Fix links

generate_index.py

@@ -1,7 +1,6 @@
 import collections
 import glob
 import re
-import pdb
 
 PDF_META = 'PDF'
 MD_WILDCARD = 'summaries/*.md'
@@ -10,12 +9,14 @@
 
 md_files = sorted(glob.glob(MD_WILDCARD))
 html_files = sorted(glob.glob(HTML_WILDCARD))
+assert(len(md_files) == len(html_files))
 
-ListItem = collections.namedtuple('ListItem', ['title', 'author', 'year', 'url'])
+ListItem = collections.namedtuple(
+  'ListItem', ['title', 'author', 'year', 'url'])
 list_items = []
 
 for md_file, html_file in zip(md_files, html_files):
-    with open(md_file, 'rb') as f:
+    with open(md_file, 'r') as f:
         title_line = f.readline()
         match = re.search(TITLE_PATTERN, title_line)
         title = match.group(1)
@@ -31,8 +32,8 @@
 
 list_items = sorted(list_items, key=lambda t: t.year)
 
-print '<ol>'
+print('<ol>')
 for list_item in list_items:
-    print '<li><a href="%s">%s <span class="year">(%s %d)</span></a></li>' % (
-        list_item.url, list_item.title, list_item.author, list_item.year)
-print '</ol>'
+    print('<li><a href="%s">%s <span class="year">(%s %d)</span></a></li>' % (
+        list_item.url, list_item.title, list_item.author, list_item.year))
+print('</ol>')

html/chu2013socp-codegen.html

@@ -52,7 +52,7 @@ <h2 id="novel-contributions">Novel contributions</h2>
 
 <ol>
   <li>SOCPs encompass a large class of convex programs; previous canonicalization
-suites targeted smaller problem clases, like QPs or QCQPs. Recall
+suites targeted smaller problem classes, like QPs or QCQPs. Recall
   that linear programs, QPs, and QCQPs are all SOCPs.</li>
   <li>Problem parameters must enter through specific functions; this interface
 allows the code generator to circumvent all floating point operations.</li>

html/dunning2016jump.html

@@ -46,7 +46,7 @@ <h1 id="jump-a-modeling-language-for-mathematical-optimization-duninng-2016https
 
 <p><strong>Nut graf</strong>: <a href="https://jump.readthedocs.io/en/latest/index.html">JuMP</a> is a
 Julia-embedded modeling language for mathematical optimization that was written
-with performance in mind; its salient features include automatic differentation
+with performance in mind; its salient features include automatic differentiation
 of user-defined functions, efficient parsing of updated problems, support for
 user-defined problems and solve methods, and solver callbacks. JuMP targets
 linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear
@@ -60,7 +60,7 @@ <h2 id="background">Background</h2>
 a particular standard form, and it is the responsibility of the client to
 express her problem in an acceptable fashion. It is often not
 at all obvious to clients how to encode their problems using the solving
-substrate's standard form; this encoding proces may require 
+substrate's standard form; this encoding proces may require
 clever re-expressions of the original problem or onerous stuffing of
 the problem's constraints into a rigid matrix structure.</p>
 

html/gatys2015neuralstyle.html

@@ -43,8 +43,8 @@ <h1 id="indextitle"><a class="title" href="../index.html">Papers</a></h1>
   </p>
 
 <h1 id="a-neural-algorithm-of-artistic-style-gatys-2015httpsarxivorgabs150806576"><a href="https://arxiv.org/abs/1508.06576">A Neural Algorithm of Artistic Style (Gatys 2015)</a></h1>
-<p>Gatys et al. present a way to repurpose a deep network trained for image 
-classification to redraw images in the style of some reference image. 
+<p>Gatys et al. present a way to repurpose a deep network trained for image
+classification to redraw images in the style of some reference image.
 For example, this method can be used to render arbitrary photographs in the
 style of Van Gogh’s <em>The Starry Night</em>.</p>
 
@@ -75,7 +75,7 @@ <h3 id="the-algorithm">The Algorithm</h3>
 \end{array}
 \end{equation*} %]]></script>
 
-<p>where <script type="math/tex">\ell</script> is fixed, <script type="math/tex">w_l</script> is a weight that incorporates the the size
+<p>where <script type="math/tex">\ell</script> is fixed, <script type="math/tex">w_l</script> is a weight that incorporates the size
 of layer (see the paper for details), and <script type="math/tex">B</script> is the optimization variable.
 Gram matrices are used above to capture correlations between the features
 within each layer. The <script type="math/tex">\alpha</script>-weighted expression measures the similarity

html/gubin1966projections.html

@@ -199,12 +199,12 @@ <h3 id="an-acceleration-scheme-for-alternating-projections">5. An Acceleration S
 
       <p>John Von Neumann. The Geometry of Orthogonal Spaces. <em>Functional Operators
 (AM-22), Vol. II.</em> Princeton University Press, 1950. Reprint of lecture
-notes originally compiled in 1933. <a href="#fnref:1" class="reversefootnote">&#8617;</a></p>
+notes originally compiled in 1933. <a href="#fnref:1" class="reversefootnote">&#8617;</a></p>
     </li>
     <li id="fn:2">
 
