Chandan Singh | linear algebra

linear algebra

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linear basics

notation

  • $x \preceq y$ - these are vectors and x is less than y elementwise
  • $X \preceq Y$ - matrices, $Y-X$ is PSD
    • $v^TXv \leq v^TYv :: \forall v$

linearity

  • inner product $<X, Y> = tr(X^TY) = \sum_i \sum_j X_{ij} Y_{ij}$
    • like inner product if we collapsed into big vector
    • linear
    • symmetric
    • gives angle back
  • linear
    1. superposition $f(x+y) = f(x)+f(y) $
    2. proportionality $f(k\cdot x) = k \cdot f(x)$
  • bilinear just means a function is linear in 2 variables
  • vector space
    1. closed under addition
    2. contains identity
  • det - sum of products including one element from each row / column with correct sign
    • absolute value = area of parallelogram made by rows (or cols)
    • 220px-Area_parallellogram_as_determinant.svg
  • lin independent: $c_1x_1+c_2x_2=0 \implies c_1=c_2=0$
  • cauchy-schwartz inequality: $ x^T y \leq   x   _2   y     _2$
    • implies triangle inequality: $   x+y   ^2 \leq (   x   +   y   )^2$

matrix properties

  • $x^TAx = tr(xx^TA)$
  • nonsingular = invertible = nonzero determinant = null space of zero
    • only square matrices
    • rank of mxn matrix- max number of linearly independent columns / rows
      • rank==m==n, then nonsingular
    • ill-conditioned matrix - matrix is close to being singular - very small determinant
  • inverse
    • orthogonal matrix: all columns are orthonormal
      • $A^{-1} = A^T$
      • preserves the Euclidean norm $   Ax   _2 =   x   _2$
    • if diagonal, inverse is invert all elements
    • inverting 3x3 - transpose, find all mini dets, multiply by signs, divide by det
    • psuedo-inverse = Moore-Penrose inverse $A^\dagger = (A^T A)^{-1} A^T$
      • if A is nonsingular, $A^\dagger = A^{-1}$
      • if rank(A) = m, then must invert using $A A^T$
      • if rank(A) = n, then must use $A^T A$
    • inversion of matrix is $\approx O(n^3)$
    • inverse of psd symmetric matrix is also psd and symmetric
    • if A, B invertible $(AB)^{-1} = B^{-1} A^{-1}$
  • orthogonal complement - set of orthogonal vectors
    • define R(A) to be range space of A (column space) and N(A) to be null space of A
    • R(A) and N(A) are orthogonal complements
    • dim $R(A)$ = r
    • dim $N(A)$ = n-r
    • dim $R(A^T)$ = r
    • dim $N(A^T)$ = m-r
  • adjoint - compute with mini-dets
    • $A^{-1} = adj(A) / \det(A)$
  • Schur complement of $X = \begin{bmatrix} A & B \ C & D\end{bmatrix}$
    • $M/D = A - BD^{-1}C$
    • $M/A = D-CA^{-1}B$
    • $X \succeq 0 \iff M/D \succeq 0$

