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< | In [[numerical linear algebra]], the [[conjugate gradient method]] is an [[iterative method]] for numerically solving the [[System of linear equations|linear system]] | ||
:<math>\boldsymbol{Ax}=\boldsymbol{b}</math> | |||
where <math>\boldsymbol{A}</math> is [[Symmetric matrix|symmetric]] [[Positive-definite matrix|positive-definite]]. The conjugate gradient method can be derived from several different perspectives, including specialization of the [[conjugate direction method]] for [[Optimization (mathematics)|optimization]], and variation of the [[Arnoldi iteration|Arnoldi]]/[[Lanczos iteration|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== | |||
{{Expand section|date=April 2010}} | |||
The conjugate gradient method can be seen as a special case of the conjugate direction method applied to minimization of the quadratic function | |||
:<math>f(\boldsymbol{x})=\boldsymbol{x}^\mathrm{T}\boldsymbol{A}\boldsymbol{x}-2\boldsymbol{b}^\mathrm{T}\boldsymbol{x}\text{.}</math> | |||
===The conjugate direction method=== | |||
In the conjugate direction method for minimizing | |||
:<math>f(\boldsymbol{x})=\boldsymbol{x}^\mathrm{T}\boldsymbol{A}\boldsymbol{x}-2\boldsymbol{b}^\mathrm{T}\boldsymbol{x}\text{.}</math> | |||
one starts with an initial guess <math>\boldsymbol{x}_0</math> and the corresponding residual <math>\boldsymbol{r}_0=\boldsymbol{b}-\boldsymbol{Ax}_0</math>, and computes the iterate and residual by the formulae | |||
:<math>\begin{align} | |||
\alpha_i&=\frac{\boldsymbol{p}_i^\mathrm{T}\boldsymbol{r}_i}{\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_i}\text{,}\\ | |||
\boldsymbol{x}_{i+1}&=\boldsymbol{x}_i+\alpha_i\boldsymbol{p}_i\text{,}\\ | |||
\boldsymbol{r}_{i+1}&=\boldsymbol{r}_i-\alpha_i\boldsymbol{Ap}_i | |||
\end{align}</math> | |||
where <math>\boldsymbol{p}_0,\boldsymbol{p}_1,\boldsymbol{p}_2,\ldots</math> are a series of mutually conjugate directions, i.e., | |||
:<math>\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_j=0</math> | |||
for any <math>i\neq j</math>. | |||
The conjugate direction method is imprecise in the sense that no formulae are given for selection of the directions <math>\boldsymbol{p}_0,\boldsymbol{p}_1,\boldsymbol{p}_2,\ldots</math>. Specific choices lead to various methods including the conjugate gradient method and [[Gaussian elimination]]. | |||
==Derivation from the Arnoldi/Lanczos iteration== | |||
{{see|Arnoldi iteration|Lanczos iteration}} | |||
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 <math>\boldsymbol{r}_0</math> and gradually builds an [[orthonormal]] basis <math>\{\boldsymbol{v}_1,\boldsymbol{v}_2,\boldsymbol{v}_3,\ldots\}</math> of the [[Krylov subspace]] | |||
:<math>\mathcal{K}(\boldsymbol{A},\boldsymbol{r}_0)=\{\boldsymbol{r}_0,\boldsymbol{Ar}_0,\boldsymbol{A}^2\boldsymbol{r}_0,\ldots\}</math> | |||
by defining <math>\boldsymbol{v}_i=\boldsymbol{w}_i/\lVert\boldsymbol{w}_i\rVert_2</math> where | |||
:<math>\boldsymbol{w}_i=\begin{cases} | |||
\boldsymbol{r}_0 & \text{if }i=1\text{,}\\ | |||
\boldsymbol{Av}_{i-1}-\sum_{j=1}^{i-1}(\boldsymbol{v}_j^\mathrm{T}\boldsymbol{Av}_{i-1})\boldsymbol{v}_j & \text{if }i>1\text{.} | |||
\end{cases}</math> | |||
