# Introduction

The reoptimizer module provides optimisation features, using the relauncher architecture. It can 
indeed be considered as a specialisation of this module for the closed-loop case (optimisation steps 
usually depends on the previous steps). This document will present neither an introduction to the 
optimisation problem characteristics (one simple version can be found in {{metho}}), nor a 
description of the different algorithms. It will more discuss the possible combination of runners, 
solvers and masters with (if possible) their pros and cons. For a glimpse at the way the implementation 
of a script can be done, one can look at the use-cases, provided in [](#use_cases_macro_reoptimizer).

Two kinds of solver are proposed: local search ones, starting from an initial guess point and global search ones,
starting from a random population of potential solutions. They differ on many points:

- Problem types: only global solvers offer multi-objective optimisation.

- Constraints: Global solvers deal with inequality but not equality constraints, while local ones deal 
with both of them.

- Inputs nature: all solvers deal with continuous problems, and some global solvers can deal with 
combinatory problems, with extra works.

- Convergence speed and robustness: local solvers need less evaluations to converge but can be trapped 
in a local minimum, while global ones are more robust.

- Unavailable items: only global solvers can deal with it.

- Parallelism: local solvers are intrinsically sequential, and the only way to parallelised them is 
to run many optimisations starting from different points. Global solvers are intrinsically parallel.

```{toctree}
introduction/local
introduction/global
introduction/objectives
```