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Appendix A.<br />

Decimation techniques<br />

Decimation is a powerful technique <strong>for</strong> the efficient handling <strong>of</strong> extended<br />

systems. This tutorial explains the basics as well as two <strong>of</strong> the more<br />

advanced applications. Once the reader is familiar with the basic principle<br />

behind these algorithms, one should quickly be able to model new <strong>use</strong><br />

cases <strong>of</strong> different geometries <strong>of</strong> symmetries.<br />

All <strong>of</strong> the following was written with a quasi-one-dimensional singleelectron<br />

tight-binding model in mind (more specifically a carbon nanotube<br />

in π orbital approximation). The technique, however should be just<br />

as <strong>use</strong>ful <strong>for</strong> many other areas that involve a localized basis and a local<br />

Hamiltonian in this basis that allows the exploitation <strong>of</strong> the locality <strong>of</strong><br />

each decimation step.<br />

A.1. The fundamental equation <strong>of</strong> decimation<br />

Be A an invertible matrix <strong>of</strong> the <strong>for</strong>m:<br />

A =<br />

A11 A12<br />

A21 A22<br />

where all <strong>for</strong> Aij are themselves matrices, A11 and A12 invertible.<br />

The inverse B = A−1 can be divided up in the same way:<br />

B =<br />

<br />

B11 B12<br />

<br />

B21 B22<br />

Expanding the equation AB = into these block matrices, we get <strong>for</strong><br />

equations:<br />

<br />

A11B11 + A12B21 = (A.1)<br />

A21B11 + A22B21 = 0 (A.2)<br />

A11B12 + A12B22 = 0<br />

A21B12 + A22B22 = <br />

159

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