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#Git #Cloudflare #acme.sh #部署 背景 博客采用如下部署架构： 123456本地 Mac ──git push──▶ 内网服务器 (hexo.git bare repo) │ post-receive hook ├─ hexo generate └─ rsync ──▶ 公网服务器 │ Nginx 对外提供服务 将域名 DNS 从阿里云迁移到 Cloudflare 后，连锁触发了三个问题：SSL 续签失败、git push 超时、hexo hook 不触发。以下记录完整排查过程。 一、SSL 续签失败 现象 123[ERROR] Le_OrderFinalize[ERROR] error code: 3[ERROR...

#SSL #acme.sh #Cloudflare #Nginx 背景 博客使用 acme.sh + Let’s Encrypt 签发 ECC 证书，原方案依赖阿里云 DNS API（dns_ali）自动验证。DNS 迁移到 Cloudflare 后需同步更新续签方式，同时记录中途遇到的一次手动续签失败的排查过程。 一、故障：续签时 Finalize 报错 错误现象 123[ERROR] Le_OrderFinalize[ERROR] Please refer to https://curl.haxx.se/libcurl/c/libcurl-errors.html for error code: 3[ERROR] Signing failed. Finalize code was not 200. 原因 libcurl 错误码 3 为 CURLE_URL_MALFORMAT，即 acme.sh 缓存的 ACME 订单状态中 Le_OrderFinalize URL 损坏，导致签名阶段失败。 解决 删除缓存的域名配置，强制重新发起订单： 123456rm -rf ~/.acme...

#sdsc6015 English / 中文 Mirror Descent Click to expand Mirror Descent review content Motivation Consider the simplex-constrained optimization problem: min⁡x∈△df(x)\min_{x \in \triangle_d} f(x) x∈△d​min​f(x) where the simplex △d:={x∈Rd:∑i=1dxi=1,xi≥0,∀i}\triangle_d := \{x \in \mathbb{R}^d : \sum_{i=1}^d x_i = 1, x_i \geq 0, \forall i\}△d​:={x∈Rd:∑i=1d​xi​=1,xi​≥0,∀i}. Assume the gradient’s infinity norm is bounded: ∥∇f(x)∥∞=max⁡i=1,…,d∣[∇f(x)]i∣≤1\|\nabla f(x)\|_\infty = \max_{i=1,\ldots,d} |[...

#sdsc6015 English / 中文 Projected Gradient Descent Projected Gradient Descent is an algorithm for constrained optimization problems that ensures constraint satisfaction by projecting gradient steps back onto the feasible set. Constrained Optimization Problem Definition The constrained optimization problem is formally defined as: min⁡f(x)subject tox∈X\begin{aligned} &\min f(x) \\ &\text{subject to}\quad x \in X \end{aligned} ​minf(x)subject tox∈X​ where: f:Rd→Rf: \mathbb{R}^d \righta...

#assignment #sdsc5001 题目链接SDSC5001 - Question of Assignment 2 Question 1 When the number of features p is large, there tends to be a deterioration in the performance of KNN and other local approaches that perform prediction using only observations that are near the test observation for which a prediction must be made. This phenomenon is known as the curse of dimensionality, and it ties into the fact that non-parametric approaches often perform poorly when p is large. We will now investigate t...

#assignment #sdsc5001 Question 1 When the number of features p is large, there tends to be a deterioration in the performance of KNN and other local approaches that perform prediction using only observations that are near the test observation for which a prediction must be made. This phenomenon is known as the curse of dimensionality, and it ties into the fact that non-parametric approaches often perform poorly when p is large. We will now investigate this curse. (a) Suppose tha...

#assignment #sdsc6015 题目链接SDSC6015 - Question of Assignment 2 Problem 1[10 marks] Prove that if the function f:Rd→Rf: \mathbb{R}^{d}\rightarrow \mathbb{R}f:Rd→R has a subgradient at every point in its domain, then fff is convex. Solution: Let x,y∈Rdx, y \in \mathbb{R}^dx,y∈Rd, λ∈[0,1]\lambda \in [0,1]λ∈[0,1], and define z=λx+(1−λ)yz = \lambda x + (1-\lambda)yz=λx+(1−λ)y. Since a subgradient exists at every point, for any gz∈∂f(z)g_z \in \partial f(z)gz​∈∂f(z), we have: f(x)≥f(z)+⟨gz,x−z⟩,f(y...

SDSC 5002 - Assignment 1 #assignment #sdsc5002

📅 TODO SDSC5001 - Assignment 2 SDSC6015 - Assignment 2 SDSC6007 - Assignment 2 SDSC5003 - Assignment 2 SDSC6012 - Assignment 1 SDSC5001 - Assignment 1 SDSC5002 - Assignment 1 SDSC5003 - Assignment 1 SDSC6007 - Assignment 1 SDSC6015 - Assignment 1 其他文件 总课表 作业清单 SDSC5001 Statistical Machine Learning I SDSC5001 课程信息 SDSC5001 课程 1-概率论与数理统计复习 SDSC5001 课程 2-数据探索 SDSC5001 课程 3-统计机器学习概述 SDSC5001 课程 4-线性回归 SDSC5002 Exploratory Data Analysis and Visualization SDSC5002 课程信息 SDSC5002 课程 2-EDA SDSC5003 S...

#assignment #sdsc6015