Full configuration interaction theory
Contents
3. Full configuration interaction theory¶
3.1. Slater determinants as basis states, Repetition¶
The simplest possible choice for many-body wavefunctions are product wavefunctions. That is
because we are really only good at thinking about one particle at a time. Such product wavefunctions, without correlations, are easy to work with; for example, if the single-particle states \(\phi_i(x)\) are orthonormal, then the product wavefunctions are easy to orthonormalize.
Similarly, computing matrix elements of operators are relatively easy, because the integrals factorize.
The price we pay is the lack of correlations, which we must build up by using many, many product wavefunctions. (Thus we have a trade-off: compact representation of correlations but difficult integrals versus easy integrals but many states required.)
3.2. Slater determinants as basis states, repetition¶
Because we have fermions, we are required to have antisymmetric wavefunctions, e.g.
etc. This is accomplished formally by using the determinantal formalism
Product wavefunction + antisymmetry = Slater determinant.
3.3. Slater determinants as basis states¶
Properties of the determinant (interchange of any two rows or any two columns yields a change in sign; thus no two rows and no two columns can be the same) lead to the Pauli principle:
No two particles can be at the same place (two columns the same); and
No two particles can be in the same state (two rows the same).
3.4. Slater determinants as basis states¶
As a practical matter, however, Slater determinants beyond \(N=4\) quickly become unwieldy. Thus we turn to the occupation representation or second quantization to simplify calculations.
The occupation representation or number representation, using fermion creation and annihilation operators, is compact and efficient. It is also abstract and, at first encounter, not easy to internalize. It is inspired by other operator formalism, such as the ladder operators for the harmonic oscillator or for angular momentum, but unlike those cases, the operators do not have coordinate space representations.
Instead, one can think of fermion creation/annihilation operators as a game of symbols that compactly reproduces what one would do, albeit clumsily, with full coordinate-space Slater determinants.
3.5. Quick repetition of the occupation representation¶
We start with a set of orthonormal single-particle states \(\{ \phi_i(x) \}\). (Note: this requirement, and others, can be relaxed, but leads to a more involved formalism.) Any orthonormal set will do.
To each single-particle state \(\phi_i(x)\) we associate a creation operator \(\hat{a}^\dagger_i\) and an annihilation operator \(\hat{a}_i\).
When acting on the vacuum state \(| 0 \rangle\), the creation operator \(\hat{a}^\dagger_i\) causes a particle to occupy the single-particle state \(\phi_i(x)\):
3.6. Quick repetition of the occupation representation¶
But with multiple creation operators we can occupy multiple states:
Now we impose antisymmetry, by having the fermion operators satisfy anticommutation relations:
so that
3.7. Quick repetition of the occupation representation¶
Because of this property, automatically \(\hat{a}^\dagger_i \hat{a}^\dagger_i = 0\), enforcing the Pauli exclusion principle. Thus when writing a Slater determinant using creation operators,
each index \(i,j,k, \ldots\) must be unique.
For some relevant exercises with solutions see chapter 8 of Lecture Notes in Physics, volume 936.
3.8. Full Configuration Interaction Theory¶
We have defined the ansatz for the ground state as
where the index \(i\) defines different single-particle states up to the Fermi level. We have assumed that we have \(N\) fermions. A given one-particle-one-hole (\(1p1h\)) state can be written as
while a \(2p2h\) state can be written as
and a general \(NpNh\) state as
3.9. Full Configuration Interaction Theory¶
We can then expand our exact state function for the ground state as
where we have introduced the so-called correlation operator
Since the normalization of \(\Psi_0\) is at our disposal and since \(C_0\) is by hypothesis non-zero, we may arbitrarily set \(C_0=1\) with corresponding proportional changes in all other coefficients. Using this so-called intermediate normalization we have
resulting in
3.10. Full Configuration Interaction Theory¶
We rewrite
in a more compact form as
where \(H\) stands for \(0,1,\dots,n\) hole states and \(P\) for \(0,1,\dots,n\) particle states. Our requirement of unit normalization gives
and the energy can be written as
3.11. Full Configuration Interaction Theory¶
Normally
is solved by diagonalization setting up the Hamiltonian matrix defined by the basis of all possible Slater determinants. A diagonalization
is equivalent to finding the variational minimum of
where \(\lambda\) is a variational multiplier to be identified with the energy of the system. The minimization process results in
2 3
< < < ! ! M A T H _ B L O C K
Since the coefficients \(\delta[C_H^{*P}]\) and \(\delta[C_{H'}^{P'}]\) are complex conjugates it is necessary and sufficient to require the quantities that multiply with \(\delta[C_H^{*P}]\) to vanish.
3.12. Full Configuration Interaction Theory¶
This leads to
for all sets of \(P\) and \(H\).
If we then multiply by the corresponding \(C_H^{*P}\) and sum over \(PH\) we obtain
leading to the identification \(\lambda = E\). This means that we have for all \(PH\) sets
3.13. Full Configuration Interaction Theory¶
An alternative way to derive the last equation is to start from
and if this equation is successively projected against all \(\Phi_H^P\) in the expansion of \(\Psi\), then the last equation on the previous slide results. As stated previously, one solves this equation normally by diagonalization. If we are able to solve this equation exactly (that is numerically exactly) in a large Hilbert space (it will be truncated in terms of the number of single-particle states included in the definition of Slater determinants), it can then serve as a benchmark for other many-body methods which approximate the correlation operator \(\hat{C}\).
3.14. Example of a Hamiltonian matrix¶
Suppose, as an example, that we have six fermions below the Fermi level. This means that we can make at most \(6p-6h\) excitations. If we have an infinity of single particle states above the Fermi level, we will obviously have an infinity of say \(2p-2h\) excitations. Each such way to configure the particles is called a configuration. We will always have to truncate in the basis of single-particle states. This gives us a finite number of possible Slater determinants. Our Hamiltonian matrix would then look like (where each block can have a large dimensionalities):
| $0p-0h$ | $1p-1h$ | $2p-2h$ | $3p-3h$ | $4p-4h$ | $5p-5h$ | $6p-6h$ | |
|---|---|---|---|---|---|---|---|
| $0p-0h$ | x | x | x | 0 | 0 | 0 | 0 |
| $1p-1h$ | x | x | x | x | 0 | 0 | 0 |
| $2p-2h$ | x | x | x | x | x | 0 | 0 |
| $3p-3h$ | 0 | x | x | x | x | x | 0 |
| $4p-4h$ | 0 | 0 | x | x | x | x | x |
| $5p-5h$ | 0 | 0 | 0 | x | x | x | x |
| $6p-6h$ | 0 | 0 | 0 | 0 | x | x | x |
3.15. Example of a Hamiltonian matrix with a Hartree-Fock basis¶
If we use a Hartree-Fock basis, this corresponds to a particular unitary transformation where matrix elements of the type \(\langle 0p-0h \vert \hat{H} \vert 1p-1h\rangle =\langle \Phi_0 | \hat{H}|\Phi_{i}^{a}\rangle=0\) and our Hamiltonian matrix becomes
| $0p-0h$ | $1p-1h$ | $2p-2h$ | $3p-3h$ | $4p-4h$ | $5p-5h$ | $6p-6h$ | |
|---|---|---|---|---|---|---|---|
| $0p-0h$ | $\tilde{x}$ | 0 | $\tilde{x}$ | 0 | 0 | 0 | 0 |
| $1p-1h$ | 0 | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ | 0 | 0 | 0 |
| $2p-2h$ | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ | 0 | 0 |
| $3p-3h$ | 0 | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ | 0 |
| $4p-4h$ | 0 | 0 | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ |
| $5p-5h$ | 0 | 0 | 0 | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ |
| $6p-6h$ | 0 | 0 | 0 | 0 | $\tilde{x}$ | $\tilde{x}$ | $\tilde{x}$ |
3.16. Shell-model jargon¶
If we do not make any truncations in the possible sets of Slater determinants (many-body states) we can make by distributing \(A\) nucleons among \(n\) single-particle states, we call such a calculation for Full configuration interaction theory
If we make truncations, we have different possibilities
The standard nuclear shell-model. Here we define an effective Hilbert space with respect to a given core. The calculations are normally then performed for all many-body states that can be constructed from the effective Hilbert spaces. This approach requires a properly defined effective Hamiltonian
We can truncate in the number of excitations. For example, we can limit the possible Slater determinants to only \(1p-1h\) and \(2p-2h\) excitations. This is called a configuration interaction calculation at the level of singles and doubles excitations, or just CISD.
