diff --git a/internal/ceres/CMakeLists.txt b/internal/ceres/CMakeLists.txt
index 20b28b4..f77c066 100644
--- a/internal/ceres/CMakeLists.txt
+++ b/internal/ceres/CMakeLists.txt
@@ -61,6 +61,7 @@
     file.cc
     gradient_checking_cost_function.cc
     implicit_schur_complement.cc
+    incomplete_lq_factorization.cc
     iterative_schur_complement_solver.cc
     levenberg_marquardt_strategy.cc
     line_search.cc
@@ -263,6 +264,7 @@
   CERES_TEST(graph)
   CERES_TEST(graph_algorithms)
   CERES_TEST(implicit_schur_complement)
+  CERES_TEST(incomplete_lq_factorization)
   CERES_TEST(iterative_schur_complement_solver)
   CERES_TEST(jet)
   CERES_TEST(levenberg_marquardt_strategy)
diff --git a/internal/ceres/compressed_row_sparse_matrix.cc b/internal/ceres/compressed_row_sparse_matrix.cc
index 1b61468..9fb7bd7 100644
--- a/internal/ceres/compressed_row_sparse_matrix.cc
+++ b/internal/ceres/compressed_row_sparse_matrix.cc
@@ -34,7 +34,8 @@
 #include <vector>
 #include "ceres/crs_matrix.h"
 #include "ceres/internal/port.h"
-#include "ceres/matrix_proto.h"
+#include "ceres/triplet_sparse_matrix.h"
+#include "glog/logging.h"
 
 namespace ceres {
 namespace internal {
@@ -72,28 +73,27 @@
                                                      int max_num_nonzeros) {
   num_rows_ = num_rows;
   num_cols_ = num_cols;
-  max_num_nonzeros_ = max_num_nonzeros;
+  rows_.resize(num_rows + 1, 0);
+  cols_.resize(max_num_nonzeros, 0);
+  values_.resize(max_num_nonzeros, 0.0);
 
-  VLOG(1) << "# of rows: " << num_rows_ << " # of columns: " << num_cols_
-          << " max_num_nonzeros: " << max_num_nonzeros_
+
+  VLOG(1) << "# of rows: " << num_rows_
+          << " # of columns: " << num_cols_
+          << " max_num_nonzeros: " << cols_.size()
           << ". Allocating " << (num_rows_ + 1) * sizeof(int) +  // NOLINT
-      max_num_nonzeros_ * sizeof(int) +  // NOLINT
-      max_num_nonzeros_ * sizeof(double);  // NOLINT
-
-  rows_.reset(new int[num_rows_ + 1]);
-  cols_.reset(new int[max_num_nonzeros_]);
-  values_.reset(new double[max_num_nonzeros_]);
-
-  fill(rows_.get(), rows_.get() + num_rows_ + 1, 0);
-  fill(cols_.get(), cols_.get() + max_num_nonzeros_, 0);
-  fill(values_.get(), values_.get() + max_num_nonzeros_, 0);
+      cols_.size() * sizeof(int) +  // NOLINT
+      cols_.size() * sizeof(double);  // NOLINT
 }
 
 CompressedRowSparseMatrix::CompressedRowSparseMatrix(
     const TripletSparseMatrix& m) {
   num_rows_ = m.num_rows();
   num_cols_ = m.num_cols();
-  max_num_nonzeros_ = m.max_num_nonzeros();
+
+  rows_.resize(num_rows_ + 1, 0);
+  cols_.resize(m.num_nonzeros(), 0);
+  values_.resize(m.max_num_nonzeros(), 0.0);
 
   // index is the list of indices into the TripletSparseMatrix m.
   vector<int> index(m.num_nonzeros(), 0);
@@ -105,18 +105,13 @@
   // are broken by column.
   sort(index.begin(), index.end(), RowColLessThan(m.rows(), m.cols()));
 
-  VLOG(1) << "# of rows: " << num_rows_ << " # of columns: " << num_cols_
-          << " max_num_nonzeros: " << max_num_nonzeros_
-          << ". Allocating " << (num_rows_ + 1) * sizeof(int) +  // NOLINT
-      max_num_nonzeros_ * sizeof(int) +  // NOLINT
-      max_num_nonzeros_ * sizeof(double);  // NOLINT
-
-  rows_.reset(new int[num_rows_ + 1]);
-  cols_.reset(new int[max_num_nonzeros_]);
-  values_.reset(new double[max_num_nonzeros_]);
-
-  // rows_ = 0
-  fill(rows_.get(), rows_.get() + num_rows_ + 1, 0);
+  VLOG(1) << "# of rows: " << num_rows_
+          << " # of columns: " << num_cols_
+          << " max_num_nonzeros: " << cols_.size()
+          << ". Allocating "
+          << ((num_rows_ + 1) * sizeof(int) +  // NOLINT
+              cols_.size() * sizeof(int) +     // NOLINT
+              cols_.size() * sizeof(double));  // NOLINT
 
   // Copy the contents of the cols and values array in the order given
   // by index and count the number of entries in each row.
@@ -135,49 +130,15 @@
   CHECK_EQ(num_nonzeros(), m.num_nonzeros());
 }
 
-#ifndef CERES_NO_PROTOCOL_BUFFERS
-CompressedRowSparseMatrix::CompressedRowSparseMatrix(
-    const SparseMatrixProto& outer_proto) {
-  CHECK(outer_proto.has_compressed_row_matrix());
-
-  const CompressedRowSparseMatrixProto& proto =
-      outer_proto.compressed_row_matrix();
-
-  num_rows_ = proto.num_rows();
-  num_cols_ = proto.num_cols();
-
-  rows_.reset(new int[proto.rows_size()]);
-  cols_.reset(new int[proto.cols_size()]);
-  values_.reset(new double[proto.values_size()]);
-
-  for (int i = 0; i < proto.rows_size(); ++i) {
-    rows_[i] = proto.rows(i);
-  }
-
-  CHECK_EQ(proto.rows_size(), num_rows_ + 1);
-  CHECK_EQ(proto.cols_size(), proto.values_size());
-  CHECK_EQ(proto.cols_size(), rows_[num_rows_]);
-
-  for (int i = 0; i < proto.cols_size(); ++i) {
-    cols_[i] = proto.cols(i);
-    values_[i] = proto.values(i);
-  }
-
-  max_num_nonzeros_ = proto.cols_size();
-}
-#endif
-
 CompressedRowSparseMatrix::CompressedRowSparseMatrix(const double* diagonal,
                                                      int num_rows) {
   CHECK_NOTNULL(diagonal);
 
