Add post on OR
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<a href="./posts/iclr-2020-notes.html">ICLR 2020 Notes: Speakers and Workshops</a> - May 5, 2020
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</li>
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<li>
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<a href="./posts/operations-research-references.html">Operations Research and Optimisation: where to start?</a> - April 8, 2020
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</li>
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<li>
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<a href="./posts/hierarchical-optimal-transport-for-document-classification.html">Reading notes: Hierarchical Optimal Transport for Document Representation</a> - April 5, 2020
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</li>
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@ -65,6 +65,52 @@
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</article>
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]]></summary>
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</entry>
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<entry>
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<title>Operations Research and Optimisation: where to start?</title>
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<link href="https://www.lozeve.com/posts/operations-research-references.html" />
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<id>https://www.lozeve.com/posts/operations-research-references.html</id>
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<published>2020-04-08T00:00:00Z</published>
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<updated>2020-04-08T00:00:00Z</updated>
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<summary type="html"><![CDATA[<article>
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<section class="header">
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</section>
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<section>
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<p><a href="https://en.wikipedia.org/wiki/Operations_research">Operations research</a> (OR) is a vast area comprising a lot of theory, different branches of mathematics, and too many applications to count. In this post, I will try to explain why I find it so fascinating, but also why it can be a little disconcerting to explore at first. Then I will try to ease the newcomer’s path in this rich area, by suggesting a very rough “map” of the field and a few references to get started.</p>
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<p>Keep in mind that although I studied it during my graduate studies, this is not my primary area of expertise (I’m a data scientist by trade), and I definitely don’t pretend to know everything in OR. This is a field too vast for any single person to understand in its entirety, and I talk mostly from a “amateur mathematician and computer scientist” standpoint.</p>
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<h1 id="why-is-it-hard-to-approach">Why is it hard to approach?</h1>
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<ul>
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<li>why it may be more difficult to approach than other, more recent areas like ML and DL
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<ul>
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<li>slightly longer history</li>
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<li>always very close to applications: somehow more “messy” in its notations, vocabulary, standard references, etc, as other “purer” fields of maths (similar to stats in this regard)</li>
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<li>often approached from a applied point of view means that many very different concepts are often mixed together</li>
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</ul></li>
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<li>why it is interesting and you should pursue it anyway
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<ul>
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<li>history of the field</li>
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<li>examples of applications</li>
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<li>theory perspective, rigorous field</li>
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</ul></li>
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<li>different subfields
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<ul>
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<li>optimisation: constrained and unconstrained</li>
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<li>game theory</li>
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<li>dynamic programming</li>
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<li>stochastic processes</li>
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<li>simulation</li>
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</ul></li>
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<li>how to learn and practice
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<ul>
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<li>references</li>
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<li>courses</li>
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<li>computational assets</li>
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</ul></li>
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</ul>
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</section>
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</article>
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]]></summary>
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</entry>
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<entry>
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<title>Reading notes: Hierarchical Optimal Transport for Document Representation</title>
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<link href="https://www.lozeve.com/posts/hierarchical-optimal-transport-for-document-classification.html" />
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@ -74,6 +74,10 @@ public key: RWQ6uexORp8f7USHA7nX9lFfltaCA9x6aBV06MvgiGjUt6BVf6McyD26
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<a href="./posts/iclr-2020-notes.html">ICLR 2020 Notes: Speakers and Workshops</a> - May 5, 2020
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</li>
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<li>
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<a href="./posts/operations-research-references.html">Operations Research and Optimisation: where to start?</a> - April 8, 2020
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</li>
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<li>
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<a href="./posts/hierarchical-optimal-transport-for-document-classification.html">Reading notes: Hierarchical Optimal Transport for Document Representation</a> - April 5, 2020
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</li>
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92
_site/posts/operations-research-references.html
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_site/posts/operations-research-references.html
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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta http-equiv="x-ua-compatible" content="ie=edge">
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<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=yes">
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<meta name="description" content="Dimitri Lozeve's blog: Operations Research and Optimisation: where to start?">
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<title>Dimitri Lozeve - Operations Research and Optimisation: where to start?</title>
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<link rel="stylesheet" href="../css/tufte.css" />
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<link rel="stylesheet" href="../css/pandoc.css" />
