreover it is a coordinatewise stationary point. Under some suitable assumptions, we further show that it is a {\it global} or a {\it local} minimizer of the problem. Numerical experiments are conducted to compare the performance of PG and NPG. The computational results demonstrate that NPG has substantially better solution quality than PG, and moreover, it is at least comparable to, but sometimes can be much faster than PG in terms of speed. Keywords: cardinality constraint, sparse optimization, sparse projection, nonmonotone projected gradient method Category 1: Nonlinear Optimization Category 2: Global Optimization Category 3: Combinatorial Optimization Citation: Manuscript, Department of Mathematics, Simon Fraser University, Canada. Download: [PDF]Entry Submitted: 09/28/2015Entry Accepted: 09/29/2015Entry Last Modified: 11/29/2015Modify/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 Optimization Online is supported by the Mathematical Optmization Society.