On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning

NeurIPS 2017 Xingguo LiLin YangJason GeJarvis HauptTong ZhangTuo Zhao

We propose a DC proximal Newton algorithm for solving nonconvex regularized sparse learning problems in high dimensions. Our proposed algorithm integrates the proximal newton algorithm with multi-stage convex relaxation based on the difference of convex (DC) programming, and enjoys both strong computational and statistical guarantees... (read more)

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