Advanced Computer Graphics Spring 2009

Advanced Computer Graphics Spring 2009

K. H. Ko

Department of MechatronicsGwangju Institute of Science and Technology

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Today’s Topics

Linear AlgebraSystems of Linear EquationsMatricesVector Spaces

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Systems of Linear Equations

Linear Equation

System of Linear Equations (n equations, m unknowns)

bxaxa mn 11

nmn

nmn

mn

bxdxd

bxcxc

bxaxa

11

111

111

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Systems of Linear Equations

Solve a system of n linear equations in m unknown variables

A common problem in applications In most cases m = n. The system has three cases

No solutions, one solution or infinitely many solutions

How to solve the system? Forward elimination followed by back

substitution

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Systems of Linear Equations

A closer look at two equations in two unknowns

When the solution method needs to be implemented for a computer, a practical concern is the amount of time required to compute a solution.

2222121

1212111

bxaxa

bxaxa

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Systems of Linear Equations

Division is more expensive than multiplication and addition.

• 3 additions

• 3 multiplications

• 3 divisions

• 3 additions

• 5 multiplications

• 2 divisions

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Gaussian Elimination

Forward elimination + back substitution = Gaussian elimination

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Gaussian Elimination

Basic Operations for Forward Elimination

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Gaussian Elimination

Basic Operations for Forward Elimination

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Gaussian Elimination

Basic Operations for Forward Elimination

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Gaussian Elimination

Basic Operations for Back Substitution

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Gaussian Elimination

Example

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Geometry of Linear Systems

Consider 2222121

1212111

bxaxa

bxaxa

021122211 aaaa0

0

121211

21122211

baba

aaaa 0

0

121211

21122211

baba

aaaa

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Geometry of Linear Systems

Consider 3 equations and 3 unknowns

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Numerical Issues

If the pivot is nearly zero, the division can be a source of numerical errors.

Use of floating point arithmetic with limited precision is the main cause.

/11

/1

/120

/11

1

1

21

1

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Numerical Issues

A better algorithm involves searching the entries with the pivot that is largest in absolute magnitude.

1

1

210

21

1

1

1

21

No division by ε. -> Numerically robust and stable.

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Numerical Issues

However, even the previous approach can be a problem.

Swap columns to avoid such problem.

Blackboard!!!

0,12

1

21212

211

xx

xx

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Numerical Issues

Generally, for a system of n equations in n unknowns…

Full Pivoting: Search the entire matrix of coefficients looking for the entry of largest absolute magnitude to be used as the pivot.

If that entry occurs in row r and column c, then rows r and 1 are swapped followed by a swap of column c and column 1.

After both swaps, the entry in row 1 and column 1 is the largest absolute magnitude entry in the matrix.

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Numerical Issues

Generally, for a system of n equations in n unknowns…

If that entry is nearly zero, the linear system is ill-conditioned and notify the user.

If you choose to continue, the division is performed and forward elimination begins.

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Iterative Methods for Solving Linear Systems Look for a good numerical

approximation instead of the exact mathematical solution.

Useful in sparse linear systems Approaches

Splitting Method Minimization problem

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Iterative Methods for Solving Linear Systems Splitting

Method

2222121

1212111

bxaxa

bxaxa

22

12122

11

21211

a

xabx

a

xabx

22

)(1212)1(

2

11

)(2121)1(

1

a

xabx

a

xabx

ii

ii

Issues

• Convergence

• Numerical Stability

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Iterative Methods for Solving Linear Systems Formulate the linear system Ax=b

as a minimization problem

0)()(),( 22222121

2121211121 bxaxabxaxaxxf

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Matrices

Square matrices Identity matrix Transpose of a matrix Symmetric matrix: A = AT

Skew-symmetric: A = -AT

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Matrices

Upper echelon matrix U = [uij](nxm) if uij = 0 for i > j If m=n, upper triangular matrix

Lower echelon matrix L = [lij](nxm) if lij = 0 for i < j If m=n, lower triangular matrix

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Matrices

Elementary Row Matrices

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Matrices

Elementary Row Matrices

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Matrices

Elementary Row Matrices The final result of forward elimination can be state

d in terms of elementary row matrices Ek, … E1 applied to the augmented matrix [A|b].

[U|v] = Ek … E1[A|b]

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Matrices

Inverse Matrix PA = I: P is a left inverse A-1A = I, AA-1 = I. Inverses are unique If A and B are invertible, so is AB. Its inverse

is (AB)-1 = B-1A-1

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Matrices

LU Decomposition of the matrix A The forward elimination of a matrix A produces an

upper echelon matrix U. The corresponding elementary row matrices are Ek…E1

U = Ek…E1A., L = (Ek…E1)-1. L is lower triangular. A = LU: L is lower triangular and U is upper echelo

n.

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Matrices

LDU Decomposition of the matrix A L is lower triangular, D is a diagonal matrix,

and U is upper echelon with diagonal entries either 1 or 0.

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Matrices

LDU Decomposition of the matrix A

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Matrices

In general the factorization can be written as PA = LDU.

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Matrices

If A is invertible, its LDU decomposition is unique

If A is symmetric, U in the LDU decomposition must be U = LT.

A = LDLT. If the diagonal entries of D are

nonnegative, A = (LD1/2) (LD1/2)T

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Vector Spaces

The central theme of linear algebra is the study of vectors and the sets in which they live, called vector spaces.

What is the vector???

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Vector Spaces

Definition of a Vector Space (the triple (V,+,ᆞ ) )

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Q & A?