Seminar: A Sequential Deep Learning Algorithm for Robust Mixed-integer Optimisation Problems, 12th May, 1PM

When: Thursday 12th of May, 1PM AEDT

Where: The talk will be presented online via Zoom, RSVP here.

Speaker: Mohammadreza Chamanbaz

Title: A Sequential Deep Learning Algorithm for Robust Mixed-integer Optimisation Problems

Abstract:

Optimisation has long been a key driving force in solving machine learning (ML) problems. However, there have been a number of very recent attempts to use machine learning to assist in solving continuous and mixed-integer optimisation problems. This talk uses a neural network classifier to considerably reduce the computational complexity of solving robust optimisation problems. To this end, we first approximate the robust problem with a sampled optimisation problem and use a sequential algorithm to solve the sampled optimisation problem. A sequential algorithm is proposed that uses the neural network classifier to identify the subset of constraints constructing a candidate solution. We then evaluate the algorithms’ effectiveness on a variety of computational examples.

Bio:

Mohammadreza Chamanbaz received his PhD in Control Science from the Department of Electrical & Computer Engineering, National University of Singapore, in 2014. He was with Data Storage Institute, Singapore as a research scholar from 2010 to 2014 and with Singapore University of Technology and Design as a postdoctoral research fellow from 2014 to 2017. Dr Chamanbaz was an assistant Professor at Arak University of Technology from 2017 to 2019 and a senior research fellow at iTrust Centre for Research in Cyber-security from 2019 to 2020. He is currently a research fellow at Australian Centre for Field Robotics, and his research activities are mainly focused on probabilistic and randomised algorithms for solving uncertain optimisation problems.