Add references to online courses

_site/atom.xml

@@ -43,6 +43,20 @@
 <p>For more books on linear programming, the two books <span class="citation" data-cites="dantzig1997_linear">Dantzig (<a href="#ref-dantzig1997_linear">1997</a>)</span>, <span class="citation" data-cites="dantzig2003_linear">Dantzig (<a href="#ref-dantzig2003_linear">2003</a>)</span> are very complete, if somewhat more mathematically advanced. <span class="citation" data-cites="bertsimas1997_introd">Bertsimas and Tsitsiklis (<a href="#ref-bertsimas1997_introd">1997</a>)</span> is also a great reference, if you can find it.</p>
 <p>For all the other subfields, <a href="https://or.stackexchange.com/a/870">this great StackExchange answer</a> contains a lot of useful references, including most of the above. Of particular note are <span class="citation" data-cites="peyreComputationalOptimalTransport2019">Peyré and Cuturi (<a href="#ref-peyreComputationalOptimalTransport2019">2019</a>)</span> for optimal transport, <span class="citation" data-cites="boyd2004_convex">Boyd (<a href="#ref-boyd2004_convex">2004</a>)</span> for convex optimization (<a href="https://web.stanford.edu/~boyd/cvxbook/">freely available online</a>), and <span class="citation" data-cites="nocedal2006_numer">Nocedal (<a href="#ref-nocedal2006_numer">2006</a>)</span> for numerical optimization. <span class="citation" data-cites="kochenderfer2019_algor">Kochenderfer (<a href="#ref-kochenderfer2019_algor">2019</a>)</span> is not in the list (because it is very recent) but is also excellent, with examples in Julia covering nearly every kind of optimization algorithms.</p>
 <h3 id="online-courses">Online courses</h3>
+<p>If you would like to watch video lectures, there are a few good opportunities freely available online, in particular on <a href="https://ocw.mit.edu/index.htm">MIT OpenCourseWare</a>. The list of courses at MIT is available <a href="https://orc.mit.edu/academics/course-offerings">on their webpage</a>. I haven’t actually looked in details at the courses content<span><label for="sn-4" class="margin-toggle sidenote-number"></label><input type="checkbox" id="sn-4" class="margin-toggle" /><span class="sidenote">I am more comfortable reading books than watching lecture videos online. Although I liked attending classes during my studies, I do not have the same feeling in front of a video. When I read, I can re-read three times the same sentence, pause to look up something, or skim a few paragraphs. I find that the inability to do that with a video diminishes greatly my ability to concentrate.<br />
+<br />
+</span></span>, so I cannot vouch for them directly, but MIT courses are generally of excellent quality. Most courses are also taught by Bertsimas and Bertsekas, who are very famous and wrote many excellent books.</p>
+<p>Of particular notes are:</p>
+<ul>
+<li><a href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-251j-introduction-to-mathematical-programming-fall-2009/">Introduction to Mathematical Programming</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/sloan-school-of-management/15-084j-nonlinear-programming-spring-2004/">Nonlinear Optimization</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-253-convex-analysis-and-optimization-spring-2012/">Convex Analysis and Optimization</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-972-algebraic-techniques-and-semidefinite-optimization-spring-2006/">Algebraic Techniques and Semidefinite Optimization</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/sloan-school-of-management/15-083j-integer-programming-and-combinatorial-optimization-fall-2009/">Integer Programming and Combinatorial Optimization</a>.</li>
+</ul>
+<p>Another interesting course I found online is <a href="https://www.ams.jhu.edu/~wcook12/dl/index.html">Deep Learning in Discrete Optimization</a>, at Johns Hopkins<span><label for="sn-5" class="margin-toggle">⊕</label><input type="checkbox" id="sn-5" class="margin-toggle" /><span class="marginnote"> It is taught by William Cook, who is the author of <a href="https://press.princeton.edu/books/paperback/9780691163529/in-pursuit-of-the-traveling-salesman"><em>In Pursuit of the Traveling Salesman</em></a>, a nice introduction to the TSP problem in a readable form.<br />
+<br />
+</span></span>. It contains an interesting overview of deep learning and integer programming, with a focus on connections, and applications to recent research areas in ML (reinforcement learning, attention, etc.).</p>
 <h2 id="solvers-and-computational-resources">Solvers and computational resources <span id="solvers"></span></h2>
 <h2 id="references" class="unnumbered">References</h2>
 <div id="refs" class="references">

