Shifted limited-memory variable metric methods for large-scale unconstrained optimization
نویسندگان
چکیده
منابع مشابه
A limited memory adaptive trust-region approach for large-scale unconstrained optimization
This study concerns with a trust-region-based method for solving unconstrained optimization problems. The approach takes the advantages of the compact limited memory BFGS updating formula together with an appropriate adaptive radius strategy. In our approach, the adaptive technique leads us to decrease the number of subproblems solving, while utilizing the structure of limited memory quasi-Newt...
متن کاملLimited-Memory Reduced-Hessian Methods for Large-Scale Unconstrained Optimization
Limited-memory BFGS quasi-Newton methods approximate the Hessian matrix of second derivatives by the sum of a diagonal matrix and a fixed number of rank-one matrices. These methods are particularly effective for large problems in which the approximate Hessian cannot be stored explicitly. It can be shown that the conventional BFGS method accumulates approximate curvature in a sequence of expandi...
متن کاملLimited-memory projective variable metric methods for unconstrained minimization
A new family of limited-memory variable metric or quasi-Newton methods for unconstrained minimization is given. The methods are based on a positive definite inverse Hessian approximation in the form of the sum of identity matrix and two low rank matrices, obtained by the standard scaled Broyden class update. To reduce the rank of matrices, various projections are used. Numerical experience is e...
متن کاملa limited memory adaptive trust-region approach for large-scale unconstrained optimization
this study concerns with a trust-region-based method for solving unconstrained optimization problems. the approach takes the advantages of the compact limited memory bfgs updating formula together with an appropriate adaptive radius strategy. in our approach, the adaptive technique leads us to decrease the number of subproblems solving, while utilizing the structure of limited memory quasi-newt...
متن کاملEnriched Methods for Large-Scale Unconstrained Optimization
This paper describes a class of optimization methods that interlace iterations of the limited memory BFGS method L BFGS and a Hessian free Newton method HFN in such a way that the information collected by one type of iteration improves the performance of the other Curvature information about the objective function is stored in the form of a limited memory matrix and plays the dual role of preco...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational and Applied Mathematics
سال: 2006
ISSN: 0377-0427
DOI: 10.1016/j.cam.2005.02.010