diff --git a/wpimath/src/main/java/edu/wpi/first/math/optimization/SimulatedAnnealing.java b/wpimath/src/main/java/edu/wpi/first/math/optimization/SimulatedAnnealing.java
index 820829e808..f0ead5b459 100644
--- a/wpimath/src/main/java/edu/wpi/first/math/optimization/SimulatedAnnealing.java
+++ b/wpimath/src/main/java/edu/wpi/first/math/optimization/SimulatedAnnealing.java
@@ -10,6 +10,12 @@ import java.util.function.ToDoubleFunction;
 /**
  * An implementation of the Simulated Annealing stochastic nonlinear optimization method.
  *
+ * <p>Solving optimization problems involves tweaking decision variables to try to minimize some
+ * cost function. Simulated annealing is good for solving optimization problems with many local
+ * minima and a very large search space (it’s a heuristic solver rather than an exact solver like,
+ * say, SQP or interior-point method). Simulated annealing is a popular choice for solving the
+ * traveling salesman problem (see {@link edu.wpi.first.math.path.TravelingSalesman}).
+ *
  * @see <a
  *     href="https://en.wikipedia.org/wiki/Simulated_annealing">https://en.wikipedia.org/wiki/Simulated_annealing</a>
  * @param <State> The type of the state to optimize.
diff --git a/wpimath/src/main/native/include/frc/optimization/SimulatedAnnealing.h b/wpimath/src/main/native/include/frc/optimization/SimulatedAnnealing.h
index 08ccf596e1..0a16dd963a 100644
--- a/wpimath/src/main/native/include/frc/optimization/SimulatedAnnealing.h
+++ b/wpimath/src/main/native/include/frc/optimization/SimulatedAnnealing.h
@@ -15,6 +15,13 @@ namespace frc {
  * An implementation of the Simulated Annealing stochastic nonlinear
  * optimization method.
  *
+ * Solving optimization problems involves tweaking decision variables to try to
+ * minimize some cost function. Simulated annealing is good for solving
+ * optimization problems with many local minima and a very large search space
+ * (it’s a heuristic solver rather than an exact solver like, say, SQP or
+ * interior-point method). Simulated annealing is a popular choice for solving
+ * the traveling salesman problem (see TravelingSalesman).
+ *
  * @see <a
  * href="https://en.wikipedia.org/wiki/Simulated_annealing">https://en.wikipedia.org/wiki/Simulated_annealing</a>
  * @tparam State The type of the state to optimize.