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THE IDEA BEHIND DMRG


The main problem of chain of length L, this dimension is 2^L. The DMRG proceeds by a smart reduction of the number of effective degrees of freedom and a variational search within this reduced space.

After a warmup cycle, the method splits the system into two blocks, which need not have equal sizes, and two sites in between. A set of ''representative states'' has been chosen for the block during the warmup. This set of left block + two sites + right block is known as superblock. Now a candidate for the ground state of the superblock, which is a reduced version of the full system, may be found. It may have a rather poor accuracy, but the method is Iterative and it will be improved with the forthcoming steps.

The candidate ground state which has been found is projected into the subspace for each block using a Density Matrix , whence the name. Thus, the ''relevant states'' for each block are updated.

Now one of the blocks grows at the expense of the other and the procedure is repeated. When the growing block reaches maximum size, the other starts to grow in its place. Each time we return to the original (equal sizes) situation, we say that a ''sweep'' has been completed. Normally, a few sweeps are enough to get a precision of a part in 10^{10}.

The first application of the DMRG, by Steven White and Reinhard Noack, was a ''toy model'': to find the spectrum of a Spin 0 particle in a 1D box. It had been proposed by Kenneth G. Wilson as a test for any new Renormalization Group method, since they all happened to fail with this simple problem. After that, the Heisenberg Model was tried, with the same accuracy.


TECHNICAL DETAILS ABOUT THE IMPLEMENTATION


A practical implementation of the DMRG algorithm is a lengthy work. A few of the main computational tricks are these:

  • The obtention of the ground state for the superblock is done using the Lanczos Algorithm of matrix diagonalization. Another choice is the Arnoldi Method , especially when dealing with non-hermitian matrices.


  • The Lanczos algorithm usually starts with a random seed. In DMRG, the ground state obtained in a certain DMRG step, suitably transformed, may serve as a better seed for the Lanczos algorithm at the next DMRG step.


  • In systems with symmetries, we may have conserved quantum numbers, such as total spin in a Heisenberg Model . It is convenient to find the ground state within each of the sectors into which the Hilbert space is divided.



APPLICATIONS


The DMRG has been successfully applied to get the low energy properties of spin chains: Ising Model in a transverse field, Heisenberg Model , etc., fermionic systems, such as the Hubbard Model , problems with impurities such as the Kondo Effect , Boson systems, and the physics of Quantum Dots joined with Quantum Wire s. It has been also extended to work on Tree Graph s, and has found applications in the study of Dendrimers . For 2D systems with one of the dimensions much larger than the other DMRG is also accurate, and has proved useful in the study of ladders.

The method has been extended to study equilibrium Statistical Physics in 2D, and to analyze Non-equilibrium phenomena in 1D.


THE MATRIX PRODUCT ANSATZ


The success of the DMRG for 1D systems is related to the fact that it is a variational method within the space of Matrix Product States. These are states of the form