cutnorm 0.1.10
Cutnorm approximation via Gaussian Rounding and Optimization with Orthogonality Constraints
License: MIT
Requires Python: >=3.6

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Cutnorm
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Approximation via Gaussian Rounding and Optimization with Orthogonality Constraints
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This package computes the approximations to the cutnorm of matrices using some of the techniques detailed by Alon and Noar [ALON2004]_ and a fast optimization algorithm by Wen and Yin [WEN2013]_.

Read the documentation_.

.. _documentation: https://pingkoc.github.io/cutnorm/cutnorm.html

Installation
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Use pip_ to install the package.
Install from terminal as follows::

  $ pip install cutnorm

.. _pip: http://www.pip-installer.org/en/latest/

Example Usage
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Given the adjacency matrices of two simple graphs A and B, we wish to compute a norm for the difference matrix (A - B) between the two graphs. An obvious display of the advantages of using a cutnorm over l1 norm is to consider the value of the norms on `Erdos-Renyi random graphs`_.

.. _`Erdos-Renyi random graphs`: https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93R%C3%A9nyi_model

Given two Erdos-Renyi random graphs with constant n and p=0.5, the edit distance (l1 norm) of the difference (after normalization) is 0.5 with large probability. An l1 norm of 1 implies the two matrices are completely different, 0 implies identity, and 0.5 is somewhere in between. However, these two graphs have the same global structure. As n approaches infinity, A and B converges to the same graphon object that is 0.5 everywhere. The edit distance fails as a notion of 'distance' between the two graphs in the perspective of global structural similarity as discussed by Lovasz [LOVASZ2009]_. The cutnorm is a measure of distance that reflects global structural similarity. In fact, the cutnorm of the difference for this example approaches 0 as n grows.

Below is an example of using the cutnorm package and tools. 

.. code:: python

  import numpy as np
  from cutnorm import compute_cutnorm, tools

  # Generate Erdos Renyi Random Graph (Simple/Undirected)
  n = 100
  p = 0.5
  erdos_renyi_a = tools.sbm.erdos_renyi(n, p, symmetric=True)
  erdos_renyi_b = tools.sbm.erdos_renyi(n, p, symmetric=True)

  # Compute l1 norm
  normalized_diff = (erdos_renyi_a - erdos_renyi_b) / n**2
  l1 = np.linalg.norm(normalized_diff.flatten(), ord=1)

  # Compute cutnorm
  cutn_round, cutn_sdp, info = compute_cutnorm(erdos_renyi_a, erdos_renyi_b)

  print("l1 norm: ", l1)  # prints l1 norm value near ~0.5
  print("cutnorm rounded: ",
        cutn_round)  # prints cutnorm rounded solution near ~0
  print("cutnorm sdp: ", cutn_sdp)  # prints cutnorm sdp solution near ~0

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.. [ALON2004] Noga Alon and Assaf Naor. 2004. Approximating the cut-norm via Grothendieck's inequality. In Proceedings of the thirty-sixth annual ACM symposium on Theory of computing (STOC '04). ACM, New York, NY, USA, 72-80. DOI: http://dx.doi.org/10.1145/1007352.1007371
.. [WEN2013] Zaiwen Wen and Wotao Yin. 2013. A feasible method for optimization with orthogonality constraints. Math. Program. 142, 1-2 (December 2013), 397-434. DOI: https://doi.org/10.1007/s10107-012-0584-1
.. [LOVASZ2009] Lovasz, L. 2009. Very large graphs. ArXiv:0902.0132 [Math]. Retrieved from http://arxiv.org/abs/0902.0132