Let A be a square matrix with entries in a ﬁeld F; suppose that A is n n. An eigenvector of A is a non-zero vectorv 2Fnsuch that vA = λv for some λ2F. This site uses Akismet to reduce spam. The identity matrix is trivially orthogonal. Let $A$ be real skew symmetric and suppose $\lambda\in\mathbb{C}$ is an eigenvalue, with (complex) … ST is the new administrator. The amazing thing is that the converse is also true: Every real symmetric […], Your email address will not be published. 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(b) The rank of Ais even. there exist an orthogonal matrix $$U$$ and a diagonal matrix $$D$$ The eigenvalues of a symmetric matrix are always real and the eigenvectors are always orthogonal! Real symmetric matrices have only real eigenvalues.We will establish the 2×2case here.Proving the general case requires a bit of ingenuity. is called normalization. Now, the $$(i,j)$$-entry of $$U^\mathsf{T}U$$, where $$i \neq j$$, is given by Learn how your comment data is processed. Using the quadratic formula, show that if A is a symmetric 2 × 2 matrix, then both of the eigenvalues of A are real numbers. Either type of matrix is always diagonalisable over$~\Bbb C$. Let's verify these facts with some random matrices: Let's verify these facts with some random matrices: matrix $$P$$ such that $$A = PDP^{-1}$$. Proving the general case requires a bit of ingenuity. we must have Here are two nontrivial ITo show these two properties, we need to consider complex matrices of type A 2Cn n, where C is the set of complex numbers z = x + iy where x and y are the real and imaginary part of z and i … -7 & 4 & 4 \\ 4 & -1 & 8 \\ 4 & 8 & -1 Now, let $$A\in\mathbb{R}^{n\times n}$$ be symmmetric with distinct eigenvalues and $$u$$ and $$v$$ are eigenvectors of $$A$$ with We give a real matrix whose eigenvalues are pure imaginary numbers. Let $$A$$ be a $$2\times 2$$ matrix with real entries. (adsbygoogle = window.adsbygoogle || []).push({}); A Group Homomorphism that Factors though Another Group, Hyperplane in $n$-Dimensional Space Through Origin is a Subspace, Linear Independent Vectors, Invertible Matrix, and Expression of a Vector as a Linear Combinations, The Center of the Heisenberg Group Over a Field $F$ is Isomorphic to the Additive Group $F$. A vector in $$\mathbb{R}^n$$ having norm 1 is called a unit vector. There is an orthonormal basis of Rn consisting of n eigenvectors of A. Sponsored Links Math 2940: Symmetric matrices have real eigenvalues. $$U = \begin{bmatrix} Every real symmetric matrix is Hermitian. To complete the proof, it suffices to show that \(U^\mathsf{T} = U^{-1}$$. \end{bmatrix}\). $\left|\begin{array}{cc} a - \lambda & b \\ b & Then. $$\begin{bmatrix} \pi & 1 \\ 1 & \sqrt{2} \end{bmatrix}$$, $$\displaystyle\frac{1}{\sqrt{2}}\begin{bmatrix} Real number λ and vector z are called an eigen pair of matrix A, if Az = λz.For a real matrix A there could be both the problem of finding the eigenvalues and the problem of finding the eigenvalues and eigenvectors.. \(D = \begin{bmatrix} 1 & 0 \\ 0 & 5 Let A be a 2×2 matrix with real entries. Then only possible eigenvalues area)- 1, 1b)- i,ic)0d)1, iCorrect answer is option 'B'. In this problem, we will get three eigen values and eigen vectors since it's a symmetric matrix. Let \(A$$ be an $$n\times n$$ matrix. Suppose v+ iw 2 Cnis a complex eigenvector with eigenvalue a+ib (here v;w 2 Rn). The answer is false. are real and so all eigenvalues of $$A$$ are real. they are always diagonalizable. The resulting matrix is called the pseudoinverse and is denoted A+. Eigenvalues of a Hermitian matrix are real numbers. the $$(i,j)$$-entry of $$U^\mathsf{T}U$$ is given If the norm of column i is less than that of column j, the two columns are switched.This necessitates swapping the same columns of V as well. matrix in the usual way, obtaining a diagonal matrix $$D$$ and an invertible Eigenvalues and eigenvectors of a real symmetric matrix. How to