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      <title>OptOpt</title>
      <link>https://by-xin.github.io/BYNotes</link>
      <description>Last 10 notes on OptOpt</description>
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    <title>Lecture 14 · Newton Method</title>
    <link>https://by-xin.github.io/BYNotes/ConvexOptimization/14.Newton-Method</link>
    <guid>https://by-xin.github.io/BYNotes/ConvexOptimization/14.Newton-Method</guid>
    <description><![CDATA[  References Lecture Reference: www.stat.cmu.edu/~ryantibs/convexopt-F18/ 1. ]]></description>
    <pubDate>Wed, 06 May 2026 10:41:42 GMT</pubDate>
  </item><item>
    <title>Lecture 13 · Duality Uses and Correspondents</title>
    <link>https://by-xin.github.io/BYNotes/ConvexOptimization/13.Duality-Uses-and-Correspondents</link>
    <guid>https://by-xin.github.io/BYNotes/ConvexOptimization/13.Duality-Uses-and-Correspondents</guid>
    <description><![CDATA[  References Lecture: www.stat.cmu.edu/~ryantibs/convexopt-F18/ Reading: 最优化: 建模、算法与理论, 刘浩洋等, 2.6 小节 (Conjugate Functions) 一文读懂凸优化中的「对偶」概念（二）：可以用一个观点解释所有对偶吗？ - 江南FLY的文章 - 知乎 一文读懂凸优化中的「对偶」概念（三）：Fenchel 共轭是什么东西？它有什么用？ - 江南FLY的文章 - 知乎 本章将希望通过讨论对偶这一概念在数学中的各种应用, 来串联深化对对偶概念的理解. ]]></description>
    <pubDate>Wed, 06 May 2026 10:26:57 GMT</pubDate>
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    <title>Lecture 12 · Optimality Conditions for Constrained Optimization</title>
    <link>https://by-xin.github.io/BYNotes/ConvexOptimization/12.Optimality-Conditions-for-Constrained-Optimization</link>
    <guid>https://by-xin.github.io/BYNotes/ConvexOptimization/12.Optimality-Conditions-for-Constrained-Optimization</guid>
    <description><![CDATA[  References Lecture: www.stat.cmu.edu/~ryantibs/convexopt-F18/ Reading: 最优化: 建模、算法与理论, 刘浩洋等, 5.5、5.6 小节. 0. ]]></description>
    <pubDate>Wed, 06 May 2026 10:22:26 GMT</pubDate>
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    <title>Discrete Time Markov Chain</title>
    <link>https://by-xin.github.io/BYNotes/StochasticProcess/01_DiscreteTimeMarkovChain</link>
    <guid>https://by-xin.github.io/BYNotes/StochasticProcess/01_DiscreteTimeMarkovChain</guid>
    <description><![CDATA[ 1. Introduction: Markov Assumption 对于一组随机变量 X_1, X_2, \cdots, X_n，其最完整的认知是其联合分布 \mathbb{P}(X_1, X_2, \cdots, X_n). 然而对于大规模的随机变量集合, 其计算和存储都非常困难. ]]></description>
    <pubDate>Wed, 06 May 2026 10:22:26 GMT</pubDate>
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    <title>Lecture 10-11 · Duality</title>
    <link>https://by-xin.github.io/BYNotes/ConvexOptimization/10-11.Duality</link>
    <guid>https://by-xin.github.io/BYNotes/ConvexOptimization/10-11.Duality</guid>
    <description><![CDATA[  References Lecture: www.stat.cmu.edu/~ryantibs/convexopt-F18/ Reading: 最优化: 建模、算法与理论, 刘浩洋等, 5.4 小节. 1. ]]></description>
    <pubDate>Wed, 06 May 2026 10:09:12 GMT</pubDate>
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    <title>Lecture 02 · Convexity I: Convexity in Maths</title>
    <link>https://by-xin.github.io/BYNotes/ConvexOptimization/2.Convexity-I</link>
    <guid>https://by-xin.github.io/BYNotes/ConvexOptimization/2.Convexity-I</guid>
    <description><![CDATA[  References Lecture: www.stat.cmu.edu/~ryantibs/convexopt-F18/ 1. ]]></description>
    <pubDate>Wed, 06 May 2026 10:02:14 GMT</pubDate>
  </item><item>
    <title>Lecture 03 · Convexity II: Convex Optimization Problems</title>
    <link>https://by-xin.github.io/BYNotes/ConvexOptimization/3.Convexity-II</link>
    <guid>https://by-xin.github.io/BYNotes/ConvexOptimization/3.Convexity-II</guid>
    <description><![CDATA[  References Lecture: www.stat.cmu.edu/~ryantibs/convexopt-F18/ Reading: Boyd &amp; Vandenberghe, Convex Optimization, Sections 3.2.5, 4.1.3, and 4.2. ]]></description>
    <pubDate>Wed, 06 May 2026 10:02:14 GMT</pubDate>
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    <title>Lecture 04 · Canonical Problem Forms</title>
    <link>https://by-xin.github.io/BYNotes/ConvexOptimization/4.Canonical-Problem-Forms</link>
    <guid>https://by-xin.github.io/BYNotes/ConvexOptimization/4.Canonical-Problem-Forms</guid>
    <description><![CDATA[  References Lecture: www.stat.cmu.edu/~ryantibs/convexopt-F18/ Reading: Boyd &amp; Vandenberghe, Convex Optimization, Sections 4.4, 4.6.1, and 4.6.2. ]]></description>
    <pubDate>Wed, 06 May 2026 10:02:14 GMT</pubDate>
  </item><item>
    <title>Lecture 05 · Gradient Descent</title>
    <link>https://by-xin.github.io/BYNotes/ConvexOptimization/5.Gradient-Descent</link>
    <guid>https://by-xin.github.io/BYNotes/ConvexOptimization/5.Gradient-Descent</guid>
    <description><![CDATA[  References Lecture: www.stat.cmu.edu/~ryantibs/convexopt-F18/ Reading: Boyd &amp; Vandenberghe, Convex Optimization, Sections 9.1 and 9.2. 1. ]]></description>
    <pubDate>Wed, 06 May 2026 10:02:14 GMT</pubDate>
  </item><item>
    <title>Lecture 06 · Subgradient</title>
    <link>https://by-xin.github.io/BYNotes/ConvexOptimization/6.Subgradient</link>
    <guid>https://by-xin.github.io/BYNotes/ConvexOptimization/6.Subgradient</guid>
    <description><![CDATA[  References Lecture: www.stat.cmu.edu/~ryantibs/convexopt-F18/ Reading: 最优化: 建模、算法与理论, 刘浩洋等, 2.7 小节. 1. ]]></description>
    <pubDate>Wed, 06 May 2026 10:02:14 GMT</pubDate>
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