Show that if an \(n \times n\) matrix \(A\) is positive definite, then there exists a positive definite matrix \(B\) such that \(A = {B^T}B\). (Hint: Write \(A = PD{P^T}\), with\({P^T} = {P^{ - 1}}\). Produce a diagonal matrix \(C\) such that \(D = {C^T}C\), and let \(B = PC{P^T}\). Show that \(B\) works.)

Short Answer

Expert verified

It is proved that if a \(n \times n\) matrix \(A\) is positive definite, then there exists a positive definite matrix \(B\) such that \({B^T}B = A\).

Step by step solution

01

Symmetric Matrices and Quadratic Forms

When any Symmetric Matrix \(A\) is diagonalized orthogonallyas\(PD{P^{ - 1}}\) we have:

\(\begin{aligned}{}{x^T}Ax = {y^T}Dy\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left\{ {{\rm{as }}x = Py} \right\}\\{\rm{and}}\\\left\| x \right\| = \left\| {Py} \right\| = \left\| y \right\|\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\;\;\;\left\{ {\forall y \in \mathbb{R}} \right\}\end{aligned}\)

02

Show that if a \(n \times n\) matrix \(A\) is positive definite, then there exists a positive definite matrix \(B\) such that \({B^T}B = A\)

As per the question, we have:

The positive\(n \times n\)matrix\(A\),Let we have:

\(A = PD{P^T},\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left\{ {{\rm{where }}{P^T} = {P^{ - 1}}} \right\}\)

For\(C\)being its diagonal matrix with all positive eigenvalues, we have:

\(\begin{aligned}{}D &= {C^2}\\ &= {C^T}C\end{aligned}\)

For,\(B = PC{P^T}\), we have:

\(\begin{aligned}{}{B^T}B &= {\left( {PC{P^T}} \right)^T}\left( {PC{P^T}} \right)\\ &= \left( {{P^T}{C^T}{P^T}^T} \right)\left( {PC{P^T}} \right)\\ &= P{C^T}C{P^T}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left\{ {{P^T}P = 1} \right\}\\ &= PD{P^T}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left\{ {{C^T}C = D} \right\}\\ &= A\end{aligned}\)

Henceit is proved thatif a \(n \times n\) matrix \(A\) is positive definite, then there exists a positive definite matrix \(B\) such that \({B^T}B = A\).

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Most popular questions from this chapter

Question:Find the principal components of the data for Exercise 1.

(M) Compute an SVD of each matrix in Exercises 26 and 27. Report the final matrix entries accurate to two decimal places. Use the method of Examples 3 and 4.

27. \(A{\bf{ = }}\left( {\begin{array}{*{20}{c}}{\bf{6}}&{ - {\bf{8}}}&{ - {\bf{4}}}&{\bf{5}}&{ - {\bf{4}}}\\{\bf{2}}&{\bf{7}}&{ - {\bf{5}}}&{ - {\bf{6}}}&{\bf{4}}\\{\bf{0}}&{ - {\bf{1}}}&{ - {\bf{8}}}&{\bf{2}}&{\bf{2}}\\{ - {\bf{1}}}&{ - {\bf{2}}}&{\bf{4}}&{\bf{4}}&{ - {\bf{8}}}\end{array}} \right)\)

Question: 6. Let A be an \(n \times n\) symmetric matrix. Use Exercise 5 and an eigenvector basis for \({\mathbb{R}^n}\) to give a second proof of the decomposition in Exercise 4(b).

Question: 12. Exercises 12–14 concern an \(m \times n\) matrix \(A\) with a reduced singular value decomposition, \(A = {U_r}D{V_r}^T\), and the pseudoinverse \({A^ + } = {U_r}{D^{ - 1}}{V_r}^T\).

Verify the properties of\({A^ + }\):

a. For each\({\rm{y}}\)in\({\mathbb{R}^m}\),\(A{A^ + }{\rm{y}}\)is the orthogonal projection of\({\rm{y}}\)onto\({\rm{Col}}\,A\).

b. For each\({\rm{x}}\)in\({\mathbb{R}^n}\),\({A^ + }A{\rm{x}}\)is the orthogonal projection of\({\rm{x}}\)onto\({\rm{Row}}\,A\).

c. \(A{A^ + }A = A\)and \({A^ + }A{A^ + } = {A^ + }\).

Classify the quadratic forms in Exercises 9-18. Then make a change of variable, \({\bf{x}} = P{\bf{y}}\), that transforms the quadratic form into one with no cross-product term. Write the new quadratic form. Construct P using the methods of Section 7.1.

10. \({\bf{2}}x_{\bf{1}}^{\bf{2}} + {\bf{6}}{x_{\bf{1}}}{x_{\bf{2}}} - {\bf{6}}x_{\bf{2}}^{\bf{2}}\)

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