Mathematics of Computation and Optimisation
Special Interest Group of the AustMS
The McKenzie Postdoctoral Fellowships Program is a University of Melbourne scheme. The fellowship funds a three year appointment commencing at Level A.6 in the University salary band plus superannuation. Fellows will receive an additional $25,000 to be spent on project costs over the term of their Fellowship.
The EOI deadline is July 5th 2021, and the full application deadline is August 23rd 2021.
Further details:
Title: Bridges between Discrete and Continous Optimisation in Stochastic Programming
Speaker: Andrew Eberhard (RMIT University)
Date and Time: June 30th, 2021, 17:00 AEST (Register here for remote connection via Zoom)
Abstract: For many years there has been a divide between the theoretical under pinning of algorithmic analysis in discrete and continuous optimisation. As a case study, stochastic optimisation provides a classic example. Here the theoretical foundations of continuous stochastic optimisation lies in the theory of monotone operators, operator splitting and nonsmooth analysis, none of which appear to be applicable to discrete problems. In this talks we will discuss the application of ideas from continuous optimisation and variational analysis to the study of progressive hedging like methods for discrete optimisation models. The key to the success of such approaches is the acceptance of the existence of MIP and QMIP\ solvers that can be integrated in to analysis as “black box solvers” that return solutions within a broader algorithmic analysis. Here methods more familiar to continuous optimisers and nonsmooth analysts can be used to provide proofs of convergence of both primal and dual methods. Unlike continuous optimisation there still exists separate primal and dual methods and analysis in the discrete context. We will discuss this aspect and some convergent modifications that yield robust and effective versions of these methods, long with numerical validation of their effectiveness.
Friday 9th July
The New South Wales branch of ANZIAM will hold a one-day virtual conference on Friday 9th July. A Zoom link will be sent at a later date.
The conference will run from approximately 10am to 5.30pm (depending upon the number of speakers).
This meeting features the 2020 winner of WIMSIG Maryam Mirzakhani Award, Dr Hoa Bui (Curtin) as the invited speaker: https://www.maintenance.org.au/users/viewuserprofile.action?username=hoa.bui. Title: Optimisation Methods for Maintenance Scheduling in the Mining Industry.
There is no registration fee.
If you would like to give a presentation or are planning on attending please contact Dr Xiaoping Lu (xplu@uow.edu.au) by 30 June, and abstracts/titles of presentations should
be send to Xiaoping by Tuesday 6 July.
Election of Speakers
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ECRs and students are strongly encouraged to attend and present at the meeting.
In the fortunate event that we are over-subscribed with speakers, priority will be given to students and ECR, with preference given to NSW ANZIAM members. For established researchers, preference will again be given to NSW ANZIAM members.
Student prize
A prize in the value of $100, will be awarded to the best student presentation.
Organisers
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Dr Xiaoping Lu,
Centre for Financial Mathematics,
School of Mathematics and Applied Statistics,
University of Wollongong
A/P Mark Nelson
Centre for Multidisciplinary Mathematical Modelling,
School of Mathematics and Applied Statistics,
University of Wollongong
The New South Wales branch of ANZIAM will hold a one-day virtual conference on Friday 9th July. A Zoom link will be sent at a later date.
The conference will run from approximately 10am to 5.30pm (depending upon the number of speakers).
This meeting features the 2020 winner of WIMSIG Maryam Mirzakhani Award, Dr Hoa Bui (Curtin) as the invited speaker. Title: Optimisation Methods for Maintenance Scheduling in the Mining Industry.
There is no registration fee.
Title: Nonsmooth DC optimization: recent developments
Speaker: Adil Bagirov (Federation University)
Date and Time: June 23rd, 2021, 17:00 AEST (Register here for remote connection via Zoom)
Abstract: In this talk we consider unconstrained optimization problems where the objective functions are represented as a difference of two convex (DC) functions. Various applications of DC optimization in machine learning are presented. We discuss two different approaches to design methods of nonsmooth DC optimization: an approach based on the extension of bundle methods and an approach based on the DCA (difference of convex algorithm). We also discuss numerical results obtained using these methods.
Title: Error bounds, amenable cones and beyond
Speaker: Bruno F. Lourenço (Institute of Statistical Mathematics)
Date and Time: June 16th, 2021, 17:00 AEST (Register here for remote connection via Zoom)
Abstract: In this talk we present an overview of the theory of amenable cones, facial residual functions and their applications to error bounds for conic linear systems. A feature of our results is that no constraint qualifications are ever assumed, so they are applicable even to some problems with unfavourable theoretical properties. Time allowing, we will discuss some recent findings on the geometry of amenable cones and also some extensions for non-amenable cones.
