Improved diagonal Hessian approximations for large-scale unconstrained optimization.
Source
Sultan Qaboos University Journal for Science. v. 26, no.2, p.126-140.
Publisher
College of Science, Sultan Qaboos University.
English abstract
We consider some diagonal quasi-Newton methods for solving large-scale unconstrained optimization problems. A simple and effective approach for diagonal quasi-Newton algorithms is presented by proposing new updates of diagonal entries of the Hessian. Moreover, we suggest employing an extra BFGS update of the diagonal updating matrix and use its diagonal again. Numerical experiments on a collection of standard test problems show, in particular, that the proposed diagonal quasi-Newton methods perform substantially better than certain available diagonal methods.
Identifier
DOI:10.53539/squjs.vol26iss2pp126-140
