Practical inexact proximal quasi-Newton method with global complexity analysis

نویسندگان

  • Katya Scheinberg
  • Xiaocheng Tang
چکیده

Recently several methods were proposed for sparse optimization which make careful use of second-order information [11, 34, 20, 4] to improve local convergence rates. These methods construct a composite quadratic approximation using Hessian information, optimize this approximation using a first-order method, such as coordinate descent and employ a line search to ensure sufficient descent. Here we propose a general framework, which includes slightly modified versions of existing algorithms and also a new algorithm, which uses limited memory BFGS Hessian approximations, and provide a novel global convergence rate analysis, which covers methods that solve subproblems via coordinate descent.

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عنوان ژورنال:
  • Math. Program.

دوره 160  شماره 

صفحات  -

تاریخ انتشار 2016