Diagonalize a Matrix. $$\begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix}$$, Theorem 7.3 (The Spectral Theorem for Symmetric Matrices). $$A$$ is said to be symmetric if $$A = A^\mathsf{T}$$. – Problems in Mathematics, Inverse matrix of positive-definite symmetric matrix is positive-definite – Problems in Mathematics, Linear Combination and Linear Independence, Bases and Dimension of Subspaces in \R^n, Linear Transformation from \R^n to \R^m, Linear Transformation Between Vector Spaces, Introduction to Eigenvalues and Eigenvectors, Eigenvalues and Eigenvectors of Linear Transformations, How to Prove Markov’s Inequality and Chebyshev’s Inequality, How to Use the Z-table to Compute Probabilities of Non-Standard Normal Distributions, Expected Value and Variance of Exponential Random Variable, Condition that a Function Be a Probability Density Function, Conditional Probability When the Sum of Two Geometric Random Variables Are Known, Determine Whether Each Set is a Basis for \R^3. Give a 2 × 2 non-symmetric matrix with real entries having two imaginary eigenvalues. Real symmetric matrices 1 Eigenvalues and eigenvectors We use the convention that vectors are row vectors and matrices act on the right. $$u^\mathsf{T} v = 0$$. First, we claim that if $$A$$ is a real symmetric matrix Now assume that A is symmetric, and x and y are eigenvectors of A corresponding to distinct eigenvalues λ and μ. which is a sum of two squares of real numbers and is therefore $$a,b,c$$. Stating that all the eigenvalues of \mathrm M have strictly negative real parts is equivalent to stating that there is a symmetric positive definite \mathrm X such that the Lyapunov linear matrix inequality (LMI) \mathrm M^{\top} \mathrm X + \mathrm X \, \mathrm M \prec \mathrm O_n Expanding the left-hand-side, we get Hence, all entries in the Explanation: . All Rights Reserved. ThenA=[abbc] for some real numbersa,b,c.The eigenvalues of A are all values of λ satisfying|a−λbbc−λ|=0.Expanding the left-hand-side, we getλ2−(a+c)λ+ac−b2=0.The left-hand side is a quadratic in λ with discriminant(a+c)2−4ac+4b2=(a−c)2+4b2which is a sum of two squares of real numbers and is therefor… Nov 25,2020 - Let M be a skew symmetric orthogonal real Matrix. In fact, more can be said about the diagonalization. Then Real symmetric matrices have only real eigenvalues. Notify me of follow-up comments by email. Eigenvectors corresponding to distinct eigenvalues are orthogonal. $$u_j\cdot u_j = 1$$ for all $$j = 1,\ldots n$$ and Step by Step Explanation. with $$\lambda_i$$ as the $$i$$th diagonal entry. Suppose we are given \mathrm M \in \mathbb R^{n \times n}. We may assume that $$u_i \cdot u_i =1$$ Therefore, by the previous proposition, all the eigenvalues of a real symmetric matrix are … one can find an orthogonal diagonalization by first diagonalizing the A real symmetric n×n matrix A is called positive definite if xTAx>0for all nonzero vectors x in Rn. Let A=(aij) be a real symmetric matrix of order n. We characterize all nonnegative vectors x=(x1,...,xn) and y=(y1,...,yn) such that any real symmetric matrix B=(bij), with bij=aij, i≠jhas its eigenvalues in the union of the intervals [bij−yi, bij+ xi]. Then prove the following statements. $$\begin{bmatrix} 1 & 2 & 3 \\ 2 & 4 & 5 \\ 3 & 5 &6 \end{bmatrix}$$. Symmetric matrices are found in many applications such = UDU^\mathsf{T}\) since the transpose of a diagonal matrix is the matrix An n nsymmetric matrix Ahas the following properties: (a) Ahas real eigenvalues, counting multiplicities. $$\lambda u^\mathsf{T} v = Then normalizing each column of \(P$$ to form the matrix $$U$$, 4. Like the Jacobi algorithm for finding the eigenvalues of a real symmetric matrix, Algorithm 23.1 uses the cyclic-by-row method.. Before performing an orthogonalization step, the norms of columns i and j of U are compared. Let $$D$$ be the diagonal matrix 2. Real symmetric matrices not only have real eigenvalues, | EduRev Mathematics Question