Updating quasi newton matrices with limited storage Free of cost sex chatting sites
Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm using a limited amount of computer memory.
It is a popular algorithm for parameter estimation in machine learning. Like the original BFGS, L-BFGS uses an estimation to the inverse Hessian matrix to steer its search through variable space, but where BFGS stores a dense n×n approximation to the inverse Hessian (n being the number of variables in the problem), L-BFGS stores only a few vectors that represent the approximation implicitly.
Due to its resulting linear memory requirement, the L-BFGS method is particularly well suited for optimization problems with a large number of variables.
Instead of the inverse Hessian H is the inverse of the Hessian matrix.
A probabilistic analysis reveals that the popular quasi-Newton algorithms can be interpreted as approximations of Bayesian linear regression under varying prior assumptions.
Mathematics of Computation 19(92):577-593, 1965  Davidon, W.
After an L-BFGS step, the method allows some variables to change sign, and repeats the process. present an online approximation to both BFGS and L-BFGS.
Multiple other open source implementations have been produced as translations of this Fortran code (e.g. Other implementations exist: is available in Fortran 77 (and with a Fortran 90 interface) at the author's website.A popular class of modifications are called active-set methods, based on the concept of the active set.The idea is that when restricted to a small neighborhood of the current iterate, the function and constraints can be simplified.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 Le Nir (1985), which combines cycles of BFGS steps and conjugate direction steps.