Merge pull request #1878 from borglab/cg-methods

gtsam/nonlinear/NonlinearConjugateGradientOptimizer.cpp

@@ -43,7 +43,7 @@ static VectorValues gradientInPlace(const NonlinearFactorGraph& nfg,
 
 NonlinearConjugateGradientOptimizer::NonlinearConjugateGradientOptimizer(
     const NonlinearFactorGraph& graph, const Values& initialValues,
-    const Parameters& params)
+    const Parameters& params, const DirectionMethod& directionMethod)
     : Base(graph, std::unique_ptr<State>(
                       new State(initialValues, graph.error(initialValues)))),
       params_(params) {}
@@ -70,7 +70,8 @@ NonlinearConjugateGradientOptimizer::System::advance(const State& current,
 
 GaussianFactorGraph::shared_ptr NonlinearConjugateGradientOptimizer::iterate() {
   const auto [newValues, dummy] = nonlinearConjugateGradient<System, Values>(
-      System(graph_), state_->values, params_, true /* single iteration */);
+      System(graph_), state_->values, params_, true /* single iteration */,
+      directionMethod_);
   state_.reset(
       new State(newValues, graph_.error(newValues), state_->iterations + 1));
 
@@ -81,8 +82,8 @@ GaussianFactorGraph::shared_ptr NonlinearConjugateGradientOptimizer::iterate() {
 const Values& NonlinearConjugateGradientOptimizer::optimize() {
   // Optimize until convergence
   System system(graph_);
-  const auto [newValues, iterations] =
-      nonlinearConjugateGradient(system, state_->values, params_, false);
+  const auto [newValues, iterations] = nonlinearConjugateGradient(
+      system, state_->values, params_, false, directionMethod_);
   state_.reset(
       new State(std::move(newValues), graph_.error(newValues), iterations));
   return state_->values;

gtsam/nonlinear/NonlinearConjugateGradientOptimizer.h

@@ -23,6 +23,57 @@
 
 namespace gtsam {
 
+/// Fletcher-Reeves formula for computing β, the direction of steepest descent.
+template <typename Gradient>
+double FletcherReeves(const Gradient &currentGradient,
+                      const Gradient &prevGradient) {
+  // Fletcher-Reeves: beta = g_n'*g_n/g_n-1'*g_n-1
+  const double beta =
+      currentGradient.dot(currentGradient) / prevGradient.dot(prevGradient);
+  return beta;
+}
+
+/// Polak-Ribiere formula for computing β, the direction of steepest descent.
+template <typename Gradient>
+double PolakRibiere(const Gradient &currentGradient,
+                    const Gradient &prevGradient) {
+  // Polak-Ribiere: beta = g_n'*(g_n-g_n-1)/g_n-1'*g_n-1
+  const double beta =
+      std::max(0.0, currentGradient.dot(currentGradient - prevGradient) /
+                        prevGradient.dot(prevGradient));
+  return beta;
+}
+
+/// The Hestenes-Stiefel formula for computing β,
+/// the direction of steepest descent.
+template <typename Gradient>
+double HestenesStiefel(const Gradient &currentGradient,
+                       const Gradient &prevGradient,
+                       const Gradient &direction) {
+  // Hestenes-Stiefel: beta = g_n'*(g_n-g_n-1)/(-s_n-1')*(g_n-g_n-1)
+  Gradient d = currentGradient - prevGradient;
+  const double beta = std::max(0.0, currentGradient.dot(d) / -direction.dot(d));
+  return beta;
+}
+
+/// The Dai-Yuan formula for computing β, the direction of steepest descent.
+template <typename Gradient>
+double DaiYuan(const Gradient &currentGradient, const Gradient &prevGradient,
+               const Gradient &direction) {
+  // Dai-Yuan: beta = g_n'*g_n/(-s_n-1')*(g_n-g_n-1)
+  const double beta =
+      std::max(0.0, currentGradient.dot(currentGradient) /
+                        -direction.dot(currentGradient - prevGradient));
+  return beta;
+}
+
+enum class DirectionMethod {
+  FletcherReeves,
+  PolakRibiere,
+  HestenesStiefel,
+  DaiYuan
+};
+
 /**  An implementation of the nonlinear CG method using the template below */
 class GTSAM_EXPORT NonlinearConjugateGradientOptimizer
     : public NonlinearOptimizer {
@@ -51,14 +102,16 @@ class GTSAM_EXPORT NonlinearConjugateGradientOptimizer
 
  protected:
   Parameters params_;
+  DirectionMethod directionMethod_ = DirectionMethod::PolakRibiere;
 
   const NonlinearOptimizerParams &_params() const override { return params_; }
 
  public:
   /// Constructor
-  NonlinearConjugateGradientOptimizer(const NonlinearFactorGraph &graph,
-                                      const Values &initialValues,
-                                      const Parameters &params = Parameters());
+  NonlinearConjugateGradientOptimizer(
+      const NonlinearFactorGraph &graph, const Values &initialValues,
+      const Parameters &params = Parameters(),
+      const DirectionMethod &directionMethod = DirectionMethod::PolakRibiere);
 
