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KEY THEOREMS KEY IDEASKEY ALGORITHMS LINKED TO EXAMPLES next.

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Presentation on theme: "KEY THEOREMS KEY IDEASKEY ALGORITHMS LINKED TO EXAMPLES next."— Presentation transcript:

1 KEY THEOREMS KEY IDEASKEY ALGORITHMS LINKED TO EXAMPLES next

2 key theorems key ideaskey algorithms  n vectorsn vectors in an n dimensional vector space VECTOR SPACE independent span Solve system equations basisFind dot product coordinates Take matrix times vector  dimension dimension domain,null space, range of a linear mapping LINEAR MAPPINGWrite matrix equation domain null space range Find matrix for lin map Take product of matrices  detA  0 detA  0 matrix forFind inverse of matrix composition inverse Find determinant of matrix  similarity, similarity, eigenstuff EIGENSTUFFFind eigenstuffeigenstuff similaritySimilar diagonal matrixdiagonal

3 V is a vector space of dimension n. S = { v 1, v 2, v 3,..., v n } then S is INDEPENDENT if and only if S SPANS V. Return to outline

4 If T is a LINEAR MAPPING then: the dimension of the DOMAIN of T = the dimension of the NULL SPACE of T + the dimension of the RANGE of T Return to outline 0

5 A and B are SIMILAR matrices if and only if there exists a matrix P such that: B= P –1 A P If A is the matrix for T relative to the standard basis then B is the matrix for T relative to the columns of P If B is diagonal then the diagonal entries of B are eigenvalues and the columns of P are eigenvectors Return to outline

6 next

7 Reduces to:

8 next Reduces to:

9 next

10

11

12 return to outline

13 ( 3 ) 4 2 ( 2 ) 5 3 -2 = next

14 ( 3 ) 4 2 ( 2 ) 5 3 -2 = 6 + -5 + 12 + -4 = 9 return to outline

15 ( 3 2 )( 1 ) = () 2 1 4 3 next

16 ( 3 2 )( 1 ) = ( 5 ) 2 1 4 3 nextdot product of row 1 of matrix with vector = entry 1 of answer

17 ( 3 2 )( 1 ) = ( 5 ) 2 1 43 3 dot product of row 2 of matrix with vector = entry 2 of answer return to outline

18 System of linear equations: Equivalent matrix equation: return to outline

19 A toy maker manufactures bears and dolls. It takes 4 hours and costs $3 to make 1 bear. It takes 2 hours and costs $5 to make 1 doll. Find the matrix for T next

20 A toy maker manufactures bears and dolls. It takes 4 hours and costs $3 to make 1 bear. It takes 2 hours and costs $5 to make 1 doll. Find the matrix for T return to outline

21 ( 3 2 )( 1 2 ) = () 2 1 4 1 3 1 next

22 AB ( 3 2 )( 1 2 ) = ( 5 ) 2 1 4 1 3 1 dot product of row 1 of A with column 1 of B = entry in row 1 column 1 of AB next

23 AB ( 3 2 )( 1 2 ) = ( 57 ) 2 1 4 1 3 1 dot product of row 1 of A with column 2 of B = entry in row 1 column 2 of AB next

24 AB ( 3 2 )( 1 2 ) = ( 57 ) 2 1 4 13 3 1 dot product of row 2 of A with column 1 of B = entry in row 2 column 1 of AB next

25 AB ( 3 2 )( 1 2 ) = ( 57 ) 2 1 4 134 3 1 dot product of row 2 of A with column 2 of B = entry in row 2 column 2 of AB return to outline

26 Reduces to next

27 Reduces to A A -1 return to outline

28 next To find eigenvalues for A, solve for :

29 next To find eigenvalues for A, solve for : The eigenvalues are 2 and 4

30 next To find eigenvalues for A, solve for : The eigenvalues are 2 and 4 An eigenvector belonging to 2 is in the null space of 2I - A

31 next To find eigenvalues for A, solve for : The eigenvalues are 2 and 4 An eigenvector belonging to 2 is in the null space of 2I - A 2I - A

32 next To find eigenvalues for A, solve for : The eigenvalues are 2 and 4 An eigenvector belonging to 2 is in the null space of 2I - A 2I - A an eigenvector belonging to 2 is any nonzero multiple of

33 next To find eigenvalues for A, solve for : The eigenvalues are 2 and 4 eigenvectors are:

34 next The eigenvalues are 2 and 4 eigenvectors are: A is similar to the diagonal matrix B

35 The eigenvalues are 2 and 4 eigenvectors are: B = P –1 A P = return to outline

36 next

37

38

39

40 Return to outline

41 A is an n  n matrix detA  0 iff A is nonsingular (invertible) iff The columns of A are a basis for R n iff The null space of A contains only the zero vector iff A is the matrix for a 1-1 linear transformation


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