JuliaCon 2023

Box-constrained optimization problems are ubiquitous in many areas of science and engineering. Our package includes methods tailored to this class of optimization problems. Due to Julia's Iterator interface, Progradio's solvers can be paused, resumed, or terminated early. Since the first release, we have included a stricter line-search procedure, and support for simplex constraints (Σ x_j = 1). Progradio's unique features make it attractive to be used as a sub-routine for dynamic optimization.

Dynamic optimization problems include optimal control, state estimation, and system identification. Our newly developed integrated residual methods generalize the state-of-the-art direct collocation method. Interesso.jl implements a selection of Lagrange polynomial and Gauss quadrature node distributions. The iterative Progradio.jl optimizer allows for efficient mesh refinement. We include an example of optimizing the trajectory of a space-shuttle landing.