       <p>Boyd, S., &amp; Dattorro, J. (2003). Alternating projections. <em>EE392o, Stanford
-University.</em> <a href="#fnref:2" class="reversefootnote">&#8617;</a></p>
+University.</em> <a href="#fnref:2" class="reversefootnote">&#8617;</a></p>
     </li>
   </ol>
 </div>

index.html

@@ -18,20 +18,20 @@ <h1 id="indextitle">Papers</h1>
 
 <ol>
 <li><a href="html/gubin1966projections.html">The Method of Projections for Finding the Common Point of Convex Sets <span class="year">(Gubin 1966)</span></a></li>
-<li><a href="html/lipton2017.html">YALMIP: A Toolbox for Modeling and Optimization in MATLAB <span class="year">(Lofberg 2004)</span></a></li>
+<li><a href="html/lofberg2004yalmip.html">YALMIP: A Toolbox for Modeling and Optimization in MATLAB <span class="year">(Lofberg 2004)</span></a></li>
 <li><a href="html/grant2008graph.html">Graph Implementations for Nonsmooth Convex Programs <span class="year">(Grant 2008)</span></a></li>
 <li><a href="html/chu2013socp-codegen.html">Code Generation for Embedded Second-Order Cone Programming <span class="year">(Chu 2013)</span></a></li>
 <li><a href="html/gatys2015neuralstyle.html">A Neural Algorithm of Artistic Style <span class="year">(Gatys 2015)</span></a></li>
 <li><a href="html/arora2016pmi-embeddings.html">A Latent Variable Model Approach to PMI-based Word Embeddings <span class="year">(Arora 2016)</span></a></li>
 <li><a href="html/diamond2016cvxpy.html">CVXPY: A Python-Embedded Modeling Language for Convex Optimization <span class="year">(Diamond 2016)</span></a></li>
 <li><a href="html/dunning2016jump.html">JuMP: A Modeling Language for Mathematical Optimization <span class="year">(Duninng 2016)</span></a></li>
-<li><a href="html/gubin1996projections.html">Train Faster, Generalize Better: Stability of Stochastic Gradient Descent <span class="year">(Hardt 2016)</span></a></li>
-<li><a href="html/lofberg2004yalmip.html">TensorFlow: A System for Large-Scale Machine Learning <span class="year">(Mongat 2016)</span></a></li>
+<li><a href="html/hardt2016sgd-stability.html">Train Faster, Generalize Better: Stability of Stochastic Gradient Descent <span class="year">(Hardt 2016)</span></a></li>
+<li><a href="html/monga2017tensorflow.html">TensorFlow: A System for Large-Scale Machine Learning <span class="year">(Mongat 2016)</span></a></li>
 <li><a href="pdf/odonoghue2016scs.pdf">Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding <span class="year">(O'Donoghue 2016)</span></a></li>
 <li><a href="html/amos2017optnet.html">OptNet: Differentiable Optimization as a Layer in Neural Networks <span class="year">(Amos 2017)</span></a></li>
 <li><a href="html/arora2017sentence-embeddings.html">A Simple but Tough-to-Beat Baseline for Sentence Embeddings <span class="year">(Arora 2017)</span></a></li>
-<li><a href="html/hardt2016sgd-stability.html">Occupy the Cloud: Distributed Computing for the 99% <span class="year">(Jonas 2017)</span></a></li>
-<li><a href="html/jonas2017pywren.html">The Mythos of Model Interpretability <span class="year">(Lipton 2017)</span></a></li>
+<li><a href="html/jonas2017pywren.html">Occupy the Cloud: Distributed Computing for the 99% <span class="year">(Jonas 2017)</span></a></li>
+<li><a href="html/lipton2017.html">The Mythos of Model Interpretability <span class="year">(Lipton 2017)</span></a></li>
 </ol>
   </div>
 </body>

reading_list.md

@@ -45,3 +45,14 @@ Recent Advances and Limitations
 * Lifted Neural Networks (El Ghaoui)
 * Minimizing Finite Sums with the Stochastic Average Gradient (Shmidt)
 * How to escape saddle points efficiently (jordan)
+* Local Minima and Convergence in Low-Rank Semidefinite Programming (Burer, Monteiro)
+* The non-convex Burer-Monteiro approach works on smooth semidefinite programs (Boumal)
+* Fast Exact Multiplication by the Hessian (Pearlmutter 1994)
+* The geometry of graphs and some of its algorithmic applications [LLR '94]
+* Expander Flows, Geometric Embeddings and Graph Partitioning [Arora 09]
+* Representation Tradeoffs for Hyperbolic Embeddings
+* improving distributional similarity with lessons learned from word embeddings
+* A La Carte Embedding:
+Cheap but Effective Induction of Semantic Feature Vectors
+* A CONVERGENCE
+ANALYSIS OF GRADIENT DESCENT FOR DEEP LINEAR NEURAL NETWORKS

style.css

@@ -19,7 +19,7 @@ body {
   color: #333;
   margin: 0;
   padding: 0;
-  font-size: 18px;
+  font-size: 14px;
 }
 
 blockquote {