“…It is a stochastic, multistart, global optimizer that relies on random sampling and local searches. Aside from MLSL itself, some of its variants also belong to our portfolio: an interior-point version for constrained nonlinear problems (fmincon, Pál, 2013), a quasi-Newton version, which approximates the Hessian using BFGS (Broyden, 1970) (fminunc, Pál, 2013, and a hybrid variant whose most important improvements are related to its sampling phase (HMLSL, Pál, 2013). The final optimizer belonging to this group is the multilevel coordinate search (MCS, Huyer and Neumaier, 2009), which splits the search space into smaller boxes -each containing a known observation -and then starts local searches from promising boxes.…”