Mathematics & Statistics
Texas Tech University
Kent Pearce

Department of Mathematics and Statistics
Texas Tech University
Lubbock, Texas 79409-1042
Voice: (806)742-2566 x 226
FAX: (806)742-1112
Email: kent.pearce@ttu.edu

Math 2360
Linear Algebra
Fall 2009
Leon, Steven J
Linear Algebra
Pearson

Review Exam II
Section Content      Suggested Problems
Section 3.1
  • vector space (linear space)
    • binary operation addition
      • commutative
      • associative
      • existence of an additive identity (called 0)
      • existence of additive inverse for each a (called -a)
    • scalar multiplication
      • distributive property i
      • distributive property ii
      • associativity of scalar multiplication
      • unity property of scalar multiplication
  • Example: Rn
    • addition = component-wise addition
    • scalar multiplication = component-wise scalar multiplication
    • R2, R3
      • vectors identified with "directed line segments"
      • component-wise addition identified with geometry of parallolegram rule
      • scalar multiplication identified with geometric scaling of vector
      • length of vector given by Pathagorean rule
  • Example: Rnxm
    • addition = matrix addition
    • scalar multiplication = scalar multiplication on matrices
    Example: Pn
    • addition = polynomial addition
    • scalar multiplicaiton = multiplication of scalars on polynomials
  • Example: C[a,b}
    • addition = function addition
    • scalar multiplicaiton = multiplication of scalars on functions
  • Theorem 3.1.1 (Additional Properties of Vector Spaces)
4, 6, 8, 9, 10, 11, 12
Section 3.2
  • definition of a subspace
  • examples for R2
  • examples for R2x2
  • examples for P3
  • null space of a matrix N(A)
  • span of a set of vectors
  • Theorem 3.2.1
  • spanning set for a vector space V
1, 2, 3, 5, 7, 9, 10, 11, 14, 17
Section 3.3
  • linear independence
  • linear dependence
  • linear independence of two vectors
  • Theorem 3.3.1 Linear independence of n vectors in Rn
  • Theorem 3.3.2
  • Linear Independence Conditions/Criteria
    • Rn
    • Pn
    • Rnxm
    • Cn-1[a,b]
      • Wronskian
      • Theorem 3.3.3
1, 2, 6, 7, 11
Section 3.4
  • basis
  • Examples
    • R2
    • R3
    • R2x2
    • P3
  • Theorem 3.4.1
  • Corolary 3.4.2
  • dimension
    • finite dimensional vector space
      • examples
    • infinite dimensional vector space
      • examples
    • Theorem 3.4.3
    • Standard Bases
1, 2, 3, 5, 7, 10, 11, 14
Section 3.5
  • Rn
    • Standard Basis for Rn
    • Ordered Basis in Rn
        Coordinates w.r.t Ordered Basis
    • Problem
      • 1. Given coordinates in standard basis find coordinates in ordered basis
      • 2. Given coordinates in ordered basis find coordinates in standard basis
    • Solution
      • Problem 2: Transition Matrix from Ordered Basis to Standard Basis U
      • Problem 1: Transition Matrix from Standard Basis to Ordered Basis U-1
    • Transition Matrix S from Ordered Basis F to Ordered Basis E
  • General Finite Dimensional Vector Space
    • Ordered Basis
        Coordinates w.r.t Ordered Basis
    • Problem
      • Given coordinates in ordered basis E find coordinates in ordered basis F
    • Solution
      • Problem 2: Transition Matrix S from Ordered Basis F to Ordered Basis E
1, 2, 3, 5, 6, 10
Section 3.6
  • vector space (linear space)
    • Row Space of A
    • Column Space of A
    • Theorem: Two row equivalent matrices have the same row space
    • Theorem 3.6.2 Consistency Theorem for Linear Systems
    • Theorem: A (nxn) is non-singular iff the column space of A = Rn
    • Rank of A
    • Nullity of A
    • Theorem 3.6.5. Rank-Nullity Theorem
    • Meta-Theorem: Two row equivalent matrices have different column spaces, but the same column dependency relationships
    • Theorem 3.6.6 dim(row space of A) = dim(col space of A)
1, 2, 4, 7, 8, 10, 11, 12





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Last modified on: Monday, 10-Aug-2015 12:47:29 CDT