updated docs

davidlizeng · Oct 29, 2014 · b24b46e · b24b46e
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diff --git a/docs/_build/doctrees/credits.doctree b/docs/_build/doctrees/credits.doctree
diff --git a/docs/_build/doctrees/environment.pickle b/docs/_build/doctrees/environment.pickle
diff --git a/docs/_build/doctrees/index.doctree b/docs/_build/doctrees/index.doctree
diff --git a/docs/_build/doctrees/julia_examples.doctree b/docs/_build/doctrees/julia_examples.doctree
diff --git a/docs/_build/html/_sources/credits.txt b/docs/_build/html/_sources/credits.txt
@@ -5,13 +5,13 @@ LLS has been implemented in following languages:
 
 - Julia: `LinearLeastSquares.jl <https://github.com/davidlizeng/LinearLeastSquares.jl>`_ by David Zeng and Karanveer Mohan
 - Python: `lsqpy <https://github.com/keegango/lsqpy>`_ by Keegan Go
-- Matlab: lsq by Alex Lemon
 
 All implementations of LLS are released under the MIT license.
 
-The design of LLS was inspired by Steven Diamond's
-`CVXPY <http://cvxpy.readthedocs.org/en/latest/>`_,
-a similar software packages for solving the much more general class of
+Much of the design of LLS was inspired by
+`Convex.jl <https://github.com/cvxgrp/Convex.jl>`_ and
+Steven Diamond's `CVXPY <http://cvxpy.readthedocs.org/en/latest/>`_,
+similar software packages for solving the much more general class of
 convex optimization problems.
 
 A huge thanks to Stephen Boyd for his feedback in both the design and

diff --git a/docs/_build/html/_sources/index.txt b/docs/_build/html/_sources/index.txt
@@ -28,7 +28,7 @@ underdetermined system can be easily setup and solved with the following code:
   optimal_value = minimize!(objective, constraint)
 
 This example showcases the Julia implementation of LLS; other
-implementations include Python and Matlab.
+implementations include Python.
 
 
 In Depth Docs

diff --git a/docs/_build/html/_sources/julia_examples.txt b/docs/_build/html/_sources/julia_examples.txt
@@ -172,7 +172,7 @@ have physical meaning, so we often want to find a sequence inputs that also
 minimizes a least squares objective like the following:
 
 .. math::
-  \sum_{t = 1}^T \|Fx(t)\|^2_2 + \|Gu(t)\|^2_2,
+  \sum_{t = 0}^T \|Fx(t)\|^2_2 + \sum_{t = 1}^T\|Gu(t)\|^2_2,
 
 where :math:`F` and :math:`G` are numerical matrices.
 

diff --git a/docs/_build/html/credits.html b/docs/_build/html/credits.html
@@ -52,12 +52,12 @@ <h1>Credits<a class="headerlink" href="#credits" title="Permalink to this headli
 <ul class="simple">
 <li>Julia: <a class="reference external" href="https://github.com/davidlizeng/LinearLeastSquares.jl">LinearLeastSquares.jl</a> by David Zeng and Karanveer Mohan</li>
 <li>Python: <a class="reference external" href="https://github.com/keegango/lsqpy">lsqpy</a> by Keegan Go</li>
-<li>Matlab: lsq by Alex Lemon</li>
 </ul>
 <p>All implementations of LLS are released under the MIT license.</p>
-<p>The design of LLS was inspired by Steven Diamond&#8217;s
-<a class="reference external" href="http://cvxpy.readthedocs.org/en/latest/">CVXPY</a>,
-a similar software packages for solving the much more general class of
+<p>Much of the design of LLS was inspired by
+<a class="reference external" href="https://github.com/cvxgrp/Convex.jl">Convex.jl</a> and
+Steven Diamond&#8217;s <a class="reference external" href="http://cvxpy.readthedocs.org/en/latest/">CVXPY</a>,
+similar software packages for solving the much more general class of
 convex optimization problems.</p>
 <p>A huge thanks to Stephen Boyd for his feedback in both the design and
 documentation of LLS.</p>

diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html
@@ -72,7 +72,7 @@ <h1>Welcome to LinearLeastSquares<a class="headerlink" href="#welcome-to-linearl
 </pre></div>
 </div>
 <p>This example showcases the Julia implementation of LLS; other
-implementations include Python and Matlab.</p>
+implementations include Python.</p>
 <div class="section" id="in-depth-docs">
 <h2>In Depth Docs<a class="headerlink" href="#in-depth-docs" title="Permalink to this headline">¶</a></h2>
 <div class="toctree-wrapper compound">

diff --git a/docs/_build/html/julia_examples.html b/docs/_build/html/julia_examples.html
@@ -192,7 +192,7 @@ <h2>Control<a class="headerlink" href="#control" title="Permalink to this headli
 have physical meaning, so we often want to find a sequence inputs that also
 minimizes a least squares objective like the following:</p>
 <div class="math">
-\[\sum_{t = 1}^T \|Fx(t)\|^2_2 + \|Gu(t)\|^2_2,\]</div>
+\[\sum_{t = 0}^T \|Fx(t)\|^2_2 + \sum_{t = 1}^T\|Gu(t)\|^2_2,\]</div>
 <p>where <span class="math">\(F\)</span> and <span class="math">\(G\)</span> are numerical matrices.</p>
 <p>We&#8217;ll now apply the basic format of the control problem to an example of controlling
 the motion of an object in a fluid over <span class="math">\(T\)</span> intervals, each of <span class="math">\(h\)</span> seconds.

diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js
diff --git a/docs/credits.rst b/docs/credits.rst
@@ -5,13 +5,13 @@ LLS has been implemented in following languages:
 
 - Julia: `LinearLeastSquares.jl <https://github.com/davidlizeng/LinearLeastSquares.jl>`_ by David Zeng and Karanveer Mohan
 - Python: `lsqpy <https://github.com/keegango/lsqpy>`_ by Keegan Go
-- Matlab: lsq by Alex Lemon
 
 All implementations of LLS are released under the MIT license.
 
-The design of LLS was inspired by Steven Diamond's
-`CVXPY <http://cvxpy.readthedocs.org/en/latest/>`_,
-a similar software packages for solving the much more general class of
+Much of the design of LLS was inspired by
+`Convex.jl <https://github.com/cvxgrp/Convex.jl>`_ and
+Steven Diamond's `CVXPY <http://cvxpy.readthedocs.org/en/latest/>`_,
+similar software packages for solving the much more general class of
 convex optimization problems.
 
 A huge thanks to Stephen Boyd for his feedback in both the design and

diff --git a/docs/index.rst b/docs/index.rst
@@ -28,7 +28,7 @@ underdetermined system can be easily setup and solved with the following code:
   optimal_value = minimize!(objective, constraint)
 
 This example showcases the Julia implementation of LLS; other
-implementations include Python and Matlab.
+implementations include Python.
 
 
 In Depth Docs

diff --git a/docs/julia_examples.rst b/docs/julia_examples.rst
@@ -172,7 +172,7 @@ have physical meaning, so we often want to find a sequence inputs that also
 minimizes a least squares objective like the following:
 
 .. math::
-  \sum_{t = 1}^T \|Fx(t)\|^2_2 + \|Gu(t)\|^2_2,
+  \sum_{t = 0}^T \|Fx(t)\|^2_2 + \sum_{t = 1}^T\|Gu(t)\|^2_2,
 
 where :math:`F` and :math:`G` are numerical matrices.