Posts for: #optimization

In general nonlinear optimization, we seek to solve problems of the form $$ \begin{equation}\tag{NLP} \begin{aligned} \min_z{} &\ell(z) \ \suchthat &c(z) = 0 \end{aligned} \end{equation} $$ This kind of formulation is often encountered in machine learning, control problems and others where structured, constrained optimization crops up.

The goal of optimal transport problems, is to find optimal mappings between probability meaures: these mappings are also called transport plans, and can take the form of functional transforms (in Monge's original problem) or joint probability distributions (in the Kantorovitch relaxation).