Séminaire FDD-FiME // K. Liu
Kang Liu (CMAP, Ecole Polytechnique)
Title : Large-scale nonconvex optimization: randomization, gap estimation, and numerical resolution
Abstract : We address a large-scale and nonconvex optimization problem, involving an aggregative term. This term can be interpreted as the sum of the contributions of N agents to some common good, with N large. We investigate a relaxation of this problem, obtained by randomization. The relaxation gap is proved to converge to zeros as N goes to infinity, Read more [...]
