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<!doctype html><html><head><meta name=generator content="Hugo 0.121.2"><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><link rel=stylesheet type=text/css href=/css/style.css><link rel=icon type=image/x-icon href=/images/favicon.ico><title>Yi Chen</title></head><body><div id=content><header><nav><a href=/>[Home]</a>
<!doctype html><html><head><meta name=generator content="Hugo 0.123.7"><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><link rel=stylesheet type=text/css href=/css/style.css><link rel=icon type=image/x-icon href=/images/favicon.ico><title>Yi Chen</title></head><body><div id=content><header><nav><a href=/>[Home]</a>
<a href=/notes/>[Notes]</a>
<a href=/publications/>[Publications]</a>
<a href=/projects/>[Projects]</a>
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4 changes: 3 additions & 1 deletion index.xml
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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Yi Chen</title><link>https://ychen878.github.io/</link><description>Recent content on Yi Chen</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Tue, 28 Feb 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://ychen878.github.io/index.xml" rel="self" type="application/rss+xml"/><item><title>LP Duality</title><link>https://ychen878.github.io/notes/lp-duality/</link><pubDate>Tue, 28 Feb 2023 00:00:00 +0000</pubDate><guid>https://ychen878.github.io/notes/lp-duality/</guid><description>Estimating LP bounds Given an optimization problem $$ \begin{align} \max_{f, s} &amp;amp;\quad 12f + 9s \ \st &amp;amp;\quad 4f + 2s \leq 4800 \ &amp;amp;\quad f + s \leq 1750 \ &amp;amp;\quad 0 \leq f \leq 1000 \ &amp;amp;\quad 0 \leq s \leq 1500 \ \end{align} $$ Suppose the maximum profit is $p^\star$. How can we bound $p^\star$? The lower bound of $p^\star$ can be found by picking any feasible point (since maximization).</description></item><item><title>RPC</title><link>https://ychen878.github.io/notes/rpc/</link><pubDate>Sat, 25 Feb 2023 22:51:15 -0600</pubDate><guid>https://ychen878.github.io/notes/rpc/</guid><description>Networks Network Interface Controllers (NICs) can connect a computer to different physical mediums, such as Ethernet and Wi-Fi. Every NIC in the world has a unique MAC (media access control) address. It consists of 48 bits. Therefore, there are 28 trillion possible MAC addresses. Some devices randomly change their MAC address for privacy.
<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Yi Chen</title><link>https://ychen878.github.io/</link><description>Recent content on Yi Chen</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Thu, 02 Mar 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://ychen878.github.io/index.xml" rel="self" type="application/rss+xml"/><item><title>Nuclear Norm via SDP</title><link>https://ychen878.github.io/notes/nuclear-norm-sdp/</link><pubDate>Thu, 02 Mar 2023 00:00:00 +0000</pubDate><guid>https://ychen878.github.io/notes/nuclear-norm-sdp/</guid><description>:PROPERTIES: :CUSTOM_ID: matrix-norm :END:
Matrix norms Given a matrix $X \in \mathbb{R}^{m \times n}$, $\sigma_{i}(X)$ denotes the $i$-th largest singular value of $X$ and is equal to the square root of the $i$-th largest eigenvalue of $XX&amp;rsquo;$. The rank of $X$, denoted as $\mathrm{rank}(X) = r$ is the number of non-zero singular values.
Inner Product Given $X, Y \in \mathbb{R}^{m \times n}$, the inner product between $X$ and $Y$, denoted by $\langle X, Y\rangle$, is defined as $$ \langle X, Y \rangle := \mathrm{Tr}(X&amp;rsquo;Y) = \sum_{i=1}^m \sum_{j=1}^n X_{ij}Y_{ij} = \mathrm{Tr}(Y&amp;rsquo;X).</description></item><item><title>LP Duality</title><link>https://ychen878.github.io/notes/lp-duality/</link><pubDate>Tue, 28 Feb 2023 00:00:00 +0000</pubDate><guid>https://ychen878.github.io/notes/lp-duality/</guid><description>Estimating LP bounds Given an optimization problem $$ \begin{align} \max_{f, s} &amp;amp;\quad 12f + 9s \ \st &amp;amp;\quad 4f + 2s \leq 4800 \ &amp;amp;\quad f + s \leq 1750 \ &amp;amp;\quad 0 \leq f \leq 1000 \ &amp;amp;\quad 0 \leq s \leq 1500 \ \end{align} $$ Suppose the maximum profit is $p^\star$. How can we bound $p^\star$? The lower bound of $p^\star$ can be found by picking any feasible point (since maximization).</description></item><item><title>RPC</title><link>https://ychen878.github.io/notes/rpc/</link><pubDate>Sat, 25 Feb 2023 22:51:15 -0600</pubDate><guid>https://ychen878.github.io/notes/rpc/</guid><description>Networks Network Interface Controllers (NICs) can connect a computer to different physical mediums, such as Ethernet and Wi-Fi. Every NIC in the world has a unique MAC (media access control) address. It consists of 48 bits. Therefore, there are 28 trillion possible MAC addresses. Some devices randomly change their MAC address for privacy.
