Add DynamicSparseNormalCholeskySolver

This code was currently buried under a bool inside SparseNormalCholeskySolver.
Pulling this out in its own solver makes the code simpler more readable
and more performant in the case of SuiteSparse.

Change-Id: I72379ca9ca162abbb83c12f7ee8ff92bc71e772c

internal/ceres/CMakeLists.txt

@@ -61,6 +61,7 @@ set(CERES_INTERNAL_SRC
     dogleg_strategy.cc
     dynamic_compressed_row_jacobian_writer.cc
     dynamic_compressed_row_sparse_matrix.cc
+    dynamic_sparse_normal_cholesky_solver.cc
     evaluator.cc
     file.cc
     gradient_checker.cc

internal/ceres/dynamic_sparse_normal_cholesky_solver.cc

@@ -0,0 +1,292 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2017 Google Inc. All rights reserved.
+// http://ceres-solver.org/
+//
+// 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/dynamic_sparse_normal_cholesky_solver.h"
+
+#include <algorithm>
+#include <cstring>
+#include <ctime>
+#include <sstream>
+
+#include "Eigen/SparseCore"
+#include "ceres/compressed_row_sparse_matrix.h"
+#include "ceres/cxsparse.h"
+#include "ceres/internal/eigen.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/linear_solver.h"
+#include "ceres/suitesparse.h"
+#include "ceres/triplet_sparse_matrix.h"
+#include "ceres/types.h"
+#include "ceres/wall_time.h"
+
+#ifdef CERES_USE_EIGEN_SPARSE
+#include "Eigen/SparseCholesky"
+#endif
+
+namespace ceres {
+namespace internal {
+
+DynamicSparseNormalCholeskySolver::DynamicSparseNormalCholeskySolver(
+    const LinearSolver::Options& options)
+    : options_(options) {}
+
+LinearSolver::Summary DynamicSparseNormalCholeskySolver::SolveImpl(
+    CompressedRowSparseMatrix* A,
+    const double* b,
+    const LinearSolver::PerSolveOptions& per_solve_options,
+    double* x) {
+  const int num_cols = A->num_cols();
+  VectorRef(x, num_cols).setZero();
+  A->LeftMultiply(b, x);
+
+  if (per_solve_options.D != NULL) {
+    // Temporarily append a diagonal block to the A matrix, but undo
+    // it before returning the matrix to the user.
+    scoped_ptr<CompressedRowSparseMatrix> regularizer;
+    if (A->col_blocks().size() > 0) {
+      regularizer.reset(CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(
+          per_solve_options.D, A->col_blocks()));
+    } else {
+      regularizer.reset(
+          new CompressedRowSparseMatrix(per_solve_options.D, num_cols));
+    }
+    A->AppendRows(*regularizer);
+  }
+
+  LinearSolver::Summary summary;
+  switch (options_.sparse_linear_algebra_library_type) {
+    case SUITE_SPARSE:
+      summary = SolveImplUsingSuiteSparse(A, x);
+      break;
+    case CX_SPARSE:
+      summary = SolveImplUsingCXSparse(A, x);
+      break;
+    case EIGEN_SPARSE:
+      summary = SolveImplUsingEigen(A, x);
+      break;
+    default:
+      LOG(FATAL) << "Unknown sparse linear algebra library : "
+                 << options_.sparse_linear_algebra_library_type;
+  }
+
+  if (per_solve_options.D != NULL) {
+    A->DeleteRows(num_cols);
+  }
+
+  return summary;
+}
+
+LinearSolver::Summary DynamicSparseNormalCholeskySolver::SolveImplUsingEigen(
+    CompressedRowSparseMatrix* A, double* rhs_and_solution) {
+#ifndef CERES_USE_EIGEN_SPARSE
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 0;
+  summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+  summary.message =
+      "SPARSE_NORMAL_CHOLESKY cannot be used with EIGEN_SPARSE "
+      "because Ceres was not built with support for "
+      "Eigen's SimplicialLDLT decomposition. "
+      "This requires enabling building with -DEIGENSPARSE=ON.";
+  return summary;
+
+#else
+
+  EventLogger event_logger("DynamicSparseNormalCholeskySolver::Eigen::Solve");
+
+  Eigen::MappedSparseMatrix<double, Eigen::RowMajor> a(A->num_rows(),
+                                                       A->num_cols(),
+                                                       A->num_nonzeros(),
+                                                       A->mutable_rows(),
+                                                       A->mutable_cols(),
+                                                       A->mutable_values());
+
+  Eigen::SparseMatrix<double> lhs = a.transpose() * a;
+  Eigen::SimplicialLDLT<Eigen::SparseMatrix<double> > solver;
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 1;
+  summary.termination_type = LINEAR_SOLVER_SUCCESS;
+  summary.message = "Success.";
+
+  solver.analyzePattern(lhs);
+  if (VLOG_IS_ON(2)) {
+    std::stringstream ss;
+    solver.dumpMemory(ss);
+    VLOG(2) << "Symbolic Analysis\n" << ss.str();
+  }
+
+  event_logger.AddEvent("Analyze");
+  if (solver.info() != Eigen::Success) {
+    summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+    summary.message = "Eigen failure. Unable to find symbolic factorization.";
