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    <div class="section" id="introduction">
  <span id="intro"></span><h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h1>
  <p>CVXOPT is a free software package for convex optimization based on the
  Python programming language.  It can be used with the interactive Python
  interpreter, on the command line by executing Python scripts, or integrated
  in other software via Python extension modules.  Its main purpose is to
  make the development of software for convex optimization applications
  straightforward by building on Python’s extensive standard library and on
  the strengths of Python as a high-level programming language.</p>
  <p>CVXOPT extends the built-in Python objects with two matrix objects: a
  <a class="reference internal" href="matrices.html#cvxopt.matrix" title="cvxopt.matrix"><code class="xref py py-class docutils literal notranslate"><span class="pre">matrix</span></code></a>  object for dense matrices and an
  <a class="reference internal" href="matrices.html#cvxopt.spmatrix" title="cvxopt.spmatrix"><code class="xref py py-class docutils literal notranslate"><span class="pre">spmatrix</span></code></a> object for sparse matrices.  These two
  matrix types are introduced in the chapter <a class="reference internal" href="matrices.html#c-matrices"><span class="std std-ref">Dense and Sparse Matrices</span></a>, together
  with the arithmetic operations and functions defined for them.  The
  following chapters (<a class="reference internal" href="blas.html#c-blas"><span class="std std-ref">The BLAS Interface</span></a> and <a class="reference internal" href="spsolvers.html#c-spsolvers"><span class="std std-ref">Sparse Linear Equations</span></a>) describe
  interfaces to several libraries for dense and sparse matrix computations.
  The CVXOPT optimization routines are described in the chapters
  <a class="reference internal" href="coneprog.html#c-coneprog"><span class="std std-ref">Cone Programming</span></a> and <a class="reference internal" href="modeling.html#c-modeling"><span class="std std-ref">Modeling</span></a>.
  These include convex optimization solvers written in Python,
  interfaces to a few other optimization libraries, and a modeling tool
  for piecewise-linear convex optimization problems.</p>
  <p>CVXOPT is organized in different modules.</p>
  <dl class="simple">
  <dt><code class="xref py py-mod docutils literal notranslate"><span class="pre">cvxopt.blas</span></code></dt><dd><p>Interface to most of the double-precision real and complex BLAS
  (<a class="reference internal" href="blas.html#c-blas"><span class="std std-ref">The BLAS Interface</span></a>).</p>
  </dd>
  <dt><code class="xref py py-mod docutils literal notranslate"><span class="pre">cvxopt.lapack</span></code></dt><dd><p>Interface to dense double-precision real and complex linear equation
  solvers and eigenvalue routines from LAPACK (<a class="reference internal" href="lapack.html#c-lapack"><span class="std std-ref">The LAPACK Interface</span></a>).</p>
  </dd>
  <dt><code class="xref py py-mod docutils literal notranslate"><span class="pre">cvxopt.fftw</span></code></dt><dd><p>An optional interface to the discrete transform routines from FFTW
  (<a class="reference internal" href="fftw.html#c-fftw"><span class="std std-ref">Discrete Transforms</span></a>).</p>
  </dd>
  <dt><code class="xref py py-mod docutils literal notranslate"><span class="pre">cvxopt.amd</span></code></dt><dd><p>Interface to the approximate minimum degree ordering routine from AMD
  (<a class="reference internal" href="spsolvers.html#s-orderings"><span class="std std-ref">Matrix Orderings</span></a>).</p>
  </dd>
  <dt><code class="xref py py-mod docutils literal notranslate"><span class="pre">cvxopt.umfpack</span></code></dt><dd><p>Interface to the sparse LU solver from UMFPACK (<a class="reference internal" href="spsolvers.html#s-umfpack"><span class="std std-ref">General Linear Equations</span></a>).</p>
  </dd>
  <dt><code class="xref py py-mod docutils literal notranslate"><span class="pre">cvxopt.cholmod</span></code></dt><dd><p>Interface to the sparse Cholesky solver from CHOLMOD (<a class="reference internal" href="spsolvers.html#s-cholmod"><span class="std std-ref">Positive Definite Linear Equations</span></a>).</p>
  </dd>
  <dt><code class="xref py py-mod docutils literal notranslate"><span class="pre">cvxopt.solvers</span></code></dt><dd><p>Convex optimization routines and optional interfaces to solvers from
  GLPK, MOSEK, and DSDP5 (<a class="reference internal" href="coneprog.html#c-coneprog"><span class="std std-ref">Cone Programming</span></a> and <a class="reference internal" href="solvers.html#c-solvers"><span class="std std-ref">Nonlinear Convex Optimization</span></a>).</p>
  </dd>
  <dt><code class="xref py py-mod docutils literal notranslate"><span class="pre">cvxopt.modeling</span></code></dt><dd><p>Routines for specifying and solving linear programs and convex
  optimization problems with piecewise-linear cost and constraint functions
  (<a class="reference internal" href="modeling.html#c-modeling"><span class="std std-ref">Modeling</span></a>).</p>
  </dd>
  <dt><code class="xref py py-mod docutils literal notranslate"><span class="pre">cvxopt.info</span></code></dt><dd><p>Defines a string <code class="xref py py-const docutils literal notranslate"><span class="pre">version</span></code> with the version number of the CVXOPT
  installation and a function <code class="xref py py-func docutils literal notranslate"><span class="pre">license</span></code> that prints the CVXOPT
  license.</p>
  </dd>
  <dt><code class="xref py py-mod docutils literal notranslate"><span class="pre">cvxopt.printing</span></code></dt><dd><p>Contains functions and parameters that control how matrices are formatted.</p>
  </dd>
  </dl>
  <p>The modules are described in detail in this manual and in the on-line Python
  help facility <strong class="program">pydoc</strong>.  Several example scripts are included in
  the distribution.</p>
  </div>
  
  
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