diff --git a/internal/ceres/detect_structure.cc b/internal/ceres/detect_structure.cc
index 6bb7bb4..a87f6ba6 100644
--- a/internal/ceres/detect_structure.cc
+++ b/internal/ceres/detect_structure.cc
@@ -54,7 +54,6 @@
     if (row.cells.front().block_id >= num_eliminate_blocks) {
       break;
     }
-    const int e_block_id = row.cells.front().block_id;
 
     if (*row_block_size == 0) {
       *row_block_size = row.block.size;
@@ -66,7 +65,7 @@
       *row_block_size = Eigen::Dynamic;
     }
 
-
+    const int e_block_id = row.cells.front().block_id;
     if (*e_block_size == 0) {
       *e_block_size = bs.cols[e_block_id].size;
     } else if (*e_block_size != Eigen::Dynamic &&
diff --git a/internal/ceres/detect_structure.h b/internal/ceres/detect_structure.h
index fb8bbb4..602581c 100644
--- a/internal/ceres/detect_structure.h
+++ b/internal/ceres/detect_structure.h
@@ -51,6 +51,10 @@
 // For more details about e_blocks and f_blocks, see
 // schur_eliminator.h. This information is used to initialized an
 // appropriate template specialization of SchurEliminator.
+//
+// Note: The structure of rows without any e-blocks has no effect on
+// the values returned by this function. It is entirely possible that
+// the f_block_size and row_blocks_size is not constant in such rows.
 void DetectStructure(const CompressedRowBlockStructure& bs,
                      const int num_eliminate_blocks,
                      int* row_block_size,
diff --git a/internal/ceres/linear_least_squares_problems.cc b/internal/ceres/linear_least_squares_problems.cc
index 15da333..0a69375 100644
--- a/internal/ceres/linear_least_squares_problems.cc
+++ b/internal/ceres/linear_least_squares_problems.cc
@@ -58,6 +58,8 @@
       return LinearLeastSquaresProblem2();
     case 3:
       return LinearLeastSquaresProblem3();
+    case 4:
+      return LinearLeastSquaresProblem4();
     default:
       LOG(FATAL) << "Unknown problem id requested " << id;
   }
@@ -505,6 +507,106 @@
   return problem;
 }
 
