From: Ilqar Ramazanli <iramazanli@fb.com>
Date: Wed, 8 Sep 2021 22:20:52 +0000 (-0700)
Subject: To add Stochastic Gradient Descent to Documentation (#63805)
X-Git-Tag: accepted/tizen/8.0/unified/20231005.095509~363
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=149f1114fe94dc90fbb7000669a98c2337e25a54;p=platform%2Fupstream%2Fpytorch.git

To add Stochastic Gradient Descent to Documentation (#63805)

Summary:
It has been discussed before that adding description of Optimization algorithms to PyTorch Core documentation may result in a nice Optimization research tutorial. In the following tracking issue we mentioned about all the necessary algorithms and links to the originally published paper  https://github.com/pytorch/pytorch/issues/63236.

In this PR we are adding description of Stochastic Gradient Descent to the documentation.

<img width="466" alt="SGDalgo" src="https://user-images.githubusercontent.com/73658284/132585881-b351a6d4-ece0-4825-b9c0-126d7303ed53.png">

Pull Request resolved: https://github.com/pytorch/pytorch/pull/63805

Reviewed By: albanD

Differential Revision: D30818947

Pulled By: iramazanli

fbshipit-source-id: 3812028e322c8a64f4343552b0c8c4582ea382f3
---

diff --git a/torch/optim/sgd.py b/torch/optim/sgd.py
index a7a67ff..a4a7222 100644
--- a/torch/optim/sgd.py
+++ b/torch/optim/sgd.py
@@ -6,6 +6,32 @@ from .optimizer import Optimizer, required
 class SGD(Optimizer):
     r"""Implements stochastic gradient descent (optionally with momentum).
 
+    .. math::
+       \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta)
+                \text{ (objective)}, \: \lambda \text{ (weight decay)},                          \\
+            &\hspace{13mm} \:\mu \text{ (momentum)}, \:\tau \text{ (dampening)},\:nesterov\\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+            &\hspace{5mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})           \\
+            &\hspace{5mm}\textbf{if} \: \lambda \neq 0                                           \\
+            &\hspace{10mm} g_t \leftarrow g_t + \lambda  \theta_{t-1}                            \\
+            &\hspace{5mm}\textbf{if} \: \mu \neq 0                                               \\
+            &\hspace{10mm}\textbf{if} \: t > 1                                                   \\
+            &\hspace{15mm} \textbf{b}_t \leftarrow \mu \textbf{b}_{t-1} + (1-\tau) g_t           \\
+            &\hspace{10mm}\textbf{else}                                                          \\
+            &\hspace{15mm} \textbf{b}_t \leftarrow g_t                                           \\
+            &\hspace{10mm}\textbf{if} \: nesterov                                                \\
+            &\hspace{15mm} g_t \leftarrow g_{t-1} + \mu \textbf{b}_t                             \\
+            &\hspace{10mm}\textbf{else}                                                   \\[-1.ex]
+            &\hspace{15mm} g_t  \leftarrow  \textbf{b}_t                                         \\
+            &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \gamma g_t                    \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+       \end{aligned}
+
     Nesterov momentum is based on the formula from
     `On the importance of initialization and momentum in deep learning`__.