We can limit the number of excitations in terms of the excitation energies. If we do not define a core, this defines normally what is called the no-core shell-model approach.
What happens if we have a three-body interaction and a Hartree-Fock basis?
3.17. FCI and the exponential growth¶
Full configuration interaction theory calculations provide in principle, if we can diagonalize numerically, all states of interest. The dimensionality of the problem explodes however quickly.
The total number of Slater determinants which can be built with say \(N\) neutrons distributed among \(n\) single particle states is
For a model space which comprises the first for major shells only \(0s\), \(0p\), \(1s0d\) and \(1p0f\) we have \(40\) single particle states for neutrons and protons. For the eight neutrons of oxygen-16 we would then have
and multiplying this with the number of proton Slater determinants we end up with approximately with a dimensionality \(d\) of \(d\sim 10^{18}\).
3.18. Exponential wall¶
This number can be reduced if we look at specific symmetries only. However, the dimensionality explodes quickly!
For Hamiltonian matrices of dimensionalities which are smaller than \(d\sim 10^5\), we would use so-called direct methods for diagonalizing the Hamiltonian matrix
For larger dimensionalities iterative eigenvalue solvers like Lanczos’ method are used. The most efficient codes at present can handle matrices of \(d\sim 10^{10}\).
3.19. A non-practical way of solving the eigenvalue problem¶
To see this, we look at the contributions arising from
in Eq. (1), that is we multiply with \(\langle \Phi_0 |\) from the left in
If we assume that we have a two-body operator at most, Slater’s rule gives then an equation for the correlation energy in terms of \(C_i^a\) and \(C_{ij}^{ab}\) only. We get then
or
where the energy \(E_0\) is the reference energy and \(\Delta E\) defines the so-called correlation energy. The single-particle basis functions could be the results of a Hartree-Fock calculation or just the eigenstates of the non-interacting part of the Hamiltonian.
3.20. A non-practical way of solving the eigenvalue problem¶
To see this, we look at the contributions arising from
in Eq. (1), that is we multiply with \(\langle \Phi_0 |\) from the left in
3.21. A non-practical way of solving the eigenvalue problem¶
If we assume that we have a two-body operator at most, Slater’s rule gives then an equation for the correlation energy in terms of \(C_i^a\) and \(C_{ij}^{ab}\) only. We get then
or
where the energy \(E_0\) is the reference energy and \(\Delta E\) defines the so-called correlation energy. The single-particle basis functions could be the results of a Hartree-Fock calculation or just the eigenstates of the non-interacting part of the Hamiltonian.
3.22. Rewriting the FCI equation¶
In our notes on Hartree-Fock calculations, we have already computed the matrix \(\langle \Phi_0 | \hat{H}|\Phi_{i}^{a}\rangle \) and \(\langle \Phi_0 | \hat{H}|\Phi_{ij}^{ab}\rangle\). If we are using a Hartree-Fock basis, then the matrix elements \(\langle \Phi_0 | \hat{H}|\Phi_{i}^{a}\rangle=0\) and we are left with a correlation energy given by
3.23. Rewriting the FCI equation¶
Inserting the various matrix elements we can rewrite the previous equation as
This equation determines the correlation energy but not the coefficients \(C\).
3.24. Rewriting the FCI equation, does not stop here¶
We need more equations. Our next step is to set up
as this equation will allow us to find an expression for the coefficents \(C_i^a\) since we can rewrite this equation as
3.25. Rewriting the FCI equation, please stop here¶
We see that on the right-hand side we have the energy \(E\). This leads to a non-linear equation in the unknown coefficients. These equations are normally solved iteratively ( that is we can start with a guess for the coefficients \(C_i^a\)). A common choice is to use perturbation theory for the first guess, setting thereby
3.26. Rewriting the FCI equation, more to add¶
The observant reader will however see that we need an equation for \(C_{jk}^{bc}\) and \(C_{jkl}^{bcd}\) as well. To find equations for these coefficients we need then to continue our multiplications from the left with the various \(\Phi_{H}^P\) terms.
For \(C_{jk}^{bc}\) we need then
4 4
< < < ! ! M A T H _ B L O C K
and we can isolate the coefficients \(C_{kl}^{cd}\) in a similar way as we did for the coefficients \(C_{i}^{a}\).
3.27. Rewriting the FCI equation, more to add¶
A standard choice for the first iteration is to set
At the end we can rewrite our solution of the Schroedinger equation in terms of \(n\) coupled equations for the coefficients \(C_H^P\). This is a very cumbersome way of solving the equation. However, by using this iterative scheme we can illustrate how we can compute the various terms in the wave operator or correlation operator \(\hat{C}\). We will later identify the calculation of the various terms \(C_H^P\) as parts of different many-body approximations to full CI. In particular, we can relate this non-linear scheme with Coupled Cluster theory and many-body perturbation theory.
3.28. Summarizing FCI and bringing in approximative methods¶
If we can diagonalize large matrices, FCI is the method of choice since:
It gives all eigenvalues, ground state and excited states
The eigenvectors are obtained directly from the coefficients \(C_H^P\) which result from the diagonalization
We can compute easily expectation values of other operators, as well as transition probabilities
Correlations are easy to understand in terms of contributions to a given operator beyond the Hartree-Fock contribution. This is the standard approach in many-body theory.
3.29. Definition of the correlation energy¶
The correlation energy is defined as, with a two-body Hamiltonian,
The coefficients \(C\) result from the solution of the eigenvalue problem. The energy of say the ground state is then
where the so-called reference energy is the energy we obtain from a Hartree-Fock calculation, that is
3.30. FCI equation and the coefficients¶
However, as we have seen, even for a small case like the four first major shells and a nucleus like oxygen-16, the dimensionality becomes quickly intractable. If we wish to include single-particle states that reflect weakly bound systems, we need a much larger single-particle basis. We need thus approximative methods that sum specific correlations to infinite order.
Popular methods are
Many-body perturbation theory (in essence a Taylor expansion)
Green’s function approaches (matrix inversion)
Similarity group transformation methods (coupled ordinary differential equations)
All these methods start normally with a Hartree-Fock basis as the calculational basis.
3.31. Important ingredients to have in codes¶
Be able to validate and verify the algorithms.
Include concepts like unit testing. Gives the possibility to test and validate several or all parts of the code.
Validation and verification are then included naturally and one can develop a better attitude to what is meant with an ethically sound scientific approach.
3.32. A structured approach to solving problems¶
In the steps that lead to the development of clean code you should think of
How to structure a code in terms of functions (use IDEs or advanced text editors like sublime or atom)
How to make a module
How to read input data flexibly from the command line or files
How to create graphical/web user interfaces
How to write unit tests
How to refactor code in terms of classes (instead of functions only)
How to conduct and automate large-scale numerical experiments
How to write scientific reports in various formats (LaTeX, HTML, doconce)
3.33. Additional benefits¶
Many of the above aspetcs will save you a lot of time when you incrementally extend software over time from simpler to more complicated problems. In particular, you will benefit from many good habits:
New code is added in a modular fashion to a library (modules)
Programs are run through convenient user interfaces
It takes one quick command to let all your code undergo heavy testing
Tedious manual work with running programs is automated,
Your scientific investigations are reproducible, scientific reports with top quality typesetting are produced both for paper and electronic devices. Use version control software like git and repositories like github
3.34. Unit Testing¶
Unit Testing is the practice of testing the smallest testable parts, called units, of an application individually and independently to determine if they behave exactly as expected.
Unit tests (short code fragments) are usually written such that they can be preformed at any time during the development to continually verify the behavior of the code.
In this way, possible bugs will be identified early in the development cycle, making the debugging at later stages much easier.
3.35. Unit Testing, benefits¶
There are many benefits associated with Unit Testing, such as
It increases confidence in changing and maintaining code. Big changes can be made to the code quickly, since the tests will ensure that everything still is working properly.
Since the code needs to be modular to make Unit Testing possible, the code will be easier to reuse. This improves the code design.
Debugging is easier, since when a test fails, only the latest changes need to be debugged.
Different parts of a project can be tested without the need to wait for the other parts to be available.
A unit test can serve as a documentation on the functionality of a unit of the code.
3.36. Simple example of unit test¶
Look up the guide on how to install unit tests for c++ at course webpage. This is the version with classes.
