Matthieu Brucher's blog

My last blog post on optimization helped me generate orthogonal sequences. Now, I will use those sequences to separate two signals. The basic use case is a linear system with two inputs, one output, and instead of recording the response of one input at a time, one plays both inputs simultaneously with specific sequences so that they can be separated in another process.
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In my last post about optimization, I’ve derived my function analytically. Sometimes, it’s not as easy. Sometimes also, a simple gradient optimization is not enough.

scikits.optimization has a special class for handling numerical differentiation, and several tools for conjugate gradients.
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Last time, I’ve made a simple example of a gradient-free optimization. Now, I’d like to use the gradient of my function (analytical gradient I’ve computed) to be able to get the global minimum in less iterations.
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Some months ago, I’ve finished my manifold learning posts serie. As support for the manifold learning toolkit, I’ve also developed an optimization framework, which I’ll be blogging about, starting now.
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Python can be used for many things, and is mainly known for the shell scripts people wrote. Shai Vangast proposes using the langage for data analysis and visualization.
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I’ve promised to make an update whenever I would find a solution to the problem I had some months ago when I tried to use the latest MKL with numpy. Well, there was a simple hack that did the trick. It is far from being perfect, but at least, the tests pass now.
So the only thing you have to do is to export the LD_PRELOAD variable:

export LD_PRELOAD=/path/to/the/MKL/lib/libmkl_core.so

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