|Speaker: Dr. Matthew Tam, the School of Mathematics and Statistics at the University of Melbourne.
Title: Algorithms derived from dynamical systems
Date and Time: Friday, March 13th, 3.00pm – 4.00pm, 2020 (Talk & Q/A)
Location: AGR Building 15, level 03, room 10 (Request for remote Zoom connect email@example.com)
|Abstract: The study of continuous time dynamical systems associated with iterative algorithms for solving optimisation problems has a long history which can be traced back at least to 1950s. The relationship between the continuous and discrete versions of an algorithm provides a unifying perspective which gives insights into their behaviour and properties. In this talk, I will report on new algorithms for solving minmax problems which were discovered by exploiting this connection.
Bio: Matthew Tam is Lecturer in Operations Research and a DECRA Fellow in the School of Mathematics and Statistics at the University of Melbourne. He received a PhD from the University of Newcastle in 2015 under the supervision of Jonathan Borwein, where he worked on iterative projection algorithms for optimisation. He then moved to the University of Göttingen (Germany) where he was a post-doctoral researcher with Russell Luke, supported initially by DFG-RTG2088 (“Discovering structure in complex data”) and later by a fellowship from the Alexander von Humboldt Foundation. Prior to joining the University of Melbourne, he was Junior Professor for Mathematical Optimisation within the Institute for Numerical and Applied Mathematics, also at the University of Göttingen.
|Speaker: Dr. Patrick Johnstone, MSIS Department of the Rutgers Business School.
Title: Projective Splitting: A New Breed of First-Order Proximal Algorithms
Date and Time: Friday, February 28th, 3.30pm – 4.30pm, 2020 (Talk & Q/A)
Location: AGR Building 15, level 3, room 10 (Request for remote connect firstname.lastname@example.org)
|Abstract: Projective splitting is a proximal operator splitting framework for solving convex optimization problems and monotone inclusions. Unlike many operator splitting methods, projective splitting is not based on a fixed-point iteration. Instead, at each iteration a separating hyperplane is constructed between the current point and the primal-dual solution set. This gives more freedom in terms of stepsize selection, incremental updates, and asynchronous parallel computation. Despite these advantages, projective splitting had two important drawbacks which we have rectified in this work. First, the method uses calculations entirely based on the proximal operator of the functions in the objective. However, for many functions this is intractable. We develop new calculations based on forward steps – explicit evaluations of the gradient – whenever the gradient is Lipschitz continuous. This extends the scope of the method to a much wider class of problems. Second, no convergence rates were previously known for the method. We derive an O(1/k) rate for convex optimization problems, which is unimprovable for this algorithm and problem class. Furthermore, we derive a linear convergence rate under certain strong convexity and smoothness conditions.
Bio: Patrick R. Johnstone is a postdoctoral associate in the MSIS Department of the Rutgers Business School where he is advised by Prof. Jonathan Eckstein. In May 2017 he received his PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (advised by Prof. Pierre Moulin). He also received the MSc degree in ECE from UIUC. He received the BSc degree in Electrical Engineering from the University of New South Wales (UNSW) in Sydney, Australia. At UNSW he received the university medal, given to the top student in electrical engineering. He has worked as a research intern at Qualcomm Research, Rambus Labs, and CSIRO. For his work at Qualcomm, he received the Roberto Padovani award for outstanding interns. His research interests are in continuous optimization, first-order proximal splitting methods, parallel and distributed algorithms, machine learning, and signal processing.
|Speaker: Dr. Inmaculada Flores Garcia., Complutense Unversity of Madrid
Title: Multi-criteria optimization for disaster evacuation of people and relief distribution
Date and Time: Friday, November 1 st, 3.00pm – 4.00pm
Location: Megaflex (near RMIT Connect), Building 8 Level 4 Room 13, RMIT City campus
|Abstract: Disasters and their consequences strike the world population from the beginning of history and their occurrences have an upward trend. Palliating the effects of a disaster is the main objective of the work that will be presented, which is focused on the supported evacuation of the population from the affected area to safer places, with the aim of preventing people from suffering the consequences of the catastrophic event. Furthermore, and once people are located at a safe place, basic supplies must be delivered in order to assure that the evacuated population have their basic necessities covered after the occurrence of a disaster. To tackle this complex problem, a mathematical mixed integer programming model based on time dependence is presented. The problem is multimodal in terms of different fleets of vehicles, types of affected population and of commodities to be distributed.