#include <unittest++/UnitTest++.h>
class MyMultiplyClass{
public:
double multiply(double x, double y) {
return x * y;
}
};
TEST(MyMath) {
MyMultiplyClass my;
CHECK_EQUAL(56, my.multiply(7,8));
}
int main()
{
return UnitTest::RunAllTests();
}
3.37. Simple example of unit test¶
And without classes
#include <unittest++/UnitTest++.h>
double multiply(double x, double y) {
return x * y;
}
TEST(MyMath) {
CHECK_EQUAL(56, multiply(7,8));
}
int main()
{
return UnitTest::RunAllTests();
}
For Fortran users, the link at http://sourceforge.net/projects/fortranxunit/ contains a similar software for unit testing. For Python go to https://docs.python.org/2/library/unittest.html.
3.38. Unit tests¶
There are many types of unit test libraries. One which is very popular with C++ programmers is Catch
Catch is header only. All you need to do is drop the file(s) somewhere reachable from your project - either in some central location you can set your header search path to find, or directly into your project tree itself!
This is a particularly good option for other Open-Source projects that want to use Catch for their test suite.
3.39. Examples¶
Computing factorials
inline unsigned int Factorial( unsigned int number ) {
return number > 1 ? Factorial(number-1)*number : 1;
}
3.40. Factorial Example¶
Simple test where we put everything in a single file
#define CATCH_CONFIG_MAIN // This tells Catch to provide a main()
#include "catch.hpp"
inline unsigned int Factorial( unsigned int number ) {
return number > 1 ? Factorial(number-1)*number : 1;
}
TEST_CASE( "Factorials are computed", "[factorial]" ) {
REQUIRE( Factorial(0) == 1 );
REQUIRE( Factorial(1) == 1 );
REQUIRE( Factorial(2) == 2 );
REQUIRE( Factorial(3) == 6 );
REQUIRE( Factorial(10) == 3628800 );
}
This will compile to a complete executable which responds to command line arguments. If you just run it with no arguments it will execute all test cases (in this case there is just one), report any failures, report a summary of how many tests passed and failed and return the number of failed tests.
3.41. What did we do (1)?¶
All we did was
#define
one identifier and
#include
one header and we got everything - even an implementation of main() that will respond to command line arguments. Once you have more than one file with unit tests in you’ll just need to
#include "catch.hpp"
and go. Usually it’s a good idea to have a dedicated implementation file that just has
#define CATCH_CONFIG_MAIN
#include "catch.hpp".
You can also provide your own implementation of main and drive Catch yourself.
3.42. What did we do (2)?¶
We introduce test cases with the
TEST_CASE
macro.
The test name must be unique. You can run sets of tests by specifying a wildcarded test name or a tag expression. All we did was define one identifier and include one header and we got everything.
We write our individual test assertions using the
REQUIRE
macro.
3.43. Unit test summary and testing approach¶
Three levels of tests
Microscopic level: testing small parts of code, use often unit test libraries
Mesoscopic level: testing the integration of various parts of your code
Macroscopic level: testing that the final result is ok
3.44. Coding Recommendations¶
Writing clean and clear code is an art and reflects your understanding of
derivation, verification, and implementation of algorithms
what can go wrong with algorithms
overview of important, known algorithms
how algorithms are used to solve mathematical problems
reproducible science and ethics
algorithmic thinking for gaining deeper insights about scientific problems
Computing is understanding and your understanding is reflected in your abilities to write clear and clean code.
3.45. Summary and recommendations¶
Some simple hints and tips in order to write clean and clear code
Spell out the algorithm and have a top-down approach to the flow of data
Start with coding as close as possible to eventual mathematical expressions
Use meaningful names for variables
Split tasks in simple functions and modules/classes
Functions should return as few as possible variables
Use unit tests and make sure your codes are producing the correct results
Where possible use symbolic coding to autogenerate code and check results
Make a proper timing of your algorithms
Use version control and make your science reproducible
Use IDEs or smart editors with debugging and analysis tools.
Automatize your computations interfacing high-level and compiled languages like C++ and Fortran.
…..
3.46. Building a many-body basis¶
Here we will discuss how we can set up a single-particle basis which we can use in the various parts of our projects, from the simple pairing model to infinite nuclear matter. We will use here the simple pairing model to illustrate in particular how to set up a single-particle basis. We will also use this do discuss standard FCI approaches like:
Standard shell-model basis in one or two major shells
Full CI in a given basis and no truncations
CISD and CISDT approximations
No-core shell model and truncation in excitation energy
3.47. Building a many-body basis¶
An important step in an FCI code is to construct the many-body basis.
While the formalism is independent of the choice of basis, the effectiveness of a calculation will certainly be basis dependent.
Furthermore there are common conventions useful to know.
First, the single-particle basis has angular momentum as a good quantum number. You can imagine the single-particle wavefunctions being generated by a one-body Hamiltonian, for example a harmonic oscillator. Modifications include harmonic oscillator plus spin-orbit splitting, or self-consistent mean-field potentials, or the Woods-Saxon potential which mocks up the self-consistent mean-field. For nuclei, the harmonic oscillator, modified by spin-orbit splitting, provides a useful language for describing single-particle states.
3.48. Building a many-body basis¶
Each single-particle state is labeled by the following quantum numbers:
Orbital angular momentum \(l\)
Intrinsic spin \(s\) = 1/2 for protons and neutrons
Angular momentum \(j = l \pm 1/2\)
\(z\)-component \(j_z\) (or \(m\))
Some labeling of the radial wavefunction, typically \(n\) the number of nodes in the radial wavefunction, but in the case of harmonic oscillator one can also use the principal quantum number \(N\), where the harmonic oscillator energy is \((N+3/2)\hbar \omega\).
In this format one labels states by \(n(l)_j\), with \((l)\) replaced by a letter: \(s\) for \(l=0\), \(p\) for \(l=1\), \(d\) for \(l=2\), \(f\) for \(l=3\), and thenceforth alphabetical.
3.49. Building a many-body basis¶
In practice the single-particle space has to be severely truncated. This truncation is typically based upon the single-particle energies, which is the effective energy from a mean-field potential.
Sometimes we freeze the core and only consider a valence space. For example, one may assume a frozen \(^{4}\mbox{He}\) core, with two protons and two neutrons in the \(0s_{1/2}\) shell, and then only allow active particles in the \(0p_{1/2}\) and \(0p_{3/2}\) orbits.
Another example is a frozen \(^{16}\mbox{O}\) core, with eight protons and eight neutrons filling the \(0s_{1/2}\), \(0p_{1/2}\) and \(0p_{3/2}\) orbits, with valence particles in the \(0d_{5/2}, 1s_{1/2}\) and \(0d_{3/2}\) orbits.
Sometimes we refer to nuclei by the valence space where their last nucleons go.
So, for example, we call \(^{12}\mbox{C}\) a \(p\)-shell nucleus, while \(^{26}\mbox{Al}\) is an
\(sd\)-shell nucleus and \(^{56}\mbox{Fe}\) is a \(pf\)-shell nucleus.
3.50. Building a many-body basis¶
There are different kinds of truncations.
For example, one can start with `filled’ orbits (almost always the lowest), and then allow one, two, three… particles excited out of those filled orbits. These are called 1p-1h, 2p-2h, 3p-3h excitations.
Alternately, one can state a maximal orbit and allow all possible configurations with particles occupying states up to that maximum. This is called full configuration.
Finally, for particular use in nuclear physics, there is the energy truncation, also called the \(N\hbar\Omega\) or \(N_{max}\) truncation.
3.51. Building a many-body basis¶
Here one works in a harmonic oscillator basis, with each major oscillator shell assigned a principal quantum number \(N=0,1,2,3,...\). The \(N\hbar\Omega\) or \(N_{max}\) truncation: Any configuration is given an noninteracting energy, which is the sum of the single-particle harmonic oscillator energies. (Thus this ignores spin-orbit splitting.)
Excited state are labeled relative to the lowest configuration by the number of harmonic oscillator quanta.
This truncation is useful because if one includes all configuration up to some \(N_{max}\), and has a translationally invariant interaction, then the intrinsic motion and the center-of-mass motion factor. In other words, we can know exactly the center-of-mass wavefunction.
In almost all cases, the many-body Hamiltonian is rotationally invariant. This means it commutes with the operators \(\hat{J}^2, \hat{J}_z\) and so eigenstates will have good \(J,M\). Furthermore, the eigenenergies do not depend upon the orientation \(M\).
