A direct approach to second-order MCSCF calculations using a norm extended optimization scheme

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Abstract
Using configuration amplitudes and the unitary generators of orbital rotation the NEO algorithm has been derived. NEO (acronym for norm extended optimization) can be implemented as a direct second‐order restricted step MCSCF optimization procedure where the quadratic convergence is obtained through solving a Hessian‐type eigenvalue problem instead of a set of linear equations. Because configuration amplitudes are used as variables, the computations in each iteration can be made comparable to those of a direct CI calculation. The NEO is especially promising because convergence is assured to a state with the desired number of negative eigenvalues of the Hessian. With the NEO procedure one achieves: (1) Any set of configurations used in a direct CI can also be used for MCSCF; (2) excellent convergence characteristics including guaranteed convergence in ground state calculations; and (3) the converged state has the desired number of negative Hessian eigenvalues.
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