You can use command ifconfig to check you network interface controller and its corresponding MAC address. There exists virtual interfaces as well.</description></item><item><title>Docker</title><link>https://ychen878.github.io/notes/docker/</link><pubDate>Sat, 25 Feb 2023 22:49:32 -0600</pubDate><guid>https://ychen878.github.io/notes/docker/</guid><description>Virtualization Virtualization is the illusion of private resources, provided by the software. We have virtual memory, virtual machine (hardware), virtual machine (languages), virtual operating system (container).
Each process using a virtual address space is not aware of other processes using memory (illusion of private memory). Virtualized resources include CPU, RAM, disks, network devices, etc. VMs rarely use all their allocated resources, so overbooking is possible. If each program is deployed to a different VM, operating system overheads dominate.</description></item><item><title>Perceptron Learning Algorithm</title><link>https://ychen878.github.io/notes/pla/</link><pubDate>Wed, 30 Nov 2022 00:00:00 +0000</pubDate><guid>https://ychen878.github.io/notes/pla/</guid><description>Given a dataset \(\mathcal{D} = \{(\vec{x}_1, y_1), \cdots, (\vec{x}_N, y_N)\}\) and a hypothesis set \(\mathcal{H}\), our learning algorithm \(\mathcal{A}\) tries to learn a function \(g \in \mathcal{H}\) that approximates the underlying, true function \(f: \mathcal{X} \to \mathcal{Y}\), which generates the points in \(\mathcal{D}\).
Credit Card Approve Problem Given a customer who is applying for a credit card, we want to build a system that determines if we should grant the application or not based on the customer&amp;#39;s information such as age, annual salary, year in job, etc.</description></item><item><title>Clustering</title><link>https://ychen878.github.io/notes/clustering/</link><pubDate>Sat, 08 Oct 2022 00:00:00 +0000</pubDate><guid>https://ychen878.github.io/notes/clustering/</guid><description>In unsupervised learning, there are no labels associated with features. Generally speaking, the ultimate goal of unsupervised learning is to find patterns and structures that help us to better understand data. Sometimes, we also use unsupervised learning to model a distribution. But we generally will not make predictions.
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<a href=https://archive.casouri.cc>[BHL0388]</a></nav></header><h1>Docker</h1><i></i>
<time datetime=2023-02-25>Feb 25, 2023</time>
<i></i>
<a href=https://ychen878.github.io/tags/big-data>big data</a><br><br><h1 id=virtualization>Virtualization</h1><p>Virtualization is the illusion of private resources, provided by the software. We have virtual memory, virtual machine (hardware), virtual machine (languages), virtual operating system (container).</p><ul><li>Each process using a virtual address space is not aware of other processes using memory (illusion of private memory).</li><li>Virtualized resources include CPU, RAM, disks, network devices, etc. VMs rarely use all their allocated resources, so overbooking is possible. If each program is deployed to a different VM, operating system overheads dominate.</li><li>JVM or PVM runs Java Bytecode and Python Bytecode. Programs written in Java and Python are compiled to their corresponding byte code instead of a specific machine code.</li><li>Virtual operating systems, or a containers, are run on a some flavor of Linux. You can have a container of Ubuntu and a container on Debian (but not Windows, since they are running on top of Linux). Containers are more efficient than virtual machines, but less flexible.</li></ul><p>Containers and Virtual Machines are Sandboxes.</p><h1 id=docker-containers>Docker Containers</h1><p>Containers are lightweight alternative to virtual machines. Virtual machines form a cluster. Resources of the cluster are limited to those of a single VM that runs there containers.</p><h1 id=registries-images-containers-and-dockerfiles>Registries, Images, Containers, and Dockerfiles</h1><p>The Images and the Containers are going to be on your VM. The images are pulled from the Registries (dockerhub, for example). Images are snapshots of installed software. From these images, we can run them to start a container. We can also build our own images and run them later.</p><h1 id=docker-commands>Docker Commands</h1><ol><li><code>docker images</code>: to list all the images I currently have.</li><li><code>docker pull IMAGENAME</code>: to pull an image. If the image is not installed, it will try to pull it first.</li><li><code>docker tag IMAGE:TAG NEW_TAG</code>: to create a new tag for an existing image.</li><li><code>docker run TAG COMMAND</code>: to run a container and let the container runs the command.</li><li><code>docker run -it TAG COMMAND</code>: to run a container with the command interactively.