Linear Algebra Part 1

May 3, 2016

This is the summary of the topics covered by Gilbert Strang in his lectures

Table of Contents:

Lectures 1-5

Covers the basic matrix concepts

  • Matrix Row picture and Column picture
  • Can we solve for \(Ax=b\) for all \(b\) ?. No, only for \(b\)’s in the column space of \(A\).

Four ways of matrix multiplication

  • Dot product
  • Column picture
  • Row picture
  • Column times Row

Gaussian Elimination

  • Operates on the rows of matrix \(A\), finally puts the matrix in Row Reduced Echelon(REF) form
  • Gaussian elimination leads to \(A = LU\) form, and \(A = LDU\) form

Gauss-Jordon form

  • Helps to put the elements of \(A\), in the Row Reduced Echelon Form (RREF) form
  • Can be used to solve for \(x\) in \(Ax = b\), by finding **_RREF([\(A\) \(b\)])** which results in **[\(I\) \(x\)]_**
  • Can be used to find the inverse of the matrix \(A\) by finding **_RREF[\(A\) \(I\)]** which results in **[\(I\) \(A^{-1}\)]_**

Permutation matrix

  • Number of permutation matrix \(A_{N*N} = N!\)
  • \(P^T = P\) (where \(P^T\) is transpose(\(P\)))
  • \(P P^T = I\) (Can you prove? Hint: use definition of matrix multiplication) * If the number of equations are less than number of variables, why we cannot solve the equation? * This is because rank of the matrix will be less than the number of variables we need to solve, so it will have infinite number of solutions * Use RREF(\(A\)) in matlab/octave and check
  • \(A A^T\) is symmetric, prove!

Lectures 6-10

Vector Space

  • All linear combination of vectors are in the space, called Vector Space

  • Say \(P\) and \(L\) are two subspaces

    • Is \(P\cup L\) a subspace? No, because some combination of vector \(A \in P\) and vector \(B \in L\) might not be in the subspace \(P\cup L\).

Column Space and Null Space

  • Column Space C(\(A\)) is all possible linear combinations of column vectors in a matrix \(A\)

  • Null Space N(\(A\)) is all the \(x\)’s for which \(Ax = 0\)

  • Elimination process will change Column Space of \(A\), but not the Null space of \(A\)

    • This is because in the elimination process we change \(A\), but not \(x\).

Basic and Free Variables

  • The real difference between basic variables and free variables is that the free variables can be anything, and the basic variables are determined by solving the equations

  • Basic variables are also called as Pivot variables. The columns with these variables are called Pivot columns

  • Number of Pivot columns = Rank r of the matrix = Number of independent columns in the matrix

  • Number of Free columns = n - r ( n = number of columns in matrix \(A))

Solution for Ax = b

Rank tells you everything about the number of solutions

  • Full column rank matrix: Unique solution exists, if \(b\) lies in column space of \(A\)
  • Full row rank matrix: Infinite number of solutions
  • Full row rank/ full column rank: Unique solution always exists for all \(b\)
  • No full column rank/ No full row rank: Infinitely many solution exists if \(b\) lies in column space of \(A\)


  • Basis of a vector space is sequence of vectors \(v_1, v_2, v_3, …, v_N\) with the following properties
    • They are independent
    • They span a space

Four Subspaces

For a matrix \(A_{m*n}\)

  • Column space C(\(A\)): combination of columns of \(A\), lies in \(R^n\)
  • Null space N(\(A^T\)) : left null space of \(A\), lies in \(R^n\)
  • Row space R(\(A\)): combination of columns of \(A^T\), lies in \(R^m\)
  • Null space N(\(A\)): right null space of \(A\), lies in \(R^m\)
  • Row operation on a matrix \(A\) changes the column space of \(A\), but preserves the row space of \(A\)

## Lectures 11-15

Vector Space of Matrices

  • Consider vector space of matrices \(A_{N*N}\)
    • Dimension of vector space of symmetric matrices = \((N^2 + N)/2\)
    • Dimension of vector space of skew symmetric matrices = \((N^2 - N)/2\)
    • Dimension of vector space of upper triangular matrices = \((N^2 + N)/2\)
    • (Symmetric matrix \(\cap\) Upper triangular matrices) = Diagonal matrices. Dimension of vector space = \((N^2 - N)/2\)
    • (Symmetric matrix \(\cup\) Upper triangular matrices) = All \(N*N\) matrices. Dimension of vector space = \(N^2\)

Rank-1 Matrices

  • They are special case
  • They are building blocks for every other matrices
  • Matrices with Rank-1 are separable matrices ( this is the application in image processing for separating the filters)


  • Prove that two vectors \(X\) and \(Y\) are orthogonal to each other if \(X^T Y = 0 = Y^T X\)
  • Row space is perpendicular to Right null space , i.e., every vector in row space is perpendicular to every other vector in right null space
  • Column space is perpendicular to left null space, i.e., every vector in column space is perpendicular to every other vector in left null space

Projection Matrix

  • Projection of vector \(b\) onto vector \(a\) is defined as \(p = Pb\), where \(P = (a a^T)/(a^T a) \)

  • Column space of projection matrix \(P\) is column space of \(a\). i.e., line through \(a\)
  • Properties of Projection matrices
    • \(P^T = P\)
    • \(P^2 = P\)
  • Projection matrix in general
    • We have \(A^T A \hat{x} = A^T B\)
    • \(P = A \hat{x}\), where \(\hat{x} = (A^T A)^{-1} A^T B\) and \(P = A (A^T A)^{-1} A^T \)

Least square problem

  • Why projection ?
    • When we have only few unknowns, but many equations to solve \(Ax = b\), \(b\) might not be the column space of \(A\)
    • To solve for \(x\), we project \(b\) onto columns space of \(A\), so that the new vector \(\bar{b}\) is closest to \(b\)
      • Eg. Measuring pulse of the heart, but repeatedly we take measurements so that on an average we can estimate the pulse. For more detailed treatment of least squares, link
  • The pictorial representation of solving least squares is shown below
    • Least squares: \(\bar{x}\) minimizes \(b - Ax\) by solving \(A^T A \bar{x} = A^T b\)

  • \(A^T A\) is invertible, if \(A\) has independent columns, prove it