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Convergence Analysis on an Accelerated Proximal Point Algorithm for Linearly Constrained Optimization Problems

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  • Sha Lu
  • Zengxin Wei

Abstract

Proximal point algorithm is a type of method widely used in solving optimization problems and some practical problems such as machine learning in recent years. In this paper, a framework of accelerated proximal point algorithm is presented for convex minimization with linear constraints. The algorithm can be seen as an extension to G ler’s methods for unconstrained optimization and linear programming problems. We prove that the sequence generated by the algorithm converges to a KKT solution of the original problem under appropriate conditions with the convergence rate of .

Suggested Citation

  • Sha Lu & Zengxin Wei, 2020. "Convergence Analysis on an Accelerated Proximal Point Algorithm for Linearly Constrained Optimization Problems," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, November.
    • Handle: RePEc:hin:jnlmpe:8873507
    DOI: 10.1155/2020/8873507
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