diff --git a/chapters/6_incremental.tex b/chapters/6_incremental.tex
index 16b60ba..209269b 100644
--- a/chapters/6_incremental.tex
+++ b/chapters/6_incremental.tex
@@ -366,10 +366,31 @@ on. We can model this strategy restarts as such:
 
 \highlightfile{assets/mzn/6_lex_minimize.mzn}
 
-Another optimisation
+The lexicographic objective changes the objective at each stage in the
+evaluation. Initially the stage is 1. Otherwise is we have an UNSAT result, then
+the last stage has been completed (proved optimal). We increase the stage by one
+if we have stages to go otherwise we finish. Otherwise if the last all was SAT
+we maintain the same stage, and store the objective value (for this stage) in
+the \mzninline{best} array. For normal computation we fix all the earlier stage
+variables to their best value. If we are not in the first run for a stage we add
+the branch and bound cut to try to find better solutions. Finally we set the
+objective to be the objective for the current stage.
+
+There is not always a clear order of importance for different objectives in a
+problem. In these cases we might instead look for a number of diverse solutions
+and allow the user to pick the most acceptable options. The following fragment
+shows a \gls{meta-search} for the pareto optimality of a pair of objectives:
 
 \highlightfile{assets/mzn/6_pareto_optimal.mzn}
 
+In this implementation we keep track of the number of solutions found so far
+using \mzninline{nsol}. There is a maximum number we can handle
+(\mzninline{ms}). At the start the number of solutions is 0. If we find no
+solutions we finish the entire search. Otherwise we record that we have one more
+solution. We store the solution values in \mzninline{s1} and \mzninline{s2}
+arrays. Before each restart we add constraints removing pareto dominated
+solutions, based on each previous solution.
+
 \section{Compilation of Meta-Search Specifications}
 \label{sec:6-solver-extension}