+/*
+      A = [1 2 0 0 0 1 1
+           1 4 0 0 0 5 6
+           0 0 9 0 0 3 1]
+
+      b = [0
+           1
+           2]
+*/
+// BlockSparseMatrix version
+//
+// This problem has the unique property that it has two different
+// sized f-blocks, but only one of them occurs in the rows involving
+// the one e-block. So performing Schur elimination on this problem
+// tests the Schur Eliminator's ability to handle non-e-block rows
+// correctly when their structure does not conform to the static
+// structure determined by DetectStructure.
+//
+// NOTE: This problem is too small and rank deficient to be solved without
+// the diagonal regularization.
+LinearLeastSquaresProblem* LinearLeastSquaresProblem4() {
+  int num_rows = 3;
+  int num_cols = 7;
+
+  LinearLeastSquaresProblem* problem = new LinearLeastSquaresProblem;
+
+  problem->b.reset(new double[num_rows]);
+  problem->D.reset(new double[num_cols]);
+  problem->num_eliminate_blocks = 1;
+
+  CompressedRowBlockStructure* bs = new CompressedRowBlockStructure;
+  scoped_array<double> values(new double[num_rows * num_cols]);
+
+  // Column block structure
+  bs->cols.push_back(Block());
+  bs->cols.back().size = 2;
+  bs->cols.back().position = 0;
+
+  bs->cols.push_back(Block());
+  bs->cols.back().size = 3;
+  bs->cols.back().position = 2;
+
+  bs->cols.push_back(Block());
+  bs->cols.back().size = 2;
+  bs->cols.back().position = 5;
+
+  int nnz = 0;
+
+  // Row 1 & 2
+  {
+    bs->rows.push_back(CompressedRow());
+    CompressedRow& row = bs->rows.back();
+    row.block.size = 2;
+    row.block.position = 0;
+
+    row.cells.push_back(Cell(0, nnz));
+    values[nnz++] = 1;
+    values[nnz++] = 2;
+    values[nnz++] = 1;
+    values[nnz++] = 4;
+
+    row.cells.push_back(Cell(2, nnz));
+    values[nnz++] = 1;
+    values[nnz++] = 1;
+    values[nnz++] = 5;
+    values[nnz++] = 6;
+  }
+
+  // Row 3
+  {
+    bs->rows.push_back(CompressedRow());
+    CompressedRow& row = bs->rows.back();
+    row.block.size = 1;
+    row.block.position = 2;
+
+    row.cells.push_back(Cell(1, nnz));
+    values[nnz++] = 9;
+    values[nnz++] = 0;
+    values[nnz++] = 0;
+
+    row.cells.push_back(Cell(2, nnz));
+    values[nnz++] = 3;
+    values[nnz++] = 1;
+  }
+
+  BlockSparseMatrix* A = new BlockSparseMatrix(bs);
+  memcpy(A->mutable_values(), values.get(), nnz * sizeof(*A->values()));
+
+  for (int i = 0; i < num_cols; ++i) {
+    problem->D.get()[i] = (i + 1) * 100;
+  }
+
+  for (int i = 0; i < num_rows; ++i) {
+    problem->b.get()[i] = i;
+  }
+
+  problem->A.reset(A);
+  return problem;
+}
+
 namespace {
 bool DumpLinearLeastSquaresProblemToConsole(const SparseMatrix* A,
                                             const double* D,
diff --git a/internal/ceres/linear_least_squares_problems.h b/internal/ceres/linear_least_squares_problems.h
index 1dccc90..384efb5 100644
--- a/internal/ceres/linear_least_squares_problems.h
+++ b/internal/ceres/linear_least_squares_problems.h
@@ -68,6 +68,7 @@
 LinearLeastSquaresProblem* LinearLeastSquaresProblem1();
 LinearLeastSquaresProblem* LinearLeastSquaresProblem2();
 LinearLeastSquaresProblem* LinearLeastSquaresProblem3();
+LinearLeastSquaresProblem* LinearLeastSquaresProblem4();
 
 // Write the linear least squares problem to disk. The exact format
 // depends on dump_format_type.
diff --git a/internal/ceres/schur_complement_solver_test.cc b/internal/ceres/schur_complement_solver_test.cc
index 4473613..cf9b09b 100644
--- a/internal/ceres/schur_complement_solver_test.cc
+++ b/internal/ceres/schur_complement_solver_test.cc
@@ -32,6 +32,7 @@
 #include "ceres/block_sparse_matrix.h"
 #include "ceres/block_structure.h"
 #include "ceres/casts.h"
+#include "ceres/detect_structure.h"
 #include "ceres/internal/scoped_ptr.h"
 #include "ceres/linear_least_squares_problems.h"
 #include "ceres/linear_solver.h"
@@ -59,9 +60,9 @@
     num_rows = A->num_rows();
     num_eliminate_blocks = problem->num_eliminate_blocks;
 
-    x.reset(new double[num_cols]);
-    sol.reset(new double[num_cols]);
-    sol_d.reset(new double[num_cols]);
+    x.resize(num_cols);
+    sol.resize(num_cols);
+    sol_d.resize(num_cols);
 
     LinearSolver::Options options;
     options.type = DENSE_QR;
@@ -75,12 +76,12 @@
 
     // Gold standard solutions using dense QR factorization.
     DenseSparseMatrix dense_A(triplet_A);
-    qr->Solve(&dense_A, b.get(), LinearSolver::PerSolveOptions(), sol.get());
+    qr->Solve(&dense_A, b.get(), LinearSolver::PerSolveOptions(), sol.data());
 
