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**Chapter 13 Gaussian Elimination (III) Bunch-Parlett diagonal pivoting**

Speaker: Lung-Sheng Chien Reference: James R. Bunch and Linda Kaufman, Some Stable Methods for Calculating Inertia and Solving Symmetric Linear Systems, Mathematics of Computation, volume 31, number 137, January 1977, page

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OutLine Preliminary - 1x1, 2x2 pivoting in Gaussian Elimination - real symmetric indefinite matrix Symmetric permutation LDL’ decomposition (diagonal pivoting) Example of complete diagonal pivoting Algorithm of complete diagonal pivoting

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**Pivoting in Traditional Gaussian Elimination**

consider then one step LU-factorization leads to where and It is easy to show from Gaussian Elimination We use principal submatrix as pivoting, called pivoting since Question: can we use or higher pivoting, i.e.

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**2x2 pivoting in block Gaussian Elimination**

If principal sub-matrix is non-singular, then one-step GE leads to Example 1: pivoting where and

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**3x3 pivoting in block Gaussian Elimination**

Example 2: pivoting where and Question: what is criterion to choose 1x1, 2x2 or 3x3 pivoting? stability performance Question: what is the cost to estimate the criterion ?

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**Real symmetry indefinite matrix A**

is symmetric implies and From linear algebra, if A is real symmetric, then A has real spectrum decomposition (譜分解) and or write in matrix form and without loss of generality, we can rearrange eigen-values to be increasing Definition: suppose matrix A is real symmetric, then we say “A is indefinite” if there exists an eigen-value of A less than zero. If A is nonsingular, then Thereafter we focus on LU-decomposition of symmetry indefinite matrix A

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**LU-factorization for real symmetry indefinite matrix A**

where and Question: why not LU-decomposition? A is real symmetric, we only store lower triangle part of A, say

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**Pointer array to store lower triangular part of A**

row-major based col-major based Question: what is the cost to fetch one element of A ? That is, operation count for Question: can you find another representation for lower part of matrix A ? Question: if one uses row-major to store lower part of matrix A, then how to fetch a column of A efficiently?

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OutLine Preliminary Symmetric permutation - change diagonal element to (1,1) position - change off-diagonal element to (2,1) position - implementation of symmetric permutation LDL’ decomposition (diagonal pivoting) Example of complete diagonal pivoting Algorithm of complete diagonal pivoting

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**How to do permutation on real symmetry indefinite matrix A**

Let Define permutation matrix interchange row 2, 3 interchange column 2, 3 Symmetric permutation: 1 2 3 1 2 3 1 3 2 interchange row 2, 3 interchange column 2, 3 2 4 5 3 5 6 3 6 5 3 5 6 2 4 5 2 5 4

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**Change diagonal element to (1,1) position [1]**

suppose we want to change to position (1,1) , then consider permutation and do symmetric permutation on A, say 8 6 3 9 1 2 3 4 3 2 1 4 6 5 2 7 2 5 6 7 interchange col 1, 3 6 5 2 7 interchange row 1, 3 3 2 1 4 3 6 8 9 8 6 3 9 9 7 4 10 4 7 9 10 9 7 4 10 Question: we only store lower triangle part of matrix A, above permutation does not work, how to modify it? Observation 1: 1 2 3 4 8 2 3 4 2 5 6 7 2 5 6 7 3 6 8 9 3 6 1 9 4 7 9 10 4 7 9 10

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**Change diagonal element to (1,1) position [2]**

Observation 2: we only need to update lower triangle part of A (diagonal term is excluded ). 8 2 3 4 2 5 6 7 2 3 6 1 9 6 4 7 9 10 4 7 9 diagonal term does not changed we have changed does not changed

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**Change diagonal element to (1,1) position [3]**

6 2 2 6 2 2 6 interchange col 1, 3 6 2 interchange row 1, 3 2 6 6 9 7 4 4 7 9 9 7 4 Keep lower triangle part 8 8 6 5 Fill-in A(3,1) 6 5 Fill-in diagonal term 6 3 2 1 2 1 2 9 7 4 10 9 7 4 10 9 7 4 Question: any compact form to represent above interchange?

