\noindent{}In previous chapters we explored the compilation of \cmodels{} for a \gls{solver} as a definitive linear process, but to solve real-world problems \gls{meta-optimisation} algorithms a.re often used.
These methods usually require solving almost the same combinatorial problem repeatedly, with only slight modifications, thousands of times.
Examples of these methods are:

\begin{itemize}
	\item Multi-objective search \autocite{jones-2002-multi-objective}.
		Optimising multiple objectives is often not supported directly in solvers.
		Instead, it can be solved using a \gls{meta-optimisation} approach: find a solution to a (single-objective) problem, then add more \constraints{} to the original problem and repeat.
	\item \gls{lns} \autocite{shaw-1998-local-search}.
		This is a very successful \gls{meta-optimisation} algorithm to quickly improve solution quality.
		After finding a (sub-optimal) solution to a problem, \constraints{} are added to restrict the search in the \gls{neighbourhood} of that solution.
		When a new solution is found, the \constraints{} are removed, and \constraints{} for a new \gls{neighbourhood} are added.
	\item Online Optimisation \autocite{jaillet-2021-online}.
		These techniques can be employed when the problem rapidly changes.
		A problem instance is continuously updated with new data, such as newly available jobs to be scheduled or customer requests to be processed.
	\item Diverse Solution Search \autocite{hebrard-2005-diverse}.
		Here we aim to provide a set of solutions that are sufficiently different from each other in order to give human decision makers an overview of the solution space.
		Diversity can be achieved by repeatedly solving a problem instance with different objectives.
	\item Interactive Optimisation \autocite{belin-2014-interactive}.
		In some scenarios it can be useful to allow a user to directly provide feedback on solutions found by the solver.
		The feedback in the form of \constraint{} are added back into the problem, and a new solution is generated.
		Users may also take back some earlier feedback and explore different aspects of the problem to arrive at the best solution that suits their needs.
\end{itemize}

All of these examples have in common that a problem instance is solved, new \constraints{} are added, the resulting instance is solved again, and \constraints{} may be removed again.

The usage of these methods is not new to \constraint{} modelling, and they have proven to be very useful \autocite{schrijvers-2013-combinators, rendl-2015-minisearch, schiendorfer-2018-minibrass, ek-2020-online, ingmar-2020-diverse}.
In its most basic form, a simple scripting language is sufficient to implement these methods, by repeatedly calling on the \constraint{} modelling infrastructure to compile and solve the adjusted \constraint{} models.
While improvements of the compilation of \cmodels{}, such as the ones discussed in previous chapters, can increase the performance of these approaches, the overhead of re-compiling an almost identical model may still prove prohibitive, warranting direct support from the \constraint{} modelling  infrastructure.
In this chapter we introduce two methods to provide this support:

\begin{itemize}
	\item We introduce the notion of restart-based \gls{meta-optimisation} algorithms.
		Using a minimal extension to a \cml\ and its target solver, we can model some \gls{meta-optimisation} algorithms and compile \gls{meta-optimisation} algorithms into efficient solver-level specifications based on solver restarts, avoiding re-compilation all-together.
	\item Alternatively, we can add an incremental interface for adding and removing \constraints{} to the infrastructure of the \cml{}.
		Although this does not avoid the need for re-compilation, it can reduce the work to only the part of the \constraint{} model that has changed.
		This approach can be used when an algorithm cannot be described using restart-based \gls{meta-optimisation} or required extension is not available for the solver.
\end{itemize}

The rest of the chapter is organised as follows.
\Cref{sec:6-modelling} discusses the declarative modelling of restart-based \gls{meta-optimisation} algorithms that can be modelled directly in a \cml{}.
\Cref{sec:6-solver-extension} introduces the method to compile these \gls{meta-optimisation} specifications into efficient solver-level specifications that only require a small extension of existing \glspl{solver}.
\Cref{sec:6-incremental-compilation} introduces the alternative method that extends the \constraint{} modelling infrastructure with an interface to add and remove \constraints{} from an existing model while avoiding recompilation.
\Cref{sec:inc-experiments} reports on the experimental results of both approaches.