lin-cg-notes

# lin-cg-notes - Notes on the linear conjugate gradient...

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Unformatted text preview: Notes on the linear conjugate gradient method 1 The conjugacy property Minimizing a convex quadratic function f ( x ) = 1 2 x T Ax- b T x + c is equivalent to solving the linear system ∇ f ( x ) = Ax- b = . For positive definite A this can be done using a method called the conjugate gradient method, that has close ties to optimization. The basic idea is to write x k = x + α p + α 1 p 1 + ··· + α k- 1 p k- 1 where the α i ’s are scalars, and the p i ’s are search directions. Then f ( x k ) is a quadratic function of the α i ’s, and finding the minimizing α i ’s can be done by solving a linear system. But linear systems can be most easily solved when they are diagonal . The linear system for the minimizing α i ’s is p T i " A ( x + k- 1 ∑ j = α j p j )- b # = for i = , 1 , 2 , ..., k- 1 . The linear system is k × k with ( i , j ) entry given by p T i Ap j . This matrix is diagonal if p T i Ap j = for all i 6 = j . That is, if the p i ’s are A-conjugate or just conjugate if A is understood. If the p i ’s are conjugate (with respect to A ) then the system of linear equations becomes simply p T i Ap i α i = p T i ( b- Ax ) = p T i " b- A ( x + i- 1 ∑ j = α j p j ) # . Note that increasing k does not change the value of α i for i ≤ k . This means that x k + 1 = x k + α k p k . Note that x k minimizes f ( x + ∑ k- 1 i = α i p i ) over all α i ’s; that is, x k minimizes f ( z ) over all z ∈ x o + span { p , ..., p k- 1 } . This is the conjugate gradient minimization property. Let r k = ∇ f ( x k )= Ax k- b . In optimization, this is clearly the gradient; in linear algebra it is called the residual for x k . If we had a sequence p , p 1 , ... of conjugate vectors then we could design an iterative algorithm for minimizing f ( x ) : 1 Given: A , b , x , p , p 1 , p 2 ,... for k ← , 1 , 2 , ..., n r k ← Ax k- b α k ← - p T k r k / p T k Ap k x k + 1 ← x k + α k p k end for The problem now is to find out how to generate the conjugate p i ’s. At the beginning, any p by itself is conjugate. So we have a place to start. We can then proceed using mathematical induction. Now let’s suppose we have generated p , p 1 , ..., p k which are, so far, all conjugate. We will also show that the residualswhich are, so far, all conjugate....
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## This note was uploaded on 04/01/2012 for the course 22M 174 taught by Professor Davidstewart during the Spring '12 term at University of Iowa.

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lin-cg-notes - Notes on the linear conjugate gradient...

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