_site/posts/operations-research-references.html

@@ -72,6 +72,20 @@
 <p>For more books on linear programming, the two books <span class="citation" data-cites="dantzig1997_linear">Dantzig (<a href="#ref-dantzig1997_linear">1997</a>)</span>, <span class="citation" data-cites="dantzig2003_linear">Dantzig (<a href="#ref-dantzig2003_linear">2003</a>)</span> are very complete, if somewhat more mathematically advanced. <span class="citation" data-cites="bertsimas1997_introd">Bertsimas and Tsitsiklis (<a href="#ref-bertsimas1997_introd">1997</a>)</span> is also a great reference, if you can find it.</p>
 <p>For all the other subfields, <a href="https://or.stackexchange.com/a/870">this great StackExchange answer</a> contains a lot of useful references, including most of the above. Of particular note are <span class="citation" data-cites="peyreComputationalOptimalTransport2019">Peyré and Cuturi (<a href="#ref-peyreComputationalOptimalTransport2019">2019</a>)</span> for optimal transport, <span class="citation" data-cites="boyd2004_convex">Boyd (<a href="#ref-boyd2004_convex">2004</a>)</span> for convex optimization (<a href="https://web.stanford.edu/~boyd/cvxbook/">freely available online</a>), and <span class="citation" data-cites="nocedal2006_numer">Nocedal (<a href="#ref-nocedal2006_numer">2006</a>)</span> for numerical optimization. <span class="citation" data-cites="kochenderfer2019_algor">Kochenderfer (<a href="#ref-kochenderfer2019_algor">2019</a>)</span> is not in the list (because it is very recent) but is also excellent, with examples in Julia covering nearly every kind of optimization algorithms.</p>
 <h3 id="online-courses">Online courses</h3>
+<p>If you would like to watch video lectures, there are a few good opportunities freely available online, in particular on <a href="https://ocw.mit.edu/index.htm">MIT OpenCourseWare</a>. The list of courses at MIT is available <a href="https://orc.mit.edu/academics/course-offerings">on their webpage</a>. I haven’t actually looked in details at the courses content<span><label for="sn-4" class="margin-toggle sidenote-number"></label><input type="checkbox" id="sn-4" class="margin-toggle" /><span class="sidenote">I am more comfortable reading books than watching lecture videos online. Although I liked attending classes during my studies, I do not have the same feeling in front of a video. When I read, I can re-read three times the same sentence, pause to look up something, or skim a few paragraphs. I find that the inability to do that with a video diminishes greatly my ability to concentrate.<br />
+<br />
+</span></span>, so I cannot vouch for them directly, but MIT courses are generally of excellent quality. Most courses are also taught by Bertsimas and Bertsekas, who are very famous and wrote many excellent books.</p>
+<p>Of particular notes are:</p>
+<ul>
+<li><a href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-251j-introduction-to-mathematical-programming-fall-2009/">Introduction to Mathematical Programming</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/sloan-school-of-management/15-084j-nonlinear-programming-spring-2004/">Nonlinear Optimization</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-253-convex-analysis-and-optimization-spring-2012/">Convex Analysis and Optimization</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-972-algebraic-techniques-and-semidefinite-optimization-spring-2006/">Algebraic Techniques and Semidefinite Optimization</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/sloan-school-of-management/15-083j-integer-programming-and-combinatorial-optimization-fall-2009/">Integer Programming and Combinatorial Optimization</a>.</li>
+</ul>
+<p>Another interesting course I found online is <a href="https://www.ams.jhu.edu/~wcook12/dl/index.html">Deep Learning in Discrete Optimization</a>, at Johns Hopkins<span><label for="sn-5" class="margin-toggle">⊕</label><input type="checkbox" id="sn-5" class="margin-toggle" /><span class="marginnote"> It is taught by William Cook, who is the author of <a href="https://press.princeton.edu/books/paperback/9780691163529/in-pursuit-of-the-traveling-salesman"><em>In Pursuit of the Traveling Salesman</em></a>, a nice introduction to the TSP problem in a readable form.<br />
+<br />
+</span></span>. It contains an interesting overview of deep learning and integer programming, with a focus on connections, and applications to recent research areas in ML (reinforcement learning, attention, etc.).</p>
 <h2 id="solvers-and-computational-resources">Solvers and computational resources <span id="solvers"></span></h2>
 <h2 id="references" class="unnumbered">References</h2>
 <div id="refs" class="references">

