Limited-memory BFGS with displacement aggregation
نویسندگان
چکیده
منابع مشابه
Global convergence of online limited memory BFGS
Global convergence of an online (stochastic) limited memory version of the Broyden-FletcherGoldfarb-Shanno (BFGS) quasi-Newton method for solving optimization problems with stochastic objectives that arise in large scale machine learning is established. Lower and upper bounds on the Hessian eigenvalues of the sample functions are shown to suffice to guarantee that the curvature approximation ma...
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 کاملSolving Limited-Memory BFGS Systems with Generalized Diagonal Updates
In this paper, we investigate a formula to solve systems of the form (Bk + D)x = y, where Bk comes from a limited-memory BFGS quasi-Newton method and D is a diagonal matrix with diagonal entries di,i ≥ σ for some σ > 0. These types of systems arise naturally in large-scale optimization. We show that provided a simple condition holds on B0 and σ, the system (Bk + D)x = y can be solved via a recu...
متن کاملLimited Memory Bfgs Updating in a Trust–region Framework
The limited memory BFGS method pioneered by Jorge Nocedal is usually implemented as a line search method where the search direction is computed from a BFGS approximation to the inverse of the Hessian. The advantage of inverse updating is that the search directions are obtained by a matrix–vector multiplication. In this paper it is observed that limited memory updates to the Hessian approximatio...
متن کاملON THE LIMITED MEMORY BFGS METHOD FORLARGE SCALE OPTIMIZATIONbyDong
We study the numerical performance of a limited memory quasi-Newton method for large scale optimization, which we call the L-BFGS method. We compare its performance with that of the method developed by Buckley and LeNir (1985), which combines cyles of BFGS steps and conjugate direction steps. Our numerical tests indicate that the L-BFGS method is faster than the method of Buckley and LeNir, and...
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: Mathematical Programming
سال: 2021
ISSN: 0025-5610,1436-4646
DOI: 10.1007/s10107-021-01621-6