Speaker: Prof. Hong Kun Xu
Hangzhou Dianzi University
Time and Location: Thursday, August 10 at 11:30 AM – 12:30 PM, T121, Mount Helen Campus
Title: Projection methods for constrained minimization of a finite sum of convex functions.
Abstract: Projection methods are introduced to minimize a finite sum of convex functions over the intersection finitely many closed convex subsets of a Hilbert space. These algorithms consist of two steps. The first step is an inner circle of gradient descent process to be executed through each component function and the second step is projection process (e.g., sequential or parallel or cyclic projection) that is applied to produce the next iterate. Read more
Speaker: A/Prof Adil Bagirov Date: Thursday 20 July 2017, 11.30am Room: T121, T Building, Mt Helen Campus (Visimeet ID: 1242508) Title: Smoothing techniques in nonsmooth optimisation and applications Abstract: In this talk, we consider an application of smoothing techniques to a broad class of nonsmooth optimisation problems. This class includes functions represented as a difference of two, in general nonsmooth, convex functions and also functions represented as a difference of two Clarke regular functions. However, we assume that component functions in these representations are either maximum functions or smooth composition of discrete maximum functions. Numerical results with nonsmooth optimisation test problems are presented and different smoothing techniques are compared using these results. Finally, we demonstrate the application of smoothing techniques for solving some problems in data mining. Read more
Speaker: A/Prof. Adil Bagirov
Federation University Australia
Title: Nonsmooth DC Optimisation: Methods and Applications
Date and time: Friday, 31 March 2017, 3:00–4:00pm Location: Building 8 Level 9 Room 66 (AGR) RMIT City campus
Abstract: An unconstrained optimisation problem with the objective function represented as a difference of two convex functions is considered. Bundle methods for solving such problems are designed and their convergence is discussed. Applications of these methods in machine learning and regression analysis are also considered.
Bio: Adil Bagirov is an Associate Professor of Optimisation at Faculty of Science and Technology, Federation University Australia. He obtained his MSc degree in Applied Mathematics in 1983 from Baku State University, Azerbaijan and PhD degree in Mathematics in 2002 from the University of Ballarat. His research is focused on nonsmooth optimisation, nonconvex optimisation and applications.
Speaker: Ms Hoa Thi Bui
Federation University Australia
Title: Quasiconvexity and robust quasiconvexity
Date and time: Friday, 3 February 2017, 4:00-5:00pm. Location: Building 8 Level 9 Room 66 (AGR) RMIT City campus
Abstract: A quasiconvex function, or a level-convex function, is a function whose sublevel sets are convex. And, the class of functions which are stable in term of the quasiconvexity under the sufficiently small linear perturbations is called robustly quasiconvex functions. This aims to give a concept of generalised convexity, at which many important properties of the convexity still hold under a relatively small perturbation. Then, the quasiconvexity and robust quasiconvexity of lower semicontinuous functions are characterized by means of Fréchet and limiting subdifferentials.
Bio: Hoa Thi Bui has recently arrived from Vietnam to take up a PhD position at Federation University. She comes from Quang Ngai, Vietnam and up until recent graduation, studied Mathematics at Ho Chi Minh City, University of Pedagogy, Vietnam.
Her PhD Supervisors are Prof. Alex Kruger and Prof. David Yost.