LECTURE 5: MATRIX DIAGONALIZATION, SINGULAR VALUE DECOMPOSITION AND PRINCIPAL COMPONENT ANALYSIS • We wish to diagonalize a square NxN matrix A: Ax=lx. Here x is the eigenvector and l the eigenvalue of A • We could solve det|A-lI|=0 and expand it terms of a polynomial of N-th order in l • There is a shift symmetry: (A+tI)x=(l+t)x that does not change eigenvectors, but changes eigenvalues

Common matrix types • Symmetric: A=AT or aij=aji • Hermitian (self-adjoint): A=A+ or aij=aji*. Important concept because eigenvalues are real (quantum mechanics!) • Orthogonal: transpose equals inverse AAT=ATA=I • Unitary: Hermitian conjugate equals inverse AA+=I • Normal: commutes with Hermitian conjugate AA+=A+A. Important because its eigenvectors are complete and orthogonal • For real matrices Hermitian is symmetric and unitary is orthogonal, both of which are normal. • A normal non-symmetric matrix is hard to diagonalize • Symmetric matrices are easier to diagonalize

Matrix forms and similarity transform • We can define the matrix of right eigenvectors as XR and the matrix of eigenvalues as D, which is diagonal with li on the diagonal: AXR=XRD • We can also define left eigenvectors XLA=DXL • XLXRD=DXLXR implies XLXR=I (with appropriate normalization) and so A=XRDXL=XRDXR-1 and D=XLAXR=XR-1AXR • For a symmetric matrix A=XRDXRT • a transformation Z-1AZ leaves eigensystem unchanged: det| Z-1AZ-lI|= det| Z-1AZ-lZ-1IZ|= det| Z-1| det| A-lI| det|Z|= det| A-lI| • Many of the methods consist of a series of such transforms • For real symmetric matrices Z-1=ZT

QR decomposition

We can show (by induction) that qiqj=dij Note that this is closely related to Gram-Schmidt orthogonalization

Gram-Schmidt orthogonalization and QR/RQ decomposition • The RQ decomposition transforms a matrix A into the product of an upper triangular matrix R (also known as right-triangular) and an orthogonal matrix Q. The only difference from QR decomposition is the order of these matrices. • QR decomposition is Gram–Schmidt orthogonalization of columns of A, started from the first column. • RQ decomposition is Gram–Schmidt orthogonalization of rows of A, started from the last row.

QR decomposition

• We have obtained a decomposition into an orthogonal matrix Q and upper diagonal matrix R

QR diagonalization for symmetric A • We can repeat these QR steps

General packaged solutions • QR works better if the matrix A is first transformed into tridiagonal form (Hauseholder or Givens method): standard method for symmetric A • Diagonalization methods: there are many different methods depending on what matrix we have: A can be real banded symmetric, real symmetric, complex Hermitian, real non-symmetric… • Depending of what we need: just the eigenvectors or both eigenvectors and eigenvalues • Look at numpy.linalg and choose depending on what your needs are: eigh, eigvalsh…

Generalized problems • Ax=lBx can be handled as (B-1A)x=lx • If A and B symmetric B-1 is not, but we can use Cholesky decomposition B=LLT and by multiplying with L-1 we get C(LTx)=lLTx where C=L-1A(L-1)T is a symmetric matrix. This diagonalization gives the same eigenvalues with eigenvectors given by LTx. • Nonlinear problems: (Al2+Bl+C)x=0 can be solved by solving 2Nx2N problem

“Shortcomings” of diagonalization • eigenvectors are not orthogonal for a nonsymmetric NxN matrix • There may not be enough eigenvectors, ie they may not be complete: A is “defective” • It cannot be defined as eigenvalue problem Ax=lx unless a is NxN • All these “defects” can be cured by SVD • None of these “defects” apply to a symmetric NxN matrix: SVD and diagonalization are the same in that case if A is semi-positive definite

