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A numerical Approach toward Approximate Algebraic Computatition. Zhonggang Zeng Northeastern Illinois University, USA. Oct. 18, 2006, Institute of Mathematics and its Applications. What would happen when we try numerical computation on algebraic problems?.

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slide1

A numerical Approachtoward

Approximate Algebraic Computatition

Zhonggang Zeng

Northeastern Illinois University, USA

Oct. 18, 2006, Institute of Mathematicsand its Applications

slide2

What would happen

when we try numerical computation

on algebraic problems?

A numerical analyst got a surprise 50 years ago on a deceptively simple problem.

1

slide3

Start of project: 1948

Completed: 1950

Add time: 1.8 microseconds

Input/output: cards

Memory size: 352 32-digit words

Memory type: delay lines

Technology: 800 vacuum tubes

Floor space: 12 square feet

Project leader: J. H. Wilkinson

Britain’s Pilot Ace

James H. Wilkinson (1919-1986)

2

slide4

The Wilkinson polynomial

p(x) = (x-1)(x-2)...(x-20)

= x20 - 210 x19 + 20615 x18 + ...

Wilkinson wrote in 1984:

Speaking for myself I regard it as the most traumatic

experience in my career as a numerical analyst.

3

slide7

approximation

A factorable polynomial

irreducible

Factoring a multivariate polynomial:

6

slide8

Translation: There are two 1-dimensional solution set:

Solving polynomial systems:

Example: A distorted cyclic four system:

7

slide9

approximation

Isolated solutions

1-dimensional solution set

Distorted Cyclic Four system in floating point form:

8

slide10

tiny perturbation

in data

(< 0.00000001)

huge error

In solution

( >105 )

9

slide11

What could happen in approximate algebraic computation?

  • “traumatic” error
  • dramatic deformation of solution structure
  • complete loss of solutions
  • miserable failure of classical algorithms
  • Polynomial division
  • Euclidean Algorithm
  • Gaussian elimination
  • determinants
  • … …

10

slide12

Either

or

So, why bother with approximation in algebra?

  • You may have no choice (e.g. Abel’s Impossibility Theorem)

All subsequent computations become approximate

11

slide13

blurred image

blurred image

true image

Application: Image restoration (Pillai & Liang)

So, why bother with approximation solutions?

  • You may have no choice
  • Approximate solutions are better!
slide14

blurred image

blurred image

restored image

Application: Image restoration (Pillai & Liang)

true image

Approximate solution is better than exact solution!

13

slide15

Approximate solutions

by Bertini

(Courtesy of Bates, Hauenstein,

Sommese, Wampler)

Perturbed Cyclic 4

Exact solutions by Maple: 16 isolated (codim 4) solutions

slide16

Perturbed Cyclic 4

Exact solutions by Maple: 16 isolated (codim 4) solutions

Or, by an experimental approximate elimination combined with approximate GCD

Approximate solutions are better than exact ones

, arguably

15

slide17

Special case:

Example: JCF computation

Maple takes 2 hours

On a similar 8x8 matrix, Maple and Mathematica run out of memory

So, why bother with approximation solutions?

  • You may have no choice
  • Approximate solutions are better
  • Approximate solutions (usually) cost less
  • You may have no choice
  • Approximate solutions are better
  • Approximate solutions (usually) cost less

16

slide18

Pioneer works in numerical algebraic computation (incomplete list)

  • Homotopy method for solving polynomial systems
  • (Li, Sommese, Wampler, Verschelde, …)
  • Numerical Algebraic Geometry
  • (Sommese, Wampler, Verschelde, …)
  • Numerical Polynomial Algerba
  • (Stetter)

17

slide19

exact computation

approximate solution

using 8-digits precision

forward error

backward error

axact solution

-

-

-

=

2

4

2

(

x

1

)

(

10

)

0

What is an “approximate solution”?

To solve

with 8 digits precision:

backward error: 0.00000001 -- method is good

forward error: 0.0001 -- problem is bad

18

slide20

From problem

From numerical method

The condition number

[Forward error] < [Condition number] [Backward error]

A large condition number

<=> The problem is sensitive or, ill-conditioned

An infinite condition number <=> The problem is ill-posed

19

slide21

Wilkinson’s Turing Award contribution:

Backward error analysis

  • A numerical algorithm solves a “nearby” problem
  • A “good” algorithm may still get a “bad” answer, if the problem is ill-conditioned (bad)

