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Chapter 3 - 4 = uclidean & Genera Vector Spaces MATH 264 Linear Algebra
50

Introduction Continued on Next Slide Section 3.1 in Textbook.

Dec 26, 2015

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Vernon Ferguson
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  • Slide 1
  • Slide 2
  • Introduction
  • Slide 3
  • Continued on Next Slide
  • Slide 4
  • Section 3.1 in Textbook
  • Slide 5
  • Definitions Vectors with the same length and direction are said to be equivalent. The vector whose initial and terminal points coincide has length zero so we call this the zero vector and denote it as 0. The zero vector has no natural direction therefore we can assign any direction that is convenient to us for the problem at hand.
  • Slide 6
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  • Slide 10
  • Section 4.2 in Textbook
  • Slide 11
  • Intro to Subspaces It is often the case that some vector space of interest is contained within a larger vector space whose properties are known. In this section we will show how to recognize when this is the case, we will explain how the properties of the larger vector space can be used to obtain properties of the smaller vector space, and we will give a variety of important examples.
  • Slide 12
  • Definition: A subset W of vector space V is called a subspace of V if W is itself a vector space under the addition and scalar multiplication defined on V.
  • Slide 13
  • Slide 14
  • Theorem 4.2.1 If W is a set of one or more vectors in a vector space V then W is a subspace of V if and only if the following conditions are true: a) If u and v are vectors in W then u+v is in W b) If k is a scalar and u is a vector in W then ku is in W This theorem states that W is a subspace of V if and only if its closed under addition and scalar multiplication.
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  • Theorem 4.2.2: Definition:
  • Slide 19
  • Theorem 4.2.3:
  • Slide 20
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  • Example:
  • Slide 22
  • Section 4.3 in Textbook
  • Slide 23
  • Intro to Linear Independence
  • Slide 24
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  • Theorem:
  • Slide 26
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  • Example: Continued on Next Slide
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  • Example:
  • Slide 31
  • Continued on Next Slide
  • Slide 32
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  • Section 4.4 in Textbook
  • Slide 36
  • Intro to Section 4.4 We usually think of a line as being one-dimensional, a plane as two-dimensional, and the space around us as three-dimensional. It is the primary goal of this section and the next to make this intuitive notion of dimension precise. In this section we will discuss coordinate systems in general vector spaces and lay the groundwork for a precise definition of dimension in the next section.
  • Slide 37
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  • In linear algebra coordinate systems are commonly specified using vectors rather than coordinate axes. See example below:
  • Slide 39
  • Units of Measurement They are essential ingredients of any coordinate system. In geometry problems one tries to use the same unit of measurement on all axes to avoid distorting the shapes of figures. This is less important in application
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  • Slide 48
  • Questions to Get Done Suggested practice problems (11th edition) Section 3.1 #1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23 Section 3.2 #1, 3, 5, 7, 9, 11 Section 3.3 #1, 13, 15, 17, 19 Section 3.4 #17, 19, 25
  • Slide 49
  • Questions to Get Done Suggested practice problems (11th edition) Section 4.2 #1, 7, 11 Section 4.3 #3, 9, 11 Section 4.4 #1, 7, 11, 13 Section 4.5 #1, 3, 5, 13, 15, 17, 19 Section 4.7 #1-19 (only odd) Section 4.8 #1, 3, 5, 7, 9, 15, 19, 21
  • Slide 50
  • Questions to Get Done Suggested practice problems (11th edition) Section 6.2 #1, 7, 25, 27