_site/rss.xml

@@ -39,6 +39,20 @@
 <p>For more books on linear programming, the two books <span class="citation" data-cites="dantzig1997_linear">Dantzig (<a href="#ref-dantzig1997_linear">1997</a>)</span>, <span class="citation" data-cites="dantzig2003_linear">Dantzig (<a href="#ref-dantzig2003_linear">2003</a>)</span> are very complete, if somewhat more mathematically advanced. <span class="citation" data-cites="bertsimas1997_introd">Bertsimas and Tsitsiklis (<a href="#ref-bertsimas1997_introd">1997</a>)</span> is also a great reference, if you can find it.</p>
 <p>For all the other subfields, <a href="https://or.stackexchange.com/a/870">this great StackExchange answer</a> contains a lot of useful references, including most of the above. Of particular note are <span class="citation" data-cites="peyreComputationalOptimalTransport2019">Peyré and Cuturi (<a href="#ref-peyreComputationalOptimalTransport2019">2019</a>)</span> for optimal transport, <span class="citation" data-cites="boyd2004_convex">Boyd (<a href="#ref-boyd2004_convex">2004</a>)</span> for convex optimization (<a href="https://web.stanford.edu/~boyd/cvxbook/">freely available online</a>), and <span class="citation" data-cites="nocedal2006_numer">Nocedal (<a href="#ref-nocedal2006_numer">2006</a>)</span> for numerical optimization. <span class="citation" data-cites="kochenderfer2019_algor">Kochenderfer (<a href="#ref-kochenderfer2019_algor">2019</a>)</span> is not in the list (because it is very recent) but is also excellent, with examples in Julia covering nearly every kind of optimization algorithms.</p>
 <h3 id="online-courses">Online courses</h3>
+<p>If you would like to watch video lectures, there are a few good opportunities freely available online, in particular on <a href="https://ocw.mit.edu/index.htm">MIT OpenCourseWare</a>. The list of courses at MIT is available <a href="https://orc.mit.edu/academics/course-offerings">on their webpage</a>. I haven’t actually looked in details at the courses content<span><label for="sn-4" class="margin-toggle sidenote-number"></label><input type="checkbox" id="sn-4" class="margin-toggle" /><span class="sidenote">I am more comfortable reading books than watching lecture videos online. Although I liked attending classes during my studies, I do not have the same feeling in front of a video. When I read, I can re-read three times the same sentence, pause to look up something, or skim a few paragraphs. I find that the inability to do that with a video diminishes greatly my ability to concentrate.<br />
+<br />
+</span></span>, so I cannot vouch for them directly, but MIT courses are generally of excellent quality. Most courses are also taught by Bertsimas and Bertsekas, who are very famous and wrote many excellent books.</p>
+<p>Of particular notes are:</p>
+<ul>
+<li><a href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-251j-introduction-to-mathematical-programming-fall-2009/">Introduction to Mathematical Programming</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/sloan-school-of-management/15-084j-nonlinear-programming-spring-2004/">Nonlinear Optimization</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-253-convex-analysis-and-optimization-spring-2012/">Convex Analysis and Optimization</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-972-algebraic-techniques-and-semidefinite-optimization-spring-2006/">Algebraic Techniques and Semidefinite Optimization</a>,</li>
+<li><a href="https://ocw.mit.edu/courses/sloan-school-of-management/15-083j-integer-programming-and-combinatorial-optimization-fall-2009/">Integer Programming and Combinatorial Optimization</a>.</li>
+</ul>
+<p>Another interesting course I found online is <a href="https://www.ams.jhu.edu/~wcook12/dl/index.html">Deep Learning in Discrete Optimization</a>, at Johns Hopkins<span><label for="sn-5" class="margin-toggle">⊕</label><input type="checkbox" id="sn-5" class="margin-toggle" /><span class="marginnote"> It is taught by William Cook, who is the author of <a href="https://press.princeton.edu/books/paperback/9780691163529/in-pursuit-of-the-traveling-salesman"><em>In Pursuit of the Traveling Salesman</em></a>, a nice introduction to the TSP problem in a readable form.<br />
+<br />
+</span></span>. It contains an interesting overview of deep learning and integer programming, with a focus on connections, and applications to recent research areas in ML (reinforcement learning, attention, etc.).</p>
 <h2 id="solvers-and-computational-resources">Solvers and computational resources <span id="solvers"></span></h2>
 <h2 id="references" class="unnumbered">References</h2>
 <div id="refs" class="references">

