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‎doc/source/installation.rst

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@@ -52,7 +52,7 @@ Then, :program:`metis` and its dependencies can be installed automatically in `/
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.. code-block:: bash
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git clone https://github.com/PythonOptimizers/cysparse.git
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git clone https://github.com/PythonOptimizers/HSL.py.git
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2. Install the :program:`Python` dependencies:

‎doc/source/mc60_rcmk_sloan.pdf

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‎doc/source/mc60_rcmk_sloan.png

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‎doc/source/ordering.rst

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.. literalinclude:: ../../examples/demo_mc60.py
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:linenos:
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.. code-block:: none
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The number of supervariables is 4
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The variable permutation is [3 5 4 1 2]
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The profile is 10.0
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The maximum wavefront is 3.0
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The semibandwidth is 3.0
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The root-mean-square wavefront is 2.09761769634
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Using a matrix market matrix through :program:`CySparse`
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. literalinclude:: ../../examples/demo_rcmk_sloan.py
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:linenos:
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.. figure:: mc60_rcmk_sloan.*
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The sparsity pattern of the orginal matrix is shown on the left plot.
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The middle and right plots show the same matrix after a symmetric permutation of the rows and columns
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aiming to reduce the bandwidth, profile and/or wavefront. This is done respectively via the
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reverse Cuthill-McKee method and the algorithm of Sloan.
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‎doc/source/scaling.rst

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Output
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.. code-block:: python
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.. code-block:: bash
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orignal values:
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i j val

‎examples/demo_rcmk_sloan.py

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from hsl.ordering.mc60 import sloan, rcmk
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from cysparse.sparse.ll_mat import *
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import cysparse.types.cysparse_types as types
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from pyorder.tools.spy import FastSpy
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import numpy as np
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import matplotlib.pyplot as plt
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A = LLSparseMatrix(mm_filename='bcsstk01.mtx', itype=types.INT32_T, dtype=types.FLOAT64_T)
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m, n = A.shape
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irow, jcol, val = A.find()
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A_csc = A.to_csc()
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colptr, rowind, values = A_csc.get_numpy_arrays()
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perm1, rinfo1 = rcmk(n, rowind, colptr) # Reverse Cuthill-McKee
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perm2, rinfo2 = sloan(n, rowind, colptr) # Sloan's method
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left = plt.subplot(131)
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FastSpy(n, n, irow, jcol, sym=True,
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ax=left.get_axes(), title='Original')
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# Apply permutation 1 and plot reordered matrix
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middle = plt.subplot(132)
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FastSpy(n, n, perm1[irow], perm1[jcol], sym=True,
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ax=middle.get_axes(), title='Rev. Cuthill-McKee (semibandwidth=%d)' % rinfo1[2])
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# Apply permutation 2 and plot reordered matrix
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right = plt.subplot(133)
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FastSpy(n, n, perm2[irow], perm2[jcol], sym=True,
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ax=right.get_axes(), title='Sloan (semibandwidth=%d)' % rinfo2[2])
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plt.savefig('mc60_rcmk_sloan.png',bbox_inches='tight')
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# plt.show()

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