FC-01: Plenary 2 – Oliver STEIN
Chair(s): Giancarlo Bigi
Granularity — a bridge between continuous and discrete optimization
In this talk we sketch the development of the granularity concept in optimization over the past five years. Granularity relaxes the difficulties imposed by integrality conditions and often provides ways for determining good feasible points of mixed-integer optimization problems at low computational cost. Starting from error bound results for roundings in mixed-integer linear optimization, we illustrate how this concept unfolded to provide algorithms for the computation of feasible points in mixed-integer linear, convex and nonconvex optimization. We also comment on the treatment of equality constraints and explain the integration of the granularity idea into branch-and-bound frameworks.