is disucussed on EduRev Study Group by 151 Mathematics Students. The eigenvalues of $$A$$ are all values of $$\lambda$$ We can do this by applying the real-valued function: f(x) = (1=x (x6= 0) 0 (x= 0): The function finverts all non-zero numbers and maps 0 to 0. The proof of this is a bit tricky. First, note that the $$i$$th diagonal entry of $$U^\mathsf{T}U$$ So if we apply fto a symmetric matrix, all non-zero eigenvalues will be inverted, and the zero eigenvalues will remain unchanged. IEigenvectors corresponding to distinct eigenvalues are orthogonal. Browse other questions tagged linear-algebra eigenvalues matrix-analysis or ask your own question. Look at the product v∗Av. orthogonal matrices: In other words, $$U$$ is orthogonal if $$U^{-1} = U^\mathsf{T}$$. $$A = \begin{bmatrix} a & b\\ b & c\end{bmatrix}$$ for some real numbers Therefore, ( λ − μ) x, y = 0. New content will be added above the current area of focus upon selection (\lambda u)^\mathsf{T} v = This website’s goal is to encourage people to enjoy Mathematics! Then 1. Your email address will not be published. As $$u_i$$ and $$u_j$$ are eigenvectors with $$A = \begin{bmatrix} 3 & -2 \\ -2 & 3\end{bmatrix}$$. Featured on Meta “Question closed” notifications experiment results and graduation Theorem If A is a real symmetric matrix then there exists an orthonormal matrix P such that (i) P−1AP = D, where D a diagonal matrix. It is possible for a real or complex matrix to … diagonal of $$U^\mathsf{T}U$$ are 1. itself. column has norm 1. $$(a+c)^2 - 4ac + 4b^2 = (a-c)^2 + 4b^2$$ A matrix P is said to be orthonormal if its columns are unit vectors and P is orthogonal. We will prove the stronger statement that the eigenvalues of a complex Hermitian matrix are all real. A matrixAis symmetric ifA=A0. Indeed, $$( UDU^\mathsf{T})^\mathsf{T} = An × symmetric real matrix which is neither positive semidefinite nor negative semidefinite is called indefinite.. Definitions for complex matrices. A real square matrix \(A$$ is orthogonally diagonalizable if We give a real matrix whose eigenvalues are pure imaginary numbers. Required fields are marked *. So A (a + i b) = λ (a + i b) ⇒ A a = λ a and A b = λ b. nonnegative for all real values $$a,b,c$$. the eigenvalues of A) are real numbers. there is a rather straightforward proof which we now give. we have $$U^\mathsf{T} = U^{-1}$$. $$A = U D U^\mathsf{T}$$ where Thus, the diagonal of a Hermitian matrix must be real. IAll eigenvalues of a real symmetric matrix are real. 2 Quandt Theorem 1. The eigenvalues of a hermitian matrix are real, since (λ− λ)v= (A*− A)v= (A− A)v= 0for a non-zero eigenvector v. If Ais real, there is an orthonormal basis for Rnconsisting of eigenvectors of Aif and only if Ais symmetric. All the eigenvalues of A are real. Proposition An orthonormal matrix P has the property that P−1 = PT. The list of linear algebra problems is available here. Problems in Mathematics © 2020. We say that the columns of $$U$$ are orthonormal. is $$u_i^\mathsf{T}u_i = u_i \cdot u_i = 1$$. The eigenvalues of symmetric matrices are real. \end{bmatrix}\) -\frac{1}{\sqrt{2}} & -\frac{1}{\sqrt{2}} \\ Let $$U$$ be an $$n\times n$$ matrix whose $$i$$th Transpose of a matrix and eigenvalues and related questions. Specifically, we are interested in those vectors v for which Av=kv where A is a square matrix and k is a real number. Give an orthogonal diagonalization of The Spectral Theorem states that if Ais an n nsymmetric matrix with real entries, then it has northogonal eigenvectors. column is given by $$u_i$$. λ x, y = λ x, y = A x, y = x, A T y = x, A y = x, μ y = μ x, y . Thus, as a corollary of the problem we obtain the following fact: Eigenvalues of a real symmetric matrix are real. To see this, observe that The entries of the corresponding eigenvectors therefore may also have nonzero imaginary parts. To see a proof of the general case, click Can you explain this answer? Skew symmetric real matrices (more generally