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**Change diagonal element to (1,1) position [4]**

2 6 2 6 6 2 4 7 9 9 7 4 4 7 9 case 1: 4 7 9 case 2: 2 6

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**Change diagonal element to (1,1) position [5]**

For general real symmetric matrix A with dimension n, suppose we want to change interchanges rows or columns Step 1: interchange position (1,1) and (k,k), i.e. keep position (k,1) and (1,k), i.e. does not changed

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**Change diagonal element to (1,1) position [6]**

Step 2: interchange column 1 and k below row k+1, i.e Therefore

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**Change diagonal element to (1,1) position [7]**

Step 3: interchange column 1 (from row-2 to row-(k-1)) and row k (from col-2 to col-(k-1)) Therefore

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**Implementation of symmetric permutation: swapping overhead [1]**

Suppose we use col-major based pointer array to store real symmetric matrix A Step 2: interchanging column 1 and k is O.K since memory block is contiguous. Contiguous memory block

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**Implementation of symmetric permutation: swapping overhead [2]**

Step 3: interchanging column 1 and row k, i.e. is NOT efficient since is NOT contiguous. NOT contiguous, swapping is very slow. Observation: the situation is the same even we choose row-major based pointer array as storage strategy. Solution: in order to avoid high swapping overhead, we should avoid permutation.

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**Change off-diagonal element to (2,1) position [1]**

suppose we want to change to position (2,1), then consider first permutation which change first index from 4 to 2 1 4 3 2 1 2 3 4 1 4 3 2 4 10 9 7 2 5 6 7 interchange col 2, 4 2 7 6 5 interchange row 2, 4 3 9 8 6 3 6 8 9 3 9 8 6 2 7 6 5 4 7 9 10 4 10 9 7 and second permutation which change second index from 3 to 1 1 4 3 2 3 4 1 2 8 9 3 6 4 10 9 7 interchange col 1, 3 9 10 4 7 interchange row 1, 3 9 10 4 7 3 9 8 6 8 9 3 6 3 4 1 2 2 7 6 5 6 7 2 5 6 7 2 5

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**Change off-diagonal element to (2,1) position [2]**

3 4 1 4 3 2 8 9 3 6 2 5 6 7 interchange row and col 2, 4 4 10 9 7 interchange row and col 1, 3 9 10 4 7 3 6 8 9 3 9 8 6 3 4 1 2 4 7 9 10 2 7 6 5 6 7 2 5 Observation: two permutation matrices can be computed easily since 2 4 and 3 are independent.

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**Change off-diagonal element to (2,1) position [3]**

HOW to transform to position (2,1), under only lower triangle part of A is stored? Observation 1: and does not changed 1 2 3 4 1 4 3 2 2 5 6 7 interchange row/col 2, 4 4 10 9 7 3 6 8 9 3 9 8 6 4 7 9 10 2 7 6 5 Step 1: interchange position (4,4) and (2,2), 1 2 3 4 1 2 3 4 2 5 6 7 2 10 6 7 3 6 8 9 3 6 8 9 4 7 9 10 4 7 9 5

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**Change off-diagonal element to (2,1) position [4]**

Observation 2: we only need to update lower triangle part of A (diagonal term is excluded ). 1 2 3 4 2 10 6 7 2 3 6 8 9 3 6 4 7 9 5 4 9 Step 2: interchange row 2 and 4 below col 2, i.e 2 interchange row 2, 4 4 9 interchange col 2, 4 4 9 3 6 3 6 3 9 6 4 9 2 6 2 6 NOTE: Interchanging column 2, 4 does not change position (2,1) and (4,1), it suffices to interchange position (2,1) and (4,1) first.

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**Change off-diagonal element to (2,1) position [5]**

4 3 6 3 6 4 9 2 9 Step 3: interchange column 2 (from row-3 to row-3) and row 4 (from col-3 to col-3) 4 4 3 6 3 9 2 9 2 6

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**Change off-diagonal element to (2,1) position [6]**

For general real symmetric matrix A with dimension n, suppose we want to change interchanges rows or columns Step 1: interchange position (2,2) and (k,k), i.e. keep position (k,2) and (2,k), i.e. does not changed

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**Change off-diagonal element to (2,1) position [7]**

Step 2: interchange column 2 and k below row k+1, i.e Therefore

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**Change off-diagonal element to (2,1) position [8]**

Step 3: interchange row 2 (column 1) and row k (column1), i.e Therefore

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**Change off-diagonal element to (2,1) position [9]**

Step 4: interchange column 2 (from row-3 to row-(k-1)) and row k (from col-3 to col-(k-1)) Therefore