   num_rows_ = num_rows;
   num_cols_ = num_rows;
-  max_num_nonzeros_ = num_rows;
-
-  rows_.reset(new int[num_rows_ + 1]);
-  cols_.reset(new int[num_rows_]);
-  values_.reset(new double[num_rows_]);
+  rows_.resize(num_rows + 1);
+  cols_.resize(num_rows);
+  values_.resize(num_rows);
 
   rows_[0] = 0;
   for (int i = 0; i < num_rows_; ++i) {
@@ -193,7 +154,7 @@
 }
 
 void CompressedRowSparseMatrix::SetZero() {
-  fill(values_.get(), values_.get() + num_nonzeros(), 0.0);
+  fill(values_.begin(), values_.end(), 0);
 }
 
 void CompressedRowSparseMatrix::RightMultiply(const double* x,
@@ -248,83 +209,35 @@
   }
 }
 
-#ifndef CERES_NO_PROTOCOL_BUFFERS
-void CompressedRowSparseMatrix::ToProto(SparseMatrixProto* outer_proto) const {
-  CHECK_NOTNULL(outer_proto);
-
-  outer_proto->Clear();
-  CompressedRowSparseMatrixProto* proto
-      = outer_proto->mutable_compressed_row_matrix();
-
-  proto->set_num_rows(num_rows_);
-  proto->set_num_cols(num_cols_);
-
-  for (int r = 0; r < num_rows_ + 1; ++r) {
-    proto->add_rows(rows_[r]);
-  }
-
-  for (int idx = 0; idx < rows_[num_rows_]; ++idx) {
-    proto->add_cols(cols_[idx]);
-    proto->add_values(values_[idx]);
-  }
-}
-#endif
-
 void CompressedRowSparseMatrix::DeleteRows(int delta_rows) {
   CHECK_GE(delta_rows, 0);
   CHECK_LE(delta_rows, num_rows_);
 
-  int new_num_rows = num_rows_ - delta_rows;
-
-  num_rows_ = new_num_rows;
-  int* new_rows = new int[num_rows_ + 1];
-  copy(rows_.get(), rows_.get() + num_rows_ + 1, new_rows);
-  rows_.reset(new_rows);
+  num_rows_ -= delta_rows;
+  rows_.resize(num_rows_ + 1);
 }
 
 void CompressedRowSparseMatrix::AppendRows(const CompressedRowSparseMatrix& m) {
   CHECK_EQ(m.num_cols(), num_cols_);
 
-  // Check if there is enough space. If not, then allocate new arrays
-  // to hold the combined matrix and copy the contents of this matrix
-  // into it.
-  if (max_num_nonzeros_ < num_nonzeros() + m.num_nonzeros()) {
-    int new_max_num_nonzeros =  num_nonzeros() + m.num_nonzeros();
-
-    VLOG(1) << "Reallocating " << sizeof(int) * new_max_num_nonzeros;  // NOLINT
-
-    int* new_cols = new int[new_max_num_nonzeros];
-    copy(cols_.get(), cols_.get() + max_num_nonzeros_, new_cols);
-    cols_.reset(new_cols);
-
-    double* new_values = new double[new_max_num_nonzeros];
-    copy(values_.get(), values_.get() + max_num_nonzeros_, new_values);
-    values_.reset(new_values);
-
-    max_num_nonzeros_ = new_max_num_nonzeros;
+  if (cols_.size() < num_nonzeros() + m.num_nonzeros()) {
+    cols_.resize(num_nonzeros() + m.num_nonzeros());
+    values_.resize(num_nonzeros() + m.num_nonzeros());
   }
 
   // Copy the contents of m into this matrix.
-  copy(m.cols(), m.cols() + m.num_nonzeros(), cols_.get() + num_nonzeros());
-  copy(m.values(),
-       m.values() + m.num_nonzeros(),
-       values_.get() + num_nonzeros());
-
-  // Create the new rows array to hold the enlarged matrix.
-  int* new_rows = new int[num_rows_ + m.num_rows() + 1];
-  // The first num_rows_ entries are the same
-  copy(rows_.get(), rows_.get() + num_rows_, new_rows);
-
+  copy(m.cols(), m.cols() + m.num_nonzeros(), &cols_[num_nonzeros()]);
+  copy(m.values(), m.values() + m.num_nonzeros(), &values_[num_nonzeros()]);
+  rows_.resize(num_rows_ + m.num_rows() + 1);
   // new_rows = [rows_, m.row() + rows_[num_rows_]]
-  fill(new_rows + num_rows_,
-       new_rows + num_rows_ + m.num_rows() + 1,
+  fill(rows_.begin() + num_rows_,
+       rows_.begin() + num_rows_ + m.num_rows() + 1,
        rows_[num_rows_]);
 
   for (int r = 0; r < m.num_rows() + 1; ++r) {
-    new_rows[num_rows_ + r] += m.rows()[r];
+    rows_[num_rows_ + r] += m.rows()[r];
   }
 
-  rows_.reset(new_rows);
   num_rows_ += m.num_rows();
 }
 
@@ -332,23 +245,122 @@
   CHECK_NOTNULL(file);
   for (int r = 0; r < num_rows_; ++r) {
     for (int idx = rows_[r]; idx < rows_[r + 1]; ++idx) {
-      fprintf(file, "% 10d % 10d %17f\n", r, cols_[idx], values_[idx]);
+      fprintf(file,
+              "% 10d % 10d %17f\n",
+              r,
+              cols_[idx],
+              values_[idx]);
     }
   }
 }
 
 void CompressedRowSparseMatrix::ToCRSMatrix(CRSMatrix* matrix) const {
-  matrix->num_rows = num_rows();
-  matrix->num_cols = num_cols();
+  matrix->num_rows = num_rows_;
+  matrix->num_cols = num_cols_;
+  matrix->rows = rows_;
+  matrix->cols = cols_;
+  matrix->values = values_;
 
