New code and modified tests for generalized_eigenvalues
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a439121641
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714f2ac987
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@ -4,7 +4,7 @@ use serde::{Deserialize, Serialize};
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use num::Zero;
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use num::Zero;
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use num_complex::Complex;
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use num_complex::Complex;
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use simba::scalar:: RealField;
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use simba::scalar::RealField;
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use crate::ComplexHelper;
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use crate::ComplexHelper;
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use na::allocator::Allocator;
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use na::allocator::Allocator;
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@ -14,6 +14,19 @@ use na::{DefaultAllocator, Matrix, OMatrix, OVector, Scalar};
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use lapack;
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use lapack;
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/// Generalized eigenvalues and generalized eigenvectors(left and right) of a pair of N*N square matrices.
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/// Generalized eigenvalues and generalized eigenvectors(left and right) of a pair of N*N square matrices.
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///
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/// Each generalized eigenvalue (lambda) satisfies determinant(A - lambda*B) = 0
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///
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/// The right eigenvector v(j) corresponding to the eigenvalue lambda(j)
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/// of (A,B) satisfies
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///
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/// A * v(j) = lambda(j) * B * v(j).
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///
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/// The left eigenvector u(j) corresponding to the eigenvalue lambda(j)
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/// of (A,B) satisfies
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///
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/// u(j)**H * A = lambda(j) * u(j)**H * B .
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/// where u(j)**H is the conjugate-transpose of u(j).
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#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
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#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
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#[cfg_attr(
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#[cfg_attr(
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feature = "serde-serialize",
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feature = "serde-serialize",
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@ -55,11 +68,21 @@ impl<T: GEScalar + RealField + Copy, D: Dim> GE<T, D>
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where
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where
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DefaultAllocator: Allocator<T, D, D> + Allocator<T, D>,
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DefaultAllocator: Allocator<T, D, D> + Allocator<T, D>,
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{
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{
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/// Attempts to compute the generalized eigenvalues (and eigenvectors) via the raw returns from LAPACK's
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/// Attempts to compute the generalized eigenvalues, and left and right associated eigenvectors
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/// dggev and sggev routines
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/// via the raw returns from LAPACK's dggev and sggev routines
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///
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///
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/// For each e in generalized eigenvalues and the associated eigenvectors e_l and e_r (left andf right)
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/// Each generalized eigenvalue (lambda) satisfies determinant(A - lambda*B) = 0
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/// it satisfies e_l*a = e*e_l*b and a*e_r = e*b*e_r
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///
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/// The right eigenvector v(j) corresponding to the eigenvalue lambda(j)
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/// of (A,B) satisfies
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///
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/// A * v(j) = lambda(j) * B * v(j).
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///
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/// The left eigenvector u(j) corresponding to the eigenvalue lambda(j)
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/// of (A,B) satisfies
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///
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/// u(j)**H * A = lambda(j) * u(j)**H * B .
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/// where u(j)**H is the conjugate-transpose of u(j).
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///
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///
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/// Panics if the method did not converge.
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/// Panics if the method did not converge.
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pub fn new(a: OMatrix<T, D, D>, b: OMatrix<T, D, D>) -> Self {
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pub fn new(a: OMatrix<T, D, D>, b: OMatrix<T, D, D>) -> Self {
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@ -69,8 +92,18 @@ where
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/// Attempts to compute the generalized eigenvalues (and eigenvectors) via the raw returns from LAPACK's
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/// Attempts to compute the generalized eigenvalues (and eigenvectors) via the raw returns from LAPACK's
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/// dggev and sggev routines
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/// dggev and sggev routines
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///
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///
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/// For each e in generalized eigenvalues and the associated eigenvectors e_l and e_r (left andf right)
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/// Each generalized eigenvalue (lambda) satisfies determinant(A - lambda*B) = 0
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/// it satisfies e_l*a = e*e_l*b and a*e_r = e*b*e_r
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///
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/// The right eigenvector v(j) corresponding to the eigenvalue lambda(j)
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/// of (A,B) satisfies
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///
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/// A * v(j) = lambda(j) * B * v(j).
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///
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/// The left eigenvector u(j) corresponding to the eigenvalue lambda(j)
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/// of (A,B) satisfies
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///
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/// u(j)**H * A = lambda(j) * u(j)**H * B .
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/// where u(j)**H is the conjugate-transpose of u(j).
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///
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///
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/// Returns `None` if the method did not converge.
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/// Returns `None` if the method did not converge.