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**OutLine Preliminary Symmetric permutation**

LDL’ decomposition (diagonal pivoting) - growth rate of 1x1, 2x2 pivoting - criterion for pivot strategy - comparison to complete pivoting in GE Example of complete diagonal pivoting Algorithm of complete diagonal pivoting

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**LDL’ decomposition: 1x1, 2x2 pivoting**

diagonal pivoting method with complete pivoting: Bunch-Parlett, “Direct methods fro solving symmetric indefinite systems of linear equations,” SIAM J. Numer. Anal., v. 8, 1971, pp diagonal pivoting method with partial pivoting: Bunch-Kaufman, “Some Stable Methods for Calculating Inertia and Solving Symmetric Linear Systems,” Mathematics of Computation, volume 31, number 137, January 1977, page

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**Growth rate in LDL’ decomposition [1]**

is of order maximal element of matrix A Define and constant maximal diagonal element of matrix A Case 1: s = 1 where with with and with

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**Growth rate in LDL’ decomposition [2]**

Therefore Observation: in PA=LU, we choose such that implies Therefore Observation: large element growth will not occur for a pivot if is large relative to

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**Growth rate in LDL’ decomposition [3]**

Case 2: s = 2 where and with and with and with implies

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**Growth rate in LDL’ decomposition [4]**

for Therefore

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**Growth rate in LDL’ decomposition [5]**

Therefore

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**Criterion for pivot strategy [1]**

Fix a number ( later on, we will determine optimal value of ) maximal element of matrix A maximal diagonal element of matrix A Case 1: suppose we interchange 1st and k-th rows and columns by introducing permutation matrix and do symmetric permutation Then do on with pivot, written as

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**Criterion for pivot strategy [2]**

Recall for pivot, , we have growth rate and Now for and Case 2: suppose , we interchange q-th and 1st rows and columns, and then r-th and 2nd rows and columns by introducing permutation matrix and do symmetric permutation Question: we must change q1 first, then change r 2, why?

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**Criterion for pivot strategy [3]**

transforms 1 2 3 (boundary case) but

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**Criterion for pivot strategy [4]**

Then do on with pivot, written as Recall for pivot, , we have growth rate and

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**Criterion for pivot strategy [5]**

Now for To sum up Case 1: and Case 2: and Question: How to choose value of

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**Criterion for pivot strategy [6]**

worst case analysis : choose such that growth rate of pivot + pivot growth rate of pivot or equivalently growth rate of pivot growth rate of pivot Define where satisfies Exercise: verify and

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**Diagonal pivoting versus complete pivoting of Gaussian Elimination [1]**

We must search for the largest element in each reduced matrix, this is a complete pivoting strategy analogous to Gaussian Elimination with complete pivoting 1 maximal element of matrix A maximal diagonal element of matrix A complete pivoting of GE (Gaussian Elimination) (1) find a such that (2) swap row and row (2) swap column and column then with

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**Diagonal pivoting versus complete pivoting of Gaussian Elimination [2]**

growth rate 2 diagonal pivoting complete pivoting in GE Remark: Bunch [1] proves that the element growth in the diagonal pivoting method with complete pivoting is bounded by in comparison with for Gaussian Elimination with complete pivoting, where [1] J.R. Bunch, “Analysis of the diagonal pivoting method”, SIAM J. Numer. Anal., v. 8, 1971, pp

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**OutLine Preliminary Symmetric permutation**

LDL’ decomposition (diagonal pivoting) Example of complete diagonal pivoting Algorithm of complete diagonal pivoting

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**Example (complete pivoting) [1]**

6 12 3 -6 12 -8 -13 4 we choose pivot 3 -13 -7 1 swap row/column and by permutation matrix -6 4 1 6 6 -8 -8 -13 12 4 12 -8 12 6 -13 -7 3 1 3 -13 -7 -13 3 -7 12 3 6 -6 -6 4 1 6 4 -6 1 6 4 1 -6 6

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**Example (complete pivoting) [2]**

-8 -13 12 4 1 -8 -13 -13 -7 3 1 1 -13 -7 12 3 6 -6 0.3982 1 4.7257 4 1 -6 6 0.1327 1 5.8584 Recursively do the same procedure for we choose pivot swap row/column by permutation matrix such that

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**Example (complete pivoting) [3]**