In other words, for <math>i>1</math>, <math>\boldsymbol{v}_i</math> is found by [[Gram-Schmidt orthogonalization|Gram-Schmidt orthogonalizing]] <math>\boldsymbol{Av}_{i-1}</math> against <math>\{\boldsymbol{v}_1,\boldsymbol{v}_2,\ldots,\boldsymbol{v}_{i-1}\}</math> followed by normalization. | |||
Put in matrix form, the iteration is captured by the equation | |||
:<math>\boldsymbol{AV}_i=\boldsymbol{V}_{i+1}\boldsymbol{\tilde{H}}_i</math> | |||
where | |||
:<math>\begin{align} | |||
\boldsymbol{V}_i&=\begin{bmatrix} | |||
\boldsymbol{v}_1 & \boldsymbol{v}_2 & \cdots & \boldsymbol{v}_i | |||
\end{bmatrix}\text{,}\\ | |||
\boldsymbol{\tilde{H}}_i&=\begin{bmatrix} | |||
h_{11} & h_{12} & h_{13} & \cdots & h_{1,i}\\ | |||
h_{21} & h_{22} & h_{23} & \cdots & h_{2,i}\\ | |||
& h_{32} & h_{33} & \cdots & h_{3,i}\\ | |||
& & \ddots & \ddots & \vdots\\ | |||
& & & h_{i,i-1} & h_{i,i}\\ | |||
& & & & h_{i+1,i} | |||
\end{bmatrix}=\begin{bmatrix} | |||
\boldsymbol{H}_i\\ | |||
h_{i+1,i}\boldsymbol{e}_i^\mathrm{T} | |||
\end{bmatrix} | |||
\end{align}</math> | |||
with | |||
:<math>h_{ji}=\begin{cases} | |||
\boldsymbol{v}_j^\mathrm{T}\boldsymbol{Av}_i & \text{if }j\leq i\text{,}\\ | |||
\lVert\boldsymbol{w}_{i+1}\rVert_2 & \text{if }j=i+1\text{,}\\ | |||
0 & \text{if }j>i+1\text{.} | |||
\end{cases}</math> | |||
When applying the Arnoldi iteration to solving linear systems, one starts with <math>\boldsymbol{r}_0=\boldsymbol{b}-\boldsymbol{Ax}_0</math>, the residual corresponding to an initial guess <math>\boldsymbol{x}_0</math>. After each step of iteration, one computes <math>\boldsymbol{y}_i=\boldsymbol{H}_i^{-1}(\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{e}_1)</math> and the new iterate <math>\boldsymbol{x}_i=\boldsymbol{x}_0+\boldsymbol{V}_i\boldsymbol{y}_i</math>. | |||
===The direct Lanczos method=== | |||
For the rest of discussion, we assume that <math>\boldsymbol{A}</math> is symmetric positive-definite. With symmetry of <math>\boldsymbol{A}</math>, the [[upper Hessenberg matrix]] <math>\boldsymbol{H}_i=\boldsymbol{V}_i^\mathrm{T}\boldsymbol{AV}_i</math> becomes symmetric and thus tridiagonal. It then can be more clearly denoted by | |||
:<math>\boldsymbol{H}_i=\begin{bmatrix} | |||
a_1 & b_2\\ | |||
b_2 & a_2 & b_3\\ | |||
& \ddots & \ddots & \ddots\\ | |||
& & b_{i-1} & a_{i-1} & b_i\\ | |||
& & & b_i & a_i | |||
\end{bmatrix}\text{.}</math> | |||
This enables a short three-term recurrence for <math>\boldsymbol{v}_i</math> in the iteration, and the Arnoldi iteration is reduced to the Lanczos iteration. | |||
Since <math>\boldsymbol{A}</math> is symmetric positive-definite, so is <math>\boldsymbol{H}_i</math>. Hence, <math>\boldsymbol{H}_i</math> can be [[LU factorization|LU factorized]] without [[partial pivoting]] into | |||
:<math>\boldsymbol{H}_i=\boldsymbol{L}_i\boldsymbol{U}_i=\begin{bmatrix} | |||
1\\ | |||
c_2 & 1\\ | |||
& \ddots & \ddots\\ | |||
& & c_{i-1} & 1\\ | |||
& & & c_i & 1 | |||
\end{bmatrix}\begin{bmatrix} | |||
d_1 & b_2\\ | |||
& d_2 & b_3\\ | |||
& & \ddots & \ddots\\ | |||
& & & d_{i-1} & b_i\\ | |||
& & & & d_i | |||
\end{bmatrix}</math> | |||
with convenient recurrences for <math>c_i</math> and <math>d_i</math>: | |||
:<math>\begin{align} | |||