</li><li><code>docker ps</code>: to show all running containers</li><li><code>docker ps -a</code>: to show all containers</li><li><code>docker ps -a -q</code>: to show containers with ID only</li><li><code>docker rm $(docker ps -a -q)</code>: to delete all containers</li><li><code>docker rmi IMAGE</code>: to remove an image</li><li><code>docker run -d IMAGE COMMADN</code>: to run a docker container in a deamon mode.</li><li><code>docker logs CONTAINER_ID</code>: to show the output of a container</li><li><code>docker exec [-it] CONTAINER_ID</code>: to jump into an existing running container</li><li><code>docker build DIRECTORY -t TAG</code>: make a container from the dockerfile (in the current directory) and save it into DIRECTORY with a TAG.</li><li><code>docker run -d -p 127.0.0.1:300:80 IMAGE</code>: redirect incoming data from port 300 to port 80</li></ol><h1 id=dockerfile-instructions>Dockerfile instructions</h1><ol><li><code>FROM</code>: run which image</li><li><code>RUN</code>: run which command</li><li><code>COPY</code>: copy a program on my computer into the image</li><li><code>CMD</code>: default command you want to run</li></ol><div class=highlight><pre tabindex=0 style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-Dockerfile data-lang=Dockerfile><span style=display:flex><span> FROM ubuntu:22.10<span style=color:#960050;background-color:#1e0010>
<a href=https://ychen878.github.io/tags/>[big data]</a><br><br><h1 id=virtualization>Virtualization</h1><p>Virtualization is the illusion of private resources, provided by the software. We have virtual memory, virtual machine (hardware), virtual machine (languages), virtual operating system (container).</p><ul><li>Each process using a virtual address space is not aware of other processes using memory (illusion of private memory).</li><li>Virtualized resources include CPU, RAM, disks, network devices, etc. VMs rarely use all their allocated resources, so overbooking is possible. If each program is deployed to a different VM, operating system overheads dominate.</li><li>JVM or PVM runs Java Bytecode and Python Bytecode. Programs written in Java and Python are compiled to their corresponding byte code instead of a specific machine code.</li><li>Virtual operating systems, or a containers, are run on a some flavor of Linux. You can have a container of Ubuntu and a container on Debian (but not Windows, since they are running on top of Linux). Containers are more efficient than virtual machines, but less flexible.</li></ul><p>Containers and Virtual Machines are Sandboxes.</p><h1 id=docker-containers>Docker Containers</h1><p>Containers are lightweight alternative to virtual machines. Virtual machines form a cluster. Resources of the cluster are limited to those of a single VM that runs there containers.</p><h1 id=registries-images-containers-and-dockerfiles>Registries, Images, Containers, and Dockerfiles</h1><p>The Images and the Containers are going to be on your VM. The images are pulled from the Registries (dockerhub, for example). Images are snapshots of installed software. From these images, we can run them to start a container. We can also build our own images and run them later.</p><h1 id=docker-commands>Docker Commands</h1><ol><li><code>docker images</code>: to list all the images I currently have.</li><li><code>docker pull IMAGENAME</code>: to pull an image. If the image is not installed, it will try to pull it first.</li><li><code>docker tag IMAGE:TAG NEW_TAG</code>: to create a new tag for an existing image.</li><li><code>docker run TAG COMMAND</code>: to run a container and let the container runs the command.</li><li><code>docker run -it TAG COMMAND</code>: to run a container with the command interactively.</li><li><code>docker ps</code>: to show all running containers</li><li><code>docker ps -a</code>: to show all containers</li><li><code>docker ps -a -q</code>: to show containers with ID only</li><li><code>docker rm $(docker ps -a -q)</code>: to delete all containers</li><li><code>docker rmi IMAGE</code>: to remove an image</li><li><code>docker run -d IMAGE COMMADN</code>: to run a docker container in a deamon mode.</li><li><code>docker logs CONTAINER_ID</code>: to show the output of a container</li><li><code>docker exec [-it] CONTAINER_ID</code>: to jump into an existing running container</li><li><code>docker build DIRECTORY -t TAG</code>: make a container from the dockerfile (in the current directory) and save it into DIRECTORY with a TAG.</li><li><code>docker run -d -p 127.0.0.1:300:80 IMAGE</code>: redirect incoming data from port 300 to port 80</li></ol><h1 id=dockerfile-instructions>Dockerfile instructions</h1><ol><li><code>FROM</code>: run which image</li><li><code>RUN</code>: run which command</li><li><code>COPY</code>: copy a program on my computer into the image</li><li><code>CMD</code>: default command you want to run</li></ol><div class=highlight><pre tabindex=0 style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-Dockerfile data-lang=Dockerfile><span style=display:flex><span> FROM ubuntu:22.10<span style=color:#960050;background-color:#1e0010>
