Maximum Likelihood for Matrices with Rank Constraints

  • Bernd Sturmfels (University of California, Berkeley, USA)
E1 05 (Leibniz-Saal)


Maximum likelihood estimation is a fundamental computational task in statistics. We discuss this problem for manifolds of low rank matrices. These represent mixtures of independent distributions of two discrete random variables. This non-convex optimization problems leads to some beautiful geometry, topology, and combinatorics. We explain how numerical algebraic geometry is used to find the global maximum of the likelihood function, and we present a remarkable duality theorem due to Draisma and Rodriguez.