Modèle LaTeX Article de recherche académique

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\vspace{6pt}

\section{Introduction}

Let $f:\mathbb{R}^n\to\mathbb{R}$ be an $L$-smooth function, i.e., its gradient satisfies
$\|\nabla f(x) - \nabla f(y)\| \le L\|x-y\|$ for all $x,y\in\mathbb{R}^n$.
The \emph{gradient descent} iteration is defined by
\[
  x_{t+1} = x_t - \eta\,\nabla f(x_t),
  \qquad \eta \in \Bigl(0,\,\tfrac{1}{L}\Bigr],
\]
where $\eta$ is the step size. Understanding the convergence of this simple procedure in the non-convex setting is fundamental to modern machine learning theory.

\begin{theorem}{Main Convergence Result}{convergence}
  Let $f:\mathbb{R}^n\to\mathbb{R}$ be $L$-smooth and bounded below.
  Running gradient descent with step size $\eta = 1/\sqrt{T}$ for $T$ iterations yields
  \[
    \min_{0 \le t < T}\|\nabla f(x_t)\|^2
    \;\le\;
    \frac{2L\bigl(f(x_0)-f^*\bigr)}{\sqrt{T}}
    \;=\;
    \mathcal{O}\!\left(\frac{1}{\sqrt{T}}\right).
  \]
\end{theorem}

\begin{proof-env}
  Applying the $L$-smoothness descent lemma repeatedly and telescoping yields the bound.
\end{proof-env}

\begin{lemma}{Descent Lemma}{descent}
  For any $L$-smooth $f$ and step size $\eta \le 1/L$,
  \[
    f(x_{t+1}) \;\le\; f(x_t) - \frac{\eta}{2}\|\nabla f(x_t)\|^2.
  \]
\end{lemma}

\section{Methodology}

\subsection{Convergence Analysis}

Summing the descent lemma over $t = 0,\ldots,T-1$ and using the bounded-below assumption $f(x)\ge f^*$ gives a total decrease bound. Dividing by $T$ and minimizing over all iterates establishes the convergence rate.

\subsection{Practical Considerations}

\begin{itemize}[leftmargin=*,itemsep=3pt]
  \item \textbf{Step size selection:} Setting $\eta=1/L$ (if $L$ is known) yields the sharpest constant.
  \item \textbf{Stochastic variant:} Mini-batch SGD achieves the same $\mathcal{O}(1/\sqrt{T})$ rate in expectation under standard noise assumptions.
  \item \textbf{Adaptive methods:} Adam and AdaGrad adapt the step size per coordinate but lack matching worst-case guarantees in general.
\end{itemize}

\section{Related Work}

Classical convergence analysis of first-order methods dates to Nesterov (1983, 2004). The non-convex analysis presented here follows the framework of Ghadimi and Lan (2013). Stochastic extensions and variance-reduction techniques have been extensively studied; see the survey by Bottou et al.\ (2018) for an overview.

\section{Conclusion}

We have established an $\mathcal{O}(1/\sqrt{T})$ convergence rate for gradient descent on $L$-smooth non-convex functions — a fundamental guarantee underpinning the empirical success of gradient-based optimization in deep learning.

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