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CSE 245: Computer Aided Circuit Simulation and Verification. Fall 2004, Oct 19 Lecture 7: Matrix Solver II -Iterative Method. Outline. Iterative Method Stationary Iterative Method (SOR, GS,Jacob) Krylov Method (CG, GMRES) Multigrid Method. Iterative Methods. Stationary:

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cse 245 computer aided circuit simulation and verification

CSE 245: Computer Aided Circuit Simulation and Verification

Fall 2004, Oct 19

Lecture 7:

Matrix Solver II

-Iterative Method

outline
Outline
  • Iterative Method
    • Stationary Iterative Method (SOR, GS,Jacob)
    • Krylov Method (CG, GMRES)
    • Multigrid Method

Zhengyong (Simon) Zhu, UCSD

iterative methods
Iterative Methods

Stationary:

x(k+1)=Gx(k)+c

where G and c do not depend on iteration count (k)

Non Stationary:

x(k+1)=x(k)+akp(k)

where computation involves information that change at each iteration

courtesy Alessandra Nardi, UCB

stationary jacobi method
Stationary: Jacobi Method

In the i-th equation solve for the value of xi while assuming the other entries of x remain fixed:

In matrix terms the method becomes:

where D, -L and -U represent the diagonal, the strictly lower-trg and strictly upper-trg parts of M

M=D-L-U

courtesy Alessandra Nardi, UCB

stationary gause seidel
Stationary-Gause-Seidel

Like Jacobi, but now assume that previously computed results are used as soon as they are available:

In matrix terms the method becomes:

where D, -L and -U represent the diagonal, the strictly lower-trg and strictly upper-trg parts of M

M=D-L-U

courtesy Alessandra Nardi, UCB

stationary successive overrelaxation sor
Stationary: Successive Overrelaxation (SOR)

Devised by extrapolation applied to Gauss-Seidel in the form of weighted average:

In matrix terms the method becomes:

where D, -L and -U represent the diagonal, the strictly lower-trg and strictly upper-trg parts of M

M=D-L-U

courtesy Alessandra Nardi, UCB

slide7
SOR
  • Choose w to accelerate the convergence
    • W =1 : Jacobi / Gauss-Seidel
    • 2>W>1: Over-Relaxation
    • W < 1: Under-Relaxation

Zhengyong (Simon) Zhu, UCSD

convergence of stationary method
Convergence of Stationary Method
  • Linear Equation: MX=b
  • A sufficient condition for convergence of the solution(GS,Jacob) is that the matrix M is diagonally dominant.
  • If M is symmetric positive definite, SOR converges for any w (0<w<2)
  • A necessary and sufficient condition for the convergence is the magnitude of the largest eigenvalue of the matrix G is smaller than 1
    • Jacobi:
    • Gauss-Seidel
    • SOR:

Zhengyong (Simon) Zhu, UCSD

outline1
Outline
  • Iterative Method
    • Stationary Iterative Method (SOR, GS,Jacob)
    • Krylov Method (CG, GMRES)
      • Steepest Descent
      • Conjugate Gradient
      • Preconditioning
    • Multigrid Method

Zhengyong (Simon) Zhu, UCSD

linear equation an optimization problem
Linear Equation: an optimization problem
  • Quadratic function of vector x
  • Matrix A is positive-definite, if for any nonzero vector x
  • If A is symmetric, positive-definite, f(x) is minimized by the solution

Zhengyong (Simon) Zhu, UCSD

linear equation an optimization problem1
Linear Equation: an optimization problem
  • Quadratic function
  • Derivative
  • If A is symmetric
  • If A is positive-definite

is minimized by setting to 0

Zhengyong (Simon) Zhu, UCSD

for symmetric positive definite matrix a
For symmetric positive definite matrix A

from J. R. Shewchuk "painless CG"

gradient of quadratic form
Gradient of quadratic form

The points in the direction of steepest increase of f(x)

from J. R. Shewchuk "painless CG"

slide14

Symmetric Positive-Definite Matrix A

  • If A is symmetric positive definite
    • P is the arbitrary point
    • X is the solution point

since

We have,

If p != x

Zhengyong (Simon) Zhu, UCSD

if a is not positive definite
If A is not positive definite
  • Positive definite matrix b) negative-definite matrix
  • c) Singular matrix d) positive indefinite matrix

from J. R. Shewchuk "painless CG"

non stationary iterative method
Non-stationary Iterative Method
  • State from initial guess x0, adjust it until close enough to the exact solution
  • How to choose direction and step size?

