<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data-Struct on 技术笔记</title><link>https://elliot-blog.com/categories/data-struct/</link><description>Recent content in Data-Struct on 技术笔记</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Tue, 17 Feb 2026 15:06:11 +0800</lastBuildDate><atom:link href="https://elliot-blog.com/categories/data-struct/index.xml" rel="self" type="application/rss+xml"/><item><title>数据结构-多维数组的超平面视角：从索引到地址的映射</title><link>https://elliot-blog.com/posts/202602/data-struct-array/</link><pubDate>Tue, 17 Feb 2026 00:00:00 +0000</pubDate><guid>https://elliot-blog.com/posts/202602/data-struct-array/</guid><description>&lt;h1 id="数据结构-多维数组的超平面视角从索引到地址的映射"&gt;数据结构-多维数组的超平面视角：从索引到地址的映射&lt;/h1&gt;
&lt;h2 id="核心思想"&gt;核心思想&lt;/h2&gt;
&lt;p&gt;多维数组在逻辑上是嵌套的子空间结构，在物理上是一段连续的内存。&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;寻址的本质：每一维的索引跳过若干个完整的子空间；未满的那个子空间，进入下一维继续定位，直到 0 维。&lt;/strong&gt;&lt;/p&gt;</description></item><item><title>算法复杂度的渐进符号定义,证明与理解</title><link>https://elliot-blog.com/posts/202602/algorithm-complexity/</link><pubDate>Fri, 13 Feb 2026 00:00:00 +0000</pubDate><guid>https://elliot-blog.com/posts/202602/algorithm-complexity/</guid><description>&lt;h1 id="算法复杂度的渐进符号定义与理解"&gt;算法复杂度的渐进符号定义与理解&lt;/h1&gt;
&lt;h2 id="常数形式定义"&gt;常数形式定义&lt;/h2&gt;
&lt;h3 id="big-o-上界"&gt;Big-O (上界)&lt;/h3&gt;
&lt;p&gt;$T(N) = O(f(N))$ if there are positive constants $c$ and $n_0$ such that $T(N) \leq c \cdot f(N)$ when $N \geq n_0$.&lt;/p&gt;
&lt;h3 id="big-omega-下界"&gt;Big-Omega (下界)&lt;/h3&gt;
&lt;p&gt;$T(N) = \Omega(g(N))$ if there are positive constants $c$ and $n_0$ such that $T(N) \geq c \cdot g(N)$ when $N \geq n_0$.&lt;/p&gt;
&lt;h3 id="big-theta-紧确界"&gt;Big-Theta (紧确界)&lt;/h3&gt;
&lt;p&gt;$T(N) = \Theta(h(N))$ if and only if $T(N) = O(h(N))$ and $T(N) = \Omega(h(N))$.&lt;/p&gt;</description></item></channel></rss>