+  // Trim.
   matrix->rows.resize(matrix->num_rows + 1);
-  matrix->cols.resize(num_nonzeros());
-  matrix->values.resize(num_nonzeros());
-
-  copy(rows_.get(), rows_.get() + matrix->num_rows + 1, matrix->rows.begin());
-  copy(cols_.get(), cols_.get() + num_nonzeros(), matrix->cols.begin());
-  copy(values_.get(), values_.get() + num_nonzeros(), matrix->values.begin());
+  matrix->cols.resize(matrix->rows[matrix->num_rows]);
+  matrix->values.resize(matrix->rows[matrix->num_rows]);
 }
 
+void CompressedRowSparseMatrix::SolveLowerTriangularInPlace(
+    double* solution) const {
+  for (int r = 0; r < num_rows_; ++r) {
+    for (int idx = rows_[r]; idx < rows_[r + 1] - 1; ++idx) {
+      solution[r] -= values_[idx] * solution[cols_[idx]];
+    }
+    solution[r] /=  values_[rows_[r + 1] - 1];
+  }
+};
+
+void CompressedRowSparseMatrix::SolveLowerTriangularTransposeInPlace(
+    double* solution) const {
+  for (int r = num_rows_ - 1; r >= 0; --r) {
+    solution[r] /= values_[rows_[r + 1] - 1];
+    for (int idx = rows_[r + 1] - 2; idx >= rows_[r]; --idx) {
+      solution[cols_[idx]] -= values_[idx] * solution[r];
+    }
+  }
+};
+
+CompressedRowSparseMatrix* CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(
+    const double* diagonal,
+    const vector<int>& blocks) {
+  int num_rows = 0;
+  int num_nonzeros = 0;
+  for (int i = 0; i < blocks.size(); ++i) {
+    num_rows += blocks[i];
+    num_nonzeros += blocks[i] * blocks[i];
+  }
+
+  CompressedRowSparseMatrix* matrix =
+      new CompressedRowSparseMatrix(num_rows, num_rows, num_nonzeros);
+
+  int* rows = matrix->mutable_rows();
+  int* cols = matrix->mutable_cols();
+  double* values = matrix->mutable_values();
+  fill(values, values + num_nonzeros, 0.0);
+
+  int idx_cursor = 0;
+  int col_cursor = 0;
+  for (int i = 0; i < blocks.size(); ++i) {
+    const int block_size = blocks[i];
+    for (int r = 0; r < block_size; ++r) {
+      *(rows++) = idx_cursor;
+      values[idx_cursor + r] = diagonal[col_cursor + r];
+      for (int c = 0; c < block_size; ++c, ++idx_cursor) {
+        *(cols++) = col_cursor + c;
+      }
+    }
+    col_cursor += block_size;
+  }
+  *rows = idx_cursor;
+
+  *matrix->mutable_row_blocks() = blocks;
+  *matrix->mutable_col_blocks() = blocks;
+
+  CHECK_EQ(idx_cursor, num_nonzeros);
+  CHECK_EQ(col_cursor, num_rows);
+  return matrix;
+}
+
+CompressedRowSparseMatrix* CompressedRowSparseMatrix::Transpose() const {
+  CompressedRowSparseMatrix* transpose =
+      new CompressedRowSparseMatrix(num_cols_, num_rows_, num_nonzeros());
+
+  int* transpose_rows = transpose->mutable_rows();
+  int* transpose_cols = transpose->mutable_cols();
+  double* transpose_values = transpose->mutable_values();
+
+  for (int idx = 0; idx < num_nonzeros(); ++idx) {
+    ++transpose_rows[cols_[idx] + 1];
+  }
+
+  for (int i = 1; i < transpose->num_rows() + 1; ++i) {
+    transpose_rows[i] += transpose_rows[i - 1];
+  }
+
+  for (int r = 0; r < num_rows(); ++r) {
+    for (int idx = rows_[r]; idx < rows_[r + 1]; ++idx) {
+      const int c = cols_[idx];
+      const int transpose_idx = transpose_rows[c]++;
+      transpose_cols[transpose_idx] = r;
+      transpose_values[transpose_idx] = values_[idx];
+    }
+  }
+
+  for (int i = transpose->num_rows() - 1; i > 0 ; --i) {
+    transpose_rows[i] = transpose_rows[i - 1];
+  }
+  transpose_rows[0] = 0;
+
+  return transpose;
+}
+
+
 }  // namespace internal
 }  // namespace ceres
diff --git a/internal/ceres/compressed_row_sparse_matrix.h b/internal/ceres/compressed_row_sparse_matrix.h
index c9c904b..d65d5c7 100644
--- a/internal/ceres/compressed_row_sparse_matrix.h
+++ b/internal/ceres/compressed_row_sparse_matrix.h
@@ -32,14 +32,10 @@
 #define CERES_INTERNAL_COMPRESSED_ROW_SPARSE_MATRIX_H_
 
 #include <vector>
-
-#include "ceres/internal/eigen.h"
 #include "ceres/internal/macros.h"
 #include "ceres/internal/port.h"
 #include "ceres/sparse_matrix.h"
-#include "ceres/triplet_sparse_matrix.h"
 #include "ceres/types.h"
-#include "glog/logging.h"
 
 namespace ceres {
 
@@ -47,7 +43,7 @@
 
 namespace internal {
 
-class SparseMatrixProto;
+class TripletSparseMatrix;
 
 class CompressedRowSparseMatrix : public SparseMatrix {
  public:
@@ -58,9 +54,6 @@
   //
   // We assume that m does not have any repeated entries.
   explicit CompressedRowSparseMatrix(const TripletSparseMatrix& m);
-#ifndef CERES_NO_PROTOCOL_BUFFERS
-  explicit CompressedRowSparseMatrix(const SparseMatrixProto& proto);
-#endif
 