c_i&=b_i/d_{i-1}\text{,}\\ | |||
d_i&=\begin{cases} | |||
a_1 & \text{if }i=1\text{,}\\ | |||
a_i-c_ib_i & \text{if }i>1\text{.} | |||
\end{cases} | |||
\end{align}</math> | |||
Rewrite <math>\boldsymbol{x}_i=\boldsymbol{x}_0+\boldsymbol{V}_i\boldsymbol{y}_i</math> as | |||
:<math>\begin{align} | |||
\boldsymbol{x}_i&=\boldsymbol{x}_0+\boldsymbol{V}_i\boldsymbol{H}_i^{-1}(\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{e}_1)\\ | |||
&=\boldsymbol{x}_0+\boldsymbol{V}_i\boldsymbol{U}_i^{-1}\boldsymbol{L}_i^{-1}(\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{e}_1)\\ | |||
&=\boldsymbol{x}_0+\boldsymbol{P}_i\boldsymbol{z}_i | |||
\end{align}</math> | |||
with | |||
:<math>\begin{align} | |||
\boldsymbol{P}_i&=\boldsymbol{V}_{i}\boldsymbol{U}_i^{-1}\text{,}\\ | |||
\boldsymbol{z}_i&=\boldsymbol{L}_i^{-1}(\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{e}_1)\text{.} | |||
\end{align}</math> | |||
It is now important to observe that | |||
:<math>\begin{align} | |||
\boldsymbol{P}_i&=\begin{bmatrix} | |||
\boldsymbol{P}_{i-1} & \boldsymbol{p}_i | |||
\end{bmatrix}\text{,}\\ | |||
\boldsymbol{z}_i&=\begin{bmatrix} | |||
\boldsymbol{z}_{i-1}\\ | |||
\zeta_i | |||
\end{bmatrix}\text{.} | |||
\end{align}</math> | |||
In fact, there are short recurrences for <math>\boldsymbol{p}_i</math> and <math>\zeta_i</math> as well: | |||
:<math>\begin{align} | |||
\boldsymbol{p}_i&=\frac{1}{d_i}(\boldsymbol{v}_i-b_i\boldsymbol{p}_{i-1})\text{,}\\ | |||
\zeta_i&=-c_i\zeta_{i-1}\text{.} | |||
\end{align}</math> | |||
With this formulation, we arrive at a simple recurrence for <math>\boldsymbol{x}_i</math>: | |||
:<math>\begin{align} | |||
\boldsymbol{x}_i&=\boldsymbol{x}_0+\boldsymbol{P}_i\boldsymbol{z}_i\\ | |||
&=\boldsymbol{x}_0+\boldsymbol{P}_{i-1}\boldsymbol{z}_{i-1}+\zeta_i\boldsymbol{p}_i\\ | |||
&=\boldsymbol{x}_{i-1}+\zeta_i\boldsymbol{p}_i\text{.} | |||
\end{align}</math> | |||
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 <math>\boldsymbol{p}_i</math> to scale and compensate for the scaling in the constant factor, we potentially can have simpler recurrences of the form: | |||
:<math>\begin{align} | |||
\boldsymbol{x}_i&=\boldsymbol{x}_{i-1}+\alpha_{i-1}\boldsymbol{p}_{i-1}\text{,}\\ | |||
\boldsymbol{r}_i&=\boldsymbol{r}_{i-1}-\alpha_{i-1}\boldsymbol{Ap}_{i-1}\text{,}\\ | |||
\boldsymbol{p}_i&=\boldsymbol{r}_i+\beta_{i-1}\boldsymbol{p}_{i-1}\text{.} | |||
\end{align}</math> | |||
As premises for the simplification, we now derive the orthogonality of <math>\boldsymbol{r}_i</math> and conjugacy of <math>\boldsymbol{p}_i</math>, i.e., for <math>i\neq j</math>, | |||
:<math>\begin{align} | |||
\boldsymbol{r}_i^\mathrm{T}\boldsymbol{r}_j&=0\text{,}\\ | |||
\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_j&=0\text{.} | |||
\end{align}</math> | |||
The residuals are mutually orthogonal because <math>\boldsymbol{r}_i</math> is essentially a multiple of <math>\boldsymbol{v}_{i+1}</math> since for <math>i=0</math>, <math>\boldsymbol{r}_0=\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{v}_1</math>, for <math>i>0</math>, | |||
:<math>\begin{align} | |||
\boldsymbol{r}_i&=\boldsymbol{b}-\boldsymbol{Ax}_i\\ | |||
