Regularization of Limited Memory Quasi-Newton Methods for Large-Scale Nonconvex Minimization

This paper deals with regularized Newton methods, a flexible class of unconstrained optimization algorithms that is competitive with line search and trust region methods and potentially combines attractive elements of both. The particular focus is on combining regularization with limited memory quasi-Newton methods by exploiting the special structure of limited memory algorithms. Global convergence of regularization methods is shown under mild assumptions and the details of regularized limited memory quasi-Newton updates are discussed including their compact representations. Numerical results using all large-scale test problems from the CUTEst collection indicate that our regularized version of L-BFGS is competitive with state-of-the-art line search and trust-region L-BFGS algorithms and previous attempts at combining L-BFGS with regularization, while potentially outperforming some of them, especially when nonmonotonicity is involved.Comment: 23 pages, 4 figure

A new Newton method for convex optimization problems with singular Hessian matrices

In this paper, we propose a new Newton method for minimizing convex optimization problems with singular Hessian matrices including the special case that the Hessian matrix of the objective function is singular at any iteration point. The new method we proposed has some updates in the regularized parameter and the search direction. The step size of our method can be obtained by using Armijo backtracking line search. We also prove that the new method has global convergence. Some numerical experimental results show that the new method performs well for solving convex optimization problems whose Hessian matrices of the objective functions are singular everywhere