diff --git a/chapter_optimization/sgd.md b/chapter_optimization/sgd.md
index f076516b7..62960df96 100644
--- a/chapter_optimization/sgd.md
+++ b/chapter_optimization/sgd.md
@@ -60,6 +60,20 @@ $$\mathbb{E}_i \nabla f_i(\mathbf{x}) = \frac{1}{n} \sum_{i = 1}^n \nabla f_i(\m
 
 This means that, on average, the stochastic gradient is a good estimate of the gradient.
 
+Modern analyses of SGD trace back to the **Robbins–Monro** stochastic approximation framework :cite:`robbins1951stochastic`. In that view, SGD seeks a root of the gradient mapping using a decreasing stepsize sequence that satisfies
+
+$$
+\sum_{t=0}^\infty \eta_t = \infty \quad \text{and} \quad \sum_{t=0}^\infty \eta_t^2 < \infty,
+$$
+
+which ensures convergence under mild regularity conditions. A standard assumption in contemporary proofs is **bounded variance** of the stochastic gradient noise:
+
+$$
+\mathbb{E}\!\left[\|\nabla f_{i_t}(x_t)-\nabla f(x_t)\|^2 \mid x_t\right] \le \sigma^2 .
+$$
+
+together with lower-bounded objectives and smoothness (Lipschitz gradients). Under these, one obtains the classical $\mathcal{O}(1/\sqrt{T})$ rate for convex problems with appropriately decayed $\{\eta_t\}$, and constant-stepsize variants converge to a noise-dominated neighborhood. See :cite:`bottou2018optimization` for a tutorial-style survey.
+
 Now, we will compare it with gradient descent by adding random noise with a mean of 0 and a variance of 1 to the gradient to simulate a stochastic gradient descent.
 
 ```{.python .input}
diff --git a/d2l.bib b/d2l.bib
index 16ad6ce96..dd5bccf81 100644
--- a/d2l.bib
+++ b/d2l.bib
@@ -4510,3 +4510,25 @@ @article{penedo2023refinedweb
   journal={Ar{X}iv:2306.01116},
   year={2023}
 }
+
+@article{robbins1951stochastic,
+  author  = {Robbins, Herbert and Monro, Sutton},
+  title   = {A Stochastic Approximation Method},
+  journal = {The Annals of Mathematical Statistics},
+  year    = {1951},
+  volume  = {22},
+  number  = {3},
+  pages   = {400--407},
+  doi     = {10.1214/aoms/1177729586}
+}
+
+@article{bottou2018optimization,
+  author  = {Bottou, L{\'e}on and Curtis, Frank E. and Nocedal, Jorge},
+  title   = {Optimization Methods for Large-Scale Machine Learning},
+  journal = {SIAM Review},
+  year    = {2018},
+  volume  = {60},
+  number  = {2},
+  pages   = {223--311},
+  doi     = {10.1137/16M1080173}
+}