RMITOpt Seminar: Dr. Gregorio Tirado Domínguez – Universidad Complutense de Madrid

 Speaker:   Dr. Gregorio Tirado Domínguez – Universidad Complutense de Madrid

Title:  Multi-criteria Models and Heuristics for the Post-disaster Distribution of Humanitarian Aid

Date and Time:  Friday, August 16th, 3.00pm – 4.00pm

Location: Building 8 Level 9 Room 66 (AGR) RMIT City campus

Abstract:   The distribution of humanitarian aid is one of the most important logistic operations to be carried out to assist the affected population after the strike of a disaster. In this context, it is crucial to minimize the response time, as well as taking into account operation costs and designing reliable and safe itineraries that lead to equitable distribution plans. This situation can be modelled in a natural way by using multi-criteria mathematical programming models. In particular, this seminar will focus on the development of a lexicographical goal programming model for the distribution of humanitarian aid, together with the design of appropriate solution methods. Some results regarding their application to several realistic case studies will be presented to illustrate their performance.

Bio:    Gregorio Tirado Domínguez is an Associate Professor at the Departament of Actuarial and Financial Economics and Statistics of the Faculty of Economics and Management of the University Complutense of Madrid. He holds a PhD degree in Mathematics from the University Complutense of Madrid since 2009. His main research lines comprise combinatorial optimization, logistics (vehicle routing problems and humanitarian logistics, in particular), mathematical programming and heuristics.

RMITOpt Seminar: Dr. Nadia Sukhorukova, Swinburn University

 Speaker: Dr. Nadia Sukhorukova, Swinburn University

Title:   Chebyshev approximation problems and Remez method applicability.

Date and Time:  Friday, August 2nd, 3.00pm – 4.00pm

Location: Building 8 Level 9 Room 66 (AGR) RMIT City campus

Abstract:   In this presentation, I am going to talk about three types of Chebyshev approximation problem: approximation by univariate polynomials, multivariate polynomials and rational functions. The objective function in the first two problems are convex and the corresponding optimisation problems can be solved by applying a general convex approximation method, while the third type is a quasi-convex and needs a specially developed method to solve it.   In this talk, I will demonstrate the transformation of the problem and highlight surprising similarities passing from one formulation to another.

Bio: Dr. Sukhorukova is holding two PhD degrees: one from the University of Ballarat (currently, Federation University Australia), completed in 2004 and another one is from Saint-Petersburg State University (Russia) completed in 2006. Dr. Nadia Sukhorukova is working in the area of Mathematical Optimisation and its application to data analysis, data mining, signal processing, location-allocation problems and other real-life applications. Dr. Sukhorukova is also working on a number of mathematical problems appearing in the area of Chebyshev (uniform) approximation (ARC Discovery grant DP180100602).

RMITOpt Seminar: Dr. Chayne Planiden, University of Wollongong

Speaker:   Dr. Chayne Planiden, University of Wollongong

Title:   A Derivative-free VU-algorithm for Convex Minimisation

Date and Time:  Friday, July 19th, 3.00pm – 4.00pm

Location: Building 8 Level 9 Room 66 (AGR) RMIT City campus

Abstract:   The VU-algorithm is a superlinearly convergent method for minimizing nonsmooth functions. The algorithm separates the space into the V-space and the orthogonal U-space, such that the nonsmoothness of the objective function is concentrated on the V-space, and on the U-space the function behaves smoothly. This structure allows for an alternation between a fast, Newton-like step in the U-space and a proximal-point step in the V-space, thus increasing convergence speed.

We establish a derivative-free variant of the VU-algorithm, the first of its kind, and show  global convergence. We provide numerical results from a proof-of-concept implementation, which demonstrate the feasibility and practical value of the approach.

Bio: Chayne Planiden received his Ph.D. from University of British Columbia in Kelowna, Canada last year. He works at University of Wollongong and specialises in nonsmooth optimisation, including the Moreau envelope and proximal mapping, proximal point algorithms, and derivative-free optimisation methods.


RMITOpt Seminar: Bill Moran, Melbourne University

 Speaker: Prof. Bill Moran, Melbourne University

Title:  Random Thoughts on Randomization

Date and Time:  Friday, May 24th, 3.00pm – 4.00pm

Location: Building 8 Level 9 Room 66 (AGR) RMIT City campus (To connect via Zoom please contact

Abstract:     Surprisingly, the randomization of a problem space can often make it easier to solve than the deterministic original.  This trick occurs across a range of areas of mathematics, statistics, computer science, and even engineering. Early instances are Borel’s Theorem on normal numbers and mixed strategies in game theory. Other examples include Erdos-Renyi  graphs and their work on additivity properties  of sequences of integers. I will give a survey of the various ideas and, as time permits, discuss a range of applications to: number theory, graph theory, signal processing, optimization, computer science, game theory, and statistics.