i=0,1,2,3,……

Adjustment Direction

Step Size

Zhengyong (Simon) Zhu, UCSD

steepest descent method 1
Steepest Descent Method (1)
  • Choose the direction in which f decrease most quickly: the direction opposite of
  • Which is also the direction of residue

Zhengyong (Simon) Zhu, UCSD

steepest descent method 2
Steepest Descent Method (2)
  • How to choose step size ?
    • Line Search

should minimize f, along the direction of , which means

Orthogonal

Zhengyong (Simon) Zhu, UCSD

steepest descent algorithm
Steepest Descent Algorithm

Given x0, iterate until residue is smaller than error tolerance

Zhengyong (Simon) Zhu, UCSD

steepest descent method example
Steepest Descent Method: example
  • Starting at (-2,-2) take the
  • direction of steepest descent of f
  • b) Find the point on the intersec-
  • tion of these two surfaces that
  • minimize f
  • c) Intersection of surfaces.
  • d) The gradient at the bottommost
  • point is orthogonal to the gradient
  • of the previous step

from J. R. Shewchuk "painless CG"

iterations of steepest descent method
Iterations of Steepest Descent Method

from J. R. Shewchuk "painless CG"

convergence of steepest descent 1
Convergence of Steepest Descent-1

let

Eigenvector:

EigenValue:

j=1,2,…,n

Energy norm:

Zhengyong (Simon) Zhu, UCSD

convergence of steepest descent 2
Convergence of Steepest Descent-2

Zhengyong (Simon) Zhu, UCSD

convergence study n 2
Convergence Study (n=2)

assume

let

Spectral condition number

let

Zhengyong (Simon) Zhu, UCSD

plot of w
Plot of w

from J. R. Shewchuk "painless CG"

case study
Case Study

from J. R. Shewchuk "painless CG"

bound of convergence
Bound of Convergence

It can be proved that it is

also valid for n>2, where

from J. R. Shewchuk "painless CG"

conjugate gradient method
Conjugate Gradient Method
  • Steepest Descent
    • Repeat search direction
  • Why take exact one step for each direction?

Search direction of Steepest

descent method

figure from J. R. Shewchuk "painless CG"

orthogonal direction
Orthogonal Direction

Pick orthogonal search direction:

  • We don’t know !!!

Zhengyong (Simon) Zhu, UCSD

orthogonal a orthogonal
Orthogonal  A-orthogonal
  • Instead of orthogonal search direction, we make search direction A –orthogonal (conjugate)

from J. R. Shewchuk "painless CG"

search step size
Search Step Size

Zhengyong (Simon) Zhu, UCSD

iteration finish in n steps
Iteration finish in n steps

Initial error:

A-orthogonal

The error component at direction dj

is eliminated at step j. After n steps,

all errors are eliminated.

Zhengyong (Simon) Zhu, UCSD

conjugate search direction
Conjugate Search Direction
  • How to construct A-orthogonal search directions, given a set of n linear independent vectors.
  • Since the residue vector in steepest descent method is orthogonal, a good candidate to start with

Zhengyong (Simon) Zhu, UCSD

construct search direction 1
Construct Search Direction -1
  • In Steepest Descent Method
    • New residue is just a linear combination of previous residue and
  • Let

We have

Krylov SubSpace: repeatedly applying a matrix to a vector

Zhengyong (Simon) Zhu, UCSD

construct search direction 2
Construct Search Direction -2

let

For i > 0

Zhengyong (Simon) Zhu, UCSD

construct search direction 3
Construct Search Direction -3
  • can get next direction from the previous one, without saving them all.

let

then

Zhengyong (Simon) Zhu, UCSD

conjugate gradient algorithm
Conjugate Gradient Algorithm

Given x0, iterate until residue is smaller than error tolerance

Zhengyong (Simon) Zhu, UCSD

conjugate gradient convergence
Conjugate gradient: Convergence
  • In exact arithmetic, CG converges in n steps (completely unrealistic!!)
  • Accuracy after k steps of CG is related to:
    • consider polynomials of degree k that are equal to 1 at 0.
    • how small can such a polynomial be at all the eigenvalues of A?
  • Thus, eigenvalues close together are good.
  • Condition number:κ(A) = ||A||2 ||A-1||2 = λmax(A) / λmin(A)
  • Residual is reduced by a constant factor by O(κ1/2(A)) iterations of CG.