Do symmetry permutation for and -8 -13 -8 -13 -13 -7 -13 -7 4.7257 5.8584 5.8584 4.7257 1 1 1 1 0.3982 1 0.1327 1 0.1327 1 0.3982 1 5.8584 we do pivot on 4.7257

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**Example (complete pivoting) [4]**

5.8584 1 5.8584 1 4.7257 1 1 Or write in original matrix form -8 -13 1 -8 -13 -13 -7 1 -13 -7 5.8584 1 5.8584 4.7257 1

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**Example (complete pivoting) [5]**

1 -8 -13 1 -13 -7 0.1327 1 5.8584 0.3982 1 In practice, we have two key issues col-major based 1 we only store lower part of matrix 6 6 -8 -7 6 12 -8 12 -13 1 3 -13 -7 3 4 -6 4 1 6 -6

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**Example (complete pivoting) [6]**

2 we store decomposition and into original 1 -8 1 -13 -7 0.1327 1 5.8584 0.3982 1 -8 -13 -7 0.1327 5.8584 0.3982 Question: How can you judge correct decomposition from

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**Example (complete pivoting) [7]**

Case 1: four 1x1 pivot 1 -8 13 1 -7 0.1327 1 5.8584 0.3982 1 Case 2: two 2x2 pivot 1 -8 1 -13 -7 0.1327 1 5.8584 0.3982 1

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**Example (complete pivoting) [8]**

Case 3: 1x1 pivot + 2x2 pivot + 1x1 pivot -8 1 -7 -13 1 5.8584 0.1327 1 0.3982 1 Case 4: 2x2 pivot + 1x1 pivot + 1x1 pivot 1 -8 1 -13 -7 0.1327 1 5.8584 0.3982 1 Solution: we need an array to record pivot sequence 1x1 pivot pivot 2 1 1 2x2 pivot

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**OutLine Preliminary Symmetric permutation**

LDL’ decomposition (diagonal pivoting) Example of complete diagonal pivoting Algorithm of complete diagonal pivoting

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**Algorithm ( PAP’ = LDL’ ) [1]**

Given symmetric indefinite matrix , construct initial lower triangle matrix use permutation vector to record permutation matrix let , and while we have compute update original matrix , where combines all lower triangle matrix and store in

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**Algorithm ( PAP’ = LDL’ ) [2]**

1 compute and Case 1: define permutation to do symmetric permutation 2 To compute , we only update lower triangle of 1 1 3 2 3 3 then 2

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**Algorithm ( PAP’ = LDL’ ) [3]**

To compute 4 We only update lower triangle matrix then 5 do 1x1 pivot : then 6 and

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**Algorithm ( PAP’ = LDL’ ) [4]**

Case 2: define permutation to interchange q-th and k-th rows and columns, and then r-th and (k+1)-th rows and columns 7 and then To compute , we only update lower triangle of (1) do interchange row/col 1 1 8 2 3 3 then 2

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**Algorithm ( PAP’ = LDL’ ) [5]**

9 (2) do interchange row/col 1 2 3 4 1 3 4 then where 2

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**Algorithm ( PAP’ = LDL’ ) [6]**

To compute 10 (1) do interchange row 11 (2) do interchange row then

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**Algorithm ( PAP’ = LDL’ ) [7]**

12 do 2x2 pivot : then 13 and means is 2x2 pivot endwhile

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**Question: recursion implementation**

Initialization - check algorithm holds for k=1 Recursion formula - check algorithm holds for k or k+1, if k-1 is true Termination condition - check algorithm holds for k=n-1 Breakdown of algorithm - check which condition PAP’=LDL’ fails No extension: algorithm works only for square, symmetric indefinite matrix. normal abnormal Extension of algorithm

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**MATLAB implementation [1]**

Given a (full) symmetric indefinite matrix , compute factorization return four quantities Remark: try to neglect upper triangle part of in MATLAB implementation Example : 6 12 3 -6 12 -8 -13 4 2 3 4 1 return : 3 -13 -7 1 2 1 1 -6 4 1 6 1 -8 1 -13 -7 0.1327 1 5.8584 0.3982 1

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**MATLAB implementation [2]**

When factorization is complete, , you need to provide linear solver 1 define , then by forward substitution 2 define , then by diagonal block inversion Example: , then 3 define , then by backward substitution However you cannot transpose explicitly in MATLAB 4 , you must scan once to determine

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