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pub fn try_new(mut a: OMatrix<T, D, D>, mut b: OMatrix<T, D, D>) -> Option<Self> {
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pub fn try_new(mut a: OMatrix<T, D, D>, mut b: OMatrix<T, D, D>) -> Option<Self> {
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@ -147,9 +180,24 @@ where
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}
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}
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/// Calculates the generalized eigenvectors (left and right) associated with the generalized eigenvalues
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/// Calculates the generalized eigenvectors (left and right) associated with the generalized eigenvalues
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/// Outputs two matrices, the first one containing the left eigenvectors of the generalized eigenvalues
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/// as columns and the second matrix contains the right eigenvectors of the generalized eigenvalues
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/// as columns
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///
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/// The right eigenvector v(j) corresponding to the eigenvalue lambda(j)
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/// of (A,B) satisfies
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///
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/// A * v(j) = lambda(j) * B * v(j).
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///
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/// The left eigenvector u(j) corresponding to the eigenvalue lambda(j)
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/// of (A,B) satisfies
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///
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/// u(j)**H * A = lambda(j) * u(j)**H * B .
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/// where u(j)**H is the conjugate-transpose of u(j).
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pub fn eigenvectors(self) -> (OMatrix<Complex<T>, D, D>, OMatrix<Complex<T>, D, D>)
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pub fn eigenvectors(self) -> (OMatrix<Complex<T>, D, D>, OMatrix<Complex<T>, D, D>)
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where
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where
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DefaultAllocator: Allocator<Complex<T>, D, D> + Allocator<Complex<T>, D>,
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DefaultAllocator:
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Allocator<Complex<T>, D, D> + Allocator<Complex<T>, D> + Allocator<(Complex<T>, T), D>,
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{
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{
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let n = self.vsl.shape().0;
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let n = self.vsl.shape().0;
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let mut l = self
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let mut l = self
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@ -199,9 +247,10 @@ where
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(l, r)
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(l, r)
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}
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}
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/// computes the generalized eigenvalues
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/// computes the generalized eigenvalues i.e values of lambda that satisfy the following equation
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/// determinant(A - lambda* B) = 0
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#[must_use]
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#[must_use]
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pub fn eigenvalues(&self) -> OVector<Complex<T>, D>
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fn eigenvalues(&self) -> OVector<Complex<T>, D>
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where
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where
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DefaultAllocator: Allocator<Complex<T>, D>,
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DefaultAllocator: Allocator<Complex<T>, D>,
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{
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{
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@ -233,6 +282,26 @@ where
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out
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out
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}
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}
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/// outputs the unprocessed (almost) version of generalized eigenvalues ((alphar, alpai), beta)
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/// straight from LAPACK
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#[must_use]
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pub fn raw_eigenvalues(&self) -> OVector<(Complex<T>, T), D>
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where
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DefaultAllocator: Allocator<(Complex<T>, T), D>,
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{
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let mut out = Matrix::from_element_generic(
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self.vsl.shape_generic().0,
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Const::<1>,
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(Complex::zero(), T::RealField::zero()),
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);
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for i in 0..out.len() {
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out[i] = (Complex::new(self.alphar[i], self.alphai[i]), self.beta[i])
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}
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out
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}
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}
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}
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/*
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/*
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@ -17,21 +17,29 @@ proptest! {
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let a_condition_no = a.clone().try_inverse().and_then(|x| Some(EuclideanNorm.norm(&x)* EuclideanNorm.norm(&a)));
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let a_condition_no = a.clone().try_inverse().and_then(|x| Some(EuclideanNorm.norm(&x)* EuclideanNorm.norm(&a)));
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let b_condition_no = b.clone().try_inverse().and_then(|x| Some(EuclideanNorm.norm(&x)* EuclideanNorm.norm(&b)));
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let b_condition_no = b.clone().try_inverse().and_then(|x| Some(EuclideanNorm.norm(&x)* EuclideanNorm.norm(&b)));
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if a_condition_no.unwrap_or(200000.0) < 10.0 && b_condition_no.unwrap_or(200000.0) < 10.0 {
