Official course-hosted technical exposition: 2.4 Ordering.
Relevant locators: §2.4, Minimum Remaining Values; §2.4.1, Structure, the upward support pass, downward assignment and cutset conditioning. The residual tree computation is linear in the number of variables for fixed maximum constraint domain size.
Cycle cutset 2026-10-25
A set of vertices whose deletion leaves a forest. For a binary constraint satisfaction problem, fixing these variables and filtering the remaining constraint domains leaves independent instances of tree-structured constraint satisfaction. A valid cutset need not be minimum; smaller cutsets reduce the exponential enumeration in cutset conditioning.
Cutset conditioning selects a cycle cutset : deleting its vertices makes the binary constraint graph a forest. Enumerate assignments to , reject those violating internal constraints, and filter each remaining constraint domain against the chosen cutset values. Solve the residual components by tree-structured constraint satisfaction. Stop at a successful assignment, or report failure after all cutset assignments fail. Completeness follows by considering the restriction of any full solution to ; soundness follows by checking internal, crossing and residual constraints. With maximum constraint domain size and cutset variables, a typical bound is for the residual forest work, with cutset and crossing checks added explicitly.[ref]
Here take . The residual constraint graph has vertices and only edges , a tree rooted at . Choose the consistent cutset values . Filtering against these assignments givesThe upward support pass from deletes , because can only equal . The pass from retains , which has support or . Assign at the root and then supported child values . Together with , this gives the solution in (c).
No singleton cycle cutset exists: deleting or leaves, respectively, a triangle on or ; deleting leaves the cycle in a graph . Thus the two-element choice is also minimum, although the algorithm only requires a valid cutset.