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ENGG2013 Unit 15 Rank, determinant, dimension, and the links between themPowerPoint Presentation

ENGG2013 Unit 15 Rank, determinant, dimension, and the links between them

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ENGG2013 Unit 15 Rank, determinant, dimension, and the links between them

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ENGG2013 Unit 15 Rank, determinant, dimension, and the links between them

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ENGG2013 Unit 15Rank, determinant, dimension,and the links between them

Mar, 2011.

- “row-rank of a matrix” counts the max. number of linearly independent rows.
- “column-rank of a matrix” counts the max. number of linearly independent columns.
- One application: Given a large system of linear equations, count the number of essentially different equations.
- The number of essentially different equations is just the row-rank of the augmented matrix.

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Linearly independent

Linearly independent

Linearly independent

Linearly dependent

Linearly independent

Linearly independent

Linearly dependent

Row-Rank = 2

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Row reductions

Row-rank = 2

Row-rank = 2

Because row reductionsdo not affect the numberof linearly independent rows

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List all combinationsof columns

Column-Rank = 2

Linearly independent??

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

N

N

N

N

N

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Given any matrix, its row-rank and column-rank are equal.

In view of this property, we can just say the “rank of a matrix”. It means either the row-rank of column-rank.

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- If some column vectors are linearly dependent, they remain linearly dependent after any elementary row operation
- For example, are linearly dependent

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- Any row operation does not change the column- rank.
- By the same argument, apply to the transpose of the matrix, we conclude that any column operation does not change the row-rank as well.

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Apply row reductions.row-rank and column-rankdo not change.

Apply column reductions.row-rank and column-rankdo not change.

The top-left corner isan identity matrix.

The row-rank and column-rank of this “normal form” is certainlythe size of this identity submatrix,

and are therefore equal.

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- y = ax2+bx+c
- Discirminant of ax2+bx+c = b2-4ac.
- It determines whether the roots are distinct or not

y

x

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- y = x2+bx+c. Let the roots be and .
- y = (x – )(x – ). Discriminant = ( – )2.
- Discriminant is zero means that the two roots coincide.

y

( – )2

x

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- If we substitute u= x – t into y = ax2+bx+c, (t is any real constant), then the discriminant of a(u+t)2+b(u+t)+c, as a polynomial in u, is the same as before.

y

( – )2

u

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- The determinant of a square matrix determine whether the matrix is invertible or not.
- Zero determinant: not invertible
- Non-zero determinant: invertible.

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- 22 determinant measures the area of a parallelogram.
- 33 determinant measures the volume of a parallelopiped.
- nn determinant measures the “volume” of some “parallelogram” in n-dimension.
- Determinant is zero means that the columns vectors lie in some lower-dimensional space.

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- Shearing action = third kind of elementary row or column operation

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- The rank of a matrix counts the maximal number of linearly independent rows.
- It also counts the maximal number of linearly independent columns.
- It is an integer.
- If the matrix is mn, then the rank is an integer between 0 and min(m,n).

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Rank = 2

Rank = 2

Rank = 2

Rank = 2

Rank = 2

Rank = 2

Rank = 2

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Determinant

Rank

Integer

Defined to any rectangular matrix

When applied to nn square matrix, rank=n implies existence of inverse.

- Real number
- Defined to square matrix only
- Non-zero det implies existence of inverse.
- When det is zero, we only know that all the columns (or rows) together are linearly dependent, but don’t know any information about subset of columns (or rows) which are linearly independent.

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- For any given vector in
if there is one and only one choice for the coefficients c1, c2, …,ck, such that

we say that these k vectors form a basis of .

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- Recall that a subspace W in is a subset which is
- Closed under addition: Sum of any two vectors in W stay in W.
- Closed under scalar multiplication: scalar multiple of any vector in W stays in W as well.

W

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W

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x – 3y + z = 0

W

z

y

x

W is the planegenerated, or spanned,by these vectors.

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- A basis of a subspace W is a set of linearly independent vectors which span W.
- A rigorous definition of the dimension is:
Dim(W) = the number of vectors in a basis of W.

z

y

W

x

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- In this context, the rank of a matrix is the dimension of the subspace spanned by the rows of this matrix.
- The least number of row vectors required to span the subspace spanned by the rows.

- The rank is also the dimension of the subspace spanned by the column of this matrix.
- The least number of column vectors required to span the subspace spanned by the columns

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x – 2y + z = 0

z

y

Rank = 2

The three row vectorslie on the same plane.

Two of them is enoughto describe the plane.

x

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- Given n points, find a polynomial of degree n-1 which goes through these n points.
- Technical requirements:
- All x-coordinates must be distinct
- y-coordinates need not be distinct.

y3

y2

y4

y1

x1

x2

x3

x4

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- Lagrange interpolating polynomial for four data points:

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- We want to solve for coefficients c3, c2, c1, and c0, such that
or equivalently

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- The determinant of a vandermonde matrix
is non-zero, if all xi’s are distinct. Hence, we can always find the matrix inverse and solve the system of linear equations.

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