diff --git a/optimization/optimizers/conjugate_gradient_descent/conjugate_gradient_descent.h b/optimization/optimizers/conjugate_gradient_descent/conjugate_gradient_descent.h
index c9d0d38bcf5f63fe33e419ce272ca5d32f48b6e2..85fe5be55a12333c7949a187cf83fc91f9a3973a 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 73276d594679f83d185a789988f26f8f7121a711..df00eca0c9bf0336f4dc01d2e0628cef453dfaa4 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';