We present a trust region algorithm for solving nonconvex optimization problems that, in the worst-case, is able to drive the norm of the gradient of the objective below a prescribed threshold $\epsilon > 0$ after at most ${\cal O}(\epsilon^{-3/2})$ function evaluations, gradient evaluations, or iterations.  Our work has been inspired by the recently proposed Adaptive Regularisation framework using Cubics (i.e., the ARC algorithm), which attains the same worst-case complexity bound.  Our algorithm is modeled after a traditional trust region algorithm, but employs modified step acceptance criteria and a novel trust region updating mechanism that allows it to achieve this desirable property.  Importantly, our method also maintains standard global and fast local convergence guarantees.

Numerical simulation tools for fluid and solid mechanics are often based on the discretisation of coupled systems of partial differential equations, which can easily be identified in terms of physical
conservation laws.  In contrast, much physical insight is often gained from the equivalent formulation of the relevant energy or free-energy functional, possibly subject to constraints.  Motivated by the
nonlinear static and dynamic behaviour of nematic liquid crystals and of magnetosensitive elastomers, we propose a finite-element framework for minimising these free-energy functionals, using Lagrange multipliers to enforce additional constraints.  This talk will highlight challenges, limitations, and successes, both in the formulation of these models and their use in numerical simulation.
This is joint work with PhD students Thomas Benson, David Emerson, and Dong Han, and with James Adler, Timothy Atherton, and Luis Dorfmann.

In this talk, we discuss the development of fast iterative solvers for matrix systems arising from various constrained optimization problems. In particular, we seek to exploit the saddle point structure of these problems to construct powerful preconditioners for the resulting systems, using appropriate approximations of the (1,1)-block and Schur complement.

The problems we consider arise from two well-studied subject areas within computational optimization. Specifically, we investigate the
numerical solution of PDE-constrained optimization problems, and the interior point method (IPM) solution of linear/quadratic programming
problems. Indeed a particular focus in this talk is the interior point method solution of PDE-constrained optimization problems with
additional inequality constraints on the state and control variables.

We present a range of optimization problems which we seek to solve using our methodology, and examine the theoretical and practical
convergence properties of our iterative methods for these problems.
 

Nonlinear systems of partial differential equations (PDEs) may permit several distinct solutions. The typical current approach to finding distinct solutions is to start Newton's method with many different initial guesses, hoping to find starting points that lie in different basins of attraction. In this talk, we present an infinite-dimensional deflation algorithm for systematically modifying the residual of a nonlinear PDE problem to eliminate known solutions from consideration. This enables the Newton--Kantorovitch iteration to converge to several different solutions, even starting from the same initial guess. The deflated Jacobian is dense, but an efficient preconditioning strategy is devised, and the number of Krylov iterations is observed not to grow as solutions are deflated. The technique is then applied to computing distinct solutions of nonlinear PDEs, tracing bifurcation diagrams, and to computing multiple local minima of PDE-constrained optimisation problems.