cotengra.pathfinders.path_compressed_greedy

Greedy contraction tree finders.

Attributes

_allowed_perms

Classes

GreedyCompressed	A greedy contraction path finder that takes into account the effect of
GreedySpan	A contraction path optimizer that greedily generates spanning trees out

Functions

_binary_combine(func, x, y)
greedy_compressed(inputs, output, size_dict[, ...])
trial_greedy_compressed(inputs, output, size_dict, ...)
greedy_span(inputs, output, size_dict[, memory_limit])
trial_greedy_span(inputs, output, size_dict, **kwargs)

Module Contents

cotengra.pathfinders.path_compressed_greedy._binary_combine(func, x, y)

class cotengra.pathfinders.path_compressed_greedy.GreedyCompressed(chi, coeff_size_compressed=1.0, coeff_size=0.0, coeff_size_inputs=0.0, score_size_inputs='max', coeff_subgraph=0.0, score_subgraph='sum', coeff_centrality=0.0, centrality_combine='max', score_centrality='diff', temperature=0.0, score_perm='', early_terminate_size=None, seed=None)

A greedy contraction path finder that takes into account the effect of compression, and can also make use of subgraph size and centrality.

Parameters:

• chi (int) – The maximum bond size between nodes to compress to.

• coeff_size_compressed (float, optional) – When assessing contractions, how to weight the size of the output tensor, post compression.

• coeff_size (float, optional) – When assessing contractions, how to weight the size of the output tenor, pre compression.

• coeff_size_inputs (float, optional) – When assessing contractions, how to weight the maximum size of the inputs tensors.

• score_size_inputs ({'sum', 'mean', 'max', 'min', 'diff'}, optional) – When assessing contractions, how to score the combination of the two input tensor sizes.

• coeff_subgraph_size (float, optional) – When assessing contractions, how to weight the total subgraph size corresponding to the inputs tensors.

• score_subgraph_size ({'sum', 'mean', 'max', 'min', 'diff'}, optional) – When assessing contractions, how to score the combination of the two input subgraph sizes.

• coeff_centrality (float, optional) – When assessing contractions, how to weight the combined centrality of the inputs tensors.

• centrality_combine ({'sum', 'mean', 'max', 'min'}, optional) – When performing the contraction, how to combine the two input tensor centralities to produce a new one.

• score_centrality ({'sum', 'mean', 'max', 'min', 'diff'}, optional) – When assessing contractions, how to score the combination of the two input tensor centralities.

• temperature (float, optional) – A noise level to apply to the scores when choosing nodes to expand to.

chi

coeff_size_compressed

coeff_size

coeff_size_inputs

score_size_inputs

coeff_subgraph

score_subgraph

coeff_centrality

centrality_combine

score_centrality

temperature

score_perm

early_terminate_size

gumbel

_score(i1, i2)

get_ssa_path(inputs, output, size_dict)

search(inputs, output, size_dict)

__call__(inputs, output, size_dict, memory_limit=None)

cotengra.pathfinders.path_compressed_greedy.greedy_compressed(inputs, output, size_dict, memory_limit=None, **kwargs)

cotengra.pathfinders.path_compressed_greedy.trial_greedy_compressed(inputs, output, size_dict, **kwargs)

class cotengra.pathfinders.path_compressed_greedy.GreedySpan(start='max', coeff_connectivity=1.0, coeff_ndim=1.0, coeff_distance=-1.0, coeff_next_centrality=0.0, weight_bonds=True, temperature=0.0, score_perm='CNDLTI', distance_p=1, distance_steal='abs', seed=None)

A contraction path optimizer that greedily generates spanning trees out of particular nodes, suitable for e.g. compressed contraction paths.

Parameters:

• start ({'max', 'min'}, optional) – Whether to start the span from the maximum or minimum centrality point.

• coeff_connectivity (float, optional) – When considering adding nodes to the span, how to weight the nodes connectivity to the current span.

• coeff_ndim (float, optional) – When considering adding nodes to the span, how to weight the nodes total rank.

• coeff_distance (float, optional) – When considering adding nodes to the span, how to weight the nodes distance to the starting point.

• coeff_next_centrality (float, optional) – When considering adding nodes to the span, how to weight the nodes centrality.

• weight_bonds

• temperature (float, optional) – A noise level to apply to the scores when choosing nodes to expand to.

• score_perm

• distance_p

• distance_steal

start

coeff_connectivity

coeff_ndim

coeff_distance

coeff_next_centrality

weight_bonds

temperature

score_perm

distance_p

distance_steal

rng

gumbel

get_ssa_path(inputs, output, size_dict)

search(inputs, output, size_dict)

__call__(inputs, output, size_dict, memory_limit=None)

cotengra.pathfinders.path_compressed_greedy.greedy_span(inputs, output, size_dict, memory_limit=None, **kwargs)

cotengra.pathfinders.path_compressed_greedy.trial_greedy_span(inputs, output, size_dict, **kwargs)

cotengra.pathfinders.path_compressed_greedy._allowed_perms