   // Use this constructor only if you know what you are doing. This
   // creates a "blank" matrix with the appropriate amount of memory
@@ -91,15 +84,12 @@
   virtual void ScaleColumns(const double* scale);
 
   virtual void ToDenseMatrix(Matrix* dense_matrix) const;
-#ifndef CERES_NO_PROTOCOL_BUFFERS
-  virtual void ToProto(SparseMatrixProto* proto) const;
-#endif
   virtual void ToTextFile(FILE* file) const;
   virtual int num_rows() const { return num_rows_; }
   virtual int num_cols() const { return num_cols_; }
   virtual int num_nonzeros() const { return rows_[num_rows_]; }
-  virtual const double* values() const { return values_.get(); }
-  virtual double* mutable_values() { return values_.get(); }
+  virtual const double* values() const { return &values_[0]; }
+  virtual double* mutable_values() { return &values_[0]; }
 
   // Delete the bottom delta_rows.
   // num_rows -= delta_rows
@@ -112,11 +102,11 @@
   void ToCRSMatrix(CRSMatrix* matrix) const;
 
   // Low level access methods that expose the structure of the matrix.
-  const int* cols() const { return cols_.get(); }
-  int* mutable_cols() { return cols_.get(); }
+  const int* cols() const { return &cols_[0]; }
+  int* mutable_cols() { return &cols_[0]; }
 
-  const int* rows() const { return rows_.get(); }
-  int* mutable_rows() { return rows_.get(); }
+  const int* rows() const { return &rows_[0]; }
+  int* mutable_rows() { return &rows_[0]; }
 
   const vector<int>& row_blocks() const { return row_blocks_; }
   vector<int>* mutable_row_blocks() { return &row_blocks_; }
@@ -124,14 +114,25 @@
   const vector<int>& col_blocks() const { return col_blocks_; }
   vector<int>* mutable_col_blocks() { return &col_blocks_; }
 
- private:
-  scoped_array<int> cols_;
-  scoped_array<int> rows_;
-  scoped_array<double> values_;
+  // Non-destructive array resizing method.
+  void set_num_rows(const int num_rows) { num_rows_ = num_rows; };
+  void set_num_cols(const int num_cols) { num_cols_ = num_cols; };
 
+  void SolveLowerTriangularInPlace(double* solution) const;
+  void SolveLowerTriangularTransposeInPlace(double* solution) const;
+
+  CompressedRowSparseMatrix* Transpose() const;
+
+  static CompressedRowSparseMatrix* CreateBlockDiagonalMatrix(
+      const double* diagonal,
+      const vector<int>& blocks);
+
+ private:
   int num_rows_;
   int num_cols_;
-  int max_num_nonzeros_;
+  vector<int> rows_;
+  vector<int> cols_;
+  vector<double> values_;
 
   // If the matrix has an underlying block structure, then it can also
   // carry with it row and column block sizes. This is auxilliary and
diff --git a/internal/ceres/compressed_row_sparse_matrix_test.cc b/internal/ceres/compressed_row_sparse_matrix_test.cc
index c9c3f14..63b66a0 100644
--- a/internal/ceres/compressed_row_sparse_matrix_test.cc
+++ b/internal/ceres/compressed_row_sparse_matrix_test.cc
@@ -35,8 +35,8 @@
 #include "ceres/internal/eigen.h"
 #include "ceres/internal/scoped_ptr.h"
 #include "ceres/linear_least_squares_problems.h"
-#include "ceres/matrix_proto.h"
 #include "ceres/triplet_sparse_matrix.h"
+#include "glog/logging.h"
 #include "gtest/gtest.h"
 
 namespace ceres {
@@ -146,30 +146,6 @@
   }
 }
 
-#ifndef CERES_NO_PROTOCOL_BUFFERS
-TEST_F(CompressedRowSparseMatrixTest, Serialization) {
-  SparseMatrixProto proto;
-  crsm->ToProto(&proto);
-
-  CompressedRowSparseMatrix n(proto);
-  ASSERT_EQ(n.num_rows(), crsm->num_rows());
-  ASSERT_EQ(n.num_cols(), crsm->num_cols());
-  ASSERT_EQ(n.num_nonzeros(), crsm->num_nonzeros());
-
-  for (int i = 0; i < n.num_rows() + 1; ++i) {
-    ASSERT_EQ(crsm->rows()[i], proto.compressed_row_matrix().rows(i));
-    ASSERT_EQ(crsm->rows()[i], n.rows()[i]);
-  }
-
-  for (int i = 0; i < crsm->num_nonzeros(); ++i) {
-    ASSERT_EQ(crsm->cols()[i], proto.compressed_row_matrix().cols(i));
-    ASSERT_EQ(crsm->cols()[i], n.cols()[i]);
-    ASSERT_EQ(crsm->values()[i], proto.compressed_row_matrix().values(i));
-    ASSERT_EQ(crsm->values()[i], n.values()[i]);
-  }
-}
-#endif
-
 TEST_F(CompressedRowSparseMatrixTest, ToDenseMatrix) {
   Matrix tsm_dense;
   Matrix crsm_dense;
@@ -199,5 +175,155 @@
   }
 }
 