Singular Value Decomposition (SVD) • Is a decomposition of a general NxM matrix • Define rank r of matrix A as the maximum number of linearly independent column vectors in the matrix or the maximum number of linearly independent row vectors in the matrix (both definitions are equivalent) • Another way: define range as the subspace of b reached by Ax=b. Rank is its dimension. • Define two orthonormal bases, with vectors u in RM and v in RN. In matrix form U is MxM and V is NxN • u1 , . . . , ur is an orthonormal basis for the column space • ur+1 ,...,um is an orthonormal basis for the left null space N (defined as ATx=0) • v1, . . . , vr is an orthonormal basis for the row space • Vr+1,...,vN is an orthonormal basis for the null space N(Ax=0). • These bases “diagonalize” A: Av1=s1u1, Av2=s2u2… • Also called Karhunen-Loeve (KL) Transform

SVD continued • Add null space, which is already orthogonal to first r v’s and u’s to go from reduced to full SVD

• Now the last matrix Σ is MxN with first r on the diagonal si, and the rest 0. U and V are now square matrices (MxM and NxN) hence AV=UΣ or A=UΣVT=u1s1v1+…ursrvr • Rank order s1>s2>…>sr

SVD in pictures

• SVD as a sequence of rotation-stretch-rotation A=UΣVT


• Av1=s1u1

Example • If A = xyT (rank 1) with unit vectors x and y, what is the SVD of A?

Example • If A = xyT (rank 1) with unit vectors x and y, what is the SVD of A? • Solution The reduced SVD is exactly xyT, with rank r = 1. It has u1 = x and v1 = y and σ1 = 1. For the full SVD, complete u1 = x to an orthonormal basis of u’s, and complete v1 = y to an orthonormal basis of v’s. No new σ’s, only σ1 = 1.

How to construct u’s and v’s? • • • •

v’s will be orthonormal eigenvectors of ATA: ATA=(UΣVT)TUΣVT=VΣTUTUΣVT=VΣTΣVT We see that s2=l of ATA. u’s can easily be computed from Avi=siui. They are orthogonal:

• Show that u’s are eigenvectors of AAT

How to construct u’s and v’s? • • • •

v’s will be orthonormal eigenvectors of ATA: ATA=(UΣVT)TUΣVT=VΣTUTUΣVT=VΣTΣVT We see that s2=l of ATA. u’s can easily be computed from Avi=siui. They are orthogonal:

• Show that u’s are eigenvectors of AAT • Soln: AAT=UΣVT(UΣVT)T=UΣVTVΣTUT=UΣΣTUT

Linear algebra with SVD • Linear algebra Ax=b, assume A is NxN • Let’s do matrix inversion: A-1=VΣ-1UT • This can be solved unless some si=0: matrix is singular, i.e. there is non-zero null space • More generally we can define condition number s1/sN: if this is large the matrix is ill-conditioned • SVD will diagnose the condition situation of the matrix • If we want to solve homogeneous equation Ax=b for b=0 we use the null space of U/V, corresponding to si=0

Solving singular matrix problems • Ax=b can be solved if b is in the range of A. If so we have an infinite number of solutions, since we can add null space solutions to it in any linear combination • We can put additional constraint, such as the norm ||x|| is minimized (i.e. pick the solution with the shortest length |x|2). Then we can set the null space solutions to 0. This is achieved by setting si-1=0 wherever si=0 (null space) and solve x=VΣ-1UTb • If b is not in range we will not have an exact solution. But we can still use x=VΣ-1UTb as the solution which minimizes r=|Ax-b|

Proofs that x=A-1b =VΣ-1UTb

• b is in range: suppose x’ is in null space, and Σ-1 is the modified inverse of Σ: |x+x’|=|VΣ-1UTb+x’|= |V(Σ-1UTb+VTx’|= |Σ-1UTb+VTx’|>|x| unless x’=0 because the two are orthogonal (x’ is in null space, x is not). • b is not in range: we add x’ to x, Ax-b is modified by b’=Ax’ (in range), show r=|Ax-b| is minimized: |Ax-b+b’|=|(UΣVT) VΣ-1UTb-b+b’|= |(UΣΣ-1UT-1)b+b’|=|U[(ΣΣ-1-1)UTb+Utb’]| = |(ΣΣ-1-1)UTb+Utb’|> |(ΣΣ-1-1)Utb| unless b’=0. • In practice we want to fix ill-conditioned matrices with SVD: throw away all singular values whose value is too small to be meaningful (machine precision roundoff errors etc). So if si
Non-square matrices • If there are fewer equations N than variables M the system is underdetermined and we have M-N dimensional family of solutions. SVD provides the family of solutions by providing si

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