In this paper we study new preconditioners to be used within the Nonlinear Conjugate Gradient (NCG) method, for large scale unconstrained optimization. The rationale behind our proposal draws inspiration from quasi– Newton updates, and its aim is to possibly approximate in some sense the inverse of the Hessian matrix. In particular, at the current iteration of the NCG we consider some precondit...

The BFGS method is one of the most famous quasi-Newton algorithms for unconstrained optimization. In 1984, Powell presented an example of a function of two variables that shows that the Polak–Ribière–Polyak (PRP) conjugate gradient method and the BFGS quasi-Newton method may cycle around eight nonstationary points if each line search picks a local minimum that provides a reduction in the object...

In this paper we present a modified BFGS algorithm for unconstrained optimization. The BFGS algorithm updates an approximate Hessian which satisfies the most recent quasi-Newton equation. The quasi-Newton condition can be interpreted as the interpolation condition that the gradient value of the local quadratic model matches that of the objective function at the previous iterate. Our modified al...

We show how the quasi-Newton least squares method (QN-LS) relates to Krylov subspace methods in general and to GMRes in particular.

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We study Newton’s method and method based on linearization for solving quasi-variational inequalities in a finite-dimensional real vector space. Projection methods were the most studied methods for solving quasi-variational inequalities and they have linear rates of the convergence. In the paper we establish sufficient conditions for the convergence of Newton’s method and method of linearizatio...

We describe a parallel method for unconstrained optimization based on the quasi-Newton descent method of Broyden, Fletcher, Goldfarb, and Shanno. Our algorithm is suitable for both single-instruction and multiple-instruction parallel architectures and has only linear memory requirements in the number of parameters used to ®t the data. We also present the results of numerical testing on both sin...

A new method for the solution of minimization problems with simple bounds is presented. Global convergence of a general scheme requiring the solution of a single linear system at each iteration is proved and a superlinear convergence rate is established without requiring the strict complementarity assumption. The theory presented covers Newton and Quasi-Newton methods, allows rapid changes in t...

In this report we present computational methods for the best multilinear rank approximation problem. We consider algorithms build on quasi-Newton methods operating on product of Grassmann manifolds. Specifically we test and compare methods based on BFGS and L-BFGS updates in local and global coordinates with the Newton-Grassmann and alternating least squares methods. The performance of the quas...

In this paper, the authors prove some existence results of solutions for a new class of generalized bi-quasi-variational inequalities (GBQVI) for quasi-pseudo-monotone type I operators in non-compact settings in locally convex Hausdorff topological vector spaces. In obtaining these results on GBQVI for quasi-pseudo-monotone type I operators in non-compact settings, we shall use the concept of e...