# The Branching Path¶

While the optimal path is guaranteed to find the smallest estimate FLOP cost, it spends a lot of time exploring paths which are not likely to result in an optimal path. For instance, outer products are usually not advantageous unless absolutely necessary. Additionally, by trying a ‘good’ path first, it should be possible to quickly establish a threshold FLOP cost which can then be used to prune many bad paths.

The branching strategy (provided by branch()) does this by taking the recursive, depth-first approach of optimal(), whilst also sorting potential contractions based on a heuristic cost, as in greedy().

There are two main flavours:

• optimize='branch-all': explore all inner products, starting with those that look best according to the cost heuristic.
• optimize='branch-2': similar, but at each step only explore the estimated best two possible contractions, leading to a maximum of 2^N paths assessed.

In both cases, branch() takes an active approach to pruning paths well before they hit the best total FLOP count, by comparing them to the FLOP count (times some factor) achieved by the best path at the same point in the contraction.

There is also 'branch-1', which, since it only explores a single path at each step does not really ‘branch’ - this is essentially the approach of 'greedy'. In comparison, 'branch-1' will be slower for large expressions, but for small to medium expressions it might find slightly higher quality contractions due to considering individual flop costs at each step.

The default optimize='auto' mode of opt_einsum will use 'branch-all' for 5 or 6 tensors, though it should be able to handle 12-13 tensors in a matter or seconds. Likewise, 'branch-2' will be used for 7 or 8 tensors, though it should be able to handle 20-22 tensors in a matter of seconds. Finally, 'branch-1' will be used by 'auto' for expressions of up to 14 tensors.

## Customizing the Branching Path¶

The ‘branch and bound’ path can be customized by creating a custom BranchBound instance. For example:

optimizer = oe.BranchBound(nbranch=3, minimize='size', cutoff_flops_factor=None)
path, path_info = oe.contract_path(eq, *arrays, optimize=optimizer)


You could then tweak the settings (e.g. optimizer.nbranch = 4) and the best bound found so far will persist and be used to prune paths on the next call:

optimizer.nbranch = 4
path, path_info = oe.contract_path(eq, *arrays, optimize=optimizer)