<div dir="ltr">Nice! I will look at your implementation to see if I can learn something from this. :)</div><div class="gmail_extra"><br><br><div class="gmail_quote">On Sun, Apr 14, 2013 at 9:59 PM, Levent Erkok <span dir="ltr"><<a href="mailto:erkokl@gmail.com" target="_blank">erkokl@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">I'm happy to announce the first release of conjugateGradient, implementing the Conjugate Gradient algorithm for solving linear systems of equations over sparse matrices:<div>
<br></div><div> <a href="http://hackage.haskell.org/package/conjugateGradient" target="_blank">http://hackage.haskell.org/package/conjugateGradient</a><br>
</div><div><br></div><div>The conjugate gradient algorithm only applies to the cases when the input matrix is symmetric and positive definite. See: <a href="http://en.wikipedia.org/wiki/Conjugate_gradient_method" target="_blank">http://en.wikipedia.org/wiki/Conjugate_gradient_method</a></div>
<div><br></div><div>The advantage of this method is that it can handle really large sparse systems quite well, when other direct methods (such as LU decomposition) are just not practical due to algorithmic complexity. Such large and sparse systems naturally arise in engineering applications, such as in ASIC placement algorithms and when solving partial differential equations.</div>
<div><br></div><div>Bug reports, feedback, and improvements are always welcome.</div><span class="HOEnZb"><font color="#888888"><div><br></div><div>-Levent.</div></font></span></div>
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<br></blockquote></div><br><br clear="all"><div><br></div>-- <br><div dir="ltr"><div>E-mail sent by Daniel Díaz Casanueva<br></div><div><div><br>let f x = x in x<br></div></div></div>
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