24 June, 3.00pm AEST
Speaker's Name: | Professor Mark Giesbrecht |
Speaker's Institution: | University of Waterloo |
Title of Seminar: | Algorithms and statistics for additive polynomials |
Host Institution: |
CARMA, University of Newcastle |
Time and Date: |
Tuesday 24 June, 3.00pm AEST |
Seminar Abstract: |
The additive or linearized polynomials were introduced by Ore in 1933 as an analogy over finite fields to his theory of difference and difference equations over function fields. The additive polynomials over a finite field field F=GF(q), where q=p^e for some p, are those of the form f = f_0x+f_1x^p + f_2x^{p^2} + ... + f_mx^{p^m} in F[x]. |
Seminar Convenors: |
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19 June, 12.00pm AEST
Speaker's Name: | Arnab Sur |
Speaker's Institution: | Department of Mathematics and Statistics, Indian Institute of Technology Kanpur |
Title of Seminar: | M-stationarity Concept for a Class of Stochastic MPCC Problems |
Host Institution: |
CARMA, University of Newcastle |
Time and Date: |
Thursday 19 June, 12.00pm AEST |
Seminar Abstract: |
In this talk we are going to discuss the importance of M-stationary conditions for a special class of one-stage stochastic mathematical programming problem with complementarity constraints (SMPCC, for short). M-stationarity concept is well known for deterministic MPCC problems. Now using the results of deterministic MPCC problems we can easily derive the M-stationarity for SMPCC problems under some well known constraint qualifications. It is well observed that under MPCC-linear independence constraint qualification we obtain strong stationarity conditions at a local minimum, which is a stronger notion than M-stationarity. Same result cab be derived for SMPCC problems under SMPCC-LICQ. Then the question that will arise is: What is the importance to study M-stationarity under the assumption of SMPCC-LICQ. To answer this question we have to discuss sample average approximation (SAA) method, which is a common technique to solve stochastic optimization problems. Here one has to discretize the underlying probability space and then using the strong Law of Large Numbers one has to approximate the expectation functionals. Now the main result of this discussion as follows: If we consider a sequence of M-type Fritz John points of the SAA problems then any accumulation point of this sequence will be an M-stationarity point under SMPCC-LICQ. But this kind of result, in general, does not hold for strong stationarity conditions. |
Seminar Convenors: |
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7 July, 3.00pm AEDT
11 July, 3.00pm AEDT
Speakers: |
Prof David Bailey and Prof Jon Borwein |
Speakers' Institutions: |
Lawrence Berkeley Lab (retired) and U.C. Davis, USA; The University of Newcastle |
Title of Seminar: |
AMSI AGR National Seminar: Big data computing: Science and pseudoscience |
Host Institution: |
University of Newcastle (CARMA) |
Time and Date: |
Friday 11 July, 3.00pm AEST |
Seminar Abstract: |
The relentless advance of computer technology, a gift of Moore’s Law, and the data deluge available via the Internet and other sources, has been a gift to both scientific research and business/industry. Researchers in many fields are hard at work exploiting this data. The discipline of “machine learning,” for instance, attempts to automatically classify, interpret and find patterns in big data. It has applications as diverse as supernova astronomy, protein molecule analysis, cybersecurity, medicine and finance. However, with this opportunity comes the danger of “statistical overfitting,” namely attempting to find patterns in data beyond prudent limits, thus producing results that are statistically meaningless. |
Seminar Convenor: |
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17 June, 3.00pm AEST
Speaker's Name: | Professor Joydeep Dutta |
Speaker's Institution: | Department of Mathematics and Statistics, Indian Institute of Technology Kanpur |
Title of Seminar: | Gap Functions, Error Bounds and Regularization of Variational Inequalities: Part 2 |
Host Institution: |
CARMA, University of Newcastle |
Time and Date: |
Tuesday 17 June, 3.00pm AEST |
Seminar Abstract: |
Our aim in this talk is to show that D-gap function can play a pivotal role in developing inexact descent methods to solve monotone variational inequality problem where the feasible set of the variational inequality is a closed convex set rather than just the non-negative orthant. We also focus on the issue of regularization of variational inequality. Freidlander and Tseng has shown in 2007 that by the regularizing the convex objective function by using another convex function which in practice is chosen correctly can make the solution of the problem simpler. Tseng and Freiedlander has provided a criteria for exact regularization of convex optimization problems. In this section we ask the question as to what extent one can extend the idea of exact regularization in the context of variational inequalities. We study this in this talk and we show the central role played by the dual gap function in this analysis. |
Seminar Convenors: |
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