+TEST(CompressedRowSparseMatrix, CreateBlockDiagonalMatrix) {
+  vector<int> blocks;
+  blocks.push_back(1);
+  blocks.push_back(2);
+  blocks.push_back(2);
+
+  Vector diagonal(5);
+  for (int i = 0; i < 5; ++i) {
+    diagonal(i) = i + 1;
+  }
+
+  scoped_ptr<CompressedRowSparseMatrix> matrix(
+      CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(
+          diagonal.data(), blocks));
+
+  EXPECT_EQ(matrix->num_rows(), 5);
+  EXPECT_EQ(matrix->num_cols(), 5);
+  EXPECT_EQ(matrix->num_nonzeros(), 9);
+  EXPECT_EQ(blocks, matrix->row_blocks());
+  EXPECT_EQ(blocks, matrix->col_blocks());
+
+  Vector x(5);
+  Vector y(5);
+
+  x.setOnes();
+  y.setZero();
+  matrix->RightMultiply(x.data(), y.data());
+  for (int i = 0; i < diagonal.size(); ++i) {
+    EXPECT_EQ(y[i], diagonal[i]);
+  }
+
+  y.setZero();
+  matrix->LeftMultiply(x.data(), y.data());
+  for (int i = 0; i < diagonal.size(); ++i) {
+    EXPECT_EQ(y[i], diagonal[i]);
+  };
+
+  Matrix dense;
+  matrix->ToDenseMatrix(&dense);
+  EXPECT_EQ((dense.diagonal() - diagonal).norm(), 0.0);
+}
+
+class SolveLowerTriangularTest : public ::testing::Test {
+ protected:
+  void SetUp() {
+    matrix_.reset(new CompressedRowSparseMatrix(4, 4, 7));
+    int* rows = matrix_->mutable_rows();
+    int* cols = matrix_->mutable_cols();
+    double* values = matrix_->mutable_values();
+
+    rows[0] = 0;
+    cols[0] = 0;
+    values[0] = 0.50754;
+
+    rows[1] = 1;
+    cols[1] = 1;
+    values[1] = 0.80483;
+
+    rows[2] = 2;
+    cols[2] = 1;
+    values[2] = 0.14120;
+    cols[3] = 2;
+    values[3] = 0.3;
+
+    rows[3] = 4;
+    cols[4] = 0;
+    values[4] = 0.77696;
+    cols[5] = 1;
+    values[5] = 0.41860;
+    cols[6] = 3;
+    values[6] = 0.88979;
+
+    rows[4] = 7;
+  }
+
+  scoped_ptr<CompressedRowSparseMatrix> matrix_;
+};
+
+TEST_F(SolveLowerTriangularTest, SolveInPlace) {
+  double rhs_and_solution[] = {1.0, 1.0, 2.0, 2.0};
+  double expected[] = {1.970288,  1.242498,  6.081864, -0.057255};
+  matrix_->SolveLowerTriangularInPlace(rhs_and_solution);
+  for (int i = 0; i < 4; ++i) {
+    EXPECT_NEAR(rhs_and_solution[i], expected[i], 1e-4) << i;
+  }
+}
+
+TEST_F(SolveLowerTriangularTest, TransposeSolveInPlace) {
+  double rhs_and_solution[] = {1.0, 1.0, 2.0, 2.0};
+  const double expected[] = { -1.4706, -1.0962, 6.6667, 2.2477};
+
+  matrix_->SolveLowerTriangularTransposeInPlace(rhs_and_solution);
+  for (int i = 0; i < 4; ++i) {
+    EXPECT_NEAR(rhs_and_solution[i], expected[i], 1e-4) << i;
+  }
+}
+
+TEST(CompressedRowSparseMatrix, Transpose) {
+  //  0  1  0  2  3  0
+  //  4  6  7  0  0  8
+  //  9 10  0 11 12  0
+  // 13  0 14 15  9  0
+  //  0 16 17  0  0  0
+
+  CompressedRowSparseMatrix matrix(5, 6, 30);
+  int* rows = matrix.mutable_rows();
+  int* cols = matrix.mutable_cols();
+  double* values = matrix.mutable_values();
+
+  rows[0] = 0;
+  cols[0] = 1;
+  cols[1] = 3;
+  cols[2] = 4;
+
+  rows[1] = 3;
+  cols[3] = 0;
+  cols[4] = 1;
+  cols[5] = 2;
+  cols[6] = 5;
+
+
+  rows[2] = 7;
+  cols[7] = 0;
+  cols[8] = 1;
+  cols[9] = 3;
+  cols[10] = 4;
+
+  rows[3] = 11;
+  cols[11] = 0;
+  cols[12] = 2;
+  cols[13] = 3;
+  cols[14] = 4;
+
+  rows[4] = 15;
+  cols[15] = 1;
+  cols[16] = 2;
+  rows[5] = 17;
+
+  copy(values, values + 17, cols);
+
+  scoped_ptr<CompressedRowSparseMatrix> transpose(matrix.Transpose());
+
+  Matrix dense_matrix;
+  matrix.ToDenseMatrix(&dense_matrix);
+
+  Matrix dense_transpose;
+  transpose->ToDenseMatrix(&dense_transpose);
+  EXPECT_NEAR((dense_matrix - dense_transpose.transpose()).norm(), 0.0, 1e-14);
+}
+
 }  // namespace internal
 }  // namespace ceres
diff --git a/internal/ceres/incomplete_lq_factorization.cc b/internal/ceres/incomplete_lq_factorization.cc
new file mode 100644
index 0000000..0d51f9a
--- /dev/null
+++ b/internal/ceres/incomplete_lq_factorization.cc
@@ -0,0 +1,239 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2013 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+
+#include "ceres/incomplete_lq_factorization.h"
+
+#include <vector>
+#include <utility>
+#include <cmath>
+#include "ceres/compressed_row_sparse_matrix.h"
+#include "ceres/internal/eigen.h"
+#include "ceres/internal/port.h"
+#include "glog/logging.h"
+
+namespace ceres {
+namespace internal {
+
+// Normalize a row and return it's norm.
+inline double NormalizeRow(const int row, CompressedRowSparseMatrix* matrix) {
+  const int row_begin =  matrix->rows()[row];
+  const int row_end = matrix->rows()[row + 1];
+
+  double* values = matrix->mutable_values();
+  double norm = 0.0;
+  for (int i =  row_begin; i < row_end; ++i) {
+    norm += values[i] * values[i];
+  }
+
+  norm = sqrt(norm);
+  const double inverse_norm = 1.0 / norm;
+  for (int i = row_begin; i < row_end; ++i) {
+    values[i] *= inverse_norm;
+  }
+
+  return norm;
+}
+
+// Compute a(row_a,:) * b(row_b, :)'
+inline double RowDotProduct(const CompressedRowSparseMatrix& a,
+                            const int row_a,
+                            const CompressedRowSparseMatrix& b,
+                            const int row_b) {