Bio: Inmaculada has a 5-year degree on Mathematics by the University of Granada, including a year course at the Technische Universität München, and a master degree on Mathematical Engineering at the Complutense University of Madrid. After 6 years working on Data Analysis and Information Security in two important companies at Spain, she became a PhD student and mid-time professor at the Faculty of Mathematical Sciences and at the Faculty of Commerce and Tourism in the Complutense University of Madrid. Currently, she belongs to the Statistics and Operations Research department (Fac. Mathematical Sciences, UCM) as Project Research Staff.
We are happy to invite contribution and participation to the Workshop
OPTIMISATION METHODS IN WILDFIRE EMERGENCY MANAGEMENT
November 11 – RMIT University, city campus
The problems arising in emergency management have to take into account a high level of uncertainty. In wildfire management, in particular, weather conditions, the stress level of vegetation, location of ignition and fire behaviour dynamics constitute the main sources of uncertainty. In order to protect communities in a safer and more efficient way, emergency management can use optimisation approaches that contribute to reduce uncertainty. Depending on the type of uncertainty and the objectives, the resulting optimisation problems can be classified in different paradigms including stochastic optimisation, robust optimisation, probabilistic combinatorial optimisation or online optimisation.
The workshop will screen the state of research in optimisation for wildefire emergency management and by extension, of any mathematical modelling approach aiming to improve the efficiency of emergency management. Details and updates are available on the workshop’s website.
Two invited international experts on wildfire emergency management and optimisation methodologies will give a lecture:
Prof. Cristina Vega, Universitat de Lleida, Spain: “Towards a landscape fire management strategy in Southern Europe based on risk analysis: Unresolved spatial and mathematical issues”
Assoc. Prof. Aurélie Thiele, Southern Methodist University, Dallas, USA: “An introduction to robust optimization with applications to emergency management”
Who can participate? The workshop is primary dedicated to PhD students and early career researchers. But everybody interested in the topic is welcome.
Location: Melbourne, Vic., RMIT University – Swanston Academic Building 445 Swanston Street – Level 5, room 12.
How to participate? Is you want to attend the workshop, send an email with your details to M. Demange (email@example.com). Participation is free but registration is required (deadline 4th of November).
If you want to present an abstract, send it to Marc Demange (firstname.lastname@example.org) before October 18th, 2019 addressing the following items:
- Real problem that you are addressing.
- Methodological approach that you are using to solve that problem.
- Main challenges identified from the approach you are using.
- Final outcome expected from your research and how this will be used to solve real life problems.
OPPORTUNITIES FOR PARTICIPANTS
Australian attendees from AMSI Member organisations have access to funding via the AMSI Travel Fund. Students or early career researchers from AMSI member universities without access to a suitable research grant or other source of funding may apply to the Head of Mathematical Sciences for subsidy of travel and accommodation out of the departmental travel allowance. We recommend applying as soon as possible. http://research.amsi.org.au/travel-funding/
The Workshop will be followed by GEO-SAFE International Wildefire Conference from November 12 to November 15. Early bird rates available until October 18.
Organisers: Marc Demange, John Hearne, David Ellison (RMIT, Australia), Marta Yebra (Australia National University, Australia), Jagannath Aryal (University of Tasmania, Australia), Núria Prat-Guitart (Pau Costa Foundation, Spain).