commit d5b93bf9ec492f3ea079d6dd2cdfb79b0f915066
author Sameer Agarwal <sameeragarwal@google.com> Fri Apr 26 21:17:49 2013 -0700
committer Sameer Agarwal <sameeragarwal@google.com> Mon Apr 29 11:18:13 2013 -0700
tree 64b458306e2dc6184863f556d1090fd249e45c54
parent 344c09f5bc72d08ac5226611954445887a2a6392

More pre-ordering support.

1. CX_SPARSE supports pre-ordering of the jacobian.
2. Add support for constrained approximate minimum degree ordering
   for SuiteSparse versions >= 4.2.0
3. Using 2, support for pre-ordering for SPARSE_SCHUR when used
   with SUITE_SPARSE.
4. Using 2, support for user orderings in SPARSE_NORMAL_CHOLESKY.
5. Minor cleanups in documentation and code all around.
6. Test update and refactoring.

Change-Id: Ibfe3ac95d59d54ab14d1d60a07f767688070f29f

internal/ceres/unsymmetric_linear_solver_test.cc

diff --git a/internal/ceres/unsymmetric_linear_solver_test.cc b/internal/ceres/unsymmetric_linear_solver_test.cc
index c8a15c0..34b03be 100644
--- a/internal/ceres/unsymmetric_linear_solver_test.cc
+++ b/internal/ceres/unsymmetric_linear_solver_test.cc
@@ -56,12 +56,7 @@
     sol_regularized_.reset(problem->x_D.release());
   }
 
-  void TestSolver(
-      LinearSolverType linear_solver_type,
-      SparseLinearAlgebraLibraryType sparse_linear_algebra_library) {
-    LinearSolver::Options options;
-    options.type = linear_solver_type;
-    options.sparse_linear_algebra_library = sparse_linear_algebra_library;
+  void TestSolver(const LinearSolver::Options& options) {
     scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
 
     LinearSolver::PerSolveOptions per_solve_options;
@@ -72,13 +67,22 @@
 
     scoped_ptr<SparseMatrix> transformed_A;
 
-    if (linear_solver_type == DENSE_QR ||
-        linear_solver_type == DENSE_NORMAL_CHOLESKY) {
+    if (options.type == DENSE_QR ||
+        options.type == DENSE_NORMAL_CHOLESKY) {
       transformed_A.reset(new DenseSparseMatrix(*A_));
-    } else if (linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
-      transformed_A.reset(new   CompressedRowSparseMatrix(*A_));
+    } else if (options.type == SPARSE_NORMAL_CHOLESKY) {
+      CompressedRowSparseMatrix* crsm =  new CompressedRowSparseMatrix(*A_);
+      // Add row/column blocks structure.
+      for (int i = 0; i < A_->num_rows(); ++i) {
+        crsm->mutable_row_blocks()->push_back(1);
+      }
+
+      for (int i = 0; i < A_->num_cols(); ++i) {
+        crsm->mutable_col_blocks()->push_back(1);
+      }
+      transformed_A.reset(crsm);
     } else {
-      LOG(FATAL) << "Unknown linear solver : " << linear_solver_type;
+      LOG(FATAL) << "Unknown linear solver : " << options.type;
     }
     // Unregularized
     unregularized_solve_summary =
@@ -115,22 +119,50 @@
 };
 
 TEST_F(UnsymmetricLinearSolverTest, DenseQR) {
-  TestSolver(DENSE_QR, SUITE_SPARSE);
+  LinearSolver::Options options;
+  options.type = DENSE_QR;
+  TestSolver(options);
 }
 
 TEST_F(UnsymmetricLinearSolverTest, DenseNormalCholesky) {
-  TestSolver(DENSE_NORMAL_CHOLESKY, SUITE_SPARSE);
+  LinearSolver::Options options;
+  options.type = DENSE_NORMAL_CHOLESKY;
+  TestSolver(options);
 }
 
 #ifndef CERES_NO_SUITESPARSE
-TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingSuiteSparse) {
-  TestSolver(SPARSE_NORMAL_CHOLESKY, SUITE_SPARSE);
+TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingSuiteSparsePreOrdering) {
+  LinearSolver::Options options;
+  options.sparse_linear_algebra_library = SUITE_SPARSE;
+  options.type = SPARSE_NORMAL_CHOLESKY;
+  options.use_postordering = false;
+  TestSolver(options);
+}
+
+TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingSuiteSparsePostOrdering) {
+  LinearSolver::Options options;
+  options.sparse_linear_algebra_library = SUITE_SPARSE;
+  options.type = SPARSE_NORMAL_CHOLESKY;
+  options.use_postordering = true;
+  TestSolver(options);
 }
 #endif
 
 #ifndef CERES_NO_CXSPARSE
-TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingCXSparse) {
-  TestSolver(SPARSE_NORMAL_CHOLESKY, CX_SPARSE);
+TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingCXSparsePreOrdering) {
+  LinearSolver::Options options;
+  options.sparse_linear_algebra_library = CX_SPARSE;
+  options.type = SPARSE_NORMAL_CHOLESKY;
+  options.use_postordering = false;
+  TestSolver(options);
+}
+
+TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingCXSparsePostOrdering) {
+  LinearSolver::Options options;
+  options.sparse_linear_algebra_library = CX_SPARSE;
+  options.type = SPARSE_NORMAL_CHOLESKY;
+  options.use_postordering = true;
+  TestSolver(options);
 }
 #endif