20

slide22

Ill-posed problems are common in applications

  • image restoration - deconvolution
  • IVP for stiction damped oscillator - inverse heat conduction
  • some optimal control problems - electromagnetic inverse scatering
  • air-sea heat fluxes estimation - the Cauchy prob. for Laplace eq.
  • … …
  • A well-posed problem: (Hadamard, 1923)
  • the solution satisfies
      • existence
      • uniqueness
      • continuity w.r.t data

An ill-posed problem is infinitely sensitive to perturbation

tiny perturbation  huge error

21

slide23

- Multiple roots

- Polynomial GCD

- Factorization of multivariate polynomials

- The Jordan Canonical Form

- Multiplicity structure/zeros of polynomial systems

- Matrix rank

Ill-posed problems are common

in algebraic computing

22

slide24

Ill-posed problems

are infinitely sensitive

to data perturbation

A numerical algorithm

seeks the exact solution

of a nearby problem

If the answer is highly sensitive to perturbations, you have probably asked the wrong question.

Maxims about numerical mathematics, computers,

science and life, L. N. Trefethen. SIAM News

Does that mean:

(Most) algebraic problems are wrong problems?

Conclusion: Numerical computation is incompatible

with ill-posed problems.

Solution: Formulate the right problem.

23

slide25

Solution

P

P

Data

P

Challenge in solving ill-posed problems:

Can we recover the lost solution

when the problem is inexact?

P:Data Solution

24

slide26

William Kahan:

This is a misconception

Are ill-posed problems really sensitive to perturbations?

Kahan’s discovery in 1972:

Ill-posed problems are sensitive to arbitrary perturbation,

but insensitive to structure preserving perturbation.

25

slide27

W. Kahan’s observation (1972)

  • Problems with certain solution structure form a “pejorative manifold”

Plot of pejorative manifolds of degree 3 polynomials with multiple roots

Why are ill-posed problems infinitely sensitive?

  • The solution structure is lost when the problem leaves the manifold due to an arbitrary perturbation
  • The problem may not be sensitive at all if the problem stays on the manifold, unless it is near another pejorative manifold

26

slide28

Geometry of ill-posed algebraic problems

Similar manifold stratification exists for problems like

factorization, JCF, multiple roots …

27

slide29

Manifolds of 4x4 matrices defined by Jordan structures

(Edelman, Elmroth and Kagstrom 1997)

e.g. {2,1} {1} is the structure of 2 eigenvalues in 3 Jordan blocks of sizes 2, 1 and 1

28

slide30

?

B

A

?

perturbation

Problem A

Problem B

Illustration of pejorative manifolds

The “nearest” manifold may not be the answer

The right manifold is of highest codimension within a certain distance

29

slide31

Finding approximate solution is (likely) a well-posed problem

Approximate solution is a generalization of exact solution.

A “three-strikes” principle for formulating

an “approximate solution” to an ill-posed problem:

  • Backward nearness: The approximate solution is the exact solution of a nearby problem
  • Maximum codimension: The approximate solution is the exact solution of a problem on the nearby pejorative manifold of the highest codimension.
  • Minimum distance: The approximate solution is the exact solution of the nearest problem on the nearby pejorative manifold of the highest codimension.

30

slide33

Formulation of the approximate rank /kernel:

Backward nearness:app-rank of A is

the exact rank of certain matrix B within q.

The approximate rank of A within q :

Maximum codimension:That matrix B

is on the pejorative manifold P possessing the

highest co-dimension and intersecting the

q-neighborhoodof A.

The approximate kernel of A within q :

with

Minimum distance:That B is the nearest

matrix on the pejorative manifold P.

  • An exact rank is the app-rank within sufficiently small q.
  • App-rank is continuous (or well-posed)

31

slide34

= 4

nullity = 2

= 4

nullityq = 2

After reformulating the rank:

Rank

Rankq

Ill-posedness is removed successfully.

kernel

App-rank/kernel can be computed by SVD and other rank-revealing algorithms

(e.g. Li-Zeng, SIMAX, 2005)

basis

+ eE = 6

nullity = 0

+ eE = 4

nullityq = 2

32

slide35

The AGCD within e :

Formulation of the approximate GCD

  • Finding AGCD is well-posed if qk(f,g) is sufficiently small
  • EGCD is an special case of AGCD for sufficiently small e(Z. Zeng, Approximate GCD of inexact polynomials, part I&II)

33

slide36

Similar formulation strikes out ill-posedness in problems such as

  • Approximate rank/kernel(Li,Zeng 2005, Lee, Li, Zeng 2006)
  • Approximate multiple roots/factorization(Zeng 2005)
  • Approximate GCD(Zeng-Dayton 2004, Gao-Kaltofen-May-Yang-Zhi 2004)
  • Approximate Jordan Canonical Form(Zeng-Li 2006)
  • Approximate irreducible factorization(Sommesse-Wampler-Verschelde 2003, Gao et al 2003, 2004, in progress)
  • Approximate dual basis and multiplicity structure(Dayton-Zeng 05, Bates-Peterson-Sommese ’06)
  • Approximate elimination ideal(in progress)

34

slide37

after formulating the approximate solution to problem P within e

Stage I: Find the pejorative manifold

P of the highest dimension s.t.