Bio:  Professor Bill Moran (M’95) currently serves, since 2017, as Professor of Defence Technology in the University of Melbourne. From 2014 to 2017,  he was Director of the Signal Processing and Sensor Control Group in the School of Engineering at RMIT University,  from 2001 to 2014,  a  Professor in the Department of Electrical Engineering, University of Melbourne,    Director of Defence Science Institute  in University of Melbourne (2011-14), Professor of Mathematics (1976–1991), Head of the Department of Pure Mathematics (1977–79, 1984–86), Dean of Mathematical and Computer Sciences (1981, 1982, 1989) at the University of Adelaide, and Head of the Mathematics Discipline at the Flinders University of South Australia (1991–95). He was Head of the Medical Signal Processing Program (1995–99) in the Cooperative Research Centre for Sensor Signal and information Processing. He was a member of the Australian Research Council College of Experts from 2007 to 2009.  He was elected to the Fellowship of the Australian Academy of Science in 1984. He holds a Ph.D. in Pure Mathematics from the University of Sheffield, UK (1968), and a First Class Honours B.Sc. in Mathematics from the University of Birmingham (1965). He has been a Principal Investigator on numerous research grants and contracts, in areas spanning pure mathematics to radar development, from both Australian and US Research Funding Agencies, including DARPA, AFOSR, AFRL, Australian Research Council (ARC), Australian Department of Education, Science and Training, and Defence Science and Technology, Australia.  His main areas of research interest are in signal processing both theoretically and in applications to radar, waveform design and radar theory, sensor networks, and sensor management. He also works in various areas of mathematics including harmonic analysis, representation theory, and number theory.

RMITOpt Seminar: Adil Bagirov – Federation University

Speaker:   A\Prof. Adil Bagirov–Federation University

Title:  Aggregated Subgradient Methods in Nonsmooth Optimization

Date and Time:  Friday, May 10th, 3.00pm – 4.00pm

Location: Building 8 Level 9 Room 66 (AGR) RMIT City campus

Abstract:   In this talk, we discuss aggregate subgradient methods for solving nonsmooth optimization problems. Our emphasize will be on the aggregate subgradient method for solving unconstrained nonsmooth difference of convex (DC) optimization problems. We discuss convergence results, present results of numerical experiments using some academic test problems and compare the aggregate subgradient method with several other nonsmooth DC optimization solvers

Bio:      Adil Bagirov is Associate Professor of Optimisation at School of Science, Engineering and Information Technology, Federation University Australia since 2010. 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. A. Bagirov has won several Australian Research Council Discovery and Linkage projects. His research is focused on nonsmooth optimisation, nonconvex optimisation and their applications.

AMSI Optimise 2019 – Hyatt Regency Perth


AMSI Optimise 2019

17-21 June | Hyatt Regency Perth

AMSI Optimise is an annual networking and research-training event that aims to strengthen mathematical optimisation research engagement and its applications across industry.

Now in its third year, AMSI Optimise moves to Perth, bringing together industry leaders from the natural resources sector, academic experts and the nation’s current postgraduate talent. The event provides a platform to understand industry drivers and foster research collaborations, connecting business with Australia’s future workforce.

This event will comprise a three-day industry-focused conference, followed by a two-day research workshop. The symposium features expert and end-user talks, international guest speakers, collaboration showcases, industry challenge sessions and tutorials. The themes of the 2019 conference are Mining, Oil & Gas, Agriculture & Water.

AMSI Optimise is aimed at:

  • anyone using optimisation, with opportunities to learn more about the current state of the art and to connect with others who have similar interests
  • industry practitioners interested in exploring the benefits of engagement with optimisation research
  • academics and postgraduate students wanting to better understand drivers and needs in this area

Full conference information, a call for submission of abstracts, and registration details can be found on the AMSI Optimise Website –

RMITOpt Seminar – Dr. James Saunderson – Monash University


 Speaker:   Dr. James Saunderson – Monash University

Title:  Hyperbolic cubic polynomials

Date and Time:  Friday, April 26th, 3.00pm – 4.00pm

Location: Building 8 Level 9 Room 66 (AGR) RMIT City campus

Abstract:     Hyperbolic polynomials are multivariate homogeneous polynomials with certain real-rootedness properties. These give rise to a class of efficiently solvable optimisation problems called hyperbolic programs, which generalize semidefinite programs.

In this talk I’ll introduce these ideas and then focus on the case of hyperbolic polynomials of degree three (i.e. hyperbolic cubics). In particular I plan to discuss questions like “How hard is it to decide hyperbolicity of a cubic?” and “Can (powers of) hyperbolic cubics always be expressed in terms of determinants?”. We will see that (sums of squares relaxations of) polynomial optimisation problems on the sphere play an important role in studying these questions.

Bio:     James Saunderson is a Lecturer in the Department of Electrical and Computer Systems Engineering at Monash.  He obtained a PhD in Electrical Engineering and Computer Science from MIT in June 2015. Before joining Monash he was a postdoc in Electrical Engineering jointly at Caltech and the University of Washington.

RMITOpt Seminar – Andrew Eberhard (RMIT)

  Speaker:   Prof. Andrew Eberhard – RMIT University

Title:  Divide and Concur – An Overview and some examples

Date and Time:  Friday, April 5th, 3.00pm – 4.00pm

Location: Building 8 Level 9 Room 66 (AGR) RMIT City campus

Abstract:    Divide and concur is a play on words that deliberately echo computing science mantra of divide and conquer. In divide and conquer a problem is broken down into smaller and smaller subproblems until each smaller part is easily solved. In divide and concur the subproblems remain linked by a relax constraint that ultimately must be reconciled.