courtesy J.R.Gilbert, UCSB

other krylov subspace methods
Other Krylov subspace methods
  • Nonsymmetric linear systems:
    • GMRES: for i = 1, 2, 3, . . . find xi  Ki (A, b) such that ri = (Axi– b)  Ki (A, b)But, no short recurrence => save old vectors => lots more space (Usually “restarted” every k iterations to use less space.)
    • BiCGStab, QMR, etc.:Two spaces Ki (A, b)and Ki (AT, b)w/ mutually orthogonal bases Short recurrences => O(n) space, but less robust
    • Convergence and preconditioning more delicate than CG
    • Active area of current research
  • Eigenvalues: Lanczos (symmetric), Arnoldi (nonsymmetric)

courtesy J.R.Gilbert, UCSB

preconditioners
Preconditioners
  • Suppose you had a matrix B such that:
    • condition number κ(B-1A) is small
    • By = z is easy to solve
  • Then you could solve (B-1A)x = B-1b instead of Ax = b
  • B = A is great for (1), not for (2)
  • B = I is great for (2), not for (1)
  • Domain-specific approximations sometimes work
  • B = diagonal of A sometimes works
  • Better: blend in some direct-methods ideas. . .

courtesy J.R.Gilbert, UCSB

preconditioned conjugate gradient iteration
Preconditioned conjugate gradient iteration
  • One matrix-vector multiplication per iteration
  • One solve with preconditioner per iteration

x0 = 0, r0 = b, d0 = B-1r0, y0 = B-1r0

for k = 1, 2, 3, . . .

αk = (yTk-1rk-1) / (dTk-1Adk-1) step length

xk = xk-1 + αk dk-1 approx solution

rk = rk-1 – αk Adk-1 residual

yk = B-1rk preconditioning solve

βk = (yTk rk) / (yTk-1rk-1) improvement

dk = yk + βk dk-1 search direction

courtesy J.R.Gilbert, UCSB

outline2
Outline
  • Iterative Method
    • Stationary Iterative Method (SOR, GS,Jacob)
    • Krylov Method (CG, GMRES)
    • Multigrid Method

Zhengyong (Simon) Zhu, UCSD

what is the multigrid
What is the multigrid
  • A multilevel iterative method to solve
    • Ax=b
  • Originated in PDEs on geometric grids
  • Expend the multigrid idea to unstructured problem – Algebraic MG
  • Geometric multigrid for presenting the basic ideas of the multigrid method.

Zhengyong (Simon) Zhu, UCSD

the model problem

v3

v4

v1

v2

v5

v6

v8

v7

+

vs

The model problem

Ax = b

Zhengyong (Simon) Zhu, UCSD

simple iterative method
Simple iterative method
  • x(0) -> x(1) -> … -> x(k)
  • Jacobi iteration
  • Matrix form : x(k) = Rjx(k-1) + Cj
  • General form: x(k) = Rx(k-1) + C (1)
  • Stationary: x* = Rx* + C (2)

Zhengyong (Simon) Zhu, UCSD

error and convergence
Error and Convergence

Definition: errore = x* - x (3)

residualr = b – Ax (4)

e, r relation: Ae = r (5) ((3)+(4))

e(1) = x*-x(1) = Rx* + C – Rx(0)– C =Re(0)

Error equatione(k) = Rke(0) (6) ((1)+(2)+(3))

Convergence:

Zhengyong (Simon) Zhu, UCSD

error of diffenent frequency

k= 1

k= 4

k= 2

Error of diffenent frequency
  • Wavenumber k and frequency 
  • = k/n
  • High frequency error is more oscillatory between points

Zhengyong (Simon) Zhu, UCSD

iteration reduce low frequency error efficiently
Iteration reduce low frequency error efficiently
  • Smoothing iteration reduce high frequency error efficiently, but not low frequency error

Error

k = 1

k = 2

k = 4

Iterations

Zhengyong (Simon) Zhu, UCSD

multigrid a first glance

2

1

3

4

3

4

1

2

5

6

8

7

Multigrid – a first glance
  • Two levels : coarse and fine grid

2h

A2hx2h=b2h

h

Ahxh=bh

Ax=b

Zhengyong (Simon) Zhu, UCSD

idea 1 the v cycle iteration
Idea 1: the V-cycle iteration
  • Also called the nested iteration

Start with

2h

A2hx2h = b2h

A2hx2h = b2h

Iterate =>

Prolongation: 

Restriction: 

h

Ahxh = bh

Iterate to get

Question 1: Why we need the coarse grid ?

