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Numerical renormalization group

Numerical renormalization group is a physics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Numerical renormalization group rather than just read about it. In short: The numerical renormalization group (NRG) is a technique devised to solve certain quantum many-body problems where impurities plays a key role. The numerical renormalization group is an iterative procedure, which is an example of a renormalization group technique.The numerical renormalization group is an inherently non-perturbative procedure, which was originally developed by Kenneth G.

Key takeaways

  • Numerical renormalization group belongs to physics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Numerical renormalization group to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Numerical renormalization group from memory before moving on to harder problems.

Reference excerpt

The numerical renormalization group (NRG) is a technique devised to solve certain quantum many-body problems where impurities plays a key role. The numerical renormalization group is an iterative procedure, which is an example of a renormalization group technique.The numerical renormalization group is an inherently non-perturbative procedure, which was originally developed by Kenneth G. Wilson to solve the Kondo model in 1975.

History The Kondo model from the 1960s is a simplified theoretical model which describes a system of magnetic spin-1/2 impurities which couple to metallic conduction electrons (e.g. iron impurities in gold). This problem is notoriously difficult to tackle theoretically, since perturbative techniques break down at low-energy. However, Kenneth Wilson was able to prove for the first time using the numerical renormalization group that the ground state of the Kondo model is a singlet state. But perhaps more importantly, the notions of renormalization, fixed points, and renormalization group flow were introduced to the field of condensed matter physics — it is for this that Wilson won the Nobel Prize in Physics in 1982. The complete behaviour of the Kondo model, including both the high-temperature 'local moment' regime and the low-temperature 'strong coupling' regime are captured by the numerical renormalization group; an exponentially small energy scale TK (not accessible from straight perturbation theory) was shown to govern all properties at low-energies, with all physical observables such as resistivity, thermodynamics, dynamics etc. exhibiting universal scaling. This is a characteristic feature of many problems in condensed matter physics, and is a central theme of quantum impurity physics in particular. In the original example of the Kondo model, the impurity local moment is completely screened below TK by the conduction electrons via the celebrated Kondo effect; and one famous consequence is that such materials exhibit a resistivity minimum at low temperatures, contrary to expectations based purely on the standard phonon contribution, where the resistivity is predicted to decrease monotonically with temperature. The very existence of local moments in real systems of course presupposes strong electron-electron correlations. The Anderson impurity model describes a quantum level with an onsite Coulomb repulsion between electrons (rather than a spin), which is tunnel-coupled to metallic conduction electrons. In the singly occupied regime of the impurity, one can derive the Kondo model from the Anderson model, but the latter contains other physics associated with charge fluctuations. The numerical renormalization group was extended to deal with the Anderson model (capturing thereby both Kondo physics and valence fluctuation physics) by H. R. Krishnamurthy et al. in 1980. Indeed, various important developments have been made since: a comprehensive modern review has been compiled by Bulla et al.

Technique The technique consists of first dividing the conduction band into logarithmic intervals (i.e. intervals which get smaller exponentially as you move closer to the Fermi energy). One conduction band state from each interval is retained, this being the totally symmetric combination of all the states in that interval. The conduction band has now been "logarithmically discretized". The Hamiltonian is now in a position to be transformed into so-called linear chain form, in which the impurity is coupled to only one conduction band state, which is coupled to one other conduction band state and so on. Crucially, these couplings decrease exponentially along the chain, so that, even though the transformed Hamiltonian is for an infinite chain, one can consider a chain of finite length and still obtain useful results. The only restriction to the conduction-band is that it is non-interacting. Recent developments make it possible for mapping a general multi-channel conduction-band with channel mixing to a Wilson chain. Once the Hamiltonian is in linear chain form, one can begin the iterative process. First the isolated impurity is considered, which will have some characteristic set of energy levels. One then considers adding the first conduction band orbital to the chain. This causes a splitting in the energy levels for the isolated impurity. One then considers the effect of adding further orbitals along the chain, doing which splits the hitherto derived energy levels further. Because the couplings decrease along the chain, the successive splittings caused by adding orbitals to the chain decrease. When a particular number of orbitals have been added to the chain, we have a set of energy levels for that finite chain. This is obviously not the true set of energy levels for the infinite chain, but it is a good approximation to the true set in the temperature range where: the further splittings caused by adding more orbitals is negligible, and we have enough orbitals in the chain to account for splittings which are relevant in this temperature range. The results of this is that the results derived for a chain of any particular length are valid only in a particular temperature range, a range which moves to lower temperatures as the chain length increases. This means that by considering the results at many different chain lengths, one can build up a picture of the behavior of the system over a wide temperature range. The Hamiltonian for a linear chain of finite length is an example of an effective Hamiltonian. It is not the full Hamiltonian of the infinite linear chain system, but in a certain temperature range it gives similar results to the full Hamiltonian.

References

Worked examples

Example 1 — a first encounter with Numerical renormalization group

Start with the simplest possible case. Write down what Numerical renormalization group claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In physics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Numerical renormalization group before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Numerical renormalization group ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Numerical renormalization group

In research
Numerical renormalization group appears in physics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Numerical renormalization group in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Numerical renormalization group is common in secondary-school and first-year university syllabi. It links to neighbouring topics Condensed matter stubs, Quantum physics stubs, Renormalization group, so understanding it makes those chapters shorter.
In everyday life
Look for Numerical renormalization group outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Numerical renormalization group in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Numerical renormalization group means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Numerical renormalization group out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Numerical renormalization group in simple terms?

The numerical renormalization group (NRG) is a technique devised to solve certain quantum many-body problems where impurities plays a key role. The numerical renormalization group is an iterative procedure, which is an example of a renormalization group technique.The numerical renormalization group…

Why does Numerical renormalization group matter?

Because it connects several physics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Numerical renormalization group?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Numerical renormalization group.

Tags

  • Condensed matter stubs
  • Quantum physics stubs
  • Renormalization group

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