# The Dynamic Programming Path

The dynamic programming (DP) approach described in reference [1] provides an efficient way to find an asymptotically optimal contraction path by running the following steps:

1. Compute all traces, i.e. summations over indices occurring exactly in one input.
2. Decompose the contraction graph of inputs into disconnected subgraphs. Two inputs are connected if they share at least one summation index.
3. Find the contraction path for each of the disconnected subgraphs using a DP approach: The optimal contraction path for all sets of n (ranging from 1 to the number of inputs) connected tensors is found by combining sets of m and n-m tensors.

Note that computing all the traces in the very beginning can never lead to a non-optimal contraction path.

Contractions of disconnected subgraphs can be optimized independently, which still results in an optimal contraction path. However, the computational complexity of finding the contraction path is drastically reduced: If the subgraphs consist of n1, n2, ... inputs, the computational complexity is reduced from O(exp(n1 + n2 + ...)) to O(exp(n1) + exp(n2) + ...).

The DP approach will only perform pair contractions and by default will never compute intermediate outer products as in reference [1] it is shown that this always results in an asymptotically optimal contraction path.

A major optimization for DP is the cost capping strategy: The DP optimization only memorizes contractions for a subset of inputs, if the total cost for this contraction is smaller than the cost cap. The cost cap is initialized with the minimal possible cost, i.e. the product of all output dimensions, and is iteratively increased by multiplying it with the smallest dimension until a contraction path including all inputs is found.

Note that the worst case scaling of DP is exponential in the number of inputs. Nevertheless, if the contraction graph is not completely random, but exhibits a certain kind of structure, it can be used for large contraction graphs and is guaranteed to find an asymptotically optimal contraction path. For this reason it is the most frequently used contraction path optimizer in the field of tensor network states.

More specifically, the search is performed over connected subgraphs, which, for example, planar and tree-like graphs have far fewer of. As a rough guide, if the graph is planar, expressions with many tens of tensors are tractable, whereas if the graph is tree-like, expressions with many hundreds of tensors are tractable.

[1] Robert N. C. Pfeifer, Jutho Haegeman, and Frank Verstraete Phys. Rev. E 90, 033315 (2014). https://arxiv.org/abs/1304.6112

## Customizing the Dynamic Programming Path

The default optimize='dp' approach has sensible defaults but can be customized with the opt_einsum.paths.DynamicProgramming object.

import opt_einsum as oe

optimizer = oe.DynamicProgramming(
minimize='size',    # optimize for largest intermediate tensor size
search_outer=True,  # search through outer products as well
cost_cap=False,     # don't use cost-capping strategy
)

oe.contract(eq, *arrays, optimize=optimizer)


Warning

Note that searching outer products will most likely drastically slow down the optimizer on all but the smallest examples.

The values that minimize can take are:

• 'flops': minimize the total number of scalar operations.
• 'size': minimize the size of the largest intermediate.
• 'write': minimize the combined size of all intermediate tensors - approximately speaking the amount of memory that will be written. This is relevant if you were to automatically differentiate through the contraction, which naively would require storing all intermediates.
• 'combo' - minimize flops + alpha * write summed over intermediates, a default ratio of alpha=64 is used, or it can be customized with f'combo-{alpha}'.
• 'limit' - minimize max(flops, alpha * write) summed over intermediates, a default ratio of alpha=64 is used, or it can be customized with f'limit-{alpha}'.

The last two take into account the fact that real contraction performance can be bound by memory speed, and so favor paths with higher arithmetic intensity. The default value of alpha=64 is reasonable for both typical CPUs and GPUs.