&=\boldsymbol{b}-\boldsymbol{A}(\boldsymbol{x}_0+\boldsymbol{V}_i\boldsymbol{y}_i)\\ | |||
&=\boldsymbol{r}_0-\boldsymbol{AV}_i\boldsymbol{y}_i\\ | |||
&=\boldsymbol{r}_0-\boldsymbol{V}_{i+1}\boldsymbol{\tilde{H}}_i\boldsymbol{y}_i\\ | |||
&=\boldsymbol{r}_0-\boldsymbol{V}_i\boldsymbol{H}_i\boldsymbol{y}_i-h_{i+1,i}(\boldsymbol{e}_i^\mathrm{T}\boldsymbol{y}_i)\boldsymbol{v}_{i+1}\\ | |||
&=\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{v}_1-\boldsymbol{V}_i(\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{e}_1)-h_{i+1,i}(\boldsymbol{e}_i^\mathrm{T}\boldsymbol{y}_i)\boldsymbol{v}_{i+1}\\ | |||
&=-h_{i+1,i}(\boldsymbol{e}_i^\mathrm{T}\boldsymbol{y}_i)\boldsymbol{v}_{i+1}\text{.}\end{align}</math> | |||
To see the conjugacy of <math>\boldsymbol{p}_i</math>, it suffices to show that <math>\boldsymbol{P}_i^\mathrm{T}\boldsymbol{AP}_i</math> is diagonal: | |||
:<math>\begin{align} | |||
\boldsymbol{P}_i^\mathrm{T}\boldsymbol{AP}_i&=\boldsymbol{U}_i^{-\mathrm{T}}\boldsymbol{V}_i^\mathrm{T}\boldsymbol{AV}_i\boldsymbol{U}_i^{-1}\\ | |||
&=\boldsymbol{U}_i^{-\mathrm{T}}\boldsymbol{H}_i\boldsymbol{U}_i^{-1}\\ | |||
&=\boldsymbol{U}_i^{-\mathrm{T}}\boldsymbol{L}_i\boldsymbol{U}_i\boldsymbol{U}_i^{-1}\\ | |||
&=\boldsymbol{U}_i^{-\mathrm{T}}\boldsymbol{L}_i | |||
\end{align}</math> | |||
is symmetric and lower triangular simultaneously and thus must be diagonal. | |||
Now we can derive the constant factors <math>\alpha_i</math> and <math>\beta_i</math> with respect to the scaled <math>\boldsymbol{p}_i</math> by solely imposing the orthogonality of <math>\boldsymbol{r}_i</math> and conjugacy of <math>\boldsymbol{p}_i</math>. | |||
Due to the orthogonality of <math>\boldsymbol{r}_i</math>, it is necessary that <math>\boldsymbol{r}_{i+1}^\mathrm{T}\boldsymbol{r}_i=(\boldsymbol{r}_i-\alpha_i\boldsymbol{Ap}_i)^\mathrm{T}\boldsymbol{r}_i=0</math>. As a result, | |||
:<math>\begin{align} | |||
\alpha_i&=\frac{\boldsymbol{r}_i^\mathrm{T}\boldsymbol{r}_i}{\boldsymbol{r}_i^\mathrm{T}\boldsymbol{Ap}_i}\\ | |||
&=\frac{\boldsymbol{r}_i^\mathrm{T}\boldsymbol{r}_i}{(\boldsymbol{p}_i-\beta_{i-1}\boldsymbol{p}_{i-1})^\mathrm{T}\boldsymbol{Ap}_i}\\ | |||
&=\frac{\boldsymbol{r}_i^\mathrm{T}\boldsymbol{r}_i}{\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_i}\text{.} | |||
\end{align}</math> | |||
Similarly, due to the conjugacy of <math>\boldsymbol{p}_i</math>, it is necessary that <math>\boldsymbol{p}_{i+1}^\mathrm{T}\boldsymbol{Ap}_i=(\boldsymbol{r}_{i+1}+\beta_i\boldsymbol{p}_i)^\mathrm{T}\boldsymbol{Ap}_i=0</math>. As a result, | |||
:<math>\begin{align} | |||
\beta_i&=-\frac{\boldsymbol{r}_{i+1}^\mathrm{T}\boldsymbol{Ap}_i}{\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_i}\\ | |||
&=-\frac{\boldsymbol{r}_{i+1}^\mathrm{T}(\boldsymbol{r}_i-\boldsymbol{r}_{i+1})}{\alpha_i\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_i}\\ | |||
&=\frac{\boldsymbol{r}_{i+1}^\mathrm{T}\boldsymbol{r}_{i+1}}{\boldsymbol{r}_i^\mathrm{T}\boldsymbol{r}_i}\text{.} | |||
\end{align}</math> | |||
This completes the derivation. | |||
==References== | |||