</span></span></span><span style=display:flex><span><span style=color:#960050;background-color:#1e0010></span> RUN apt-get update<span style=color:#960050;background-color:#1e0010>
</span></span></span><span style=display:flex><span><span style=color:#960050;background-color:#1e0010></span> RUN apt-get install -y python3 python3-pip curl lsof<span style=color:#960050;background-color:#1e0010>
</span></span></span><span style=display:flex><span><span style=color:#960050;background-color:#1e0010></span> RUN pip3 install jupyterlab<span style=color:#f92672>==</span>3.4.5 MarkupSafe<span style=color:#f92672>==</span>2.0.1<span style=color:#960050;background-color:#1e0010>
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14 changes: 10 additions & 4 deletions notes/index.html
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<a href=/publications/>[Publications]</a>
<a href=/projects/>[Projects]</a>
<a href=/tags/>[Tags]</a><br><a href=https://github.com/kitkatdafu>[GitHub]</a>
<a href=https://archive.casouri.cc>[BHL0388]</a></nav></header><h1>Notes</h1><p><h3><a href=/notes/lp-duality/>LP Duality</a></h3><i></i>
<a href=https://archive.casouri.cc>[BHL0388]</a></nav></header><h1>Notes</h1><p><h3><a href=/notes/nuclear-norm-sdp/>Nuclear Norm via SDP</a></h3><i></i>
<time datetime=2023-03-02>Mar 2, 2023</time>
<i></i>
<a href=https://ychen878.github.io/tags/ml>ml</a>
<a href=/notes/nuclear-norm-sdp/><p>:PROPERTIES: :CUSTOM_ID: matrix-norm :END:
Matrix norms Given a matrix $X \in \mathbb{R}^{m \times n}$, $\sigma_{i}(X)$ denotes the $i$-th largest singular value of $X$ and is equal to the square root of the $i$-th largest eigenvalue of $XX&rsquo;$. The rank of $X$, denoted as $\mathrm{rank}(X) = r$ is the number of non-zero singular values.
Inner Product Given $X, Y \in \mathbb{R}^{m \times n}$, the inner product between $X$ and $Y$, denoted by $\langle X, Y\rangle$, is defined as $$ \langle X, Y \rangle := \mathrm{Tr}(X&rsquo;Y) = \sum_{i=1}^m \sum_{j=1}^n X_{ij}Y_{ij} = \mathrm{Tr}(Y&rsquo;X).</p></a></p><p><h3><a href=/notes/lp-duality/>LP Duality</a></h3><i></i>
<time datetime=2023-02-28>Feb 28, 2023</time>
<i></i>
<a href=https://ychen878.github.io/tags/optimization>optimization</a>
<a href=https://ychen878.github.io/tags/>[optimization]</a>
<a href=/notes/lp-duality/><p>Estimating LP bounds Given an optimization problem $$ \begin{align} \max_{f, s} &\quad 12f + 9s \ \st &\quad 4f + 2s \leq 4800 \ &\quad f + s \leq 1750 \ &\quad 0 \leq f \leq 1000 \ &\quad 0 \leq s \leq 1500 \ \end{align} $$ Suppose the maximum profit is $p^\star$. How can we bound $p^\star$? The lower bound of $p^\star$ can be found by picking any feasible point (since maximization).</p></a></p><p><h3><a href=/notes/rpc/>RPC</a></h3><i></i>
<time datetime=2023-02-25>Feb 25, 2023</time>
<i></i>
<a href=https://ychen878.github.io/tags/big-data>big data</a>
<a href=https://ychen878.github.io/tags/>[big data]</a>
<a href=/notes/rpc/><p>Networks Network Interface Controllers (NICs) can connect a computer to different physical mediums, such as Ethernet and Wi-Fi. Every NIC in the world has a unique MAC (media access control) address. It consists of 48 bits. Therefore, there are 28 trillion possible MAC addresses. Some devices randomly change their MAC address for privacy.
You can use command ifconfig to check you network interface controller and its corresponding MAC address. There exists virtual interfaces as well.</p></a></p><p><h3><a href=/notes/docker/>Docker</a></h3><i></i>
<time datetime=2023-02-25>Feb 25, 2023</time>
<i></i>
<a href=https://ychen878.github.io/tags/big-data>big data</a>
<a href=https://ychen878.github.io/tags/>[big data]</a>
<a href=/notes/docker/><p>Virtualization Virtualization is the illusion of private resources, provided by the software. We have virtual memory, virtual machine (hardware), virtual machine (languages), virtual operating system (container).
Each process using a virtual address space is not aware of other processes using memory (illusion of private memory). Virtualized resources include CPU, RAM, disks, network devices, etc. VMs rarely use all their allocated resources, so overbooking is possible. If each program is deployed to a different VM, operating system overheads dominate.</p></a></p><p><h3><a href=/notes/pla/>Perceptron Learning Algorithm</a></h3><i></i>
<time datetime=2022-11-30>Nov 30, 2022</time>
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