4 research outputs found
A variation of Broyden Class methods using Householder adaptive transforms
In this work we introduce and study novel Quasi Newton minimization methods
based on a Hessian approximation Broyden Class-\textit{type} updating scheme,
where a suitable matrix is updated instead of the current Hessian
approximation . We identify conditions which imply the convergence of the
algorithm and, if exact line search is chosen, its quadratic termination. By a
remarkable connection between the projection operation and Krylov spaces, such
conditions can be ensured using low complexity matrices obtained
projecting onto algebras of matrices diagonalized by products of two or
three Householder matrices adaptively chosen step by step. Extended
experimental tests show that the introduction of the adaptive criterion, which
theoretically guarantees the convergence, considerably improves the robustness
of the minimization schemes when compared with a non-adaptive choice; moreover,
they show that the proposed methods could be particularly suitable to solve
large scale problems where - performs poorly
Preconditioned Nonlinear Conjugate Gradient methods based on a modified secant equation
This paper includes a twofold result for the Nonlinear Conjugate Gradient (NCG) method, in large scale unconstrained optimization. First we consider a theoretical analysis, where preconditioning is embedded in a strong convergence framework of an NCG method from the literature. Mild conditions to be satisfied by the preconditioners are defined, in order to preserve NCG convergence. As a second task, we also detail the use of novel matrix-free preconditioners for NCG. Our proposals are based on quasi-Newton updates, and either satisfy the secant equation or a secant-like condition at some of the previous iterates. We show that, in some sense, the preconditioners we propose also approximate the inverse of the Hessian matrix. In particular, the structures of our preconditioners depend on low-rank updates used, along with different choices of specific parameters. The low-rank updates are obtained as by-product of NCG iterations. The results of an extended numerical experience using large scale CUTEst problems is reported, showing that our preconditioners can considerably improve the performance of NCG methods
AIRO 2016. 46th Annual Conference of the Italian Operational Research Society. Emerging Advances in Logistics Systems Trieste, September 6-9, 2016 - Abstracts Book
The AIRO 2016 book of abstract collects the contributions from the conference participants.
The AIRO 2016 Conference is a special occasion for the Italian Operations Research community, as AIRO annual conferences turn 46th edition in 2016. To reflect this special occasion, the Programme and Organizing Committee, chaired by Walter Ukovich, prepared a high quality Scientific Programme including the first initiative of AIRO Young, the new AIRO poster section that aims to promote the work of students, PhD students, and Postdocs with an interest in Operations Research.
The Scientific Programme of the Conference offers a broad spectrum of contributions covering the variety of OR topics and research areas with an emphasis on “Emerging Advances in Logistics Systems”.
The event aims at stimulating integration of existing methods and systems, fostering communication amongst different research groups, and laying the foundations for OR integrated research projects in the next decade.
Distinct thematic sections follow the AIRO 2016 days starting by initial presentation of the objectives and features of the Conference. In addition three invited internationally known speakers will present Plenary Lectures, by Gianni Di Pillo, Frédéric Semet e Stefan Nickel, gathering AIRO 2016 participants together to offer key presentations on the latest advances and developments in OR’s research