posts/operations-research-references.org

@@ -143,6 +143,41 @@ covering nearly every kind of optimization algorithms.
 
 ** Online courses
 
+If you would like to watch video lectures, there are a few good
+opportunities freely available online, in particular on [[https://ocw.mit.edu/index.htm][MIT
+OpenCourseWare]]. The list of courses at MIT is available [[https://orc.mit.edu/academics/course-offerings][on their
+webpage]]. I haven't actually looked in details at the courses
+content[fn:courses], so I cannot vouch for them directly, but MIT
+courses are generally of excellent quality. Most courses are also
+taught by Bertsimas and Bertsekas, who are very famous and wrote many
+excellent books.
+
+[fn:courses] I am more comfortable reading books than watching lecture
+videos online. Although I liked attending classes during my studies, I
+do not have the same feeling in front of a video. When I read, I can
+re-read three times the same sentence, pause to look up something, or
+skim a few paragraphs. I find that the inability to do that with a
+video diminishes greatly my ability to concentrate.
+
+
+Of particular notes are:
+- [[https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-251j-introduction-to-mathematical-programming-fall-2009/][Introduction to Mathematical Programming]],
+- [[https://ocw.mit.edu/courses/sloan-school-of-management/15-084j-nonlinear-programming-spring-2004/][Nonlinear Optimization]],
+- [[https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-253-convex-analysis-and-optimization-spring-2012/][Convex Analysis and Optimization]],
+- [[https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-972-algebraic-techniques-and-semidefinite-optimization-spring-2006/][Algebraic Techniques and Semidefinite Optimization]],
+- [[https://ocw.mit.edu/courses/sloan-school-of-management/15-083j-integer-programming-and-combinatorial-optimization-fall-2009/][Integer Programming and Combinatorial Optimization]].
+
+Another interesting course I found online is [[https://www.ams.jhu.edu/~wcook12/dl/index.html][Deep Learning in Discrete
+Optimization]], at Johns Hopkins[fn:cook]. It contains an interesting
+overview of deep learning and integer programming, with a focus on
+connections, and applications to recent research areas in ML
+(reinforcement learning, attention, etc.).
+
+[fn:cook] {-} It is taught by William Cook, who is the author of [[https://press.princeton.edu/books/paperback/9780691163529/in-pursuit-of-the-traveling-salesman][/In
+Pursuit of the Traveling Salesman/]], a nice introduction to the TSP
+problem in a readable form.
+
+
 * Solvers and computational resources <<solvers>>
 
 * References