P

P

S

Stage II: Find/solve problem Q such that

Q

The two-staged algorithm

Exact solution of Q is the approximate solution of P within e

which approximates the solution of S where P is perturbed from

35

slide38

Stage II: solve the (overdetermined) quadratic system

for a least squares solution (u,v,w) by Gauss-Newton iteration

Case study: Univariate approximate GCD:

Stage I: Find the pejorative manifold

(key theorem: The Jacobian of F(u,v,w) is injective.)

36

slide39

Max-codimension

k := k-1

k := k-1

Is AGCD of degree k

possible?

no

no

probably

Refine with

G-N Iteration

Successful?

yes

Min-distance

nearness

Output GCD

Start: k = n

Univariate AGCD algorithm

37

slide40

Stage II: solve the (overdetermined) quadratic system

for a least squares solution (u,v,w) by Gauss-Newton iteration

(key theorem: The Jacobian of F(u,v,w) is injective.)

Case study: Multivariate approximate GCD:

Stage I: Find the max-codimension pejorative manifold by

applying univariate AGCD algorithm on each variable xj

38

slide41

Stage II: solve the (overdetermined) polynomial system F(z1 ,…,zk )=f

(in the form of coefficient vectors)

for a least squares solution (z1 ,…,zk) by Gauss-Newton iteration

(key theorem: The Jacobian is injective.)

Case study: univariate factorization:

Stage I: Find the max-codimension pejorative manifold by

applying univariate AGCD algorithm on (f, f’ )

39

slide42

A ~ J

l 1

l 1

l

J =

Segre characteristic = [3,1]

B

l

A

codim = -1 + 3 + 3(1) = 5

l

l

s13

s23

s14

s24

s34

Wyre characteristic = [2,1,1]

lI+S=

l

l

3

1

2

1

1

Ferrer’s diagram

Case study: Finding the nearest matrix with a Jordan structure

P

Equations determining the manifold

40

slide43

A ~ J

B

A

For B not on the manifold, we can still solve

for a least squares solution :

When

is minimized, so is

of

is injective.

The crucial requirement: The Jacobian

(Zeng & Li, 2006)

Case study: Finding the nearest matrix with a Jordan structure

Equations determining the manifold

P

41

slide44

Project to tangent plane

u1 = G(z0)+J(z0)(z1- z0)

~

tangent plane P0 :

u = G(z0)+J(z0)(z- z0)

Least squares solution

u*=G(z*)

new iterate

u1=G(z1)

initial iterate

u0=G(z0)

Solving G(z) = a

a

Pejorative manifold

u = G( z )

Solve

G( z ) = a

for nonlinear least squares solutionz=z*

Solve

G(z0)+J(z0)(z - z0 ) = a

for linear least squares solutionz =z1

G(z0)+J(z0)(z - z0 ) = a

J(z0)(z - z0 ) = - [G(z0) - a ]

z1 = z0 - [J(z0)+][G(z0) - a]

42

slide45

condition number

(sensitivity measure)

Stage I: Find the nearby max-codim manifold

Stage II: Find/solve the nearest problem on the manifold via solving an overdetermined system G(z)=a for a least squares solutionz* s.t . ||G(z*)-a||=minz ||G(z)-a||

by the Gauss-Newton iteration

Key requirement: Jacobian J(z*) of G(z) at z* is injective (i.e. the pseudo-inverse exists)

43

slide46

univariate/multivariate GCDmatrix rank/kernel

dual basis for a polynomial ideal

univariate factorization

irreducible factorizationelimination ideal

… …

(to be continued in the workshop

next week)

Summary:

  • An (ill-posed) algebraic problem can be formulated using the three-strikes principle (backward nearness, maximum-codimension, and minimum distance) to remove the ill-posedness
  • The re-formulated problem can be solved by numerical computation in two stages (finding the manifold, solving least squares)
  • The combined numerical approach leads to Matlab/Maple toolbox ApaTools for approximate polynomial algebra. The toolbox consists of

44

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