matrix calc

  • overview: imagine derivative $f(x + \Delta)$
  • function f: $\text{anything} \to \mathbb{R}^m$
    • gradient vector $\nabla_A f(\mathbf{A})$- partial derivatives with respect to each element of A (vector or matrix)
    • gradient = $\frac{\partial f}{\partial A}^T$
  • these next 2 assume numerator layout (numerator-major order, so numerator constant along rows)
  • function f: $\mathbb{R}^n \to \mathbb{R}^m$
    • Jacobian matrix: \(\mathbf J = \begin{bmatrix} \dfrac{\partial \mathbf{f}}{\partial x_1} & \cdots & \dfrac{\partial \mathbf{f}}{\partial x_n} \end{bmatrix}= \begin{bmatrix} \dfrac{\partial f_1}{\partial x_1} & \cdots & \dfrac{\partial f_1}{\partial x_n}\\ \vdots & \ddots & \vdots\\ \dfrac{\partial f_m}{\partial x_1} & \cdots & \dfrac{\partial f_m}{\partial x_n} \end{bmatrix}\) - this is dim(f) x dim(x)
  • function f: $\mathbb{R}^n \to \mathbb{R}$
    • 2nd derivative is Hessian matrix
      • $\bold H = \nabla^2 f(x)_{ij} = \frac{\partial^2 f(x)}{\partial x_i \partial x_j} = \begin{bmatrix} \dfrac{\partial^2 f}{\partial x_1^2} & \dfrac{\partial^2 f}{\partial x_1\,\partial x_2} & \cdots & \dfrac{\partial^2 f}{\partial x_1\,\partial x_n} \[2.2ex] \dfrac{\partial^2 f}{\partial x_2\,\partial x_1} & \dfrac{\partial^2 f}{\partial x_2^2} & \cdots & \dfrac{\partial^2 f}{\partial x_2\,\partial x_n} \[2.2ex] \vdots & \vdots & \ddots & \vdots \[2.2ex] \dfrac{\partial^2 f}{\partial x_n\,\partial x_1} & \dfrac{\partial^2 f}{\partial x_n\,\partial x_2} & \cdots & \dfrac{\partial^2 f}{\partial x_n^2}\end{bmatrix}$
  • examples
    • $\nabla_x a^T x = a$
    • $\nabla_x x^TAx = 2Ax$ (if A symmetric, else $(A+A^T)x)$)
    • $\nabla_x^2 x^TAx = 2A$ (if A symmetric, else $A+A^T$)
    • $\nabla_x \log : \det X = X^{-1}$
  • we can calculate derivs of quadratic forms by calculating derivs of traces
    • $x^TAx = tr[x^TAx] = tr[xx^TA]$
    • $\implies \frac{\partial}{\partial A} x^TAx = \frac{\partial}{\partial A} tr[xx^TA] = [xx^T]^T = xx^T$
    • useful result: $\frac{\partial}{\partial A} log A = A^{-T}$

norms

  • def
    1. nonnegative
    2. definite f(x) = 0 iff x = 0
    3. proportionality (also called homogenous)
    4. triangle inequality
  • properties
    • convex

vector norms

  • $L_p-$norms: $   x   p = (\sum{i=1}^n x_i ^p)^{1/p}$
    • $L_0$ norm - number of nonzero elements (this is not actually a norm!)
    • $   x   _1 = \sum x_i $
    • $   x   _2$ - Euclidean norm
    • $   x   _\infty = \max_i x_i $ - also called Cheybyshev norm
  • quadratic norms
    • P-quadratic norm: $   x   _P = (x^TPx)^{1/2} =   P^{1/2} x   2$ where $P \in S{++}^n$
  • dual norm
    • given a norm $   \cdot   $, dual norm $   z   _* = sup{ z^Tx : :   x   \leq 1}$
    • dual of the dual is the original
    • dual of Euclidean is just Euclidean
    • dual of $l_1$ is $l_\infty$
    • dual of spectral norm is some of the singular values

matrix norms

  • schatten p-norms: $   X   _p = (\sum \sigma^p_i(A) )^{1/p}$ - note this is nice for organization but this p is never really mentioned
    • p=1: nuclear norm = trace norm: $   X   _* = \sum_i \sigma_i$
    • p=2: frobenius norm = euclidean norm: $||X||F^2 = \sqrt {\sum{ij} X_{ij}^2} = \sqrt{\sum_i \sigma_i^2}$ - like vector $L_2$ norm
    • p=$\infty$: spectral norm = $\mathbf{L_2}$-norm (of a matrix) = $   X   2 = \sigma\text{max}(X) $
  • entrywise norms

    • sum-absolute-value norm (like vector $l_1$)
    • maximum-absolute-value norm (like vector $l_\infty$)
  • operator norm
    • let $   \cdot   _a$ and $   \cdot   _b$ be vector norms
    • operator norm $   X   _{a,b} = sup{   Xu   _a : :   u   _b \leq 1 }$
      • represents the maximum stretching that X does to a vector u
    • if using p-norms, can get Frobenius and some others