These ideas have found their way into feasibility problems and optimisation both continuous and discrete. Some view these are project-project feasibility problems, but recent work has suggested they are really an expression the older Gauss-Seidel approach.

In this talk we will discuss a modified version of a penalty-based Gauss-Seidel method as applied to stochastic optimisation that has stronger theoretical properties for certain sub-classes of Stochastic Integer Programs (SIP). In particular we will show that the modified algorithm always converges to a feasible point and this is verified by numerical experiments. This method shares structure and properties related the feasibility pump of integer programming and progressive Hedging heuristics used for stochastic integer programming.

Bio:    Andrew Eberhard did his PhD at Adelaide University under Prof. Charles Pearce and after graduating spend some time at UniSA (then SAIT) before moving to RMIT in Melbourne in the early 1990s. He has been an active member of the Australian mathematical and optimisation community for more than 20 years. He has served on the executives of ASOR, ANZIAM, as the deputy director of AMSI and the board of AMSI. Currently he is the co-chair of the AustMS special interest group Mathematics of Computation and Optimisation (MoCaO). His interests span numerous areas including nonsmooth and variational analysis, optimisation algorithms (both continuous and discrete), systems and control theory, operations research and other more theoretical aspect of optimisation theory.


RMITOpt Seminar – Matthew Tam (University of Goettingen)

  Speaker: Matthew K. Tam (University of Goettingen)

Title: Forward-Backward Splitting Without Cocoercivity

Date and Time:  Friday, March 15th, 3.00pm – 4.00pm

Location: Building 8 Level 9 Room 66 (AGR) RMIT City campus (To connect via visimeet please contact

Abstract:   In this talk, I will discuss a simple modification of the forward-backward splitting method for finding a zero in the sum of two monotone operators. The modified method converges under the same assumptions as Tseng’s forward-backward-forward method, namely, it does not require cocoercivity of the single-valued operator. Moreover, each of its iterations only require one forward evaluation rather than two as is the case in Tseng’s method. Variants of the method incorporating a linesearch, an inertial term, or a structured three operator inclusion will also be discussed. Based on joint work with Yura Malitsky (University of Göttingen).

Bio:   Matthew Tam received a PhD from the University of Newcastle under the supervision of Jonathan Borwein, where he worked on iterative projection algorithms for optimisation. He then moved to the University of Göttingen (Germany) to take up a post-doctoral position with Russell Luke in the Institute for Numerical and Applied Mathematics, supported initially by DFG-RTG2088, (‘Discovering structure in complex data”) and later by a fellowship from the Alexander von Humboldt Foundation. Since 2018, he has been Junior Professor for Mathematical Optimisation also at the University of Göttingen.

RMIT Mathematics Seminar: Craig Bauling – Wolfram Research

  Speaker:    Craig Bauling, Wolfram Research

Title:  Highlighting Some Customer Use Cases behind Wolfram’s Exponential Growth in Education

Date and Time:  Monday, December 3rd, 3.00pm – 4.00pm

Location: Building 8 Level 9 Room 66 (AGR)

RMIT City campus

Abstract:   For 30 years, Wolfram Research has been serving Educators and Researchers. For most of those years our customers have had access to only one tool: Mathematica desktop.   In the past 10 years, we have introduced many world changing technical innovations like: Wolfram | Alpha Pro, Natural Language computation, Wolfram System Modeler, and the Wolfram Cloud products (for iPads and tablets).  Many of which are being made available through large scale deployments like Egypt, Victoria Australia, and Ecuador.  This suite is being deployed by Faculty and Students all around Victoria in numerous projects, high-stakes exams, and in thousands of classrooms.    Craig will demonstrate the key new technologies that are directly applicable for use in Education with specific examples for how it is being used. Topics of the technical talk include:

•           Practical applications in Engineering, Chemistry, Physics, and Biology

•           Computation using Natural English Language

•           On – demand Chemical, Biological, Economic, Finance and Social data

•           Creating interactive models that encourage student participation and learning

•           2 D and 3 D information visualization and 3 D Printing

•           Market Leading Statistical Analysis Functionality

•           Mathematica as a modern programming language

The content will help attendees with no prior experience get started with the Wolfram workflow. Since there is a large amount of new functionality, most intermediate users who attend these training sessions have reported learning quite a bit as well.   All attendees will receive an electronic copy of the examples, which can be adapted to individual courses.

Bio:    Craig Bauling holds a MBA from Northern Illinois University, a BS in Mathematics from Western Illinois University and an AS in Engineering from Sauk Valley Community College.  Craig has over 6 years teaching experience including Community College, Senior and Junior Secondary.  Craig’s corporate experience includes over 15 years in various Engineering roles.  He joined Wolfram Research in 2008 and his current role is to help schools, universities and businesses leverage Wolfram resources for teaching, research and workflow improvement.

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