<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>线性代数-MIT18.06 on 蛇索尼 Aiden</title><link>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/</link><description>Recent content in 线性代数-MIT18.06 on 蛇索尼 Aiden</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><managingEditor>shesl-meow@qq.com (佘崧林)</managingEditor><webMaster>shesl-meow@qq.com (佘崧林)</webMaster><lastBuildDate>Wed, 18 Jan 2023 17:27:46 +0800</lastBuildDate><atom:link href="https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/index.xml" rel="self" type="application/rss+xml"/><item><title>6.向量空间与子空间</title><link>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/6.%E5%90%91%E9%87%8F%E7%A9%BA%E9%97%B4%E4%B8%8E%E5%AD%90%E7%A9%BA%E9%97%B4/</link><pubDate>Wed, 18 Jan 2023 17:27:46 +0800</pubDate><author>shesl-meow@qq.com (佘崧林)</author><guid>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/6.%E5%90%91%E9%87%8F%E7%A9%BA%E9%97%B4%E4%B8%8E%E5%AD%90%E7%A9%BA%E9%97%B4/</guid><description>向量空间（Vector Spaces）：$$R^n$$ 是有 n 个分量的典型向量空间。 子空间（Subspace）： 某个向量空间中的部分向量组成的子</description></item><item><title>矩阵的LU分解</title><link>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/4.%E7%9F%A9%E9%98%B5%E7%9A%84lu%E5%88%86%E8%A7%A3/</link><pubDate>Sun, 29 May 2022 00:00:49 +0800</pubDate><author>shesl-meow@qq.com (佘崧林)</author><guid>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/4.%E7%9F%A9%E9%98%B5%E7%9A%84lu%E5%88%86%E8%A7%A3/</guid><description>AB 的逆：$$B^{-1} A^{-1}$$ 转置的逆，$$A^T$$ 的逆是 $$(A^{-1})^T$$ 矩阵的 LU 分解： 任意一个矩阵 A 表示一定可以表示为两个矩阵 L、U 的乘积，其中 L 是一个下</description></item><item><title>矩阵的转置</title><link>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/5.%E7%9F%A9%E9%98%B5%E7%9A%84%E8%BD%AC%E7%BD%AE/</link><pubDate>Sun, 29 May 2022 00:00:49 +0800</pubDate><author>shesl-meow@qq.com (佘崧林)</author><guid>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/5.%E7%9F%A9%E9%98%B5%E7%9A%84%E8%BD%AC%E7%BD%AE/</guid><description>什么是转置（Transpose）： 对于 A 的转置矩阵 $$A^T$$，其中的元素满足 $$(A^T){ij} = A{ji}$$ 对称矩阵（Symmetric Matrices）：满足 $$A^T</description></item><item><title>1. 方程组的几何解</title><link>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/1.%E6%96%B9%E7%A8%8B%E7%BB%84%E7%9A%84%E5%87%A0%E4%BD%95%E8%A7%A3/</link><pubDate>Thu, 05 May 2022 02:17:55 +0800</pubDate><author>shesl-meow@qq.com (佘崧林)</author><guid>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/1.%E6%96%B9%E7%A8%8B%E7%BB%84%E7%9A%84%E5%87%A0%E4%BD%95%E8%A7%A3/</guid><description>Linear equations can be written as $$A \times X = B$$ Find linear combination of columns (x&amp;rsquo;s coefficient vector, and y&amp;rsquo;s ciefficient vector); 任何一个线性方程可以从两个视角了解它： Row Picture：将每单个方程视作为空间中的某个点集，求</description></item><item><title>2. 矩阵消元法</title><link>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/2.%E7%9F%A9%E9%98%B5%E6%B6%88%E5%85%83%E6%B3%95/</link><pubDate>Thu, 05 May 2022 02:17:55 +0800</pubDate><author>shesl-meow@qq.com (佘崧林)</author><guid>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/2.%E7%9F%A9%E9%98%B5%E6%B6%88%E5%85%83%E6%B3%95/</guid><description>矩阵消元法与行列式一样都是可以用于解决方程组的方式。 矩阵消元法的步骤如下： 考虑系数矩阵，按行自上而下通过与之前行的加减法运算，将 pivot（</description></item><item><title>3. 矩阵乘法和逆</title><link>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/3.%E7%9F%A9%E9%98%B5%E4%B9%98%E6%B3%95%E5%92%8C%E9%80%86/</link><pubDate>Thu, 05 May 2022 02:17:55 +0800</pubDate><author>shesl-meow@qq.com (佘崧林)</author><guid>https://blog.haitai-social.com/note/%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA%E8%AF%BE%E7%A8%8B/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0-mit18.06/3.%E7%9F%A9%E9%98%B5%E4%B9%98%E6%B3%95%E5%92%8C%E9%80%86/</guid><description>乘法 矩阵乘法的规则，对于运算 $$A * B = C$$： C 中的元素值 $$C_{ij} = \sum_{k = 1}^n A_{ik} * B_{kj}$$； 乘法可以运算的前提条件：A 的列数等于 B 的行数； 其他理</description></item></channel></rss>