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    <title>Coordinate-Descent on As it was</title>
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    <managingEditor>maocred@gmail.com (Halois)</managingEditor>
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    <lastBuildDate>Wed, 13 May 2026 08:00:00 +0800</lastBuildDate>
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      <title>从零开始理解坐标下降：一个参数一个参数地优化</title>
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      <pubDate>Wed, 13 May 2026 08:00:00 +0800</pubDate><author>maocred@gmail.com (Halois)</author>
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      <description>&lt;p&gt;&lt;strong&gt;Reading:&lt;/strong&gt; 抛硬币 10 次得 7 次正面——正面概率的最可能估计是 0.7。但如果模型有几百个参数呢？MLE 可能没有闭式解。&lt;strong&gt;坐标下降&lt;/strong&gt;（coordinate descent）也许是解决这类问题最简单的思路：一次只优化一个参数，固定其他的，反复直到收敛。本文从抛硬币出发，经过线性回归，一直走到高维 Lasso——展示坐标下降从入门到核心应用的全景。&lt;/p&gt;</description>
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