Therefore we can choose to construct a many-body basis which has fixed \(M\); this is called an \(M\)-scheme basis.
Alternately, one can construct a many-body basis which has fixed \(J\), or a \(J\)-scheme basis.
3.52. Building a many-body basis¶
The Hamiltonian matrix will have smaller dimensions (a factor of 10 or more) in the \(J\)-scheme than in the \(M\)-scheme. On the other hand, as we’ll show in the next slide, the \(M\)-scheme is very easy to construct with Slater determinants, while the \(J\)-scheme basis states, and thus the matrix elements, are more complicated, almost always being linear combinations of \(M\)-scheme states. \(J\)-scheme bases are important and useful, but we’ll focus on the simpler \(M\)-scheme.
The quantum number \(m\) is additive (because the underlying group is Abelian): if a Slater determinant \(\hat{a}_i^\dagger \hat{a}^\dagger_j \hat{a}^\dagger_k \ldots | 0 \rangle\) is built from single-particle states all with good \(m\), then the total
This is not true of \(J\), because the angular momentum group SU(2) is not Abelian.
3.53. Building a many-body basis¶
The upshot is that
It is easy to construct a Slater determinant with good total \(M\);
It is trivial to calculate \(M\) for each Slater determinant;
So it is easy to construct an \(M\)-scheme basis with fixed total \(M\).
Note that the individual \(M\)-scheme basis states will not, in general, have good total \(J\). Because the Hamiltonian is rotationally invariant, however, the eigenstates will have good \(J\). (The situation is muddied when one has states of different \(J\) that are nonetheless degenerate.)
3.54. Building a many-body basis¶
Example: two \(j=1/2\) orbits
| Index | $n$ | $l$ | $j$ | $m_j$ |
|---|---|---|---|---|
| 1 | 0 | 0 | 1/2 | -1/2 |
| 2 | 0 | 0 | 1/2 | 1/2 |
| 3 | 1 | 0 | 1/2 | -1/2 |
| 4 | 1 | 0 | 1/2 | 1/2 |
3.55. Building a many-body basis¶
There are \(\left ( \begin{array}{c} 4 \\ 2 \end{array} \right) = 6\) two-particle states, which we list with the total \(M\):
| Occupied | $M$ |
|---|---|
| 1,2 | 0 |
| 1,3 | -1 |
| 1,4 | 0 |
| 2,3 | 0 |
| 2,4 | 1 |
| 3,4 | 0 |
3.56. Building a many-body basis¶
As another example, consider using only single particle states from the \(0d_{5/2}\) space. They have the following quantum numbers
| Index | $n$ | $l$ | $j$ | $m_j$ |
|---|---|---|---|---|
| 1 | 0 | 2 | 5/2 | -5/2 |
| 2 | 0 | 2 | 5/2 | -3/2 |
| 3 | 0 | 2 | 5/2 | -1/2 |
| 4 | 0 | 2 | 5/2 | 1/2 |
| 5 | 0 | 2 | 5/2 | 3/2 |
| 6 | 0 | 2 | 5/2 | 5/2 |
3.57. Building a many-body basis¶
There are \(\left ( \begin{array}{c} 6 \\ 2 \end{array} \right) = 15\) two-particle states, which we list with the total \(M\):
| Occupied | $M$ | Occupied | $M$ | Occupied | $M$ |
|---|---|---|---|---|---|
| 1,2 | -4 | 2,3 | -2 | 3,5 | 1 |
| 1,3 | -3 | 2,4 | -1 | 3,6 | 2 |
| 1,4 | -2 | 2,5 | 0 | 4,5 | 2 |
| 1,5 | -1 | 2,6 | 1 | 4,6 | 3 |
| 1,6 | 0 | 3,4 | 0 | 5,6 | 4 |
3.58. Shell-model project¶
The first step is to construct the \(M\)-scheme basis of Slater determinants. Here \(M\)-scheme means the total \(J_z\) of the many-body states is fixed.
The steps could be:
Read in a user-supplied file of single-particle states (examples can be given) or just code these internally;
Ask for the total \(M\) of the system and the number of particles \(N\);
Construct all the \(N\)-particle states with given \(M\). You will validate the code by comparing both the number of states and specific states.
3.59. Shell-model project¶
The format of a possible input file could be
| Index | $n$ | $l$ | $2j$ | $2m_j$ |
|---|---|---|---|---|
| 1 | 1 | 0 | 1 | -1 |
| 2 | 1 | 0 | 1 | 1 |
| 3 | 0 | 2 | 3 | -3 |
| 4 | 0 | 2 | 3 | -1 |
| 5 | 0 | 2 | 3 | 1 |
| 6 | 0 | 2 | 3 | 3 |
| 7 | 0 | 2 | 5 | -5 |
| 8 | 0 | 2 | 5 | -3 |
| 9 | 0 | 2 | 5 | -1 |
| 10 | 0 | 2 | 5 | 1 |
| 11 | 0 | 2 | 5 | 3 |
| 12 | 0 | 2 | 5 | 5 |
3.60. Shell-model project¶
To read in the single-particle states you need to:
Open the file
Read the number of single-particle states (in the above example, 12); allocate memory; all you need is a single array storing \(2\times j_z\) for each state, labeled by the index.
Read in the quantum numbers and store \(2 \times j_z\) (and anything else you happen to want).
3.61. Shell-model project¶
The next step is to read in the number of particles \(N\) and the fixed total \(M\) (or, actually, \(2 \times M\)). For this project we assume only a single species of particles, say neutrons, although this can be relaxed. Note: Although it is often a good idea to try to write a more general code, given the short time alloted we would suggest you keep your ambition in check, at least in the initial phases of the project.
You should probably write an error trap to make sure \(N\) and \(M\) are congruent; if \(N\) is even, then \(2 \times M\) should be even, and if \(N\) is odd then \(2\times M\) should be odd.
3.62. Shell-model project¶
The final step is to generate the set of \(N\)-particle Slater determinants with fixed \(M\). The Slater determinants will be stored in occupation representation. Although in many codes this representation is done compactly in bit notation with ones and zeros, but for greater transparency and simplicity we will list the occupied single particle states.
Hence we can store the Slater determinant basis states as \(sd(i,j)\), that is an array of dimension \(N_{SD}\), the number of Slater determinants, by \(N\), the number of occupied state. So if for the 7th Slater determinant the 2nd, 3rd, and 9th single-particle states are occupied, then \(sd(7,1) = 2\), \(sd(7,2) = 3\), and \(sd(7,3) = 9\).
3.63. Shell-model project¶
We can construct an occupation representation of Slater determinants by the odometer method. Consider \(N_{sp} = 12\) and \(N=4\). Start with the first 4 states occupied, that is:
\(sd(1,:)= 1,2,3,4\) (also written as \(|1,2,3,4 \rangle\))
Now increase the last occupancy recursively:
\(sd(2,:)= 1,2,3,5\)
\(sd(3,:)= 1,2,3,6\)
\(sd(4,:)= 1,2,3,7\)
\(\ldots\)
\(sd(9,:)= 1,2,3,12\)
Then start over with
\(sd(10,:)= 1,2,4,5\)
and again increase the rightmost digit
\(sd(11,:)= 1,2,4,6\)
\(sd(12,:)= 1,2,4,7\)
\(\ldots\)
\(sd(17,:)= 1,2,4,12\)
3.64. Shell-model project¶
When we restrict ourselves to an \(M\)-scheme basis, we could choose two paths. The first is simplest (and simplest is often best, at least in the first draft of a code): generate all possible Slater determinants, and then extract from this initial list a list of those Slater determinants with a given \(M\). (You will need to write a short function or routine that computes \(M\) for any given occupation.)
Alternately, and not too difficult, is to run the odometer routine twice: each time, as as a Slater determinant is calculated, compute \(M\), but do not store the Slater determinants except the current one. You can then count up the number of Slater determinants with a chosen \(M\). Then allocated storage for the Slater determinants, and run the odometer algorithm again, this time storing Slater determinants with the desired \(M\) (this can be done with a simple logical flag).