Zhengyong (Simon) Zhu, UCSD

prolongation

2

1

3

4

3

4

1

2

5

6

8

7

Prolongation
  • Prolongation (interpolation) operator

xh = x2h

Zhengyong (Simon) Zhu, UCSD

restriction

2

1

3

4

3

4

1

2

5

6

8

7

Restriction
  • Restriction operator

xh = x2h

Zhengyong (Simon) Zhu, UCSD

smoothing
Smoothing
  • The basic iterations in each level

In ph: xphold  xphnew

  • Iteration reduces the error, makes the error smooth geometrically.

So the iteration is called smoothing.

Zhengyong (Simon) Zhu, UCSD

why multilevel
Why multilevel ?
  • Coarse lever iteration is cheap.
  • More than this…
    • Coarse level smoothing reduces the error more efficiently than fine level in some way .
    • Why ? ( Question 2 )

Zhengyong (Simon) Zhu, UCSD

error restriction
Error restriction
  • Map error to coarse grid will make the error more oscillatory

K = 4,  = 

K = 4,  = /2

Zhengyong (Simon) Zhu, UCSD

idea 2 residual correction
Idea 2: Residual correction
  • Known current solution x
  • Solve Ax=b eq. to
  • MG do NOT map x directly between levels

Map residual equation to coarse level

    • Calculate rh
    • b2h= Ih2h rh ( Restriction )
    • eh =Ih2hx2h ( Prolongation )
    • xh = xh + eh

Zhengyong (Simon) Zhu, UCSD

why residual correction
Why residual correction ?
  • Error is smooth at fine level, but the actual solution may not be.
  • Prolongation results in a smooth error in fine level, which is suppose to be a good evaluation of the fine level error.
  • If the solution is not smooth in fine level, prolongation will introduce more high frequency error.

Zhengyong (Simon) Zhu, UCSD

revised v cycle with idea 2
Revised V-cycle with idea 2
  • Smoothing on xh
  • Calculate rh
  • b2h= Ih2h rh
  • Smoothing on x2h
  • eh =Ih2hx2h
  • Correct: xh = xh + eh

2h h

`

Restriction

Prolongation

Zhengyong (Simon) Zhu, UCSD

what is a 2h
What is A2h
  • Galerkin condition

Zhengyong (Simon) Zhu, UCSD

going to multilevels
Going to multilevels
  • V-cycle and W-cycle
  • Full Multigrid V-cycle

h

2h

4h

h

2h

4h

8h

Zhengyong (Simon) Zhu, UCSD

performance of multigrid
Performance of Multigrid
  • Complexity comparison

Zhengyong (Simon) Zhu, UCSD

summary of mg ideas
Summary of MG ideas

Three important ideas of MG

  • Nested iteration
  • Residual correction
  • Elimination of error:

high frequency : fine grid

low frequency : coarse grid

Zhengyong (Simon) Zhu, UCSD

amg for unstructured grids

1

2

4

3

5

6

AMG :for unstructured grids
  • Ax=b, no regular grid structure
  • Fine grid defined from A

Zhengyong (Simon) Zhu, UCSD

three questions for amg
Three questions for AMG
  • How to choose coarse grid
  • How to define the smoothness of errors
  • How are interpolation and prolongation done

Zhengyong (Simon) Zhu, UCSD

how to choose coarse grid
How to choose coarse grid
  • Idea:
    • C/F splitting
    • As few coarse grid point as possible
    • For each F-node, at least one of its neighbor is a C-node
    • Choose node with strong coupling to other nodes as C-node

1

2

4

3

5

6

Zhengyong (Simon) Zhu, UCSD

how to define the smoothness of error
How to define the smoothness of error
  • AMG fundamental concept:

Smooth error = small residuals

  • ||r|| << ||e||

Zhengyong (Simon) Zhu, UCSD

how are prolongation and restriction done
How are Prolongation and Restriction done
  • Prolongation is based on smooth error and strong connections
  • Common practice: I

Zhengyong (Simon) Zhu, UCSD

amg prolongation 2
AMG Prolongation (2)

Zhengyong (Simon) Zhu, UCSD

amg prolongation 3
AMG Prolongation (3)
  • Restriction :

Zhengyong (Simon) Zhu, UCSD

summary
Summary
  • Multigrid is a multilevel iterative method.
  • Advantage: scalable
  • If no geometrical grid is available, try Algebraic multigrid method

Zhengyong (Simon) Zhu, UCSD

the landscape of solvers

Direct

A = LU

Iterative

y’ = Ay

More General

Non-

symmetric

Symmetric

positive

definite

More Robust

The landscape of Solvers

More Robust

Less Storage (if sparse)

courtesy J.R.Gilbert, UCSB

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