More... again... new hope to correct further.md

docs/src/guide/further.md

@@ -150,17 +150,13 @@ priority values must be distinct_.
 * Minimize/maximize with respect to the linear objective of highest priority value, according to whether its `weight` is positive/negative
 
 * Add a constraint to the model so that the value of the linear objective of highest priority satsifies a bound given by the values of `abs_tolerance` and/or `rel_tolerance`.
-    + If the objective was minimized to a value ``f^*\ge0``, then the constraint ensures that the this objective value is no greater than
-``\min(f^*+abs\_tolerance,~f^*\times[1+rel\_tolerance]).``
+    + If the objective was minimized to a value ``f^*\ge0``, then the constraint ensures that the this objective value is no greater than ``\min(f^*+abs\_tolerance,~f^*\times[1+rel\_tolerance]).``
 
-    + If the objective was minimized to a value ``f^*<0``, then the constraint ensures that the this objective value is no greater than
-``\min(f^*+abs\_tolerance,~f^*\times[1-rel\_tolerance]).``
+    + If the objective was minimized to a value ``f^*<0``, then the constraint ensures that the this objective value is no greater than ``\min(f^*+abs\_tolerance,~f^*\times[1-rel\_tolerance]).``
 
-    + If the objective was maximized to a value ``f^*\ge0``, then the constraint ensures that the this objective value is no less than
-``\max(f^*-abs\_tolerance,~f^*\times[1-rel\_tolerance]).``
+    + If the objective was maximized to a value ``f^*\ge0``, then the constraint ensures that the this objective value is no less than ``\max(f^*-abs\_tolerance,~f^*\times[1-rel\_tolerance]).``
 
-    + If the objective was maximized to a value ``f^*<0``, then the constraint ensures that the this objective value is no less than
-``\max(f^*-abs\_tolerance,~f^*\times[1+rel\_tolerance]).``
+    + If the objective was maximized to a value ``f^*<0``, then the constraint ensures that the this objective value is no less than ``\max(f^*-abs\_tolerance,~f^*\times[1+rel\_tolerance]).``
 
 * Minimize/maximize with respect to the linear objective of next highest priority, and then add a corresponding objective constraint to the model, repeating until optimization with respect to the linear objective of lowest priority has taken place.