3.65. Shell-model project¶
Some example solutions: Let’s begin with a simple case, the \(0d_{5/2}\) space containing six single-particle states
| Index | $n$ | $l$ | $j$ | $m_j$ |
|---|---|---|---|---|
| 1 | 0 | 2 | 5/2 | -5/2 |
| 2 | 0 | 2 | 5/2 | -3/2 |
| 3 | 0 | 2 | 5/2 | -1/2 |
| 4 | 0 | 2 | 5/2 | 1/2 |
| 5 | 0 | 2 | 5/2 | 3/2 |
| 6 | 0 | 2 | 5/2 | 5/2 |
\(\vert 1,4 \rangle\), \(M= -2\), \(\vert 1,5 \rangle\), \(M= -1\)
\(\vert 1,5 \rangle\), \(M= 0\), \(vert 2,3 \rangle\), \(M= -2\)
\(\vert 2,4 \rangle\), \(M= -1\), \(\vert 2,5 \rangle\), \(M= 0\)
\(\vert 2,6 \rangle\), \(M= 1\), \(\vert 3,4 \rangle\), \(M= 0\)
\(\vert 3,5 \rangle\), \(M= 1\), \(\vert 3,6 \rangle\), \(M= 2\)
\(\vert 4,5 \rangle\), \(M= 2\), \(\vert 4,6 \rangle\), \(M= 3\)
\(\vert 5,6 \rangle\), \(M= 4\)
Of these, there are only 3 states with \(M=0\).
3.66. Shell-model project¶
You should try by hand to show that in this same single-particle space, that for \(N=3\) there are 3 states with \(M=1/2\) and for \(N= 4\) there are also only 3 states with \(M=0\).
To test your code, confirm the above.
Also, for the \(sd\)-space given above, for \(N=2\) there are 14 states with \(M=0\), for \(N=3\) there are 37 states with \(M=1/2\), for \(N=4\) there are 81 states with \(M=0\).
3.67. Shell-model project¶
For our project, we will only consider the pairing model. A simple space is the \((1/2)^2\) space with four single-particle states
| Index | $n$ | $l$ | $s$ | $m_s$ |
|---|---|---|---|---|
| 1 | 0 | 0 | 1/2 | -1/2 |
| 2 | 0 | 0 | 1/2 | 1/2 |
| 3 | 1 | 0 | 1/2 | -1/2 |
| 4 | 1 | 0 | 1/2 | 1/2 |
3.68. Shell-model project¶
Another, slightly more challenging space is the \((1/2)^4\) space, that is, with eight single-particle states we have
| Index | $n$ | $l$ | $s$ | $m_s$ |
|---|---|---|---|---|
| 1 | 0 | 0 | 1/2 | -1/2 |
| 2 | 0 | 0 | 1/2 | 1/2 |
| 3 | 1 | 0 | 1/2 | -1/2 |
| 4 | 1 | 0 | 1/2 | 1/2 |
| 5 | 2 | 0 | 1/2 | -1/2 |
| 6 | 2 | 0 | 1/2 | 1/2 |
| 7 | 3 | 0 | 1/2 | -1/2 |
| 8 | 3 | 0 | 1/2 | 1/2 |
3.69. Shell-model project¶
In the shell-model context we can interpret this as 4 \(s_{1/2}\) levels, with \(m = \pm 1/2\), we can also think of these are simple four pairs, \(\pm k, k = 1,2,3,4\). Later on we will assign single-particle energies, depending on the radial quantum number \(n\), that is, \(\epsilon_k = |k| \delta\) so that they are equally spaced.
3.70. Shell-model project¶
For application in the pairing model we can go further and consider only states with no “broken pairs,” that is, if \(+k\) is filled (or \(m = +1/2\), so is \(-k\) (\(m=-1/2\)). If you want, you can write your code to accept only these, and obtain the following six states:
\(| 1, 2 , 3 , 4 \rangle , \)
\(| 1 , 2 , 5 , 6 \rangle , \)
\(| 1 , 2 , 7 , 8 \rangle , \)
\(| 3 , 4 , 5 , 6 \rangle , \)
\(| 3 , 4 , 7 , 8 \rangle , \)
\(| 5 , 6 , 7 , 8 \rangle \)
3.71. Shell-model project¶
Hints for coding.
Write small modules (routines/functions) ; avoid big functions that do everything. (But not too small.)
Use Unit tests! Write lots of error traps, even for things that are `obvious.’
Document as you go along. The Unit tests serve as documentation. For each function write a header that includes:
a. Main purpose of function and/or unit test
b. names and brief explanation of input variables, if any
c. names and brief explanation of output variables, if any
d. functions called by this function
e. called by which functions
3.72. Shell-model project¶
Hints for coding
Unit tests will save time. Use also IDEs for debugging. If you insist on brute force debugging, print out intermediate values. It’s almost impossible to debug a code by looking at it - the code will almost always win a `staring contest.’
Validate code with SIMPLE CASES. Validate early and often. Unit tests!!
The number one mistake is using a too complex a system to test. For example , if you are computing particles in a potential in a box, try removing the potential - you should get particles in a box. And start with one particle, then two, then three… Don’t start with eight particles.
3.73. Shell-model project¶
Our recommended occupation representation, e.g. \(| 1,2,4,8 \rangle\), is easy to code, but numerically inefficient when one has hundreds of millions of Slater determinants.
In state-of-the-art shell-model codes, one generally uses bit representation, i.e. \(|1101000100... \rangle\) where one stores the Slater determinant as a single (or a small number of) integer.
This is much more compact, but more intricate to code with considerable more overhead. There exist bit-manipulation functions. We will discuss these in more detail at the beginning of the third week.
3.74. Example case: pairing Hamiltonian¶
We consider a space with \(2\Omega\) single-particle states, with each state labeled by \(k = 1, 2, 3, \Omega\) and \(m = \pm 1/2\). The convention is that the state with \(k>0\) has \(m = + 1/2\) while \(-k\) has \(m = -1/2\).
The Hamiltonian we consider is
where
and \(\hat{P}_- = ( \hat{P}_+)^\dagger\).
This problem can be solved using what is called the quasi-spin formalism to obtain the exact results. Thereafter we will try again using the explicit Slater determinant formalism.
3.75. Example case: pairing Hamiltonian¶
One can show (and this is part of the project) that
Now define
Finally you can show
This means the operators \(\hat{P}_\pm, \hat{P}_z\) form a so-called \(SU(2)\) algebra, and we can use all our insights about angular momentum, even though there is no actual angular momentum involved.
So we rewrite the Hamiltonian to make this explicit:
3.76. Example case: pairing Hamiltonian¶
Because of the SU(2) algebra, we know that the eigenvalues of \(\hat{P}^2\) must be of the form \(p(p+1)\), with \(p\) either integer or half-integer, and the eigenvalues of \(\hat{P}_z\) are \(m_p\) with \(p \geq | m_p|\), with \(m_p\) also integer or half-integer.
But because \(\hat{P}_z = (1/2)(\hat{N}-\Omega)\), we know that for \(N\) particles the value \(m_p = (N-\Omega)/2\). Furthermore, the values of \(m_p\) range from \(-\Omega/2\) (for \(N=0\)) to \(+\Omega/2\) (for \(N=2\Omega\), with all states filled).
We deduce the maximal \(p = \Omega/2\) and for a given \(n\) the values range of \(p\) range from \(|N-\Omega|/2\) to \(\Omega/2\) in steps of 1 (for an even number of particles)
Following Racah we introduce the notation \(p = (\Omega - v)/2\) where \(v = 0, 2, 4,..., \Omega - |N-\Omega|\) With this it is easy to deduce that the eigenvalues of the pairing Hamiltonian are
This also works for \(N\) odd, with \(v= 1,3,5, \dots\).
3.77. Example case: pairing Hamiltonian¶
Let’s take a specific example: \(\Omega = 3\) so there are 6 single-particle states, and \(N = 3\), with \(v= 1,3\). Therefore there are two distinct eigenvalues,
Now let’s work this out explicitly. The single particle degrees of freedom are defined as
| Index | $k$ | $m$ |
|---|---|---|
| 1 | 1 | -1/2 |
| 2 | -1 | 1/2 |
| 3 | 2 | -1/2 |
| 4 | -2 | 1/2 |
| 5 | 3 | -1/2 |
| 6 | -3 | 1/2 |
3.78. Example case: pairing Hamiltonian¶
In this basis, the operator
From this we can determine that
so those states all have eigenvalue 0.
3.79. Example case: pairing Hamiltonian¶
Now for further example,
so
The second term vanishes because state 3 is occupied twice, and reordering the last term we get
without picking up a phase.
