test/sparse: add test for Simplicial{LLT,LDLT}

Author: jcarpent

unittest/CMakeLists.txt

@@ -79,10 +79,6 @@ endif()
 add_lib_unit_test(bind_virtual_factory)
 
 add_python_unit_test("py-matrix" "unittest/python/test_matrix.py" "unittest")
-if(BUILD_TESTING_SCIPY)
-  add_python_unit_test("py-sparse-matrix"
-                       "unittest/python/test_sparse_matrix.py" "unittest")
-endif()
 
 add_python_unit_test("py-tensor" "unittest/python/test_tensor.py" "unittest")
 add_python_unit_test("py-geometry" "unittest/python/test_geometry.py"
@@ -146,3 +142,19 @@ add_python_unit_test("py-std-unique-ptr"
 
 add_python_unit_test("py-bind-virtual" "unittest/python/test_bind_virtual.py"
                      "unittest")
+
+if(BUILD_TESTING_SCIPY)
+  add_python_unit_test("py-sparse-matrix"
+                       "unittest/python/test_sparse_matrix.py" "unittest")
+
+  add_python_unit_test(
+    "py-SimplicialLLT"
+    "unittest/python/decompositions/sparse/test_SimplicialLLT.py" "python")
+  add_python_unit_test(
+    "py-SimplicialLDLT"
+    "unittest/python/decompositions/sparse/test_SimplicialLDLT.py" "python")
+
+  if(BUILD_WITH_CHOLMOD_SUPPORT)
+
+  endif(BUILD_WITH_CHOLMOD_SUPPORT)
+endif()

unittest/python/decompositions/sparse/test_SimplicialLDLT.py

@@ -0,0 +1,31 @@
+import eigenpy
+
+import numpy as np
+from scipy.sparse import csc_matrix
+
+dim = 100
+A = np.random.rand(dim, dim)
+A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
+
+A = csc_matrix(A)
+
+ldlt = eigenpy.SimplicialLDLT(A)
+
+assert ldlt.info() == eigenpy.ComputationInfo.Success
+
+L = ldlt.matrixL()
+U = ldlt.matrixU()
+D = csc_matrix(np.diag(ldlt.vectorD()))
+
+LDU = L @ D @ U
+assert eigenpy.is_approx(LDU.toarray(), A.toarray())
+
+X = np.random.rand(dim, 20)
+B = A.dot(X)
+X_est = ldlt.solve(B)
+assert eigenpy.is_approx(X, X_est)
+assert eigenpy.is_approx(A.dot(X_est), B)
+
+ldlt.analyzePattern(A)
+ldlt.factorize(A)
+permutation = ldlt.permutationP()

unittest/python/decompositions/sparse/test_SimplicialLLT.py

@@ -0,0 +1,30 @@
+import eigenpy
+
+import numpy as np
+from scipy.sparse import csc_matrix
+
+dim = 100
+A = np.random.rand(dim, dim)
+A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
+
+A = csc_matrix(A)
+
+llt = eigenpy.SimplicialLLT(A)
+
+assert llt.info() == eigenpy.ComputationInfo.Success
+
+L = llt.matrixL()
+U = llt.matrixU()
+
+LU = L @ U
+assert eigenpy.is_approx(LU.toarray(), A.toarray())
+
+X = np.random.rand(dim, 20)
+B = A.dot(X)
+X_est = llt.solve(B)
+assert eigenpy.is_approx(X, X_est)
+assert eigenpy.is_approx(A.dot(X_est), B)
+
+llt.analyzePattern(A)
+llt.factorize(A)
+permutation = llt.permutationP()