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Lindon Roberts

MSI Fellow

Australian National University

Biography

I am an MSI Fellow at the Mathematical Sciences Institute, Australian National University. My research interests are in numerical analysis and data science, particularly nonconvex and derivative-free optimization.

Details of my CV, publications, talks and software are below (or look at Google Scholar and Github). For general optimization resources, see below or my nonlinear optimization resources page.

Recent news:

  • (Dec-21) I will be speaking about convex-constrained DFO at WoMBaT at AustMS 2021.
  • (Nov-21) I am lead organizer for the Workshop on the Intersections of Computation and Optimisation on 22-25 November at ANU. This is the first such workshop, a new initiative of the AustMS special interest group MoCaO.
  • (Nov-21) New paper out, Model-Based Derivative-Free Methods for Convex-Constrained Optimization with Matthew Hough (University of Waterloo), where we show how use interpolation in general convex sets to do model-based derivative-free optimization. Read it here, and download the new version 1.3 of DFO-LS which can now solve problems with general convex constraints!
  • (Oct-21) I will be speaking about large-scale DFO at the INFORMS Annual Meeting, as well as organising two sessions on DFO.
  • (Oct-21) I will be speaking about bilevel learning at the Machine Intelligence and Learning Systems seminar at Université Paris Dauphine-PSL.
  • (Sep-21) I will be speaking about large-scale DFO at the University of Leicester’s CSE Mathematics Seminar.
  • (Jul-21) I will be speaking about large-scale DFO at EUROPT, EURO and SIOPT.
  • (Jul-21) Interviewed by Channel 9 National News about projections for Australia’s COVID-19 vaccine rollout: video here
  • (Jun-21) Delighted to be announced as the first prize winner of the 20th IMA Leslie Fox Prize for Numerical Analysis for my recent paper on scalable DFO! Watch the video of my talk here.
  • (Jun-21) New paper submitted, Does Model Calibration Reduce Uncertainty in Climate Projections?, led by Simon Tett (University of Edinburgh). The study shows that performing structured parameter tuning of climate models helps to significantly reduce the uncertainties in their predictions. It also shows that my DFO-LS code is an effective solver for parameter fitting of climate models. Preprint coming soon!
  • (May-21) Very happy to be shortlisted for the IMA Leslie Fox Prize for Numerical Analysis for my recent paper on scalable DFO. I will be talking about my work at the Fox Prize event on 21 June.

Awards:

Interests

  • Derivative-Free Optimization
  • Nonconvex Optimization
  • Numerical Analysis
  • Data Science

Education

  • DPhil in Mathematics, 2019

    University of Oxford

  • Bachelor of Computational Science (Honours), 2011

    Australian National University

Research

Optimization

Optimization—finding the maximum or minimum of a function—is one of the most important classes of problem in computational mathematics, arising often in scientific and industrial applications. My focus is on nonlinear optimization, where the function to be optimized (the ‘objective’ function) is some nonlinear, possibly nonconvex function usually with little known structure.

Nonlinear optimization resources

Together with Coralia Cartis and Jaroslav Fowkes (University of Oxford), I maintain a page of resources for nonlinear optimization, including a collection of software and test problems.

Derivative-Free Optimization (DFO)

Generally speaking, to optimize a nonlinear objective, you approximate it locally by some simpler function (such as a low-order Taylor series). To construct this simpler function, you need to evaluate the objective and its derivatives at some set of points. Evaluating the derivative of the objective can be done in several ways:

  • If you know the analytic form of the objective, you can compute its derivatives using calculus.
  • If you have access to computer code for evaluating the objective, automatic differentiation could be used to compute analytic derivatives.
  • Otherwise, you have to approximate the derivative, e.g. using finite differencing.

However, if the objective is black-box, expensive to evaluate or noisy (e.g. a Monte Carlo simulation, or involves the finite termination of an iterative procedure), these approaches may be impractical or inaccurate. Derivative-free optimization (DFO) is the field devoted to nonlinear optimization of objectives when you only have access to (possibly inaccurate) evaluations of the objective.

My research

I develop and study model-based DFO algorithms. This is a class of DFO method which tries to incorporate features of derivative-based methods, and ultimately build local approximations to the objective (e.g. by polynomial interpolation). I have developed several algorithms and software packages for solving least-squares problems with DFO methods, and developed techniques which make model-based DFO more robust to noise, able to local minima, and tackle large-scale problems via dimensionality reduction.

Employment

[2019 – present] MSI Fellow, Australian National University

[2015 – 2019] Doctoral Student, University of Oxford

[2012 – 2015] Senior Analyst, Macquarie Group

  • Worked in the Quantitative Applications Division of the Risk Management Group. Responsible for implementing risk management models (particularly for market risk) and reviewing pricing models.
  • Macquarie Group is Australia’s largest investment bank, headquartered in Sydney.

Software

Github | Click on each package for more details. Other useful packages not developed by me are listed on the nonlinear optimization resources webpage

DFBGN

Derivative-free block Gauss-Newton method (for large-scale nonlinear least-squares problems)

trustregion

Python routines for solving trust-region subproblems

PyCUTEst

Python interface to CUTEst optimization testing package

DFO-LS

Derivative-free solver for nonlinear least-squares problems

Py-BOBYQA

General-purpose derivative-free optimization solver

DFO-GN

Derivative-Free Gauss-Newton solver

Teaching

Australian National University

University of Oxford

Qualifications

  • 2018, Professional Development Framework Supporting Learning Award, Staff and Educational Development Association — this is a teaching qualification accredited by the UK professional association for higher education (more details here), assessed by the University of Oxford.

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