#{{cite journal|last1 = Hestenes|first1 = M. R.|authorlink1 = David Hestenes|last2 = Stiefel|first2 = E.|authorlink2 = Eduard Stiefel|title = Methods of conjugate gradients for solving linear systems|journal = Journal of Research of the National Bureau of Standards|volume = 49|issue = 6|date=December 1952|url = http://nvl.nist.gov/pub/nistpubs/jres/049/6/V49.N06.A08.pdf|format=PDF}} | |||
#{{cite book|last = Saad|first = Y.|title = Iterative methods for sparse linear systems|edition = 2nd|chapter = Chapter 6: Krylov Subspace Methods, Part I|publisher = SIAM|year = 2003|isbn = 978-0-89871-534-7}} | |||
{{Numerical linear algebra}} | |||
[[Category:Numerical linear algebra]] | |||
[[Category:Optimization algorithms and methods]] | |||
[[Category:Gradient methods]] | |||
[[Category:Articles containing proofs]] |
Revision as of 17:33, 15 July 2013
In numerical linear algebra, the conjugate gradient method is an iterative method for numerically solving the linear system
where 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
The conjugate direction method
In the conjugate direction method for minimizing
one starts with an initial guess and the corresponding residual , and computes the iterate and residual by the formulae
where are a series of mutually conjugate directions, i.e.,
The conjugate direction method is imprecise in the sense that no formulae are given for selection of the directions . 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 and gradually builds an orthonormal basis of the Krylov subspace
In other words, for , is found by Gram-Schmidt orthogonalizing against followed by normalization.
Put in matrix form, the iteration is captured by the equation
where
with
When applying the Arnoldi iteration to solving linear systems, one starts with , the residual corresponding to an initial guess . After each step of iteration, one computes and the new iterate .
The direct Lanczos method
For the rest of discussion, we assume that is symmetric positive-definite. With symmetry of , the upper Hessenberg matrix becomes symmetric and thus tridiagonal. It then can be more clearly denoted by
This enables a short three-term recurrence for in the iteration, and the Arnoldi iteration is reduced to the Lanczos iteration.
Since is symmetric positive-definite, so is . Hence, can be LU factorized without partial pivoting into
with convenient recurrences for and :
with
It is now important to observe that
In fact, there are short recurrences for and as well:
With this formulation, we arrive at a simple recurrence for :
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 to scale and compensate for the scaling in the constant factor, we potentially can have simpler recurrences of the form:
As premises for the simplification, we now derive the orthogonality of and conjugacy of , i.e., for ,
The residuals are mutually orthogonal because is essentially a multiple of since for , , for ,
To see the conjugacy of , it suffices to show that is diagonal:
is symmetric and lower triangular simultaneously and thus must be diagonal.
Now we can derive the constant factors and with respect to the scaled by solely imposing the orthogonality of and conjugacy of .
Due to the orthogonality of , it is necessary that . As a result,
Similarly, due to the conjugacy of , it is necessary that . As a result,
This completes the derivation.
References
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