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2025-06-02 23:29:32 +00:00
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@@ -8,7 +8,7 @@
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Linear Algebra &#8212; MLX 0.25.2 documentation</title>
<title>Linear Algebra &#8212; MLX 0.26.0 documentation</title>
@@ -36,7 +36,7 @@
<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
<script src="../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
<script src="../_static/documentation_options.js?v=fe4f2727"></script>
<script src="../_static/documentation_options.js?v=8cd87d29"></script>
<script src="../_static/doctools.js?v=9a2dae69"></script>
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
@@ -138,8 +138,8 @@
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.25.2 documentation - Home"/>
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.25.2 documentation - Home"/>`);</script>
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.0 documentation - Home"/>
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.0 documentation - Home"/>`);</script>
</a></div>
@@ -194,6 +194,8 @@
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.array.ndim.html">mlx.core.array.ndim</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.array.shape.html">mlx.core.array.shape</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.array.size.html">mlx.core.array.size</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.array.real.html">mlx.core.array.real</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.array.imag.html">mlx.core.array.imag</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.array.abs.html">mlx.core.array.abs</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.array.all.html">mlx.core.array.all</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.array.any.html">mlx.core.array.any</a></li>
@@ -504,6 +506,8 @@
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.linalg.cross.html">mlx.core.linalg.cross</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.linalg.qr.html">mlx.core.linalg.qr</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.linalg.svd.html">mlx.core.linalg.svd</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.linalg.eigvals.html">mlx.core.linalg.eigvals</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.linalg.eig.html">mlx.core.linalg.eig</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.linalg.eigvalsh.html">mlx.core.linalg.eigvalsh</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.linalg.eigh.html">mlx.core.linalg.eigh</a></li>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/mlx.core.linalg.lu.html">mlx.core.linalg.lu</a></li>
@@ -932,6 +936,12 @@ document.write(`
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.core.linalg.svd.html#mlx.core.linalg.svd" title="mlx.core.linalg.svd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svd</span></code></a>(a[, compute_uv, stream])</p></td>
<td><p>The Singular Value Decomposition (SVD) of the input matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.core.linalg.eigvals.html#mlx.core.linalg.eigvals" title="mlx.core.linalg.eigvals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eigvals</span></code></a>(a, *[, stream])</p></td>
<td><p>Compute the eigenvalues of a square matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.core.linalg.eig.html#mlx.core.linalg.eig" title="mlx.core.linalg.eig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eig</span></code></a>(a, *[, stream])</p></td>
<td><p>Compute the eigenvalues and eigenvectors of a square matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.core.linalg.eigvalsh.html#mlx.core.linalg.eigvalsh" title="mlx.core.linalg.eigvalsh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eigvalsh</span></code></a>(a[, UPLO, stream])</p></td>
<td><p>Compute the eigenvalues of a complex Hermitian or real symmetric matrix.</p></td>
</tr>
@@ -1013,7 +1023,7 @@ By MLX Contributors
<p class="copyright">
© Copyright 2023, MLX Contributors.
© Copyright 2023, Apple.
<br/>
</p>