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...

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...

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...

This paper studies recent modi cations of the limited memory BFGS (L-BFGS) method for solving large scale unconstrained optimization problems. Each modi cation technique attempts to improve the quality of the L-BFGS Hessian by employing (extra) updates in certain sense. Because at some iterations these updates might be redundant or worsen the quality of this Hessian, this paper proposes an upda...

Require: Algorithm parameters γ > 1 and η < 1, an auxiliary scalar vk, iteration counter iter = 0, and the maximum number of iterations maxiter. Ensure: A critical point 1: Initiate y0 and σ0. 2: repeat 3: Given yk and σk, solve (1.1) to find the minimizer Rk via the LBFGS algorithm. 4: Compute v = ∑ i ( tr(DiRkR T k )− bi )2 . 5: if v < ηvk then 6: y i = y k i − σk · ( tr(DiRkR T k )− bi ) for...

This paper studies recent modifications of the limited memory BFGS (L-BFGS) method for solving large scale unconstrained optimization problems. Each modification technique attempts to improve the quality of the L-BFGS Hessian by employing (extra) updates in a certain sense. Because at some iterations these updates might be redundant or worsen the quality of this Hessian, this paper proposes an ...