+  const int* a_rows = a.rows();
+  const int* a_cols = a.cols();
+  const double* a_values = a.values();
+
+  const int* b_rows = b.rows();
+  const int* b_cols = b.cols();
+  const double* b_values = b.values();
+
+  const int row_a_end = a_rows[row_a + 1];
+  const int row_b_end = b_rows[row_b + 1];
+
+  int idx_a = a_rows[row_a];
+  int idx_b = b_rows[row_b];
+  double dot_product = 0.0;
+  while (idx_a < row_a_end && idx_b < row_b_end) {
+    if (a_cols[idx_a] == b_cols[idx_b]) {
+      dot_product += a_values[idx_a++] * b_values[idx_b++];
+    }
+
+    while (a_cols[idx_a] < b_cols[idx_b] && idx_a < row_a_end) {
+      ++idx_a;
+    }
+
+    while (a_cols[idx_a] > b_cols[idx_b] && idx_b < row_b_end) {
+      ++idx_b;
+    }
+  }
+
+  return dot_product;
+}
+
+struct SecondGreaterThan {
+ public:
+  bool operator()(const pair<int, double>& lhs,
+                  const pair<int, double>& rhs) const {
+    return (fabs(lhs.second) > fabs(rhs.second));
+  }
+};
+
+// In the row vector dense_row(0:num_cols), drop values smaller than
+// the max_value * drop_tolerance. Of the remaining non-zero values,
+// choose at most level_of_fill values and then add the resulting row
+// vector to matrix.
+
+void DropEntriesAndAddRow(const Vector& dense_row,
+                          const int num_entries,
+                          const int level_of_fill,
+                          const double drop_tolerance,
+                          vector<pair<int, double> >* scratch,
+                          CompressedRowSparseMatrix* matrix) {
+  int* rows = matrix->mutable_rows();
+  int* cols = matrix->mutable_cols();
+  double* values = matrix->mutable_values();
+  int num_nonzeros = rows[matrix->num_rows()];
+
+  if (num_entries == 0) {
+    matrix->set_num_rows(matrix->num_rows() + 1);
+    rows[matrix->num_rows()] = num_nonzeros;
+    return;
+  }
+
+  const double max_value = dense_row.head(num_entries).cwiseAbs().maxCoeff();
+  const double threshold = drop_tolerance * max_value;
+
+  int scratch_count = 0;
+  for (int i = 0; i < num_entries; ++i) {
+    if (fabs(dense_row[i]) > threshold) {
+      pair<int, double>& entry = (*scratch)[scratch_count];
+      entry.first = i;
+      entry.second = dense_row[i];
+      ++scratch_count;
+    }
+  }
+
+  if (scratch_count > level_of_fill) {
+    nth_element(scratch->begin(),
+                scratch->begin() + level_of_fill,
+                scratch->begin() + scratch_count,
+                SecondGreaterThan());
+    scratch_count = level_of_fill;
+    sort(scratch->begin(), scratch->begin() + scratch_count);
+  }
+
+  for (int i = 0; i < scratch_count; ++i) {
+    const pair<int, double>& entry = (*scratch)[i];
+    cols[num_nonzeros] = entry.first;
+    values[num_nonzeros] = entry.second;
+    ++num_nonzeros;
+  }
+
+  matrix->set_num_rows(matrix->num_rows() + 1);
+  rows[matrix->num_rows()] = num_nonzeros;
+}
+
+// Saad's Incomplete LQ factorization algorithm.
+CompressedRowSparseMatrix* IncompleteLQFactorization(
+    const CompressedRowSparseMatrix& matrix,
+    const int l_level_of_fill,
+    const double l_drop_tolerance,
+    const int q_level_of_fill,
+    const double q_drop_tolerance) {
+  const int num_rows = matrix.num_rows();
+  const int num_cols = matrix.num_cols();
+  const int* rows = matrix.rows();
+  const int* cols = matrix.cols();
+  const double* values = matrix.values();
+
+  CompressedRowSparseMatrix* l =
+      new CompressedRowSparseMatrix(num_rows,
+                                    num_rows,
+                                    l_level_of_fill * num_rows);
+  l->set_num_rows(0);
+
+  CompressedRowSparseMatrix q(num_rows, num_cols, q_level_of_fill * num_rows);
+  q.set_num_rows(0);
+
+  int* l_rows = l->mutable_rows();
+  int* l_cols = l->mutable_cols();
+  double* l_values = l->mutable_values();
+
+  int* q_rows = q.mutable_rows();
+  int* q_cols = q.mutable_cols();
+  double* q_values = q.mutable_values();
+
+  Vector l_i(num_rows);
+  Vector q_i(num_cols);
+  vector<pair<int, double> > scratch(num_cols);
+  for (int i = 0; i < num_rows; ++i) {
+    // l_i = q * matrix(i,:)');
+    l_i.setZero();
+    for (int j = 0; j < i; ++j) {
+      l_i(j) = RowDotProduct(matrix, i, q, j);
+    }
+    DropEntriesAndAddRow(l_i,
+                         i,
+                         l_level_of_fill,
+                         l_drop_tolerance,
+                         &scratch,
+                         l);
+
+    // q_i = matrix(i,:) - q(0:i-1,:) * l_i);
+    q_i.setZero();
+    for (int idx = rows[i]; idx < rows[i + 1]; ++idx) {
+      q_i(cols[idx]) = values[idx];
+    }
+
+    for (int j = l_rows[i]; j < l_rows[i + 1]; ++j) {
+      const int r = l_cols[j];
+      const double lij = l_values[j];
+      for (int idx = q_rows[r]; idx < q_rows[r + 1]; ++idx) {
+        q_i(q_cols[idx]) -= lij * q_values[idx];
+      }
+    }
+    DropEntriesAndAddRow(q_i,
+                         num_cols,
+                         q_level_of_fill,
+                         q_drop_tolerance,
+                         &scratch,
+                         &q);