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<link rel="stylesheet" href="../css/default.css" />
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<link rel="stylesheet" href="../css/syntax.css" />
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<!-- KaTeX CSS styles -->
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.11.0/dist/katex.min.css" integrity="sha384-BdGj8xC2eZkQaxoQ8nSLefg4AV4/AwB3Fj+8SUSo7pnKP6Eoy18liIKTPn9oBYNG" crossorigin="anonymous">
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<!-- The loading of KaTeX is deferred to speed up page rendering -->
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<script defer src="https://cdn.jsdelivr.net/npm/katex@0.11.0/dist/katex.min.js" integrity="sha384-JiKN5O8x9Hhs/UE5cT5AAJqieYlOZbGT3CHws/y97o3ty4R7/O5poG9F3JoiOYw1" crossorigin="anonymous"></script>
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<!-- To automatically render math in text elements, include the auto-render extension: -->
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<script defer src="https://cdn.jsdelivr.net/npm/katex@0.11.0/dist/contrib/auto-render.min.js" integrity="sha384-kWPLUVMOks5AQFrykwIup5lo0m3iMkkHrD0uJ4H5cjeGihAutqP0yW0J6dpFiVkI" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script>
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</head>
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<body>
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<article>
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<header>
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<nav>
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<a href="../">Home</a>
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<a href="../projects.html">Projects</a>
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<a href="../archive.html">Archive</a>
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<a href="../contact.html">Contact</a>
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</nav>
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<h1 class="title">Operations Research and Optimisation: where to start?</h1>
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<p class="byline">April 8, 2020</p>
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</header>
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</article>
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<article>
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<section class="header">
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</section>
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<section>
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<p><a href="https://en.wikipedia.org/wiki/Operations_research">Operations research</a> (OR) is a vast area comprising a lot of theory, different branches of mathematics, and too many applications to count. In this post, I will try to explain why I find it so fascinating, but also why it can be a little disconcerting to explore at first. Then I will try to ease the newcomer’s path in this rich area, by suggesting a very rough “map” of the field and a few references to get started.</p>
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<p>Keep in mind that although I studied it during my graduate studies, this is not my primary area of expertise (I’m a data scientist by trade), and I definitely don’t pretend to know everything in OR. This is a field too vast for any single person to understand in its entirety, and I talk mostly from a “amateur mathematician and computer scientist” standpoint.</p>
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<h1 id="why-is-it-hard-to-approach">Why is it hard to approach?</h1>
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<ul>
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<li>why it may be more difficult to approach than other, more recent areas like ML and DL
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<ul>
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<li>slightly longer history</li>
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<li>always very close to applications: somehow more “messy” in its notations, vocabulary, standard references, etc, as other “purer” fields of maths (similar to stats in this regard)</li>
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<li>often approached from a applied point of view means that many very different concepts are often mixed together</li>
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</ul></li>
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<li>why it is interesting and you should pursue it anyway
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<ul>
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<li>history of the field</li>
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<li>examples of applications</li>
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<li>theory perspective, rigorous field</li>
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</ul></li>
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<li>different subfields
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<ul>
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<li>optimisation: constrained and unconstrained</li>
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<li>game theory</li>
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<li>dynamic programming</li>
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<li>stochastic processes</li>
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<li>simulation</li>
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</ul></li>
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<li>how to learn and practice
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<ul>
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<li>references</li>
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<li>courses</li>
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<li>computational assets</li>
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</ul></li>
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</ul>
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</section>
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</article>
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<footer>
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Site proudly generated by
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<a href="http://jaspervdj.be/hakyll">Hakyll</a>
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</footer>
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</body>
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</html>
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@ -64,6 +64,52 @@
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<guid>https://www.lozeve.com/posts/iclr-2020-notes.html</guid>
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<dc:creator>Dimitri Lozeve</dc:creator>
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</item>
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<item>
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<title>Operations Research and Optimisation: where to start?</title>
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<link>https://www.lozeve.com/posts/operations-research-references.html</link>
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<description><![CDATA[<article>
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<section class="header">
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</section>
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<section>
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<p><a href="https://en.wikipedia.org/wiki/Operations_research">Operations research</a> (OR) is a vast area comprising a lot of theory, different branches of mathematics, and too many applications to count. In this post, I will try to explain why I find it so fascinating, but also why it can be a little disconcerting to explore at first. Then I will try to ease the newcomer’s path in this rich area, by suggesting a very rough “map” of the field and a few references to get started.</p>