+    return summary;
+  }
+
+  solver.factorize(lhs);
+  event_logger.AddEvent("Factorize");
+  if (solver.info() != Eigen::Success) {
+    summary.termination_type = LINEAR_SOLVER_FAILURE;
+    summary.message = "Eigen failure. Unable to find numeric factorization.";
+    return summary;
+  }
+
+  const Vector rhs = VectorRef(rhs_and_solution, lhs.cols());
+  VectorRef(rhs_and_solution, lhs.cols()) = solver.solve(rhs);
+  event_logger.AddEvent("Solve");
+  if (solver.info() != Eigen::Success) {
+    summary.termination_type = LINEAR_SOLVER_FAILURE;
+    summary.message = "Eigen failure. Unable to do triangular solve.";
+    return summary;
+  }
+
+  return summary;
+#endif  // CERES_USE_EIGEN_SPARSE
+}
+
+LinearSolver::Summary DynamicSparseNormalCholeskySolver::SolveImplUsingCXSparse(
+    CompressedRowSparseMatrix* A, double* rhs_and_solution) {
+#ifdef CERES_NO_CXSPARSE
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 0;
+  summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+  summary.message =
+      "SPARSE_NORMAL_CHOLESKY cannot be used with CX_SPARSE "
+      "because Ceres was not built with support for CXSparse. "
+      "This requires enabling building with -DCXSPARSE=ON.";
+
+  return summary;
+
+#else
+  EventLogger event_logger(
+      "DynamicSparseNormalCholeskySolver::CXSparse::Solve");
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 1;
+  summary.termination_type = LINEAR_SOLVER_SUCCESS;
+  summary.message = "Success.";
+
+  CXSparse cxsparse;
+
+  // Wrap the augmented Jacobian in a compressed sparse column matrix.
+  cs_di a_transpose = cxsparse.CreateSparseMatrixTransposeView(A);
+
+  // Compute the normal equations. J'J delta = J'f and solve them
+  // using a sparse Cholesky factorization. Notice that when compared
+  // to SuiteSparse we have to explicitly compute the transpose of Jt,
+  // and then the normal equations before they can be
+  // factorized. CHOLMOD/SuiteSparse on the other hand can just work
+  // off of Jt to compute the Cholesky factorization of the normal
+  // equations.
+  cs_di* a = cxsparse.TransposeMatrix(&a_transpose);
+  cs_di* lhs = cxsparse.MatrixMatrixMultiply(&a_transpose, a);
+  cxsparse.Free(a);
+  event_logger.AddEvent("NormalEquations");
+
+  cs_dis* factor = cxsparse.AnalyzeCholesky(lhs);
+  event_logger.AddEvent("Analysis");
+
+  if (factor == NULL) {
+    summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+    summary.message = "CXSparse::AnalyzeCholesky failed.";
+  } else if (!cxsparse.SolveCholesky(lhs, factor, rhs_and_solution)) {
+    summary.termination_type = LINEAR_SOLVER_FAILURE;
+    summary.message = "CXSparse::SolveCholesky failed.";
+  }
+  event_logger.AddEvent("Solve");
+
+  cxsparse.Free(lhs);
+  cxsparse.Free(factor);
+  event_logger.AddEvent("TearDown");
+  return summary;
+#endif
+}
+
+LinearSolver::Summary
+DynamicSparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
+    CompressedRowSparseMatrix* A, double* rhs_and_solution) {
+#ifdef CERES_NO_SUITESPARSE
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 0;
+  summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+  summary.message =
+      "SPARSE_NORMAL_CHOLESKY cannot be used with SUITE_SPARSE "
+      "because Ceres was not built with support for SuiteSparse. "
+      "This requires enabling building with -DSUITESPARSE=ON.";
+  return summary;
+
+#else
+
+  EventLogger event_logger(
+      "DynamicSparseNormalCholeskySolver::SuiteSparse::Solve");
+  LinearSolver::Summary summary;
+  summary.termination_type = LINEAR_SOLVER_SUCCESS;
+  summary.num_iterations = 1;
+  summary.message = "Success.";
+
+  SuiteSparse ss;
+  const int num_cols = A->num_cols();
+  cholmod_sparse lhs = ss.CreateSparseMatrixTransposeView(A, 0);
+  event_logger.AddEvent("Setup");
+  cholmod_factor* factor = ss.AnalyzeCholesky(&lhs, &summary.message);
+  event_logger.AddEvent("Analysis");
+
+  if (factor == NULL) {
+    summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+    return summary;
+  }
+
+  summary.termination_type = ss.Cholesky(&lhs, factor, &summary.message);
+  if (summary.termination_type == LINEAR_SOLVER_SUCCESS) {
+    cholmod_dense* rhs =
+        ss.CreateDenseVector(rhs_and_solution, num_cols, num_cols);
+    cholmod_dense* solution = ss.Solve(factor, rhs, &summary.message);
+    event_logger.AddEvent("Solve");
+    ss.Free(rhs);
+    if (solution != NULL) {
+      memcpy(
+          rhs_and_solution, solution->x, num_cols * sizeof(*rhs_and_solution));
+      ss.Free(solution);
+    } else {
+      summary.termination_type = LINEAR_SOLVER_FAILURE;
+    }
+  }
+
+  ss.Free(factor);
+  event_logger.AddEvent("Teardown");
+  return summary;
+
+#endif
+}
+
+}  // namespace internal
+}  // namespace ceres

