Remove Cholesky solver in algebraic_connectivity

.travis.yml

@@ -9,8 +9,8 @@ python:
 before_install:
   # Current TravisCI VM is Ubuntu 12.04 which has Python 2.7 and 3.2.
   # So apt-get will only apply to those virtualenvs.
-  - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then source ~/virtualenv/python2.7_with_system_site_packages/bin/activate; sudo apt-get install -qq python-numpy python-scipy python-matplotlib python-pydot python-gdal cython libsuitesparse-dev; fi
-  - if [[ $TRAVIS_PYTHON_VERSION == '3.2' ]]; then source ~/virtualenv/python3.2_with_system_site_packages/bin/activate; sudo apt-get install -qq python3-numpy python3-scipy libsuitesparse-dev; fi
+  - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then source ~/virtualenv/python2.7_with_system_site_packages/bin/activate; sudo apt-get install -qq python-numpy python-scipy python-matplotlib python-pydot python-gdal; fi
+  - if [[ $TRAVIS_PYTHON_VERSION == '3.2' ]]; then source ~/virtualenv/python3.2_with_system_site_packages/bin/activate; sudo apt-get install -qq python3-numpy python3-scipy; fi
 
   # Additionally, the TravisCI VM has numpy preinstalled on 2.6, 2.7 and 3.2.
   # So for 3.3 and pypy, numpy is not preinstalled and must be pip-installed.
@@ -26,9 +26,6 @@ before_install:
   - pip install pyyaml --use-mirrors
   - pip install pyparsing --use-mirrors
   - pip install coveralls --use-mirrors
-  - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then pip install scikits.sparse; fi
-  - if [[ $TRAVIS_PYTHON_VERSION == '3.2' ]]; then pip install Cython; fi
-  - if [[ $TRAVIS_PYTHON_VERSION == '3.2' ]]; then pip install scikits.sparse; fi
 
   # In general, 2.6, 3.2, 3.3, and pypy will have lower coverage levels
   # due to missing dependencies.  The coverage level reported by coveralls

networkx/linalg/algebraic_connectivity.py

@@ -63,25 +63,6 @@ def solve(self, b):
             p += z
 
 
-class _CholeskySolver(object):
-    """Cholesky factorization.
-    """
-
-    def __init__(self, A):
-        if not self._cholesky:
-            raise nx.NetworkXError('Cholesky solver unavailable.')
-        self._chol = self._cholesky(A)
-
-    def solve(self, b):
-        return self._chol(b)
-
-    try:
-        from scikits.sparse.cholmod import cholesky
-        _cholesky = cholesky
-    except ImportError:
-        _cholesky = None
-
-
 class _LUSolver(object):
     """LU factorization.
     """
@@ -179,7 +160,7 @@ def gram_schmidt(X):
     if solver is None or solver == 'pcg':
         M = (1. / L.diagonal()).__mul__
         solver = _PCGSolver(L, M, tol / 10)
-    elif solver == 'chol' or solver == 'lu':
+    elif solver == 'lu':
         # Convert A to CSC to suppress SparseEfficiencyWarning.
         A = csc_matrix(L, dtype=float, copy=True)
         # Force A to be nonsingular. Since A is the Laplacian matrix of a
@@ -188,7 +169,7 @@ def gram_schmidt(X):
         # corresponding element in the solution.
         i = A.diagonal().argmin()
         A[i, i] = float('inf')
-        solver = (_CholeskySolver if solver == 'chol' else _LUSolver)(A)
+        solver = _LUSolver(A)
     else:
         raise nx.NetworkXError('unknown linear system solver.')
 
@@ -265,7 +246,6 @@ def algebraic_connectivity(G, weight='weight', normalized=False, tol=1e-8,
         Value           Solver
         =============== ========================================
         'tracemin_pcg'  Preconditioned conjugate gradient method
-        'tracemin_chol' Cholesky factorization
         'tracemin_lu'   LU factorization
         =============== ========================================
 
@@ -366,7 +346,6 @@ def fiedler_vector(G, weight='weight', normalized=False, tol=1e-8,
         Value           Solver
         =============== ========================================
         'tracemin_pcg'  Preconditioned conjugate gradient method
-        'tracemin_chol' Cholesky factorization
         'tracemin_lu'   LU factorization
         =============== ========================================
 
@@ -466,7 +445,6 @@ def spectral_ordering(G, weight='weight', normalized=False, tol=1e-8,
         Value           Solver
         =============== ========================================
         'tracemin_pcg'  Preconditioned conjugate gradient method
-        'tracemin_chol' Cholesky factorization
         'tracemin_lu'   LU factorization
         =============== ========================================
 

networkx/linalg/tests/test_algebraic_connectivity.py

@@ -4,7 +4,7 @@
 from nose import SkipTest
 from nose.tools import *
 
-methods = ('tracemin_pcg', 'tracemin_chol', 'tracemin_lu', 'lanczos', 'lobpcg')
+methods = ('tracemin_pcg', 'tracemin_lu', 'lanczos', 'lobpcg')
 
 
 try: