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Forbid simultaneous Gram-Schmidt and pseudoinverse

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Nuwan Yapa 2025-01-27 12:58:43 -05:00
parent ce8166c985
commit ad25531571
1 changed files with 12 additions and 12 deletions
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14
EC.jl
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@ -23,9 +23,11 @@ end
If a list is provided for ref_eval, they are used as reference values for picking the closest eigenvalues at each sampling point. If a list is provided for ref_eval, they are used as reference values for picking the closest eigenvalues at each sampling point.
If a single number is provided for ref_eval, it is used as a reference for the first point, and the previous eigenvalue is used as the reference for each successive point. If a single number is provided for ref_eval, it is used as a reference for the first point, and the previous eigenvalue is used as the reference for each successive point.
If pseudo_inv_rtol > 0, the GEVP is avoided using Moore-Penrose psuedoinverse, using this value as the relative tolerance for dropping redundant vectors. If pseudo_inv_rtol > 0, the GEVP is avoided using Moore-Penrose psuedoinverse, using this value as the relative tolerance for dropping redundant vectors.
If orthonormalize_threshold > 0, Gram-Schmidt orthonormalization is performed, overriding Moore-Penrose, using this value as the threshold for dropping redundant vectors. If orthonormalize_threshold > 0, Gram-Schmidt orthonormalization is performed, using this value as the threshold for dropping redundant vectors.
If both are = 0, GEVP is solved instead." If both are = 0, GEVP is solved instead."
function train!(EC::affine_EC, c_vals; ref_eval=-10.0, pseudo_inv_rtol=1e-6, orthonormalize_threshold=0, CAEC=false, verbose=true, tol=1e-5) function train!(EC::affine_EC, c_vals; ref_eval=-10.0, pseudo_inv_rtol=1e-6, gram_schmidt_threshold=0, CAEC=false, verbose=true, tol=1e-5)
@assert pseudo_inv_rtol == 0 || gram_schmidt_threshold == 0 "Cannot perform both psuedoinverse and Gram-Schmidt"
training_vecs = Vector{ComplexF64}[] training_vecs = Vector{ComplexF64}[]
for c in c_vals for c in c_vals
@ -50,24 +52,22 @@ function train!(EC::affine_EC, c_vals; ref_eval=-10.0, pseudo_inv_rtol=1e-6, ort
CAEC && append!(training_vecs, conj.(training_vecs)) CAEC && append!(training_vecs, conj.(training_vecs))
if orthonormalize_threshold ≠ 0 if gram_schmidt_threshold ≠ 0
training_vecs = gram_schmidt!(training_vecs, EC.weights, orthonormalize_threshold; verbose=verbose) training_vecs = gram_schmidt!(training_vecs, EC.weights, gram_schmidt_threshold; verbose=verbose)
end end
EC_basis = hcat(training_vecs...) EC_basis = hcat(training_vecs...)
weights_mat = spdiagm(EC.weights) weights_mat = spdiagm(EC.weights)
EC.H0_EC = transpose(EC_basis) * weights_mat * EC.H0 * EC_basis EC.H0_EC = transpose(EC_basis) * weights_mat * EC.H0 * EC_basis
EC.H1_EC = transpose(EC_basis) * weights_mat * EC.H1 * EC_basis EC.H1_EC = transpose(EC_basis) * weights_mat * EC.H1 * EC_basis
if orthonormalize_threshold == 0
EC.N_EC = transpose(EC_basis) * weights_mat * EC_basis EC.N_EC = transpose(EC_basis) * weights_mat * EC_basis
if pseudo_inv_rtol > 0 if pseudo_inv_rtol > 0
inv_N_EC = pinv(EC.N_EC; rtol=pseudo_inv_rtol) inv_N_EC = pinv(EC.N_EC; rtol=pseudo_inv_rtol)
EC.H0_EC = inv_N_EC * EC.H0_EC EC.H0_EC = inv_N_EC * EC.H0_EC
EC.H1_EC = inv_N_EC * EC.H1_EC EC.H1_EC = inv_N_EC * EC.H1_EC
EC.N_EC = nothing EC.N_EC = nothing
end end
end
EC.trained = true EC.trained = true
end end