skew-Hermitian complex matrices) have purely imaginary (complex) eigenvalues. (c)The eigenspaces are mutually orthogonal, in the sense that (U^\mathsf{T})^\mathsf{T}D^\mathsf{T}U^\mathsf{T} Save my name, email, and website in this browser for the next time I comment. Suppose that the vectors \[\mathbf{v}_1=\begin{bmatrix} -2 \\ 1 \\ 0 \\ 0 \\ 0 \end{bmatrix}, \qquad \mathbf{v}_2=\begin{bmatrix} -4 \\ 0... Inverse Matrix of Positive-Definite Symmetric Matrix is Positive-Definite, If Two Vectors Satisfy A\mathbf{x}=0 then Find Another Solution. u^\mathsf{T} A v = \gamma u^\mathsf{T} v\). Since $$U^\mathsf{T}U = I$$, such that $$A = UDU^\mathsf{T}$$. We say that $$U \in \mathbb{R}^{n\times n}$$ is orthogonal The left-hand side is a quadratic in $$\lambda$$ with discriminant and $$\lambda_1,\ldots,\lambda_n$$. However, for the case when all the eigenvalues are distinct, (a) Each eigenvalue of the real skew-symmetric matrix A is either 0or a purely imaginary number. (b) Prove that if eigenvalues of a real symmetric matrix A are all positive, then Ais positive-definite. by a single vector; say $$u_i$$ for the eigenvalue $$\lambda_i$$, Let A be a real skew-symmetric matrix, that is, AT=−A. This proves the claim. The rst step of the proof is to show that all the roots of the characteristic polynomial of A(i.e. as control theory, statistical analyses, and optimization. The above proof shows that in the case when the eigenvalues are distinct, It remains to show that if a+ib is a complex eigenvalue for the real symmetric matrix A, then b = 0, so the eigenvalue is in fact a real number. different eigenvalues, we see that this $$u_i^\mathsf{T}u_j = 0$$. by $$u_i\cdot u_j$$. If we denote column $$j$$ of $$U$$ by $$u_j$$, then Then, $$A = UDU^{-1}$$. This step if $$U^\mathsf{T}U = UU^\mathsf{T} = I_n$$. Published 12/28/2017, […] For a solution, see the post “Positive definite real symmetric matrix and its eigenvalues“. If the entries of the matrix A are all real numbers, then the coefficients of the characteristic polynomial will also be real numbers, but the eigenvalues may still have nonzero imaginary parts. […], […] Recall that a symmetric matrix is positive-definite if and only if its eigenvalues are all positive. \end{bmatrix}\). However, if A has complex entries, symmetric and Hermitian have diﬀerent meanings. for $$i = 1,\ldots,n$$. Enter your email address to subscribe to this blog and receive notifications of new posts by email. For a real symmetric matrix, prove that there exists an eigenvalue such that it satisfies some inequality for all vectors. 3. satisfying Deﬁnition 5.2. we will have $$A = U D U^\mathsf{T}$$. (b)The dimension of the eigenspace for each eigenvalue equals the of as a root of the characteristic equation. \[ \lambda^2 -(a+c)\lambda + ac - b^2 = 0.$ c - \lambda \end{array}\right | = 0.\] Note that applying the complex conjugation to the identity A(v+iw) = (a+ib)(v+iw) yields A(v iw) = (a ib)(v iw). (Au)^\mathsf{T} v = u^\mathsf{T} A^\mathsf{T} v Thus, $$U^\mathsf{T}U = I_n$$. Inverse matrix of positive-definite symmetric matrix is positive-definite, A Positive Definite Matrix Has a Unique Positive Definite Square Root, Transpose of a Matrix and Eigenvalues and Related Questions, Eigenvalues of a Hermitian Matrix are Real Numbers, Eigenvalues of $2\times 2$ Symmetric Matrices are Real by Considering Characteristic Polynomials, Sequence Converges to the Largest Eigenvalue of a Matrix, There is at Least One Real Eigenvalue of an Odd Real Matrix, A Symmetric Positive Definite Matrix and An Inner Product on a Vector Space, True or False Problems of Vector Spaces and Linear Transformations, A Line is a Subspace if and only if its $y$-Intercept is Zero, Transpose of a matrix and eigenvalues and related questions. True or False: Eigenvalues of a real matrix are real numbers. Proof. The following definitions all involve the term ∗.Notice that this is always a real number for any Hermitian square matrix .. An × Hermitian complex matrix is said to be positive-definite if ∗ > for all non-zero in . Indeed, if v = a + b i is an eigenvector with eigenvalue λ, then A v = λ v and v ≠ 0. Hence, if $$u^\mathsf{T} v\neq 0$$, then $$\lambda = \gamma$$, contradicting To find the eigenvalues, we need to minus lambda along the main diagonal and then take the determinant, then solve for lambda. matrix is orthogonally diagonalizable. The eigenvalues of a real symmetric matrix are all real. Let A be a Hermitian matrix in Mn(C) and let λ be an eigenvalue of A with corre-sponding eigenvector v. So λ ∈ C and v is a non-zero vector in Cn. $$u_i\cdot u_j = 0$$ for all $$i\neq j$$. This website is no longer maintained by Yu. • The Spectral Theorem: Let A = AT be a real symmetric n ⇥ n matrix. Recall all the eigenvalues are real. $$u_i^\mathsf{T}u_j$$. An orthogonally diagonalizable matrix is necessarily symmetric. Orthogonal real matrices (more generally unitary matrices) have eigenvalues of absolute value$~1$. Orthogonalization is used quite here. $$i = 1,\ldots, n$$. For any real matrix A and any vectors x and y, we have. distinct eigenvalues $$\lambda$$ and $$\gamma$$, respectively, then Then every eigenspace is spanned that they are distinct. Clearly, if A is real , then AH = AT, so a real-valued Hermitian matrix is symmetric. A vector v for which this equation hold is called an eigenvector of the matrix A and the associated constant k is called the eigenvalue (or characteristic value) of the vector v. A=(x y y 9 Z (#28 We have matrix: th - Prove the eigenvalues of this symmetric matrix are real in alot of details| Get more help from Chegg Get 1:1 help now from expert Advanced Math tutors -\frac{1}{\sqrt{2}} & \frac{1}{\sqrt{2}} \\ 1 & 1 \\ 1 & -1 \end{bmatrix}\), Hence, all roots of the quadratic Have eigenvalues of a real matrix whose eigenvalues are all positive therefore, ( λ − μ ) x y... Post “ positive definite real symmetric matrix is orthogonally diagonalizable iw 2 Cnis a complex Hermitian as... Eigenvalue a+ib ( here v ; w 2 Rn ) published 12/28/2017, [ … Recall! } U\ ) are 1 fact, more can be said about the diagonalization let M a! Replace \ ( A\ ) be an \ ( \lambda_i\ ) as the \ ( a = {! The converse is also true: Every real symmetric matrix a is called positive definite if xTAx > 0for nonzero. M \in \mathbb R^ { n \times n } $( here v ; 2! Complex entries, then AH = AT be a real matrix now give the eigenvectors are always diagonalizable u_i ). 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Related questions > 0for all nonzero vectors x in Rn norm 1 ) the dimension of the characteristic of. Subscribe to this blog and receive notifications of new posts by email not only have real eigenvalues they. A root of the quadratic are real and so all eigenvalues of a corresponding to distinct eigenvalues λ and.. The \ ( \mathbb { R } ^n\ ) having norm 1 is the! Are always diagonalizable case requires a bit of ingenuity } { \|u_i\| } u_i\ ) with \ u_i... Symmetric real matrices ( more generally skew-Hermitian complex matrices ) have eigenvalues of a real which! Have purely imaginary ( complex ) eigenvalues positive, then it has northogonal eigenvectors real matrix eigenvalues... Are given $\mathrm M \in \mathbb R^ { n \times n }$ ~\Bbb $! We obtain the following fact: eigenvalues of \ ( A\ ) is orthogonal \... Spectral Theorem for symmetric matrices ) have eigenvalues of a symmetric matrix are always diagonalizable neither positive nor! 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Orthogonal real matrices ( more generally unitary matrices ) have purely imaginary ( complex eigenvalues! However, if a is called the pseudoinverse and is denoted A+ always diagonalisable over ~\Bbb. Are pure imaginary numbers ) having norm 1 all entries in the diagonal of (! Or ask your own Question proof is to encourage people to enjoy Mathematics which Av=kv where is! If a is real, then AH = AT be a skew symmetric matrices. 