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MATH 3795 Lecture 5. Solving Linear Systems 3

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MATH 3795 Lecture 5. Solving Linear Systems 3 MATH 3795 Lecture 5. Solving Linear Systems 3 Dmitriy Leykekhman Fall 2008 Goals I Positive definite and definite matrices. I Cholesky decomposition. I LU-Decomposition of Tridiagonal Systems I Applications. D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 1 n×n I A 2 R is called m- banded if aij = 0 for ji − jj > m. n×n I If A 2 R is symmetric positive definite, then the LU decomposition can be computed in a stable way without permutation, i.e., A = LU I Can we use the structure of A, i.e. symmetry and the positive definiteness of A or the property that A is m-banded to compute LU-decomposition more efficiently? Symmetric Positive Definite Matrices n×n T I A 2 R is called symmetric if A = A . n×n T I A 2 R is called symmetric positive definite if A = A and vT Av > 0 for all v 2 Rn, v 6= 0. D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 2 n×n I If A 2 R is symmetric positive definite, then the LU decomposition can be computed in a stable way without permutation, i.e., A = LU I Can we use the structure of A, i.e. symmetry and the positive definiteness of A or the property that A is m-banded to compute LU-decomposition more efficiently? Symmetric Positive Definite Matrices n×n T I A 2 R is called symmetric if A = A . n×n T I A 2 R is called symmetric positive definite if A = A and vT Av > 0 for all v 2 Rn, v 6= 0. n×n I A 2 R is called m- banded if aij = 0 for ji − jj > m. D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 2 I Can we use the structure of A, i.e. symmetry and the positive definiteness of A or the property that A is m-banded to compute LU-decomposition more efficiently? Symmetric Positive Definite Matrices n×n T I A 2 R is called symmetric if A = A . n×n T I A 2 R is called symmetric positive definite if A = A and vT Av > 0 for all v 2 Rn, v 6= 0. n×n I A 2 R is called m- banded if aij = 0 for ji − jj > m. n×n I If A 2 R is symmetric positive definite, then the LU decomposition can be computed in a stable way without permutation, i.e., A = LU D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 2 Symmetric Positive Definite Matrices n×n T I A 2 R is called symmetric if A = A . n×n T I A 2 R is called symmetric positive definite if A = A and vT Av > 0 for all v 2 Rn, v 6= 0. n×n I A 2 R is called m- banded if aij = 0 for ji − jj > m. n×n I If A 2 R is symmetric positive definite, then the LU decomposition can be computed in a stable way without permutation, i.e., A = LU I Can we use the structure of A, i.e. symmetry and the positive definiteness of A or the property that A is m-banded to compute LU-decomposition more efficiently? D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 2 n×n I Fact 2: If A 2 R is symmetric positive definite and A = LU, then the diagonal entries of U are positive. I Any upper triangular matrix U we can write as U = DU~, where U~ is unit upper triangular and D = diag(u11; : : : ; unn): Symmetric Positive Definite Matrices I Fact 1: If L is a unit lower triangular matrix and U is an upper triangular matrix such that A = LU, then L and U are unique, i.e., if there L~ is a unit lower triangular matrix and U~ is an upper triangular matrix such that A = L~U~, then L = L~ and U = U~ . D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 3 I Any upper triangular matrix U we can write as U = DU~, where U~ is unit upper triangular and D = diag(u11; : : : ; unn): Symmetric Positive Definite Matrices I Fact 1: If L is a unit lower triangular matrix and U is an upper triangular matrix such that A = LU, then L and U are unique, i.e., if there L~ is a unit lower triangular matrix and U~ is an upper triangular matrix such that A = L~U~, then L = L~ and U = U~ . n×n I Fact 2: If A 2 R is symmetric positive