3.80. Example case: pairing Hamiltonian¶
Continuing in this fashion, with the previous ordering of the many-body states ( \(| 1,2,3 \rangle, |1,2,5\rangle, | 1,4,6 \rangle, | 2,3,4 \rangle, |2,3,6 \rangle, | 2,4,5 \rangle, | 2, 5, 6 \rangle, |3,4,6 \rangle, | 4,5,6 \rangle\)) the Hamiltonian matrix of this system is
This is useful for our project. One can by hand confirm that there are 3 eigenvalues \(-2G\) and 6 with value zero.
3.81. Example case: pairing Hamiltonian¶
Another example Using the \((1/2)^4\) single-particle space, resulting in eight single-particle states
| Index | $n$ | $l$ | $s$ | $m_s$ |
|---|---|---|---|---|
| 1 | 0 | 0 | 1/2 | -1/2 |
| 2 | 0 | 0 | 1/2 | 1/2 |
| 3 | 1 | 0 | 1/2 | -1/2 |
| 4 | 1 | 0 | 1/2 | 1/2 |
| 5 | 2 | 0 | 1/2 | -1/2 |
| 6 | 2 | 0 | 1/2 | 1/2 |
| 7 | 3 | 0 | 1/2 | -1/2 |
| 8 | 3 | 0 | 1/2 | 1/2 |
\(| 1, 2 , 3 , 4 \rangle , \)
\(| 1 , 2 , 5 , 6 \rangle , \)
\(| 1 , 2 , 7 , 8 \rangle , \)
\(| 3 , 4 , 5 , 6 \rangle , \)
\(| 3 , 4 , 7 , 8 \rangle , \)
\(| 5 , 6 , 7 , 8 \rangle \)
3.82. Example case: pairing Hamiltonian¶
Now we take the following Hamiltonian
where
and
We can write down the \( 6 \times 6\) Hamiltonian in the basis from the prior slide:
(You should check by hand that this is correct.)
For \(\delta = 0\) we have the closed form solution of the g.s. energy given by \(-6G\).
3.83. Building a Hamiltonian matrix¶
The goal is to compute the matrix elements of the Hamiltonian, specifically matrix elements between many-body states (Slater determinants) of two-body operators
In particular we will need to compute
where \(\alpha, \beta\) are indices labeling Slater determinants and \(p,q,r,s\) label single-particle states.
3.84. Building a Hamiltonian matrix¶
Note: there are other, more efficient ways to do this than the method we describe, but you will be able to produce a working code quickly.
As we coded in the first step, a Slater determinant \(| \alpha \rangle\) with index \(\alpha\) is a list of \(N\) occupied single-particle states \(i_1 < i_2 < i_3 \ldots i_N\).
Furthermore, for the two-body matrix elements \(V_{pqrs}\) we normally assume \(p < q\) and \(r < s\). For our specific project, the interaction is much simpler and you can use this to simplify considerably the setup of a shell-model code for project 2.
What follows here is a more general, but still brute force, approach.
3.85. Building a Hamiltonian matrix¶
Write a function that:
Has as input the single-particle indices \(p,q,r,s\) for the two-body operator and the index \(\alpha\) for the ket Slater determinant;
Returns the index \(\beta\) of the unique (if any) Slater determinant such that
as well as the phase
This is equivalent to computing
3.86. Building a Hamiltonian matrix, first step¶
The first step can take as input an initial Slater determinant (whose position in the list of basis Slater determinants is \(\alpha\)) written as an ordered listed of occupied single-particle states, e.g. \(1,2,5,8\), and the indices \(p,q,r,s\) from the two-body operator.
It will return another final Slater determinant if the single-particle states \(r\) and \(s\) are occupied, else it will return an empty Slater determinant (all zeroes).
If \(r\) and \(s\) are in the list of occupied single particle states, then replace the initial single-particle states \(ij\) as \(i \rightarrow r\) and \(j \rightarrow r\).
3.87. Building a Hamiltonian matrix, second step¶
The second step will take the final Slater determinant from the first step (if not empty), and then order by pairwise permutations (i.e., if the Slater determinant is \(i_1, i_2, i_3, \ldots\), then if \(i_n > i_{n+1}\), interchange \(i_n \leftrightarrow i_{n+1}\).
3.88. Building a Hamiltonian matrix¶
It will also output a phase. If any two single-particle occupancies are repeated, the phase is 0. Otherwise it is +1 for an even permutation and -1 for an odd permutation to bring the final Slater determinant into ascending order, \(j_1 < j_2 < j_3 \ldots\).
3.89. Building a Hamiltonian matrix¶
Example: Suppose in the \(sd\) single-particle space that the initial Slater determinant is \(1,3,9,12\). If \(p,q,r,s = 2,8,1,12\), then after the first step the final Slater determinant is \(2,3,9,8\). The second step will return \(2,3,8,9\) and a phase of -1, because an odd number of interchanges is required.
3.90. Building a Hamiltonian matrix¶
Example: Suppose in the \(sd\) single-particle space that the initial Slater determinant is \(1,3,9,12\). If \(p,q,r,s = 3,8,1,12\), then after the first step the final Slater determinant is \(3,3,9,8\), but after the second step the phase is 0 because the single-particle state 3 is occupied twice.
Lastly, the final step takes the ordered final Slater determinant and we search through the basis list to determine its index in the many-body basis, that is, \(\beta\).
3.91. Building a Hamiltonian matrix¶
The Hamiltonian is then stored as an \(N_{SD} \times N_{SD}\) array of real numbers, which can be allocated once you have created the many-body basis and know \(N_{SD}\).
3.92. Building a Hamiltonian matrix¶
Initialize \(H(\alpha,\beta)=0.0\)
Set up an outer loop over \(\beta\)
Loop over \(\alpha = 1, NSD\)
For each \(\alpha\), loop over \(a=1,ntbme\) and fetch \(V(a)\) and the single-particle indices \(p,q,r,s\)
If \(V(a) = 0\) skip. Otherwise, apply \(\hat{a}^\dagger_p\hat{a}^\dagger_q \hat{a}_s \hat{a}_r\) to the Slater determinant labeled by \(\alpha\).
Find, if any, the label \(\beta\) of the resulting Slater determinant and the phase (which is 0, +1, -1).
If phase \(\neq 0\), then update \(H(\alpha,\beta)\) as \(H(\alpha,\beta) + phase*V(a)\). The sum is important because multiple operators might contribute to the same matrix element.
Continue loop over \(a\)
Continue loop over \(\alpha\).
End the outer loop over \(\beta\).
You should force the resulting matrix \(H\) to be symmetric. To do this, when updating \(H(\alpha,\beta)\), if \(\alpha \neq \beta\), also update \(H(\beta,\alpha)\).
3.93. Building a Hamiltonian matrix¶
You will also need to include the single-particle energies. This is easy: they only
contribute to diagonal matrix elements, that is, \(H(\alpha,\alpha)\).
Simply find the occupied single-particle states \(i\) and add the corresponding \(\epsilon(i)\).
3.94. Hamiltonian matrix without the bit representation¶
Consider the many-body state \(\Psi_{\lambda}\) expressed as linear combinations of Slater determinants (\(SD\)) of orthonormal single-particle states \(\phi({\bf r})\):
Using the Slater-Condon rules the matrix elements of any one-body (\(\cal{O}_1\)) or two-body (\(\cal{O}_2\)) operator expressed in the determinant space have simple expressions involving one- and two-fermion integrals in our given single-particle basis. The diagonal elements are given by:
3.95. Hamiltonian matrix without the bit representation, one and two-body operators¶
For two determinants which differ only by the substitution of single-particle states \(i\) with a single-particle state \(j\):
For two determinants which differ by two single-particle states
All other matrix elements involving determinants with more than two substitutions are zero.
3.96. Strategies for setting up an algorithm¶
An efficient implementation of these rules requires
to find the number of single-particle state substitutions between two determinants
to find which single-particle states are involved in the substitution
to compute the phase factor if a reordering of the single-particle states has occured
We can solve this problem using our odometric approach or alternatively using a bit representation as discussed below and in more detail in
We recommend in particular the article by Simen Kvaal. It contains nice general classes for creation and annihilation operators as well as the calculation of the phase (see below).
3.97. Computing expectation values and transitions in the shell-model¶
When we diagonalize the Hamiltonian matrix, the eigenvectors are the coefficients \(C_{\lambda i}\) used to express the many-body state \(\Psi_{\lambda}\) in terms of a linear combinations of Slater determinants (\(SD\)) of orthonormal single-particle states \(\phi({\bf r})\).
