Cutset conditioning is a method for performing Tree Decomposition on a dependency graph. By identifying and conditioning on the “cut set” of the graph, a tree structure is produced.

The **cut set** of a graph is the set of variables which, if removed, will “cut” all loops in the graph and produce an acyclic graph.

**Loop-cutset decomposition** (Loop-cutset conditioning) uses [http://en.wikipedia.org/wiki/Decomposition_method_(constraint_satisfaction)#Cycle_cutset Cutset-Conditioning] to transform a belief graph into a tree structure by removing large-degree variables from the network. It improves time performance at the cost of space. The result is a forest of probability trees which can be easily processed by Belief Propagation. Unfortunately, the resulting cutset forest forms a tree which takes additional memory and must be searched over via tree search spaces.

- Input is a belief network.
- Identify the cycle cutset: the set of nodes which must be removed in order to produce a tree.
- For each node in this cutset:
- use VEC on that node
- Output is a forest of probability trees

Inference work is exponential in for cycle [http://en.wikipedia.org/wiki/Cut_set cutset] of size .

for cutset size and induced-width w. For example, in a chain of cliques, the induced width is a function of clique size while the cutset is a function of the total number of nodes in the graph. Finding a minimal cycle cutset is not easy.

Because full loop-cutset decomposition produces a very large graph, it is generally better to not condition all the way to a tree structure but to stop when some target graph width is met. This is called a **w-cutset** algorithm.

Small cycle-cutsets require a smaller number of tree-solving algorithms to be run, but finding a minimal-size cycle-cutset is [http://en.wikipedia.org/wiki/Feedback_vertex_set NP-complete]. One heuristic for quickly choosing a cutset is to perform a depth-first search.

This is an algorithm for using VEC to optimize Bucket Elimination inference:

- Use VEC to decompose the belief graph into a poly tree

#* Each sub-tree is a dependency graph of some kind

#* The main tree is a tree of variable assignments leading to a sub-tree

- Solve each sub-tree (including any evidence that applies) e.g. using Bucket Elimination
- Sum over the main tree for the final probability of all evidence

For a target treewidth , run loop-cutset decomposition but stop when the target width is achieved.

Time complexity is

Space complexity is

- * is number of variables
- * is the number of variables in the cutset
- * is the domain size
- * is the treewidth

It can be shown that . So decreasing results in a greater time requirement and lesser space requirements.