lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

adding more to codes for sim

Browse Source
This commit is contained in:
mhjensen
2018-11-06 11:02:04 +01:00
parent 0fce698a63
commit cb232e1001
31 changed files with 425 additions and 121 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/pub/svm/html/svm.html
+46 -4
View File
@@ -63,7 +63,11 @@ div { text-align: justify; text-justify: inter-word; }
('Different kernels', 2, None, '___sec18'),
('Quadratic coefficient matrix', 2, None, '___sec19'),
("Mercer's theorem", 2, None, '___sec20'),
('How do we solve these problems', 2, None, '___sec21')]}
('Mathematical optimization of convex functions',
2,
None,
'___sec21'),
('How do we solve these problems', 2, None, '___sec22')]}
end of tocinfo -->
<body>
@@ -105,7 +109,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Nov 5, 2018</h4></center> <!-- date -->
<center><h4>Nov 6, 2018</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -709,7 +713,30 @@ from which we also find \( b \).
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec21">How do we solve these problems </h2>
<h2 id="___sec21">Mathematical optimization of convex functions </h2>
<p>
A mathematical optimization problem, or just optimization problem, has the form
$$
\mathrm{minimize}\hspace{0.1cm} f(x),
$$
subject to some constraints \( g(\lambda_i) \leq b_i \) for say a selected set \( i=1,2,\dots, n \).
In our case we are optimizing with respect to the Lagrangian multipliers \( \lambda_i \), and the
vector \( \boldsymbol{\lambda}=[\lambda_1, \lambda_2,\dots, \lambda_n] \) is the optimization variable we are dealing with.
and \( f(x) \) is our objective function while \( g(\lambda_i) \leq b_i \) represents our constraint function.
<p>
In our case we are particularly interested in a class of optimization problems called convex optmization problems.
In our disussion on gradient descent methods we discussed at length the definition of a convex function.
<p>
Convex optimization problems play a central role in applied mathematics and we recommend strongly <a href="http://web.stanford.edu/~boyd/cvxbook/" target="_blank">Boyd and Vandenberghe's text on the topics</a>.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec22">How do we solve these problems </h2>
<p>
If we use Python as programming language and wish to venture beyond
@@ -756,10 +783,25 @@ from cvxopt import matrix
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>P <span style="color: #666666">=</span> matrix([[<span style="color: #666666">1.0</span>,<span style="color: #666666">0.0</span>],[<span style="color: #666666">0.0</span>,<span style="color: #666666">0.0</span>]])
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># Import the necessary packages</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span>
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">cvxopt</span> <span style="color: #008000; font-weight: bold">import</span> matrix
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">cvxopt</span> <span style="color: #008000; font-weight: bold">import</span> solvers
<span style="color: #408080; font-style: italic"># Define QP parameters (directly)</span>
P <span style="color: #666666">=</span> matrix([[<span style="color: #666666">1.0</span>,<span style="color: #666666">0.0</span>],[<span style="color: #666666">0.0</span>,<span style="color: #666666">0.0</span>]])
q <span style="color: #666666">=</span> matrix([<span style="color: #666666">3.0</span>,<span style="color: #666666">4.0</span>])
G <span style="color: #666666">=</span> matrix([[<span style="color: #666666">-1.0</span>,<span style="color: #666666">0.0</span>,<span style="color: #666666">-1.0</span>,<span style="color: #666666">2.0</span>,<span style="color: #666666">3.0</span>],[<span style="color: #666666">0.0</span>,<span style="color: #666666">-1.0</span>,<span style="color: #666666">-3.0</span>,<span style="color: #666666">5.0</span>,<span style="color: #666666">4.0</span>]])
h <span style="color: #666666">=</span> matrix([<span style="color: #666666">0.0</span>,<span style="color: #666666">0.0</span>,<span style="color: #666666">-15.0</span>,<span style="color: #666666">100.0</span>,<span style="color: #666666">80.0</span>])
<span style="color: #408080; font-style: italic"># Define QP parameters (with NumPy)</span>
P <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>diag([<span style="color: #666666">1</span>,<span style="color: #666666">0</span>]), tc<span style="color: #666666">=</span>’d’)
q <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([<span style="color: #666666">3</span>,<span style="color: #666666">4</span>]), tc<span style="color: #666666">=</span>’d’)
G <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([[<span style="color: #666666">-1</span>,<span style="color: #666666">0</span>],[<span style="color: #666666">0</span>,<span style="color: #666666">-1</span>],[<span style="color: #666666">-1</span>,<span style="color: #666666">-3</span>],[<span style="color: #666666">2</span>,<span style="color: #666666">5</span>],[<span style="color: #666666">3</span>,<span style="color: #666666">4</span>]]), tc<span style="color: #666666">=</span>’d’)
h <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([<span style="color: #666666">0</span>,<span style="color: #666666">0</span>,<span style="color: #666666">-15</span>,<span style="color: #666666">100</span>,<span style="color: #666666">80</span>]), tc<span style="color: #666666">=</span>’d’)
<span style="color: #408080; font-style: italic"># Construct the QP, invoke solver</span>
sol <span style="color: #666666">=</span> solvers<span style="color: #666666">.</span>qp(P,q,G,h)
<span style="color: #408080; font-style: italic"># Extract optimal value and solution</span>
sol[’x’] <span style="color: #408080; font-style: italic"># [7.13e-07, 5.00e+00]</span>
sol[’primal objective’]
</pre></div>
<p>