From 26cbb2f58eb632a40e34c26e464ffa152f541627 Mon Sep 17 00:00:00 2001
From: Erik Strand <erik.strand@cba.mit.edu>
Date: Fri, 17 Apr 2020 13:13:59 -0400
Subject: [PATCH] Allow move semantics for the initial point for cgd
---
.../conjugate_gradient_descent.h | 45 ++++++++++++-------
.../conjugate_gradient_descent/main.cpp | 4 +-
2 files changed, 31 insertions(+), 18 deletions(-)
diff --git a/optimization/optimizers/conjugate_gradient_descent/conjugate_gradient_descent.h b/optimization/optimizers/conjugate_gradient_descent/conjugate_gradient_descent.h
index c9d0d38..85fe5be 100644
--- a/optimization/optimizers/conjugate_gradient_descent/conjugate_gradient_descent.h
+++ b/optimization/optimizers/conjugate_gradient_descent/conjugate_gradient_descent.h
@@ -32,12 +32,37 @@ public:
VectorNs<N> const& gradient() const { return gradient_; }
Scalar value() const { return value_; }
+ // There are four cases for optimize, since you can use a log or not, and can pass an rvalue or
+ // not. These methods ultimately all call optimize_impl to do the real work.
+ // Note: I explicitly write out the Vector const& and Vector&& cases rather than passing by
+ // value and moving since Eigen forbids passing by value:
+ // https://eigen.tuxfamily.org/dox/group__TopicPassingByValue.html
template <typename Objective>
- Vector const& optimize(Objective& objective, Vector const& initial_point);
+ Vector const& optimize(Objective& objective, Vector const& initial_point) {
+ ConjugateGradientDescentLogNothing log;
+ return optimize(objective, initial_point, log);
+ }
template <typename Objective, typename Log>
- Vector const& optimize(Objective& objective, Vector const& initial_point, Log& log);
+ Vector const& optimize(Objective& objective, Vector const& initial_point, Log& log) {
+ point_ = initial_point;
+ return optimize_impl(objective, log);
+ }
+ template <typename Objective>
+ Vector const& optimize(Objective& objective, Vector&& initial_point) {
+ ConjugateGradientDescentLogNothing log;
+ return optimize(objective, std::move(initial_point), log);
+ }
+ template <typename Objective, typename Log>
+ Vector const& optimize(Objective& objective, Vector&& initial_point, Log& log) {
+ point_ = std::move(initial_point);
+ return optimize_impl(objective, log);
+ }
private:
+ // Actually does the optimization, once point_ has been initialized.
+ template <typename Objective, typename Log>
+ Vector const& optimize_impl(Objective& objective, Log& log);
+
// hyperparameters
Scalar gradient_threshold_;
Scalar progress_threshold_;
@@ -62,28 +87,16 @@ private:
static constexpr Scalar tiny_ = std::numeric_limits<Scalar>::epsilon();
};
-//..................................................................................................
-template <int32_t N>
-template <typename Objective>
-VectorNs<N> const& ConjugateGradientDescent<N>::optimize(
- Objective& objective,
- Vector const& initial_point
-) {
- ConjugateGradientDescentLogNothing log;
- return optimize(objective, initial_point, log);
-}
-
//..................................................................................................
template <int32_t N>
template <typename Objective, typename Log>
-VectorNs<N> const& ConjugateGradientDescent<N>::optimize(
+VectorNs<N> const& ConjugateGradientDescent<N>::optimize_impl(
Objective& objective,
- Vector const& initial_point,
Log& log
) {
n_evaluations_ = 0;
n_iterations_ = 0;
- point_ = initial_point;
+ // point_ has already been initialized
direction_.resize(point_.size());
gradient_.resize(point_.size());
new_point_or_last_gradient_.resize(point_.size());
diff --git a/optimization/optimizers/conjugate_gradient_descent/main.cpp b/optimization/optimizers/conjugate_gradient_descent/main.cpp
index 73276d5..df00eca 100644
--- a/optimization/optimizers/conjugate_gradient_descent/main.cpp
+++ b/optimization/optimizers/conjugate_gradient_descent/main.cpp
@@ -60,9 +60,9 @@ int main(int const argc, char const** argv) {
// Only log stuff if we're going to use it.
if (log_file_path.empty() && vis_file_path.empty()) {
- optimizer.optimize(objective, initial_point);
+ optimizer.optimize(objective, std::move(initial_point));
} else {
- optimizer.optimize(objective, initial_point, log);
+ optimizer.optimize(objective, std::move(initial_point), log);
}
std::cout << "n evaluations: " << optimizer.n_evaluations() << '\n';
--
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