With these eigenvectors we can compute say the transition likelyhood of a one-body operator as
Writing the one-body operator in second quantization as
we have
3.98. Computing expectation values and transitions in the shell-model and spectroscopic factors¶
The terms we need to evalute then are just the elements
which can be rewritten in terms of spectroscopic factors by inserting a complete set of Slater determinats as
where \(\langle SD_l\vert a_q(a_p^{\dagger})\vert SD_j\rangle\) are the spectroscopic factors. These can be easily evaluated in \(m\)-scheme. Using the Wigner-Eckart theorem we can transform these to a \(J\)-coupled scheme through so-called reduced matrix elements.
3.99. Operators in second quantization¶
In the build-up of a shell-model or FCI code that is meant to tackle large dimensionalities we need to deal with the action of the Hamiltonian \(\hat{H}\) on a Slater determinant represented in second quantization as
The time consuming part stems from the action of the Hamiltonian on the above determinant,
A practically useful way to implement this action is to encode a Slater determinant as a bit pattern.
3.100. Operators in second quantization¶
Assume that we have at our disposal \(n\) different single-particle states \(\alpha_0,\alpha_2,\dots,\alpha_{n-1}\) and that we can distribute among these states \(N\le n\) particles.
A Slater determinant can then be coded as an integer of \(n\) bits. As an example, if we have \(n=16\) single-particle states \(\alpha_0,\alpha_1,\dots,\alpha_{15}\) and \(N=4\) fermions occupying the states \(\alpha_3\), \(\alpha_6\), \(\alpha_{10}\) and \(\alpha_{13}\) we could write this Slater determinant as
The unoccupied single-particle states have bit value \(0\) while the occupied ones are represented by bit state \(1\). In the binary notation we would write this 16 bits long integer as
which translates into the decimal number
We can thus encode a Slater determinant as a bit pattern.
3.101. Operators in second quantization¶
With \(N\) particles that can be distributed over \(n\) single-particle states, the total number of Slater determinats (and defining thereby the dimensionality of the system) is
The total number of bit patterns is \(2^n\).
3.102. Operators in second quantization¶
We assume again that we have at our disposal \(n\) different single-particle orbits \(\alpha_0,\alpha_2,\dots,\alpha_{n-1}\) and that we can distribute among these orbits \(N\le n\) particles. The ordering among these states is important as it defines the order of the creation operators. We will write the determinant
in a more compact way as
The action of a creation operator is thus
which becomes
3.103. Operators in second quantization¶
Similarly
which becomes
This gives a simple recipe:
If one of the bits \(b_j\) is \(1\) and we act with a creation operator on this bit, we return a null vector
If \(b_j=0\), we set it to \(1\) and return a sign factor \((-1)^l\), where \(l\) is the number of bits set before bit \(j\).
3.104. Operators in second quantization¶
Consider the action of \(a^{\dagger}_{\alpha_2}\) on various slater determinants:
What is the simplest way to obtain the phase when we act with one annihilation(creation) operator on the given Slater determinant representation?
3.105. Operators in second quantization¶
We have an SD representation
in a more compact way as
The action of
which becomes
3.106. Operators in second quantization¶
The action
can be obtained by subtracting the logical sum (AND operation) of \(\Phi_{0,3,6,10,13}\) and a word which represents only \(\alpha_0\), that is
from \(\Phi_{0,3,6,10,13}= |1001001000100100\rangle\).
This operation gives \(|0001001000100100\rangle\).
Similarly, we can form \(a^{\dagger}_{\alpha_4}a_{\alpha_0}\Phi_{0,3,6,10,13}\), say, by adding \(|0000100000000000\rangle\) to \(a_{\alpha_0}\Phi_{0,3,6,10,13}\), first checking that their logical sum is zero in order to make sure that the state \(\alpha_4\) is not already occupied.
3.107. Operators in second quantization¶
It is trickier however to get the phase \((-1)^l\). One possibility is as follows
Let \(S_1\) be a word that represents the 1-bit to be removed and all others set to zero.
In the previous example \(S_1=|1000000000000000\rangle\)
Define \(S_2\) as the similar word that represents the bit to be added, that is in our case
\(S_2=|0000100000000000\rangle\).
Compute then \(S=S_1-S_2\), which here becomes
Perform then the logical AND operation of \(S\) with the word containing
which results in \(|0001000000000000\rangle\). Counting the number of 1-bits gives the phase. Here you need however an algorithm for bitcounting.
3.108. Bit counting¶
We include here a python program which may aid in this direction. It uses bit manipulation functions from http://wiki.python.org/moin/BitManipulation.
import math
"""
A simple Python class for Slater determinant manipulation
Bit-manipulation stolen from:
http://wiki.python.org/moin/BitManipulation
"""
# bitCount() counts the number of bits set (not an optimal function)
def bitCount(int_type):
""" Count bits set in integer """
count = 0
while(int_type):
int_type &= int_type - 1
count += 1
return(count)
# testBit() returns a nonzero result, 2**offset, if the bit at 'offset' is one.
def testBit(int_type, offset):
mask = 1 << offset
return(int_type & mask) >> offset
# setBit() returns an integer with the bit at 'offset' set to 1.
def setBit(int_type, offset):
mask = 1 << offset
return(int_type | mask)
# clearBit() returns an integer with the bit at 'offset' cleared.
def clearBit(int_type, offset):
mask = ~(1 << offset)
return(int_type & mask)
# toggleBit() returns an integer with the bit at 'offset' inverted, 0 -> 1 and 1 -> 0.
def toggleBit(int_type, offset):
mask = 1 << offset
return(int_type ^ mask)
# binary string made from number
def bin0(s):
return str(s) if s<=1 else bin0(s>>1) + str(s&1)
def bin(s, L = 0):
ss = bin0(s)
if L > 0:
return '0'*(L-len(ss)) + ss
else:
return ss
class Slater:
""" Class for Slater determinants """
def __init__(self):
self.word = int(0)
def create(self, j):
print "c^+_" + str(j) + " |" + bin(self.word) + "> = ",
# Assume bit j is set, then we return zero.
s = 0
# Check if bit j is set.
isset = testBit(self.word, j)
if isset == 0:
bits = bitCount(self.word & ((1<<j)-1))
s = pow(-1, bits)
self.word = setBit(self.word, j)
print str(s) + " x |" + bin(self.word) + ">"
return s
def annihilate(self, j):
print "c_" + str(j) + " |" + bin(self.word) + "> = ",
# Assume bit j is not set, then we return zero.
s = 0
# Check if bit j is set.
isset = testBit(self.word, j)
if isset == 1:
bits = bitCount(self.word & ((1<<j)-1))
s = pow(-1, bits)
self.word = clearBit(self.word, j)
print str(s) + " x |" + bin(self.word) + ">"
return s
# Do some testing:
phi = Slater()
phi.create(0)
phi.create(1)
phi.create(2)
phi.create(3)
print
s = phi.annihilate(2)
s = phi.create(7)
s = phi.annihilate(0)
s = phi.create(200)
Input In [1]
print "c^+_" + str(j) + " |" + bin(self.word) + "> = ",
^
SyntaxError: invalid syntax
3.109. Eigenvalue problems, basic definitions¶
Let us consider the matrix \(\mathbf{A}\) of dimension \(n\). The eigenvalues of \(\mathbf{A}\) are defined through the matrix equation
where \(\lambda^{(\nu)}\) are the eigenvalues and \(\mathbf{x}^{(\nu)}\) the corresponding eigenvectors. Unless otherwise stated, when we use the wording eigenvector we mean the right eigenvector. The left eigenvalue problem is defined as
The above right eigenvector problem is equivalent to a set of \(n\) equations with \(n\) unknowns \(x_i\).
3.110. Eigenvalue problems, basic definitions¶
The eigenvalue problem can be rewritten as
with \(\mathbf{I}\) being the unity matrix. This equation provides a solution to the problem if and only if the determinant is zero, namely
which in turn means that the determinant is a polynomial of degree \(n\) in \(\lambda\) and in general we will have \(n\) distinct zeros.
3.111. Eigenvalue problems, basic definitions¶
The eigenvalues of a matrix \(\mathbf{A}\in {\mathbb{C}}^{n\times n}\) are thus the \(n\) roots of its characteristic polynomial
or
The set of these roots is called the spectrum and is denoted as \(\lambda(\mathbf{A})\). If \(\lambda(\mathbf{A})=\left\{\lambda_1,\lambda_2,\dots ,\lambda_n\right\}\) then we have
and if we define the trace of \(\mathbf{A}\) as
then
3.112. Abel-Ruffini Impossibility Theorem¶
The Abel-Ruffini theorem (also known as Abel’s impossibility theorem) states that there is no general solution in radicals to polynomial equations of degree five or higher.