eigenstuff

eigenvalues intro - strang 5.1

  • nice viz
  • elimination changes eigenvalues
  • eigenvector application to diff eqs $\frac{du}{dt}=Au$
    • soln is exponential: $u(t) = c_1 e^{\lambda_1 t} x_1 + c_2 e^{\lambda_2 t} x_2$
  • eigenvalue eqn: $Ax = \lambda x \implies (A-\lambda I)x=0$
    • $det(A-\lambda I) = 0$ yields characteristic polynomial
  • eigenvalue properties
    • 0 eigenvalue $\implies$ A is singular
    • eigenvalues are on the main diagonal when the matrix is triangular
  • expressions when $A \in \mathbb{S}$
    • $\det(A) = \prod_i \lambda_i$
    • $tr(A) = \sum_i \lambda_i$
    • $   A   _2 = \max \lambda_i $
    • $   A   _F = \sqrt{\sum \lambda_i^2}$
    • $\lambda_{max} (A) = \sup_{x \neq 0} \frac{x^T A x}{x^T x}$
    • $\lambda_{min} (A) = \inf_{x \neq 0} \frac{x^T A x}{x^T x}$
  • defective matrices - lack a full set of eigenvalues
  • positive semi-definite: $A \in R^{nxn}$
    • basically these are always symmetric $A=A^T$
    • all eigenvalues are nonnegative
    • if $\forall x \in R^n, x^TAx \geq 0$ then A is positive semi definite (PSD)
      • like it curves up
      • Note: $x^TAx = \sum_{i, j} x_iA_{i, j} x_j$
    • if $\forall x \in R^n, x^TAx > 0$ then A is positive definite (PD)
      • PD $\to$ full rank, invertible
    • PSD + symmetric $\implies$ can be written as Gram matrix $G = X^T X $
      • if X full rank, then $G$ is PD
    • PSD notation
      • $S^n$ - set of symmetric matrices
      • $S^n_+$ - set of PSD matrices
      • $S^n_{++}$ - set of PD matrices

strang 5.2 - diagonalization

  • diagonalization = eigenvalue decomposition = spectral decomposition
  • assume A (nxn) is symmetric
    • $A = Q \Lambda Q^T$
    • Q := eigenvectors as columns, Q is orthonormal
  • only diagonalizable if n independent eigenvectors
    • not related to invertibility
    • eigenvectors corresponding to different eigenvalues are lin. independent
    • other Q matrices won’t produce diagonal
    • there are always n complex eigenvalues
    • orthogonal matrix $Q^TQ=I$
  • examples
    • if X, Y symmetric, $tr(YX) = tr(Y \sum \lambda_i q_i q_i^T)$
    • lets us easily calculate $A^2$, $sqrt(A)$
    • eigenvalues of $A^2$ are squared, eigenvectors remain same
    • eigenvalues of $A^{-1}$ are inverse eigenvalues
    • eigenvalue of rotation matrix is $i$
  • eigenvalues for $AB$ only multiply when A and B share eigenvectors
    • diagonalizable matrices share the same eigenvector matrix S iff $AB = BA$
  • generalized eigenvalue decomposition - for 2 symmetric matrices
    • $A = V \Lambda V^T$, $B=VV^T$

strang 6.3 - singular value decomposition

  • SVD for any nxp matrix: $X=U \Sigma V^T$
    • U columns (nxn) are eigenvectors of $XX^T$
    • columns of V (pxp) are eigenvectors of $X^TX$
    • r singular values on diagonal of $\Sigma$ (nxp) - square roots of nonzero eigenvalues of both $XX^T$ and $X^TX$
    • like rotating, scaling, and rotating back
    • SVD ex. $A=UDV^T \implies A^{-1} = VD^{-1} U^T$
    • $X = \sum_i \sigma_i u_i v_i^T$
  • properties
    1. for PD matrices, $\Sigma=\Lambda$, $U\Sigma V^T = Q \Lambda Q^T$
      • for other symmetric matrices, any negative eigenvalues in $\Lambda$ become positive in $\Sigma$
  • applications
    • very numerically stable because U and V are orthogonal matrices
    • condition number of invertible nxn matrix = $\sigma_{max} / \sigma_{min}$
    • $A=U\Sigma V^T = u_1 \sigma_1 v_1^T + … + u_r \sigma_r v_r^T$
      • we can throw away columns corresponding to small $\sigma_i$
    • pseudoinverse $A^+ = V \Sigma^+ U^T$

strang 5.3 - difference eqs and power $A^k$

  • compound interest
  • solving for fibonacci numbers
  • Markov matrices
    • steady-state Ax = x
    • corresponds to $\lambda = 1$
  • stability of $u_{k+1} = A u_k$
    • stable if all eigenvalues satisfy $ \lambda_i $ <1
    • neutrally stable if some $ \lambda_i =1$
    • unstable if at least one $ \lambda_i $ > 1
  • Leontief’s input-output matrix
  • Perron-Frobenius thm - if A is a positive matrix (positive values), so is its largest eigenvalue and every component of the corresponding eigenvector is also positive
    • useful for ranking, etc.
  • power method: want to find eigenvector $v$ corresponding to largest eigenvalue
    • $v = \underset{n \to \infty}{\lim} \frac{A^n v_0}{ A^nv_0 }$ where $v_0$ is nonnegative