definite and A = LU, then the diagonal entries of U are positive. D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 3 Symmetric Positive Definite Matrices I Fact 1: If L is a unit lower triangular matrix and U is an upper triangular matrix such that A = LU, then L and U are unique, i.e., if there L~ is a unit lower triangular matrix and U~ is an upper triangular matrix such that A = L~U~, then L = L~ and U = U~ . n×n I Fact 2: If A 2 R is symmetric positive definite and A = LU, then the diagonal entries of U are positive. I Any upper triangular matrix U we can write as U = DU~, where U~ is unit upper triangular and D = diag(u11; : : : ; unn): D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 3 Using that A = AT and LU decomposition is uniqie A = LU = U~ T DLT = U~ T (DLT ) = (lower unit triangular)×(upper triangular): Thus, L = U~ T and U = DLT : Symmetric Positive Definite Matrices Thus, A = LU = LDU:~ On the other hand AT = (LDU~)T = U~ T DLT : D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 4 Symmetric Positive Definite Matrices Thus, A = LU = LDU:~ On the other hand AT = (LDU~)T = U~ T DLT : Using that A = AT and LU decomposition is uniqie A = LU = U~ T DLT = U~ T (DLT ) = (lower unit triangular)×(upper triangular): Thus, L = U~ T and U = DLT : D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 4 I Recall that D = diag(u11; : : : ; unn) has positive diagonal entries. So we can define 1=2 p p D = diag( u11;::: unn): Define R := D1=2LT , then A = LDLT = LD1=2D1=2LT = RT R : Cholesky-decomposition Symmetric Positive Definite Matrices I Above we showed that if A is a symmetric positive definite matrix, then A = LDLT : D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 5 Symmetric Positive Definite Matrices I Above we showed that if A is a symmetric positive definite matrix, then A = LDLT : I Recall that D = diag(u11; : : : ; unn) has positive diagonal entries. So we can define 1=2 p p D = diag( u11;::: unn): Define R := D1=2LT , then A = LDLT = LD1=2D1=2LT = RT R : Cholesky-decomposition D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 5 Example 0 4 −4 8 1 0 1 0 0 1 0 4 −4 8 1 @ −4 8 −4 A = @ −1 1 0 A @ 0 4 4 A 8 −4 29 2 1 1 0 0 9 | {z } | {z } | {z } A L U 0 1 0 0 1 0 4 0 0 1 0 1 −1 2 1 = @ −1 1 0 A @ 0 4 0 A @ 0 1 1 A 2 1 1 0 0 9 0 0 1 | {z } | {z } | {z } L D LT 0 2 0 0 1 0 2 −2 4 1 = @ −2 2 0 A @ 0 2 2 A 4 2 3 0 0 3 | {z } | {z } RT R D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 6 T T I So far we know how to compute LDL and R R by first computing the LU-decomposition and then derive LDLT or RT R from it. No savings. Not very useful. Symmetric Positive Definite Matrices n×n I A matrix A 2 R is symmetric positive definite if and only if there exists a unit lower triangular matrix L and a positive definite diagonal matrix D such that A = LDLT . n×n I A matrix A 2 R is symmetric positive definite if and only if there exists an upper triangular matrix R with rii > 0, i = 1; : : : ; n, such that A = RT R. D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 7 Symmetric Positive Definite Matrices n×n I A matrix A 2 R is symmetric positive definite if and only if there exists a unit lower triangular matrix L and a positive definite diagonal matrix D such that A = LDLT . n×n I A matrix A 2 R is symmetric positive definite if and only if there exists an upper triangular matrix R with rii > 0, i = 1; : : : ; n, such that A = RT R. T T I So far we know how to compute LDL and R R by first computing the LU-decomposition and then derive LDLT or RT R from it. No savings. Not very useful. D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Symmetric and Banded Matrices { 7 I Can easily derive the algorithm I Fix i = 1 and let j = 1 : n; a = r r . This implies p 1j 11 1j r11 = a11, j = 1 and r1j = a1j =r11, j = 2 : n. I Fix i =p 2 and let j = 2 : n; a2j = r12r1j + r22r2j .
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