+
+    // lii = |qi|
+    l_cols[l->num_nonzeros()] = i;
+    l_values[l->num_nonzeros()] = NormalizeRow(i, &q);
+    l_rows[l->num_rows()] += 1;
+  };
+
+  return l;
+}
+
+}  // namespace internal
+}  // namespace ceres
diff --git a/internal/ceres/incomplete_lq_factorization.h b/internal/ceres/incomplete_lq_factorization.h
new file mode 100644
index 0000000..4f3da3c
--- /dev/null
+++ b/internal/ceres/incomplete_lq_factorization.h
@@ -0,0 +1,83 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2013 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+
+#include "ceres/compressed_row_sparse_matrix.h"
+
+namespace ceres {
+namespace internal {
+
+// Incomplete LQ factorization as described in
+//
+// Preconditioning techniques for indefinite and nonsymmetric linear
+// systems. Yousef Saad, Preprint RIACS-ILQ-TR, RIACS, NASA Ames
+// Research Center, Moffett Field, CA, 1987.
+//
+// An incomplete LQ factorization of a matrix A is a decomposition
+//
+//   A = LQ + E
+//
+// Where L is a lower triangular matrix, and Q is a near orthonormal
+// matrix. The extent of orthonormality depends on E. E is the "drop"
+// matrix. Each row of L has a maximum of l_level_of_fill entries, and
+// all non-zero entries are within l_drop_tolerance of the largest
+// entry. Each row of Q has a maximum of q_level_of_fill entries and
+// all non-zero entries are within q_drop_tolerance of the largest
+// entry.
+//
+// E is the error of the incomplete factorization.
+//
+// The purpose of incomplete factorizations is preconditioning and
+// there one only needs the L matrix, therefore this function just
+// returns L.
+//
+// Caller owns the result.
+CompressedRowSparseMatrix* IncompleteLQFactorization(
+    const CompressedRowSparseMatrix& matrix,
+    const int l_level_of_fill,
+    const double l_drop_tolerance,
+    const int q_level_of_fill,
+    const double q_drop_tolerance);
+
+// In the row vector dense_row(0:num_cols), drop values smaller than
+// the max_value * drop_tolerance. Of the remaining non-zero values,
+// choose at most level_of_fill values and then add the resulting row
+// vector to matrix.
+//
+// scratch is used to prevent allocations inside this function. It is
+// assumed that scratch is of size matrix->num_cols().
+void DropEntriesAndAddRow(const Vector& dense_row,
+                          const int num_entries,
+                          const int level_of_fill,
+                          const double drop_tolerance,
+                          vector<pair<int, double> >* scratch,
+                          CompressedRowSparseMatrix* matrix);
+
+}  // namespace internal
+}  // namespace ceres
diff --git a/internal/ceres/incomplete_lq_factorization_test.cc b/internal/ceres/incomplete_lq_factorization_test.cc
new file mode 100644
index 0000000..0a43f02
--- /dev/null
+++ b/internal/ceres/incomplete_lq_factorization_test.cc
@@ -0,0 +1,196 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2013 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+
+#include "ceres/incomplete_lq_factorization.h"
+
+#include "Eigen/Dense"
+#include "ceres/compressed_row_sparse_matrix.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+
+namespace ceres {
+namespace internal {
+
+void ExpectMatricesAreEqual(const CompressedRowSparseMatrix& expected,
+                            const CompressedRowSparseMatrix& actual,
+                            const double tolerance) {
+  EXPECT_EQ(expected.num_rows(), actual.num_rows());
+  EXPECT_EQ(expected.num_cols(), actual.num_cols());
+  for (int i = 0; i < expected.num_rows(); ++i) {
+    EXPECT_EQ(expected.rows()[i], actual.rows()[i]);
+  }
+
+  for (int i = 0; i < actual.num_nonzeros(); ++i) {
+    EXPECT_EQ(expected.cols()[i], actual.cols()[i]);
+    EXPECT_NEAR(expected.values()[i], actual.values()[i], tolerance);
+  }
+}
+
+TEST(IncompleteQRFactorization, OneByOneMatrix) {
+  CompressedRowSparseMatrix matrix(1, 1, 1);
+  matrix.mutable_rows()[0] = 0;
+  matrix.mutable_rows()[1] = 1;
+  matrix.mutable_cols()[0] = 0;
+  matrix.mutable_values()[0] = 2;
+
+  scoped_ptr<CompressedRowSparseMatrix> l(
+      IncompleteLQFactorization(matrix, 1, 0.0, 1, 0.0));
+  ExpectMatricesAreEqual(matrix, *l, 1e-16);
+}
+
+TEST(IncompleteLQFactorization, CompleteFactorization) {
+  double dense_matrix[] = {
+    0.00000,  0.00000,  0.20522,  0.00000,  0.49077,  0.92835,  0.00000,  0.83825,  0.00000,  0.00000,  // NOLINT
+    0.00000,  0.00000,  0.00000,  0.62491,  0.38144,  0.00000,  0.79394,  0.79178,  0.00000,  0.44382,  // NOLINT
+    0.00000,  0.00000,  0.00000,  0.61517,  0.55941,  0.00000,  0.00000,  0.00000,  0.00000,  0.60664,  // NOLINT
+    0.00000,  0.00000,  0.00000,  0.00000,  0.45031,  0.00000,  0.64132,  0.00000,  0.38832,  0.00000,  // NOLINT
+    0.00000,  0.00000,  0.00000,  0.00000,  0.00000,  0.57121,  0.00000,  0.01375,  0.70640,  0.00000,  // NOLINT
+    0.00000,  0.00000,  0.07451,  0.00000,  0.00000,  0.00000,  0.00000,  0.00000,  0.00000,  0.00000,  // NOLINT
+    0.68095,  0.00000,  0.00000,  0.95473,  0.00000,  0.00000,  0.00000,  0.00000,  0.00000,  0.00000,  // NOLINT