   /// Destructor
   ~NonlinearConjugateGradientOptimizer() override {}
@@ -140,7 +193,9 @@ double lineSearch(const S &system, const V currentValues, const W &gradient) {
 template <class S, class V>
 std::tuple<V, int> nonlinearConjugateGradient(
     const S &system, const V &initial, const NonlinearOptimizerParams &params,
-    const bool singleIteration, const bool gradientDescent = false) {
+    const bool singleIteration,
+    const DirectionMethod &directionMethod = DirectionMethod::PolakRibiere,
+    const bool gradientDescent = false) {
   // GTSAM_CONCEPT_MANIFOLD_TYPE(V)
 
   size_t iteration = 0;
@@ -177,10 +232,23 @@ std::tuple<V, int> nonlinearConjugateGradient(
     } else {
       prevGradient = currentGradient;
       currentGradient = system.gradient(currentValues);
-      // Polak-Ribiere: beta = g'*(g_n-g_n-1)/g_n-1'*g_n-1
-      const double beta =
-          std::max(0.0, currentGradient.dot(currentGradient - prevGradient) /
-                            prevGradient.dot(prevGradient));
+
+      double beta;
+      switch (directionMethod) {
+        case DirectionMethod::FletcherReeves:
+          beta = FletcherReeves(currentGradient, prevGradient);
+          break;
+        case DirectionMethod::PolakRibiere:
+          beta = PolakRibiere(currentGradient, prevGradient);
+          break;
+        case DirectionMethod::HestenesStiefel:
+          beta = HestenesStiefel(currentGradient, prevGradient, direction);
+          break;
+        case DirectionMethod::DaiYuan:
+          beta = DaiYuan(currentGradient, prevGradient, direction);
+          break;
+      }
+
       direction = currentGradient + (beta * direction);
     }
 

gtsam/nonlinear/tests/testNonlinearConjugateGradientOptimizer.cpp

@@ -79,6 +79,49 @@ TEST(NonlinearConjugateGradientOptimizer, Optimize) {
   EXPECT_DOUBLES_EQUAL(0.0, graph.error(result), 1e-4);
 }
 
+/* ************************************************************************* */
+/// Test different direction methods
+TEST(NonlinearConjugateGradientOptimizer, DirectionMethods) {
+  const auto [graph, initialEstimate] = generateProblem();
+
+  NonlinearOptimizerParams param;
+  param.maxIterations =
+      500; /* requires a larger number of iterations to converge */
+  param.verbosity = NonlinearOptimizerParams::SILENT;
+
+  // Fletcher-Reeves
+  {
+    NonlinearConjugateGradientOptimizer optimizer(
+        graph, initialEstimate, param, DirectionMethod::FletcherReeves);
+    Values result = optimizer.optimize();
+
+    EXPECT_DOUBLES_EQUAL(0.0, graph.error(result), 1e-4);
+  }
+  // Polak-Ribiere
+  {
+    NonlinearConjugateGradientOptimizer optimizer(
+        graph, initialEstimate, param, DirectionMethod::PolakRibiere);
+    Values result = optimizer.optimize();
+
+    EXPECT_DOUBLES_EQUAL(0.0, graph.error(result), 1e-4);
+  }
+  // Hestenes-Stiefel
+  {
+    NonlinearConjugateGradientOptimizer optimizer(
+        graph, initialEstimate, param, DirectionMethod::HestenesStiefel);
+    Values result = optimizer.optimize();
+
+    EXPECT_DOUBLES_EQUAL(0.0, graph.error(result), 1e-4);
+  }
+  // Dai-Yuan
+  {
+    NonlinearConjugateGradientOptimizer optimizer(graph, initialEstimate, param,
+                                                  DirectionMethod::DaiYuan);
+    Values result = optimizer.optimize();
+
+    EXPECT_DOUBLES_EQUAL(0.0, graph.error(result), 1e-4);
+  }
+}
 /* ************************************************************************* */
 int main() {
   TestResult tr;