JuliaCon 2022 (Times are UTC)

Progradio.jl - Projected Gradient Optimization
07-28, 13:30–13:40 (UTC), Blue

Most (Mathematical) Optimization problems are subject to bounds on the decision variables. In general, a nonlinear cost function f(x) is to be minimized, with the vector x constrained by simple bounds l <= x <= u. The Projected Gradient class of methods is tailored for this very optimization problem. Our package includes various Projected Gradient methods, fully implemented in Julia. We make use of Julia's Iterator interface, allowing for user-defined termination criteria.

PhD Student at Imperial College London