internal/ceres/dynamic_sparse_normal_cholesky_solver.h

@@ -0,0 +1,85 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2017 Google Inc. All rights reserved.
+// http://ceres-solver.org/
+//
+// 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)
+//
+// A solver for sparse linear least squares problem based on solving
+// the normal equations via a sparse cholesky factorization.
+
+#ifndef CERES_INTERNAL_DYNAMIC_SPARSE_NORMAL_CHOLESKY_SOLVER_H_
+#define CERES_INTERNAL_DYNAMIC_SPARSE_NORMAL_CHOLESKY_SOLVER_H_
+
+// This include must come before any #ifndef check on Ceres compile options.
+#include "ceres/internal/port.h"
+
+#include "ceres/internal/macros.h"
+#include "ceres/linear_solver.h"
+
+namespace ceres {
+namespace internal {
+
+class CompressedRowSparseMatrix;
+
+// A variant of SparseNormalCholeskySolver in the case where matrix
+// sparsity is not constant across calls to Solve. This means that
+// there is no benefit to symbolically factorizing the matrix and
+// caching this factorization.
+class DynamicSparseNormalCholeskySolver
+    : public CompressedRowSparseMatrixSolver {
+ public:
+  explicit DynamicSparseNormalCholeskySolver(
+      const LinearSolver::Options& options);
+  virtual ~DynamicSparseNormalCholeskySolver() {}
+
+ private:
+  virtual LinearSolver::Summary SolveImpl(
+      CompressedRowSparseMatrix* A,
+      const double* b,
+      const LinearSolver::PerSolveOptions& options,
+      double* x);
+
+  LinearSolver::Summary SolveImplUsingSuiteSparse(
+      CompressedRowSparseMatrix* A,
+      double* rhs_and_solution);
+
+  LinearSolver::Summary SolveImplUsingCXSparse(
+      CompressedRowSparseMatrix* A,
+      double* rhs_and_solution);
+
+  LinearSolver::Summary SolveImplUsingEigen(
+      CompressedRowSparseMatrix* A,
+      double* rhs_and_solution);
+
+  const LinearSolver::Options options_;
+  CERES_DISALLOW_COPY_AND_ASSIGN(DynamicSparseNormalCholeskySolver);
+};
+
+}  // namespace internal
+}  // namespace ceres
+
+#endif  // CERES_INTERNAL_DYNAMIC_SPARSE_NORMAL_CHOLESKY_SOLVER_H_

internal/ceres/linear_solver.cc

@@ -35,6 +35,7 @@
 #include "ceres/dense_qr_solver.h"
 #include "ceres/iterative_schur_complement_solver.h"
 #include "ceres/schur_complement_solver.h"
+#include "ceres/dynamic_sparse_normal_cholesky_solver.h"
 #include "ceres/sparse_normal_cholesky_solver.h"
 #include "ceres/types.h"
 #include "glog/logging.h"
@@ -80,6 +81,10 @@ LinearSolver* LinearSolver::Create(const LinearSolver::Options& options) {
    !defined(CERES_USE_EIGEN_SPARSE)
       return NULL;
 #else
+      if (options.dynamic_sparsity) {
+        return new DynamicSparseNormalCholeskySolver(options);
+      }
+
       return new SparseNormalCholeskySolver(options);
 #endif
 

jni/Android.mk

@@ -144,6 +144,7 @@ LOCAL_SRC_FILES := $(CERES_SRC_PATH)/array_utils.cc \
                    $(CERES_SRC_PATH)/dogleg_strategy.cc \
                    $(CERES_SRC_PATH)/dynamic_compressed_row_jacobian_writer.cc \
                    $(CERES_SRC_PATH)/dynamic_compressed_row_sparse_matrix.cc \
+                   $(CERES_SRC_PATH)/dynamic_sparse_normal_cholesky_solver.cc \
                    $(CERES_SRC_PATH)/evaluator.cc \
                    $(CERES_SRC_PATH)/file.cc \
                    $(CERES_SRC_PATH)/gradient_checker.cc \