     // Gold standard solution with appended diagonal.
     LinearSolver::PerSolveOptions per_solve_options;
     per_solve_options.D = D.get();
-    qr->Solve(&dense_A, b.get(), per_solve_options, sol_d.get());
+    qr->Solve(&dense_A, b.get(), per_solve_options, sol_d.data());
   }
 
   void ComputeAndCompareSolutions(
@@ -101,6 +102,11 @@
     options.sparse_linear_algebra_library_type =
         sparse_linear_algebra_library_type;
     options.use_postordering = use_postordering;
+    DetectStructure(*A->block_structure(),
+                    num_eliminate_blocks,
+                    &options.row_block_size,
+                    &options.e_block_size,
+                    &options.f_block_size);
 
     scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
 
@@ -110,16 +116,17 @@
       per_solve_options.D = D.get();
     }
 
-    summary = solver->Solve(A.get(), b.get(), per_solve_options, x.get());
+    summary = solver->Solve(A.get(), b.get(), per_solve_options, x.data());
+    EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_SUCCESS);
 
     if (regularization) {
-      for (int i = 0; i < num_cols; ++i) {
-        ASSERT_NEAR(sol_d.get()[i], x[i], 1e-10);
-      }
+      ASSERT_NEAR((sol_d - x).norm() / num_cols, 0, 1e-10)
+          << "Expected solution: " << sol_d.transpose()
+          << " Actual solution: " << x.transpose();
     } else {
-      for (int i = 0; i < num_cols; ++i) {
-        ASSERT_NEAR(sol.get()[i], x[i], 1e-10);
-      }
+      ASSERT_NEAR((sol - x).norm() / num_cols, 0, 1e-10)
+          << "Expected solution: " << sol.transpose()
+          << " Actual solution: " << x.transpose();
     }
   }
 
@@ -129,10 +136,10 @@
 
   scoped_ptr<BlockSparseMatrix> A;
   scoped_array<double> b;
-  scoped_array<double> x;
   scoped_array<double> D;
-  scoped_array<double> sol;
-  scoped_array<double> sol_d;
+  Vector x;
+  Vector sol;
+  Vector sol_d;
 };
 
 TEST_F(SchurComplementSolverTest, EigenBasedDenseSchurWithSmallProblem) {
@@ -145,6 +152,10 @@
   ComputeAndCompareSolutions(3, true, DENSE_SCHUR, EIGEN, SUITE_SPARSE, true);
 }
 
+TEST_F(SchurComplementSolverTest, EigenBasedDenseSchurWithVaryingFBlockSize) {
+  ComputeAndCompareSolutions(4, true, DENSE_SCHUR, EIGEN, SUITE_SPARSE, true);
+}
+
 #ifndef CERES_NO_LAPACK
 TEST_F(SchurComplementSolverTest, LAPACKBasedDenseSchurWithSmallProblem) {
   ComputeAndCompareSolutions(2, false, DENSE_SCHUR, LAPACK, SUITE_SPARSE, true);
diff --git a/internal/ceres/schur_eliminator_impl.h b/internal/ceres/schur_eliminator_impl.h
index 7e06806..f253588 100644
--- a/internal/ceres/schur_eliminator_impl.h
+++ b/internal/ceres/schur_eliminator_impl.h
@@ -194,7 +194,7 @@
                                          &row_stride, &col_stride);
       if (cell_info != NULL) {
         const int block_size = bs->cols[i].size;
-        typename EigenTypes<kFBlockSize>::ConstVectorRef
+        typename EigenTypes<Eigen::Dynamic>::ConstVectorRef
             diag(D + bs->cols[i].position, block_size);
 
