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He, B. and Yuan, X. (2010) A Contraction Method with Implementable Proximal Regularization for Linearly Constrained Convex Programming. Optimization Online, 1-14.

has been cited by the following article:

• TITLE: An Efficient Proximal Point Algorithm for Unweighted Max-Min Dispersion Problem*

  AUTHORS: Siqi Tao

  KEYWORDS: Maximum Weighted Dispersion Problem, Adaptive Custom Proximal Point Al-gorithm, NP-Hard

  JOURNAL NAME: Advances in Pure Mathematics, Vol.8 No.4, April 18, 2018

  ABSTRACT: In this paper, we first reformulate the max-min dispersion problem as a saddle-point problem. Specifically, we introduce an auxiliary problem whose optimum value gives an upper bound on that of the original problem. Then we propose the saddle-point problem to be solved by an adaptive custom proximal point algorithm. Numerical results show that the proposed algorithm is efficient.