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if a_condition_no.unwrap_or(200000.0) < 5.0 && b_condition_no.unwrap_or(200000.0) < 5.0 {
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let a_c =a.clone().map(|x| Complex::new(x, 0.0));
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let a_c = a.clone().map(|x| Complex::new(x, 0.0));
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let b_c = b.clone().map(|x| Complex::new(x, 0.0));
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let b_c = b.clone().map(|x| Complex::new(x, 0.0));
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let ge = GE::new(a.clone(), b.clone());
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let ge = GE::new(a.clone(), b.clone());
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let (vsl,vsr) = ge.clone().eigenvectors();
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let (vsl,vsr) = ge.clone().eigenvectors();
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let eigenvalues = ge.clone().eigenvalues();
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for i in 0..n {
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for (i,(alpha,beta)) in ge.raw_eigenvalues().iter().enumerate() {
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let left_eigenvector = &vsl.column(i);
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let l_a = a_c.clone() * Complex::new(*beta, 0.0);
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prop_assert!(relative_eq!((left_eigenvector.transpose()*&a_c - left_eigenvector.transpose()*&b_c*eigenvalues[i]).map(|x| x.modulus()), OMatrix::zeros_generic(Const::<1>,Dynamic::new(n)) ,epsilon = 1.0e-7));
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let l_b = b_c.clone() * *alpha;
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let right_eigenvector = &vsr.column(i);
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prop_assert!(
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prop_assert!(relative_eq!((&a_c*right_eigenvector - &b_c*right_eigenvector*eigenvalues[i]).map(|x| x.modulus()), OMatrix::zeros_generic(Dynamic::new(n), Const::<1>) ,epsilon = 1.0e-7));
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relative_eq!(
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};
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((&l_a - &l_b)*vsr.column(i)).map(|x| x.modulus()),
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OMatrix::zeros_generic(Dynamic::new(n), Const::<1>),
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epsilon = 1.0e-7));
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prop_assert!(
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relative_eq!(
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(vsl.column(i).adjoint()*(&l_a - &l_b)).map(|x| x.modulus()),
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OMatrix::zeros_generic(Const::<1>, Dynamic::new(n)),
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epsilon = 1.0e-7))
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};
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};
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};
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}
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}
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let a_condition_no = a.clone().try_inverse().and_then(|x| Some(EuclideanNorm.norm(&x)* EuclideanNorm.norm(&a)));
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let a_condition_no = a.clone().try_inverse().and_then(|x| Some(EuclideanNorm.norm(&x)* EuclideanNorm.norm(&a)));
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let b_condition_no = b.clone().try_inverse().and_then(|x| Some(EuclideanNorm.norm(&x)* EuclideanNorm.norm(&b)));
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let b_condition_no = b.clone().try_inverse().and_then(|x| Some(EuclideanNorm.norm(&x)* EuclideanNorm.norm(&b)));
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if a_condition_no.unwrap_or(200000.0) < 10.0 && b_condition_no.unwrap_or(200000.0) < 10.0{
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if a_condition_no.unwrap_or(200000.0) < 5.0 && b_condition_no.unwrap_or(200000.0) < 5.0 {
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let ge = GE::new(a.clone(), b.clone());
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let ge = GE::new(a.clone(), b.clone());
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let a_c =a.clone().map(|x| Complex::new(x, 0.0));
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let a_c =a.clone().map(|x| Complex::new(x, 0.0));
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let b_c = b.clone().map(|x| Complex::new(x, 0.0));
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let b_c = b.clone().map(|x| Complex::new(x, 0.0));
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let (vsl,vsr) = ge.eigenvectors();
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let (vsl,vsr) = ge.eigenvectors();
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let eigenvalues = ge.eigenvalues();
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let eigenvalues = ge.raw_eigenvalues();
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for i in 0..4 {
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for (i,(alpha,beta)) in eigenvalues.iter().enumerate() {
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let left_eigenvector = &vsl.column(i);
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let l_a = a_c.clone() * Complex::new(*beta, 0.0);
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prop_assert!(relative_eq!((left_eigenvector.transpose()*&a_c - left_eigenvector.transpose()*&b_c*eigenvalues[i]).map(|x| x.modulus()), OMatrix::zeros_generic(Const::<1>,Const::<4>) ,epsilon = 1.0e-7));
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let l_b = b_c.clone() * *alpha;
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let right_eigenvector = &vsr.column(i);
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prop_assert!(
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prop_assert!(relative_eq!((&a_c*right_eigenvector - &b_c*right_eigenvector*eigenvalues[i]).map(|x| x.modulus()), OMatrix::zeros_generic(Const::<4>, Const::<1>) ,epsilon = 1.0e-7));
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relative_eq!(
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};
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((&l_a - &l_b)*vsr.column(i)).map(|x| x.modulus()),
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OMatrix::zeros_generic(Const::<4>, Const::<1>),
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epsilon = 1.0e-7));
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prop_assert!(
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relative_eq!((vsl.column(i).adjoint()*(&l_a - &l_b)).map(|x| x.modulus()),
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OMatrix::zeros_generic(Const::<1>, Const::<4>),
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epsilon = 1.0e-7))
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}
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};
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};
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}
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}
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}
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}
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