         CeresMutexLock l(&cell_info->m);
@@ -205,20 +205,19 @@
     }
   }
 
-  // Eliminate y blocks one chunk at a time.  For each chunk,x3
-  // compute the entries of the normal equations and the gradient
-  // vector block corresponding to the y block and then apply
-  // Gaussian elimination to them. The matrix ete stores the normal
-  // matrix corresponding to the block being eliminated and array
-  // buffer_ contains the non-zero blocks in the row corresponding
-  // to this y block in the normal equations. This computation is
-  // done in ChunkDiagonalBlockAndGradient. UpdateRhs then applies
-  // gaussian elimination to the rhs of the normal equations,
-  // updating the rhs of the reduced linear system by modifying rhs
-  // blocks for all the z blocks that share a row block/residual
-  // term with the y block. EliminateRowOuterProduct does the
-  // corresponding operation for the lhs of the reduced linear
-  // system.
+  // Eliminate y blocks one chunk at a time.  For each chunk, compute
+  // the entries of the normal equations and the gradient vector block
+  // corresponding to the y block and then apply Gaussian elimination
+  // to them. The matrix ete stores the normal matrix corresponding to
+  // the block being eliminated and array buffer_ contains the
+  // non-zero blocks in the row corresponding to this y block in the
+  // normal equations. This computation is done in
+  // ChunkDiagonalBlockAndGradient. UpdateRhs then applies gaussian
+  // elimination to the rhs of the normal equations, updating the rhs
+  // of the reduced linear system by modifying rhs blocks for all the
+  // z blocks that share a row block/residual term with the y
+  // block. EliminateRowOuterProduct does the corresponding operation
+  // for the lhs of the reduced linear system.
 #pragma omp parallel for num_threads(num_threads_) schedule(dynamic)
   for (int i = 0; i < chunks_.size(); ++i) {
 #ifdef CERES_USE_OPENMP
@@ -594,8 +593,8 @@
 void
 SchurEliminator<kRowBlockSize, kEBlockSize, kFBlockSize>::
 NoEBlockRowOuterProduct(const BlockSparseMatrix* A,
-                     int row_block_index,
-                     BlockRandomAccessMatrix* lhs) {
+                        int row_block_index,
+                        BlockRandomAccessMatrix* lhs) {
   const CompressedRowBlockStructure* bs = A->block_structure();
   const CompressedRow& row = bs->rows[row_block_index];
   const double* values = A->values();
diff --git a/internal/ceres/schur_eliminator_test.cc b/internal/ceres/schur_eliminator_test.cc
index 40bc904..85ecd8f 100644
--- a/internal/ceres/schur_eliminator_test.cc
+++ b/internal/ceres/schur_eliminator_test.cc
@@ -193,7 +193,7 @@
   Vector sol_expected;
 };
 
-TEST_F(SchurEliminatorTest, ScalarProblem) {
+TEST_F(SchurEliminatorTest, ScalarProblemNoRegularization) {
   SetUpFromId(2);
   Vector zero(A->num_cols());
   zero.setZero();
@@ -201,11 +201,26 @@
   ComputeReferenceSolution(VectorRef(zero.data(), A->num_cols()));
   EliminateSolveAndCompare(VectorRef(zero.data(), A->num_cols()), true, 1e-14);
   EliminateSolveAndCompare(VectorRef(zero.data(), A->num_cols()), false, 1e-14);
+}
 
+TEST_F(SchurEliminatorTest, ScalarProblemWithRegularization) {
+  SetUpFromId(2);
   ComputeReferenceSolution(VectorRef(D.get(), A->num_cols()));
   EliminateSolveAndCompare(VectorRef(D.get(), A->num_cols()), true, 1e-14);
   EliminateSolveAndCompare(VectorRef(D.get(), A->num_cols()), false, 1e-14);
 }
 
+TEST_F(SchurEliminatorTest, VaryingFBlockSizeWithStaticStructure) {
+  SetUpFromId(4);
+  ComputeReferenceSolution(VectorRef(D.get(), A->num_cols()));
+  EliminateSolveAndCompare(VectorRef(D.get(), A->num_cols()), true, 1e-14);
+}
+
+TEST_F(SchurEliminatorTest, VaryingFBlockSizeWithoutStaticStructure) {
+  SetUpFromId(4);
+  ComputeReferenceSolution(VectorRef(D.get(), A->num_cols()));
+  EliminateSolveAndCompare(VectorRef(D.get(), A->num_cols()), false, 1e-14);
+}
+
 }  // namespace internal
 }  // namespace ceres