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<p>Keep in mind that although I studied it during my graduate studies, this is not my primary area of expertise (I’m a data scientist by trade), and I definitely don’t pretend to know everything in OR. This is a field too vast for any single person to understand in its entirety, and I talk mostly from a “amateur mathematician and computer scientist” standpoint.</p>
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<h1 id="why-is-it-hard-to-approach">Why is it hard to approach?</h1>
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<ul>
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<li>why it may be more difficult to approach than other, more recent areas like ML and DL
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<ul>
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<li>slightly longer history</li>
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<li>always very close to applications: somehow more “messy” in its notations, vocabulary, standard references, etc, as other “purer” fields of maths (similar to stats in this regard)</li>
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<li>often approached from a applied point of view means that many very different concepts are often mixed together</li>
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</ul></li>
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<li>why it is interesting and you should pursue it anyway
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<ul>
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<li>history of the field</li>
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<li>examples of applications</li>
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<li>theory perspective, rigorous field</li>
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</ul></li>
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<li>different subfields
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<ul>
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<li>optimisation: constrained and unconstrained</li>
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<li>game theory</li>
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<li>dynamic programming</li>
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<li>stochastic processes</li>
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<li>simulation</li>
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</ul></li>
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<li>how to learn and practice
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<ul>
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<li>references</li>
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<li>courses</li>
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<li>computational assets</li>
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</ul></li>
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</ul>
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</section>
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</article>
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]]></description>
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<pubDate>Wed, 08 Apr 2020 00:00:00 UT</pubDate>
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<guid>https://www.lozeve.com/posts/operations-research-references.html</guid>
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<dc:creator>Dimitri Lozeve</dc:creator>
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</item>
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<item>
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<title>Reading notes: Hierarchical Optimal Transport for Document Representation</title>
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<link>https://www.lozeve.com/posts/hierarchical-optimal-transport-for-document-classification.html</link>
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44
posts/operations-research-references.org
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44
posts/operations-research-references.org
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@ -0,0 +1,44 @@
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---
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title: "Operations Research and Optimisation: where to start?"
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date: 2020-04-08
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---
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[[https://en.wikipedia.org/wiki/Operations_research][Operations research]] (OR) is a vast area comprising a lot of theory,
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different branches of mathematics, and too many applications to
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count. In this post, I will try to explain why I find it so
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fascinating, but also why it can be a little disconcerting to explore
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at first. Then I will try to ease the newcomer's path in this rich
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area, by suggesting a very rough "map" of the field and a few
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references to get started.
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Keep in mind that although I studied it during my graduate studies,
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this is not my primary area of expertise (I'm a data scientist by
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trade), and I definitely don't pretend to know everything in OR. This
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is a field too vast for any single person to understand in its
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entirety, and I talk mostly from a "amateur mathematician and computer
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scientist" standpoint.
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* Why is it hard to approach?
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- why it may be more difficult to approach than other, more recent
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areas like ML and DL
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- slightly longer history
|
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- always very close to applications: somehow more "messy" in its
|
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notations, vocabulary, standard references, etc, as other "purer"
|
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fields of maths (similar to stats in this regard)
|
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- often approached from a applied point of view means that many very
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different concepts are often mixed together
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- why it is interesting and you should pursue it anyway
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- history of the field
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- examples of applications
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- theory perspective, rigorous field
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- different subfields
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- optimisation: constrained and unconstrained
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- game theory
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- dynamic programming
|
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- stochastic processes
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- simulation
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- how to learn and practice
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- references
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- courses
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- computational assets
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