BFGS with Update Skipping and Varying Memory
نویسندگان
چکیده
We give conditions under which limited-memory quasi-Newton methods with exact line searches will terminate in n steps when minimizing n-dimensional quadratic functions. We show that although all Broyden family methods terminate in n steps in their full-memory versions, only BFGS does so with limited-memory. Additionally, we show that full-memory Broyden family methods with exact line searches terminate in at most n + p steps when p matrix updates are skipped. We introduce new limited-memory BFGS variants and test them on nonquadratic minimization problems.
منابع مشابه
Modifications of the Limited Memory Bfgs Algorithm for Large-scale Nonlinear Optimization
In this paper we present two new numerical methods for unconstrained large-scale optimization. These methods apply update formulae, which are derived by considering different techniques of approximating the objective function. Theoretical analysis is given to show the advantages of using these update formulae. It is observed that these update formulae can be employed within the framework of lim...
متن کاملA Limited Memory Bfgs Algorithm with Super Relaxation Technique for Nonlinear Equations
In this paper, a trust-region algorithm combining with the limited memory BFGS (L-BFGS) update is proposed for solving nonlinear equations, where the super relaxation technique(SRT) is used. We choose the next iteration point by SRT. The global convergence without the nondegeneracy assumption is obtained under suitable conditions. Numerical results show that this method is very effective for la...
متن کاملParallel Algorithms for the BFGS Update on a Machine
A quasi-Newton algorithm using the BFGS update is one of the most widely used unconstrained numerical optimisation algorithms. We describe three parallel algorithms to perform the BFGS update on a local memory MIMD architecture such as . These algorithms are distinguished by the way in which Hessian information is stored. Cost models are developed for the algorithms and used to compare their pe...
متن کاملA Numerical Study of Limited Memory BFGS
The application of quasi-Newton methods is widespread in numerical optimization. Independently of the application, the techniques used to update the BFGS matrices seem to play an important role in the performance of the overall method. In this paper we address precisely this issue. We compare two implementations of the limited memory BFGS method for large-scale unconstrained problems. They diie...
متن کاملTransformations enabling to construct limited-memory Broyden class methods
The Broyden class of quasi-Newton updates for inverse Hessian approximation are transformed to the formal BFGS update, which makes possible to generalize the well-known Nocedal method based on the Strang recurrences to the scaled limited-memory Broyden family, using the same number of stored vectors as for the limited-memory BFGS method. Two variants are given, the simpler of them does not requ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- SIAM Journal on Optimization
دوره 8 شماره
صفحات -
تاریخ انتشار 1998