Method of conditional probabilities

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In numerical linear algebra, the conjugate gradient method is an iterative method for numerically solving the linear system

Ax=b

where A is symmetric positive-definite. The conjugate gradient method can be derived from several different perspectives, including specialization of the conjugate direction method for optimization, and variation of the Arnoldi/Lanczos iteration for eigenvalue problems.

The intent of this article is to document the important steps in these derivations.

Derivation from the conjugate direction method

Template:Expand section The conjugate gradient method can be seen as a special case of the conjugate direction method applied to minimization of the quadratic function

f(x)=xTAx2bTx.

The conjugate direction method

In the conjugate direction method for minimizing

f(x)=xTAx2bTx.

one starts with an initial guess x0 and the corresponding residual r0=bAx0, and computes the iterate and residual by the formulae

αi=piTripiTApi,xi+1=xi+αipi,ri+1=riαiApi

where p0,p1,p2, are a series of mutually conjugate directions, i.e.,

piTApj=0

for any ij.

The conjugate direction method is imprecise in the sense that no formulae are given for selection of the directions p0,p1,p2,. Specific choices lead to various methods including the conjugate gradient method and Gaussian elimination.

Derivation from the Arnoldi/Lanczos iteration

Template:See The conjugate gradient method can also be seen as a variant of the Arnoldi/Lanczos iteration applied to solving linear systems.

The general Arnoldi method

In the Arnoldi iteration, one starts with a vector r0 and gradually builds an orthonormal basis {v1,v2,v3,} of the Krylov subspace

𝒦(A,r0)={r0,Ar0,A2r0,}

by defining vi=wi/wi2 where

wi={r0if i=1,Avi1j=1i1(vjTAvi1)vjif i>1.

In other words, for i>1, vi is found by Gram-Schmidt orthogonalizing Avi1 against {v1,v2,,vi1} followed by normalization.

Put in matrix form, the iteration is captured by the equation

AVi=Vi+1H~i

where

Vi=[v1v2vi],H~i=[h11h12h13h1,ih21h22h23h2,ih32h33h3,ihi,i1hi,ihi+1,i]=[Hihi+1,ieiT]

with

hji={vjTAviif ji,wi+12if j=i+1,0if j>i+1.

When applying the Arnoldi iteration to solving linear systems, one starts with r0=bAx0, the residual corresponding to an initial guess x0. After each step of iteration, one computes yi=Hi1(r02e1) and the new iterate xi=x0+Viyi.

The direct Lanczos method

For the rest of discussion, we assume that A is symmetric positive-definite. With symmetry of A, the upper Hessenberg matrix Hi=ViTAVi becomes symmetric and thus tridiagonal. It then can be more clearly denoted by

Hi=[a1b2b2a2b3bi1ai1bibiai].

This enables a short three-term recurrence for vi in the iteration, and the Arnoldi iteration is reduced to the Lanczos iteration.

Since A is symmetric positive-definite, so is Hi. Hence, Hi can be LU factorized without partial pivoting into

Hi=LiUi=[1c21ci11ci1][d1b2d2b3di1bidi]

with convenient recurrences for ci and di:

ci=bi/di1,di={a1if i=1,aicibiif i>1.

Rewrite xi=x0+Viyi as

xi=x0+ViHi1(r02e1)=x0+ViUi1Li1(r02e1)=x0+Pizi

with

Pi=ViUi1,zi=Li1(r02e1).

It is now important to observe that

Pi=[Pi1pi],zi=[zi1ζi].

In fact, there are short recurrences for pi and ζi as well:

pi=1di(vibipi1),ζi=ciζi1.

With this formulation, we arrive at a simple recurrence for xi:

xi=x0+Pizi=x0+Pi1zi1+ζipi=xi1+ζipi.

The relations above straightforwardly lead to the direct Lanczos method, which turns out to be slightly more complex.

The conjugate gradient method from imposing orthogonality and conjugacy

If we allow pi to scale and compensate for the scaling in the constant factor, we potentially can have simpler recurrences of the form:

xi=xi1+αi1pi1,ri=ri1αi1Api1,pi=ri+βi1pi1.

As premises for the simplification, we now derive the orthogonality of ri and conjugacy of pi, i.e., for ij,

riTrj=0,piTApj=0.

The residuals are mutually orthogonal because ri is essentially a multiple of vi+1 since for i=0, r0=r02v1, for i>0,

ri=bAxi=bA(x0+Viyi)=r0AViyi=r0Vi+1H~iyi=r0ViHiyihi+1,i(eiTyi)vi+1=r02v1Vi(r02e1)hi+1,i(eiTyi)vi+1=hi+1,i(eiTyi)vi+1.

To see the conjugacy of pi, it suffices to show that PiTAPi is diagonal:

PiTAPi=UiTViTAViUi1=UiTHiUi1=UiTLiUiUi1=UiTLi

is symmetric and lower triangular simultaneously and thus must be diagonal.

Now we can derive the constant factors αi and βi with respect to the scaled pi by solely imposing the orthogonality of ri and conjugacy of pi.

Due to the orthogonality of ri, it is necessary that ri+1Tri=(riαiApi)Tri=0. As a result,

αi=riTririTApi=riTri(piβi1pi1)TApi=riTripiTApi.

Similarly, due to the conjugacy of pi, it is necessary that pi+1TApi=(ri+1+βipi)TApi=0. As a result,

βi=ri+1TApipiTApi=ri+1T(riri+1)αipiTApi=ri+1Tri+1riTri.

This completes the derivation.

References

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