An Application of the Frank-Wolfe Algorithm at Maximum Likelihood Estimation Problems
Ciprian Costin POPESCU
constrained maximum likelihood, nonlinear programming, Frank-Wolfe algorithm
This paper tackles the problem of maximum likelihood estimation  under various types of constraints (equalities and inequalities restrictions) on parameters. The initial model, which is in fact a maximization problem (here are a few methods available in literature for estimating the parameters: ERM (expectation-restricted-maximization) algorithms, GP (gradient projection) algorithms and so on) is change into a new problem, a minimization problem. This second form is suited to a variant of Frank-Wolfe method for solving linearly restricted nonlinear programming problems . In this way, some difficulties from the previous approaches are removed.