The Research Fellow is expected to conduct world-class research and provide training for research students working in industrial optimisation as a key appointee in a newly established ARC Training Centre in Optimisation Technologies, Integrated Methodologies and Applications (OPTIMA). The research program involves a focus on model-based and black-box optimisation methodologies of relevance to a broad range of industry partner optimisation challenges. A
multidisciplinary approach is expected, drawing from techniques developed in mathematics, computer science, statistics, engineering, and economics.
For more details, please refer to
http://jobs.unimelb.edu.au/caw/en/job/904801/optima-postdoctoral-research-fellow
A position of Postdoctoral Fellow is available at UNSW Sydney. The Postdoctoral Fellow will undertake collaborative and self-directed research on an ARC-funded Discovery Project. For more details and to apply please visit https://external-careers.jobs.unsw.edu.au/cw/en/job/502136/postdoctoral-fellow-computational-mathematics.
For more information please contact Thong Le Gia qlegia@unsw.edu.au or Ian Sloan i.sloan@unsw.edu.au.
Applications close July 6th, 2021
Workshop on the Intersections of Computation and Optimisations
MoCaO (Mathematics of Computation and Optimisation) is planning a new workshop for late 2021 which is sponsored by the ANU, UNSW and AMSI.
This workshop intends to bring together researchers from the areas of computation, optimisation, computing sciences and engineering interested in the cross- fertilization of ideas around the following theme:
Optimisation often faces unique issues when there is a need to efficiently compute. On the other hand, computational techniques at times utilise optimisation within their algorithms. Both areas fundamentally need to understand approximation in all its facets which is also fundamental to computation as are the associated notions of convergence. Indeed, recent research has blurred the boundaries between optimisation (continuous and discrete), computation and areas of computing science. The area of machine learning has crept into relevance everywhere. Recently research has turned to its use in computational techniques including the enhancement of optimisation algorithms and the cycle of cross fertilisation of ideas has continued to date.
Workshop Format
We intend to run the workshop in a blended format, involving a face to face component which will be held at the ANU mathematics school in conjunction with a simultaneous\parallel online format to which both group of participants will engage. Some keynotes will present in person (streamed online from ANU) and other will engage totally online in a remote format. We encourage local and international participants to take part in the online workshop. In addition to their keynote presentations, keynotes who will be invited to give a lectorial-discussion session that will promote research questions and engage emerging researchers in these areas.
Keynotes Speakers:
Prof Gerlind Plonka-Hoch (University of Goettingen, Germany)
Prof. Frances Kuo (UNSW)
Prof Stefan Wild (Argonne, USA)
Prof Stephen Wright (Wisconsin, USA)
Prof. Ian Turner (QUT)
Prof. Claudia Sagastizabal (IMECC-Unicamp and CEMEAI, Brazil)
Prof Martin Berggren (Umeå University, Sweden)
Important dates:
Registration Opens: 07/06/2021
Workshop Dates: 22/11/2021 to 25/11/2021
Local Organising Committee
Prof. Andrew Eberhard (RMIT) andy.eberhard@rmit.edu.au
Prof. Stephen Roberts (ANU) stephen.roberts@anu.edu.au
Prof. Markus Hegland (ANU) markus.hegland@anu.edu.au
Dr. Matthew Tam (UniMelb) matthew.tam@unimelb.edu.au
Dr. Nadia Sukhorukova (Swinburne) nsukhorukova@swin.edu.au
Future Announcements and Grants:
We intend to follow up with regular announcements regarding workshop accommodation, details on format and software and funding opportunities for ECR, PHD and female participants. We also wish to draw female participants attention to the possibility of applying for the WIMSIG Cheryl E. Praeger Travel Award (support for attending conferences/visiting collaborators) and/or the WIMSIG Anne Penfold Street Awards (support for careering responsible while attending conferences/visiting collaborators).
Title: The Boosted Difference of Convex Functions Algorithm
Speaker: Vuong Phan (University of Southampton)
Date and Time: June 9th, 2021, 17:00 AEST (Register here for remote connection via Zoom)
Abstract: We introduce a new algorithm for solving Difference of Convex functions (DC) programming, called Boosted Difference of Convex functions Algorithm (BDCA). BDCA accelerates the convergence of the classical difference of convex functions algorithm (DCA) thanks to an additional line search step. We prove that any limit point of the BDCA iterative sequence is a critical point of the problem under consideration and that the corresponding objective value is monotonically decreasing and convergent. The global convergence and convergence rate of the iterations are obtained under the Kurdyka Lojasiewicz property. We provide applications and numerical experiments for a hard problem in biochemistry and two challenging problems in machine learning, demonstrating that BDCA outperforms DCA. For the biochemistry problem, BDCA was ve times faster than DCA, for the Minimum Sum-of-Squares Clustering problem, BDCA was on average sixteen times faster than DCA, and for the Multidimensional Scaling problem, BDCA was three times faster than DCA.
Joint work with Francisco J. Aragón Artacho (University of Alicante, Spain).