Linear Variational Method
So far each trial state had a parameter that entered nonlinearly (a Gaussian width, an effective charge). The linear variational method instead writes the trial state as a linear combination of fixed basis functions and optimizes the coefficients. The magic is that minimizing over linear coefficients is exact and reduces to a matrix eigenvalue problem.
A basis-expansion trial state
Pick fixed, linearly independent functions and write
The coefficients are the variational parameters. Define the Hamiltonian matrix and overlap matrix
If the basis is orthonormal, and the overlap drops out.
The secular equation
Minimizing the Rayleigh quotient over the coefficients leads to the generalized eigenvalue problem
with non-trivial solutions only when the secular determinant vanishes:
For an orthonormal basis this is just — the ordinary eigenvalue equation. The roots are the linear variational energies. The smallest, , is the best variational estimate of the ground state; remarkably, are upper bounds on the excited states too (the Hylleraas–Undheim / MacDonald theorem).
The two-dimensional case
With two orthonormal basis states the Hamiltonian matrix is a real symmetric matrix
and the secular equation gives the quadratic , whose roots are
The off-diagonal coupling pushes the levels apart: the lower root drops below the smaller diagonal entry. This level repulsion is the variational improvement over using a single basis state.
Try it
For , compute and return the lower eigenvalue — the
best two-state variational estimate of the ground-state energy.
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