The content of this theorem is frequently misunderstood. It does not assert that higher-degree polynomial equations are unsolvable. In fact, if the polynomial has real or complex coefficients, and we allow complex solutions, then every polynomial equation has solutions; this is the fundamental theorem of algebra. Although these solutions cannot always be computed exactly with radicals, they can be computed to any desired degree of accuracy using numerical methods such as the Newton-Raphson method or Laguerre method, and in this way they are no different from solutions to polynomial equations of the second, third, or fourth degrees.
The theorem only concerns the form that such a solution must take. The content of the theorem is that the solution of a higher-degree equation cannot in all cases be expressed in terms of the polynomial coefficients with a finite number of operations of addition, subtraction, multiplication, division and root extraction. Some polynomials of arbitrary degree, of which the simplest nontrivial example is the monomial equation \(ax^n = b\), are always solvable with a radical.
3.113. Abel-Ruffini Impossibility Theorem¶
The Abel-Ruffini theorem says that there are some fifth-degree equations whose solution cannot be so expressed. The equation \(x^5 - x + 1 = 0\) is an example. Some other fifth degree equations can be solved by radicals, for example \(x^5 - x^4 - x + 1 = 0\). The precise criterion that distinguishes between those equations that can be solved by radicals and those that cannot was given by Galois and is now part of Galois theory: a polynomial equation can be solved by radicals if and only if its Galois group is a solvable group.
Today, in the modern algebraic context, we say that second, third and fourth degree polynomial equations can always be solved by radicals because the symmetric groups \(S_2, S_3\) and \(S_4\) are solvable groups, whereas \(S_n\) is not solvable for \(n \ge 5\).
3.114. Eigenvalue problems, basic definitions¶
In the present discussion we assume that our matrix is real and symmetric, that is \(\mathbf{A}\in {\mathbb{R}}^{n\times n}\). The matrix \(\mathbf{A}\) has \(n\) eigenvalues \(\lambda_1\dots \lambda_n\) (distinct or not). Let \(\mathbf{D}\) be the diagonal matrix with the eigenvalues on the diagonal
If \(\mathbf{A}\) is real and symmetric then there exists a real orthogonal matrix \(\mathbf{S}\) such that
and for \(j=1:n\) we have \(\mathbf{A}\mathbf{S}(:,j) = \lambda_j \mathbf{S}(:,j)\).
3.115. Eigenvalue problems, basic definitions¶
To obtain the eigenvalues of \(\mathbf{A}\in {\mathbb{R}}^{n\times n}\), the strategy is to perform a series of similarity transformations on the original matrix \(\mathbf{A}\), in order to reduce it either into a diagonal form as above or into a tridiagonal form.
We say that a matrix \(\mathbf{B}\) is a similarity transform of \(\mathbf{A}\) if
The importance of a similarity transformation lies in the fact that the resulting matrix has the same eigenvalues, but the eigenvectors are in general different.
3.116. Eigenvalue problems, basic definitions¶
To prove this we start with the eigenvalue problem and a similarity transformed matrix \(\mathbf{B}\).
We multiply the first equation on the left by \(\mathbf{S}^T\) and insert \(\mathbf{S}^{T}\mathbf{S} = \mathbf{I}\) between \(\mathbf{A}\) and \(\mathbf{x}\). Then we get
which is the same as
The variable \(\lambda\) is an eigenvalue of \(\mathbf{B}\) as well, but with eigenvector \(\mathbf{S}^T\mathbf{x}\).
3.117. Eigenvalue problems, basic definitions¶
The basic philosophy is to
Either apply subsequent similarity transformations (direct method) so that
Or apply subsequent similarity transformations so that \(\mathbf{A}\) becomes tridiagonal (Householder) or upper/lower triangular (the QR method to be discussed later).
Thereafter, techniques for obtaining eigenvalues from tridiagonal matrices can be used.
Or use so-called power methods
Or use iterative methods (Krylov, Lanczos, Arnoldi). These methods are popular for huge matrix problems.
3.118. Discussion of methods for eigenvalues¶
The general overview.
One speaks normally of two main approaches to solving the eigenvalue problem.
The first is the formal method, involving determinants and the characteristic polynomial. This proves how many eigenvalues there are, and is the way most of you learned about how to solve the eigenvalue problem, but for matrices of dimensions greater than 2 or 3, it is rather impractical.
The other general approach is to use similarity or unitary tranformations to reduce a matrix to diagonal form. This is normally done in two steps: first reduce to for example a tridiagonal form, and then to diagonal form. The main algorithms we will discuss in detail, Jacobi’s and Householder’s (so-called direct method) and Lanczos algorithms (an iterative method), follow this methodology.
3.119. Eigenvalues methods¶
Direct or non-iterative methods require for matrices of dimensionality \(n\times n\) typically \(O(n^3)\) operations. These methods are normally called standard methods and are used for dimensionalities \(n \sim 10^5\) or smaller. A brief historical overview
| Year | $n$ | |
|---|---|---|
| 1950 | $n=20$ | (Wilkinson) |
| 1965 | $n=200$ | (Forsythe et al.) |
| 1980 | $n=2000$ | Linpack |
| 1995 | $n=20000$ | Lapack |
| This decade | $n\sim 10^5$ | Lapack |
Sloppily speaking, when \(n\sim 10^4\) is cubed we have \(O(10^{12})\) operations, which is smaller than the \(10^{15}\) increase in flops.
3.120. Discussion of methods for eigenvalues¶
If the matrix to diagonalize is large and sparse, direct methods simply become impractical, also because many of the direct methods tend to destroy sparsity. As a result large dense matrices may arise during the diagonalization procedure. The idea behind iterative methods is to project the \(n-\)dimensional problem in smaller spaces, so-called Krylov subspaces. Given a matrix \(\mathbf{A}\) and a vector \(\mathbf{v}\), the associated Krylov sequences of vectors (and thereby subspaces) \(\mathbf{v}\), \(\mathbf{A}\mathbf{v}\), \(\mathbf{A}^2\mathbf{v}\), \(\mathbf{A}^3\mathbf{v},\dots\), represent successively larger Krylov subspaces.
| Matrix | $\mathbf{A}\mathbf{x}=\mathbf{b}$ | $\mathbf{A}\mathbf{x}=\lambda\mathbf{x}$ |
|---|---|---|
| $\mathbf{A}=\mathbf{A}^*$ | Conjugate gradient | Lanczos |
| $\mathbf{A}\ne \mathbf{A}^*$ | GMRES etc | Arnoldi |
3.121. Eigenvalues and Lanczos’ method¶
Basic features with a real symmetric matrix (and normally huge \(n> 10^6\) and sparse) \(\hat{A}\) of dimension \(n\times n\):
Lanczos’ algorithm generates a sequence of real tridiagonal matrices \(T_k\) of dimension \(k\times k\) with \(k\le n\), with the property that the extremal eigenvalues of \(T_k\) are progressively better estimates of \(\hat{A}\)’ extremal eigenvalues.* The method converges to the extremal eigenvalues.
The similarity transformation is
with the first vector \(\hat{Q}\hat{e}_1=\hat{q}_1\).
We are going to solve iteratively
with the first vector \(\hat{Q}\hat{e}_1=\hat{q}_1\). We can write out the matrix \(\hat{Q}\) in terms of its column vectors
3.122. Eigenvalues and Lanczos’ method, tridiagonal matrix¶
The matrix
can be written as
3.123. Eigenvalues and Lanczos’ method, tridiagonal and orthogonal matrices¶
Using the fact that
we can rewrite
as
3.124. Eigenvalues and Lanczos’ method¶
If we equate columns
we obtain
3.125. Eigenvalues and Lanczos’ method, defining the Lanczos’ vectors¶
We have thus
with \(\beta_0\hat{q}_0=0\) for \(k=1:n-1\). Remember that the vectors \(\hat{q}_k\) are orthornormal and this implies
and these vectors are called Lanczos vectors.
3.126. Eigenvalues and Lanczos’ method, basic steps¶
We have thus
with \(\beta_0\hat{q}_0=0\) for \(k=1:n-1\) and
If
is non-zero, then
with \(\beta_k=\pm ||\hat{r}_{k}||_2\).