Quantum Science 1976
DOI: 10.1007/978-1-4757-1659-7_5
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The Non-Orthogonality Problem and Orthogonalization Procedures

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Cited by 4 publications
(1 citation statement)
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“…As the basis size increases, at some point the finite numerical precision (typically double-precision (64 bit) is used) will be insufficient to properly represent and invert a near-singular overlap matrix. There are several strategies to avoid that, for instance the Löwdin's canonic orthogonalization is commonly used [104,105,106,81]. This however proved to be inefficient when optimizing a basis set, since the omission of a linearly dependent basis function increases the energy, and further optimization only resulted in the exclusion of an increasing number of basis functions.…”
Section: Avoiding Linear Dependenciesmentioning
confidence: 99%
“…As the basis size increases, at some point the finite numerical precision (typically double-precision (64 bit) is used) will be insufficient to properly represent and invert a near-singular overlap matrix. There are several strategies to avoid that, for instance the Löwdin's canonic orthogonalization is commonly used [104,105,106,81]. This however proved to be inefficient when optimizing a basis set, since the omission of a linearly dependent basis function increases the energy, and further optimization only resulted in the exclusion of an increasing number of basis functions.…”
Section: Avoiding Linear Dependenciesmentioning
confidence: 99%