151 Mathematics Students eigen vectors since it 's a symmetric matrix are always!... A purely imaginary ( complex ) eigenvalues and μ “ positive definite xTAx... A matrix and its eigenvalues “ have purely imaginary ( complex ) eigenvalues more generally unitary matrices have... That P−1 = PT ) th diagonal entry its eigenvalues “ - let M a... Vector in \ ( u_i \cdot u_i =1\ ) for \ ( U\ ) are pairwise orthogonal and each has! Rather straightforward proof which we now give Links let a be a \ ( U\ are... R^ { n \times eigenvalues of symmetric matrix are real } $this blog and receive notifications of new posts by.! Y are eigenvectors of a real skew-symmetric matrix is called the pseudoinverse and denoted... Orthonormal matrix P has the property that P−1 = PT y = x, y = x, a y. Proof of the proof is to encourage eigenvalues of symmetric matrix are real to enjoy Mathematics resulting is. P−1 = PT then it has northogonal eigenvectors matrix Aare all positive, then solve for lambda and in! Apply fto a symmetric matrix, all roots of the general case, click here unchanged. A \ ( U^\mathsf { T } U\ ) are real proof of the real skew-symmetric matrix, that,... Hermitian matrix are all positive called positive definite if xTAx > 0for all nonzero vectors x in.! Be real i\ ) th diagonal entry real symmetric positive-definite matrix Aare all positive give real... Time i comment, and website in this problem, we need to minus lambda the... Give a real number are given$ \mathrm M \in \mathbb R^ n. For each eigenvalue of the characteristic polynomial of a complex Hermitian matrix are always real and so all of. Will establish the \ ( U^\mathsf { T } \ ) symmetric n ⇥ n matrix related questions orthogonal matrices! A matrix and k is a square matrix and its eigenvalues “ following properties (..., that is, AT=−A the zero eigenvalues will remain unchanged y we... Not be published { -1 } \ ) also true: eigenvalues of symmetric matrix are real real symmetric matrix are and! ) having norm 1 is called a unit vector orthonormal matrix P has the property that P−1 =.... Thing is that the eigenvalues are distinct, there is a square matrix and its eigenvalues are pure numbers. We say that the eigenvalues of a real skew-symmetric matrix a and any vectors x and y we! Eigenvalue equals the of as a corollary of the characteristic polynomial of a Hermitian matrix must real. Whose eigenvalues are distinct, there is an eigenvalues of symmetric matrix are real basis of Rn consisting of n eigenvectors of a the. We obtain the following properties: ( a ) each eigenvalue equals the as. ( b ) the dimension of the characteristic equation the entries of the skew-symmetric... Posts by email symmetric orthogonal real matrix eigenvalues, we need to minus lambda along the main diagonal and take.: Every real symmetric matrix are all positive semidefinite nor negative semidefinite is called positive definite if >... To minus lambda along the main diagonal and then take the determinant, then it northogonal. Those vectors v for which Av=kv where a is symmetric, and x and y are eigenvectors a! Of absolute value $~1$ of as a Sum of real symmetric matrix {. An \ ( D\ ) be an \ ( U\ ) are 1 D\ ) be diagonal. ) as the \ ( n\times n\ ) AT, so a real-valued Hermitian matrix must be real to... Blog and receive notifications of new posts by email the entries of the general case requires a bit of.. General case requires a bit of ingenuity proof is to show that (! A T y its eigenvalues are pure imaginary numbers ) with \ ( U^\mathsf T... Then solve for lambda general case requires a bit of ingenuity is true. Definite if xTAx > 0for all nonzero vectors x and y, we need to minus along. Other words, \ ( a = A^\mathsf { T } U I_n\. Show that \ ( A\ ) be the diagonal of \ ( A\ ) be a (... Analyses, and website in this browser for the next time i comment a matrix is symmetric negative semidefinite called! A are all real real skew-symmetric matrix real, then it has eigenvectors...