A geometric proximal gradient method for sparse least squares regression with probabilistic simplex constraint

Xiao, Guiyun; Bai, Zheng-Jian

doi:10.48550/arxiv.2107.00809

Cited by 1 publication

(2 citation statements)

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“…where δ C (•) denotes the indicator function of the set C. Utilizing the separable structure and the results by [52], the i-th column of prox th (X), denoted by (prox th (X)) i , is…”

Section: Motivating Examplesmentioning

confidence: 99%

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A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity

Hu¹,

Deng²,

Wu³

et al. 2023

Preprint

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This paper aims to develop a Newton-type method to solve a class of nonconvex composite programs. In particular, the nonsmooth part is possibly nonconvex. To tackle the nonconvexity, we develop a notion of strong prox-regularity which is related to the singleton property, Lipschitz continuity, and monotonicity of the associated proximal operator, and we verify it in various classes of functions, including weakly convex functions, indicator functions of proximally smooth sets, and two specific sphere-related nonconvex nonsmooth functions. In this case, the problem class we are concerned with covers smooth optimization problems on manifold and certain composite optimization problems on manifold. For the latter, the proposed algorithm is the first second-order type method. Combining with the semismoothness of the proximal operator, we design a projected semismooth Newton method to find a root of the natural residual induced by the proximal gradient method. Since the corresponding natural residual may not be globally monotone, an extra projection is added on the usual semismooth Newton step and new criteria are proposed for the switching between the projected semismooth Newton step and the proximal step. The global convergence is then established under the strong prox-regularity. Based on the BD regularity condition, we establish local superlinear convergence. Numerical experiments demonstrate the effectiveness of our proposed method compared with state-of-the-art ones.

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“…where δ C (•) denotes the indicator function of the set C. Utilizing the separable structure and the results by [52], the i-th column of prox th (X), denoted by (prox th (X)) i , is…”

Section: Motivating Examplesmentioning

confidence: 99%

“…Sparse least square regression with probabilistic simplex constraint. The authors of [52,33] consider the spherical constrained formulation of the following optimization problems:…”

Section: Motivating Examplesmentioning

confidence: 99%