In this talk, we will describe innovative applications of two key ideas in the solvability of large scale optimization problems: problem structure and primal and dual decomposition. These two concepts are among the favorite to Jean-Louis Goffin and have been very useful in my research work in a wide range of optimization frameworks. We will highlight some successful uses of structure and decomposition to address both scale and difficulty in mixed-integer, nonlinear, stochastic and robust optimization.

In this session, we pay tribute to the remarkable life and legacy of Jean-Louis Goffin, a scholar whose influence transcended the boundaries of academia. While his contributions to our field are well-documented, this talk seeks to illuminate a less discussed, yet equally significant, aspect of his legacy: his profound humanity and mentorship.

The transition to a zero-carbon power grid requires far-reaching decisions
that will have large financial, societal and environmental impacts. Using
very simple optimization tools, we highlight transition pathways and the
urgency of moving now.
This is joint work with F. Mitjana and P.-O. Pineau at HEC Montréal

A case of the convex feasibility problem where the set is defined by an infinite number
of certain strongly convex self-concordant inequalities is considered. At each iteration, the
analytic centre based algorithm adds a self-concordant cut through an approximate analytic
center of the current set of localization until a feasible point is found. The algorithm is
proven to be a fully polynomial approximation scheme.