Lecture 6

Resolutionand

Generalized Inverses

SyllabusLecture 01 Describing Inverse ProblemsLecture 02 Probability and Measurement Error, Part 1Lecture 03 Probability and Measurement Error, Part 2 Lecture 04 The L2 Norm and Simple Least SquaresLecture 05 A Priori Information and Weighted Least SquaredLecture 06 Resolution and Generalized InversesLecture 07 Backus-Gilbert Inverse and the Trade Off of Resolution and VarianceLecture 08 The Principle of Maximum LikelihoodLecture 09 Inexact TheoriesLecture 10 Nonuniqueness and Localized AveragesLecture 11 Vector Spaces and Singular Value DecompositionLecture 12 Equality and Inequality ConstraintsLecture 13 L1 , L∞ Norm Problems and Linear ProgrammingLecture 14 Nonlinear Problems: Grid and Monte Carlo Searches Lecture 15 Nonlinear Problems: Newton’s Method Lecture 16 Nonlinear Problems: Simulated Annealing and Bootstrap Confidence Intervals Lecture 17 Factor AnalysisLecture 18 Varimax Factors, Empircal Orthogonal FunctionsLecture 19 Backus-Gilbert Theory for Continuous Problems; Radon’s ProblemLecture 20 Linear Operators and Their AdjointsLecture 21 Fréchet DerivativesLecture 22 Exemplary Inverse Problems, incl. Filter DesignLecture 23 Exemplary Inverse Problems, incl. Earthquake LocationLecture 24 Exemplary Inverse Problems, incl. Vibrational Problems

Purpose of the Lecture

Introduce the idea of a Generalized Inverse,the Data and Model Resolution Matrices

and the Unit Covariance Matrix

Quantify the spread of resolution and the size of the covariance

Use the maximization of resolution and/or covariance as the guiding principle for solving inverse problems

Part 1

The Generalized Inverse,

the Data and Model Resolution Matrices

and the Unit Covariance Matrix

all of the solutions

of the formmest = Md + v

mest = Md + vlet’s focus on

this matrix

mest = G-gd + vrename it the “generalized

inverse”and use the symbol G-g

(let’s ignore the vector v for a moment)

Generalized Inverse G-goperates on the data to give an estimate of the

model parametersifdpre = Gmest

thenmest = G-gdobs

Generalized Inverse G-gif dpre = Gmest then mest = G-gdobs

sort of looks like a matrix inverseexcept

M⨉N, not squareandGG-g≠I and G-gG≠I

so actuallythe generalized inverse is not a

matrix inverse at all

dpre = Gmest and mest = G-gdobs

dpre = Ndobs with N = GG-g “data resolution matrix”

plug one equation into the other

Data Resolution Matrix, N

How much does diobs contribute to its own

prediction?

dpre = Ndobs

ifN=I

dipre = di

obs

dpre = dobs

diobs completely controls its own prediction

dpre dobs=

(A)

The closer N is to I, the more diobs controls its own

prediction

0 5 100

5

10

15

z

d

0 5 100

5

10

15

z

d

straight line problem

= =

i i

j

j

dpre = N dobs

only the data at the ends control their own prediction

dobs = Gmtrue and mest = G-gdobs

mest = Rmtrue with R = G-gG “model resolution matrix”

plug one equation into the other

Model Resolution Matrix, R

How much does mitrue contribute to its own estimated value?

mest = Rmtrue

ifR=I

miest = mitrue

mest = mtrue

miest reflects mitrue only

else ifR≠Imiest =

… + Ri,i-1mi-1true + Ri,imitrue + Ri,i+1mi+1true+ …

miest is a weighted averageof all the elements of mtrue

mest mtrue=

The closer R is to I, the more miest reflects only mitrue

Discrete version ofLaplace Transform

large c: d is “shallow” average of m(z)small c: d is “deep” average of m(z)

e-chiz

z

m(z)z

z

⨉

⨉e-cloz dlo

dhi

integrate

integrate

= =

i i

j

j

mest = R mtrue

the shallowest model parameters are “best resolved”

Covariance associated with the Generalized Inverse

“unit covariance matrix”divide by σ2 to remove effect of the

overall magnitude of the measurement error

unit covariance for straight line problem

model parameters uncorrelated when this term zerohappens when data are centered about the origin

Part 2

The spread of resolution and the size of the covariance

a resolution matrix has small spread if only its main diagonal has

large elements

it is close to the identity matrix

“Dirichlet” Spread Functions

a unit covariance matrix has small size if its diagonal elements are small

error in the data corresponds to only small error in the model parameters

(ignore correlations)

Part 3

minimization ofspread of resolution

and/orsize of covariance

as the guiding principlefor creating a generalized inverse

over-determined case

note that forsimple least squaresG-g = [GTG]-1GTmodel resolutionR=G-gG = [GTG]-1GTG=I

always the identify matrix

suggests that we try to minimize the spread of the data resolution matrix, N

find G-g that minimizes spread(N)

spread of the k-th row of N

now compute

first term

second term

third term is zero

putting it all together

which is justsimple least squaresG-g = [GTG]-1GT

the simple least squares solutionminimizes the spread of data resolution

and has zero spread of the model resolution

under-determined case

note that forminimum length solutionG-g = GT [GGT]-1

data resolutionN=GG-g = G GT [GGT]-1 =Ialways the identify matrix

suggests that we try to minimize the spread of the model resolution matrix, R

find G-g that minimizes spread(R)

minimization leads to

[GGT]G-g = GT

which is justminimum length solutionG-g = GT [GGT]-1

the minimum length solutionminimizes the spread of model resolution

and has zero spread of the data resolution

general case

leads to

a Sylvester Equation, so explicit solution in terms of matrices

leads to

general case

1

special case #1

0 ε2

[GTG+ε2I]G-g=GTG-g=[GTG+ε2I]-1GT

I

damped least squares

0

special case #2

1 ε2

G-g[GGT+ε2I] =GTG-g=GT [GGT+ε2I]-1

I

damped minimum length

so

no new solutions have arisen …

… just a reinterpretation of previously-derived solutions

reinterpretation

instead of solving for estimates of the model parameters

We are solving for estimates of weighted averages of the model parameters,

where the weights are given by the model resolution matrix

criticism of Direchlet spread() functions

when m represents m(x)is that they don’t capture the sense of

“being localized” very well

These two rows of the model resolution matrix have the same spread …

Rij Rijindex, j index, ji i… but the left case is better “localized”

we will take up this issue in the next lecture