+    0.00000,  0.00000,  0.00000,  0.00000,  0.59374,  0.00000,  0.00000,  0.00000,  0.49139,  0.00000,  // NOLINT
+    0.91276,  0.96641,  0.00000,  0.00000,  0.00000,  0.00000,  0.00000,  0.00000,  0.00000,  0.91797,  // NOLINT
+    0.96828,  0.00000,  0.00000,  0.72583,  0.00000,  0.00000,  0.81459,  0.00000,  0.04560,  0.00000   // NOLINT
+  };
+
+  CompressedRowSparseMatrix matrix(10, 10, 100);
+  int* rows = matrix.mutable_rows();
+  int* cols = matrix.mutable_cols();
+  double* values = matrix.mutable_values();
+
+  int idx = 0;
+  for (int i = 0; i < 10; ++i) {
+    rows[i] = idx;
+    for (int j = 0; j < 10; ++j) {
+      const double v = dense_matrix[i * 10 + j];
+      if (fabs(v) > 1e-6) {
+        cols[idx] = j;
+        values[idx] = v;
+        ++idx;
+      }
+    }
+  }
+  rows[10] = idx;
+
+  scoped_ptr<CompressedRowSparseMatrix> lmatrix(
+      IncompleteLQFactorization(matrix, 10, 0.0, 10, 0.0));
+
+  ConstMatrixRef mref(dense_matrix, 10, 10);
+
+  // Use Cholesky factorization to compute the L matrix.
+  Matrix expected_l_matrix  = (mref * mref.transpose()).llt().matrixL();
+  Matrix actual_l_matrix;
+  lmatrix->ToDenseMatrix(&actual_l_matrix);
+
+  EXPECT_NEAR((expected_l_matrix * expected_l_matrix.transpose() -
+               actual_l_matrix * actual_l_matrix.transpose()).norm(), 0.0, 1e-10)
+      << "expected: \n" << expected_l_matrix
+      << "\actual: \n" << actual_l_matrix;
+}
+
+TEST(IncompleteLQFactorization, DropEntriesAndAddRow) {
+  // Allocate space and then make it a zero sized matrix.
+  CompressedRowSparseMatrix matrix(10,10, 100);
+  matrix.set_num_rows(0);
+
+  vector<pair<int, double> > scratch(10);
+
+  Vector dense_vector(10);
+  dense_vector(0) = 5;
+  dense_vector(1) = 1;
+  dense_vector(2) = 2;
+  dense_vector(3) = 3;
+  dense_vector(4) = 1;
+  dense_vector(5) = 4;
+
+  // Add a row with just one entry.
+  DropEntriesAndAddRow(dense_vector, 1, 1, 0, &scratch, &matrix);
+  EXPECT_EQ(matrix.num_rows(), 1);
+  EXPECT_EQ(matrix.num_cols(), 10);
+  EXPECT_EQ(matrix.num_nonzeros(), 1);
+  EXPECT_EQ(matrix.values()[0], 5.0);
+  EXPECT_EQ(matrix.cols()[0], 0);
+
+  // Add a row with six entries
+  DropEntriesAndAddRow(dense_vector, 6, 10, 0, &scratch, &matrix);
+  EXPECT_EQ(matrix.num_rows(), 2);
+  EXPECT_EQ(matrix.num_cols(), 10);
+  EXPECT_EQ(matrix.num_nonzeros(), 7);
+  for (int idx = matrix.rows()[1]; idx < matrix.rows()[2]; ++idx) {
+    EXPECT_EQ(matrix.cols()[idx], idx - matrix.rows()[1]);
+    EXPECT_EQ(matrix.values()[idx], dense_vector(idx - matrix.rows()[1]));
+  }
+
+  // Add the top 3 entries.
+  DropEntriesAndAddRow(dense_vector, 6, 3, 0, &scratch, &matrix);
+  EXPECT_EQ(matrix.num_rows(), 3);
+  EXPECT_EQ(matrix.num_cols(), 10);
+  EXPECT_EQ(matrix.num_nonzeros(), 10);
+
+  EXPECT_EQ(matrix.cols()[matrix.rows()[2]], 0);
+  EXPECT_EQ(matrix.cols()[matrix.rows()[2] + 1], 3);
+  EXPECT_EQ(matrix.cols()[matrix.rows()[2] + 2], 5);
+
+  EXPECT_EQ(matrix.values()[matrix.rows()[2]], 5);
+  EXPECT_EQ(matrix.values()[matrix.rows()[2] + 1], 3);
+  EXPECT_EQ(matrix.values()[matrix.rows()[2] + 2], 4);
+
+  // Only keep entries greater than 1.0;
+  DropEntriesAndAddRow(dense_vector, 6, 6, 0.2, &scratch, &matrix);
+  EXPECT_EQ(matrix.num_rows(), 4);
+  EXPECT_EQ(matrix.num_cols(), 10);
+  EXPECT_EQ(matrix.num_nonzeros(), 14);
+
+  EXPECT_EQ(matrix.cols()[matrix.rows()[3]], 0);
+  EXPECT_EQ(matrix.cols()[matrix.rows()[3] + 1], 2);
+  EXPECT_EQ(matrix.cols()[matrix.rows()[3] + 2], 3);
+  EXPECT_EQ(matrix.cols()[matrix.rows()[3] + 3], 5);
+
+  EXPECT_EQ(matrix.values()[matrix.rows()[3]], 5);
+  EXPECT_EQ(matrix.values()[matrix.rows()[3] + 1], 2);
+  EXPECT_EQ(matrix.values()[matrix.rows()[3] + 2], 3);
+  EXPECT_EQ(matrix.values()[matrix.rows()[3] + 3], 4);
+
+  // Only keep the top 2 entries greater than 1.0
+  DropEntriesAndAddRow(dense_vector, 6, 2, 0.2, &scratch, &matrix);
+  EXPECT_EQ(matrix.num_rows(), 5);
+  EXPECT_EQ(matrix.num_cols(), 10);
+  EXPECT_EQ(matrix.num_nonzeros(), 16);
+
+  EXPECT_EQ(matrix.cols()[matrix.rows()[4]], 0);
+  EXPECT_EQ(matrix.cols()[matrix.rows()[4] + 1], 5);
+
+  EXPECT_EQ(matrix.values()[matrix.rows()[4]], 5);
+  EXPECT_EQ(matrix.values()[matrix.rows()[4] + 1], 4);
+}
+
+
+}  // namespace internal
+}  // namespace ceres
diff --git a/internal/ceres/linear_least_squares_problems.cc b/internal/ceres/linear_least_squares_problems.cc
index d1ee7f0..df6d03f 100644
--- a/internal/ceres/linear_least_squares_problems.cc
+++ b/internal/ceres/linear_least_squares_problems.cc
@@ -36,7 +36,6 @@
 #include "ceres/block_sparse_matrix.h"
 #include "ceres/block_structure.h"
 #include "ceres/casts.h"
-#include "ceres/compressed_row_sparse_matrix.h"
 #include "ceres/file.h"
 #include "ceres/internal/scoped_ptr.h"
 #include "ceres/matrix_proto.h"
@@ -82,7 +81,7 @@
   } else if (A.has_triplet_matrix()) {
     problem->A.reset(new TripletSparseMatrix(A));
   } else {
-    problem->A.reset(new CompressedRowSparseMatrix(A));
+    LOG(FATAL) << "Broken.";
   }
 
   if (problem_proto.b_size() > 0) {