Optimization over Sparse Symmetric Sets via a Nonmonotone Projected Gradient Method
This work addresses optimization over sparse symmetric sets, which has applications in engineering and science, but it is incremental as it builds upon existing projected gradient methods with new strategies.
The authors tackled the problem of minimizing a Lipschitz differentiable function over sparse symmetric sets by proposing a nonmonotone projected gradient (NPG) method with support-changing and coordinate-swapping strategies. The results show that NPG achieves substantially better solution quality than the classical projected gradient method and is at least comparable or much faster in speed.
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 global or a 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.