Recent & Upcoming Talks

2022

Large-scale derivative-free optimization using random subspace methods [slides and video available]

Many standard optimization algorithms require being able to cheaply and accurately compute derivatives for the objective and/or …

2021

Derivative-Free Optimization with Convex Constraints [slides available]

When optimizing functions which are computationally expensive and/or noisy, gradient information is often impractical to obtain or …

Derivative-Free Optimization with Convex Constraints [slides available]

When optimizing functions which are computationally expensive and/or noisy, gradient information is often impractical to obtain or …

Inexact Derivative-Free Optimization for Bilevel Learning [slides available]

When variational regularisation methods are used to solve inverse problems, they suffer from the drawback of having potentially many …

Inexact Derivative-Free Optimization for Bilevel Learning [slides available]

When variational regularisation methods are used to solve inverse problems, they suffer from the drawback of having potentially many …

Scalable Subspace Methods for Derivative-Free Nonlinear Least-Squares Optimization [slides available]

When optimizing functions which are computationally expensive and/or noisy, gradient information is often impractical to obtain or …

Large-Scale Derivative-Free Optimization using Subspace Methods [slides available]

In existing techniques for model-based derivative-free optimization, the computational cost of constructing local models and Lagrange …

Large-Scale Derivative-Free Optimization using Subspace Methods [slides available]

In existing techniques for model-based derivative-free optimization, the computational cost of constructing local models and Lagrange …

Large-Scale Derivative-Free Optimization using Subspace Methods [slides available]

In existing techniques for model-based derivative-free optimization, the computational cost of constructing local models and Lagrange …

Inexact Derivative-Free Optimization for Bilevel Learning [slides available]

When variational regularisation methods are used to solve inverse problems, they suffer from the drawback of having potentially many …

2020

Block Methods for Scalable Derivative-Free Optimisation [slides available]

Derivative-free optimisation (DFO) methods are an important class of optimisation routines with applications in areas such as in image …

Scalable Derivative-Free Optimization for Nonlinear Least-Squares Problems [slides & video available]

Derivative-free optimisation (DFO) methods are an important class of optimisation routines with applications in areas such as in image …

Inexact Derivative-Free Optimisation for Bilevel Learning [slides available]

When variational regularisation methods are used to solve inverse problems, they suffer from the drawback of having potentially many …

Derivative-free optimisation for least-squares problems [slides available]

Least-squares problems (such as parameter estimation) are ubiquitous across quantitative disciplines. Optimisation algorithms for …

2019

Improving the scalability of model-based derivative-free optimization [slides available]

Derivative-free optimization (DFO) methods are an important class of optimization routines for many problems in data science, such as …

Improving the efficiency and robustness of black-box optimisation

In classical nonlinear optimisation, the availability of first-order information is crucial to constructing accurate local models for …

Derivative-Free Algorithms for Nonconvex Optimisation

Classical nonconvex optimisation algorithms require the availability of gradient evaluations for constructing local approximations to …

Improving the scalability of derivative-free optimization for nonlinear least-squares problems

In existing techniques for model-based derivative-free optimization, the computational cost of constructing local models and Lagrange …

Improving the scalability of derivative-free optimization for nonlinear least-squares problems

In existing techniques for model-based derivative-free optimization, the computational cost of constructing local models and Lagrange …

Improving the scalability of derivative-free optimization for nonlinear least-squares problems

In existing techniques for model-based derivative-free optimization, the computational cost of constructing local models and Lagrange …

Improving the efficiency and robustness of black-box optimisation

In classical nonlinear optimisation, the availability of first-order information is crucial to constructing accurate local models for …

Improving the Flexibility and Robustness of Derivative-Free Optimisation Solvers

Classical nonlinear optimisation algorithms require the availability of gradient evaluations for constructing local approximations to …

2018

Improving the Flexibility and Robustness of Derivative-Free Optimization Solvers

Classical nonlinear optimization algorithms require the availability of gradient evaluations for constructing local approximations to …

A flexible, robust and efficient derivative-free solver for least-squares

We present DFO-LS, a software package for derivative-free optimization (DFO) for nonlinear least-squares problems, that has simplified …

Improving the efficiency of derivative-free methods for nonlinear least squares problems

We present DFO-GN, a derivative-free version of the Gauss-Newton method for solving nonlinear least-squares problems. As is common in …

2017

Derivative-Free Optimisation Methods for Nonlinear Least-Squares Problems

Derivative-free optimisation (DFO) algorithms are a category of optimisation methods for situations when one is unable to compute or …