3. Let U E Rnxn be an orthogonal matrix, i.c., UTU = UUT-1. Show that for any vector x E Rn LXTL ...
Let A be an m × n matrix, let x Rn and let 0 be the zero vector in Rm. (a) Let u, v є Rn be any two solutions of Ax 0, and let c E R. Use the properties of matrix-vector multiplication to show that u+v and cu are also solutions of Ax O. (b) Extend the result of (a) to show that the linear combination cu + dv is a solution of Ax 0 for any c,d...
3. [2+2pt] Let n > 2. Consider a matrix A E Rnxn for which every leading principal submatrix of order less than n is non-singular. (a) Show that A can be factored in the form A = LDU, where Le Rnxn is unit lower triangular, D e Rnxn is diagonal and U E Rnxn is unit upper triangular. (b) If the factorization A = LU is known, where L is unit lower triangular and U is upper triangular, show how...
3. Let a >0, and for any A E Rnxn, define Aa aA (a) Prove that for any induced matrix norm, K(Ao) (b) Write the formula for det(Aa) in terms of det(A). estimating well/ill-conditioning of matrices. n(A) . Hint: examine IAall and IAal directly. (c) Based on your result from (a) and (b), comment on whether the determinant is useful for 3. Let a >0, and for any A E Rnxn, define Aa aA (a) Prove that for any induced...
Please show all work in READ-ABLE way. Thank you so much in advance. Problem 2.2 n and let X ε Rnxp be a full-rank matrix, and Assume p Note that H is a square n × n matrix. This problem is devoted to understanding the properties H Any matrix that satisfies conditions in (a) is an orthogonal projection matriz. In this problem, we will verify this directly for the H given in (1). Let V - Im(X). (b) Show that...
A square matrix E∈Mn×n(R) is idempotent if E2=E. It is symmetric if E = tE. (a) Let V⊆Rn be a subspace of Rn, and consider the orthogonal projection projV:Rn→Rn onto V. Show that the representing matrix E = [projV]EE of proj V relative to the standard basis E of Rn is both idempotent and symmetric. (b) Let E∈Mn×n(R) be a matrix that is both idempotent and symmetric. Show that there is a subspace V⊆Rn such that E= [projV]EE. [Hint: What...
3, (a) [5 marks] what does it mean for A E Rnxn to be (i) symmetric? (ii) orthogonal? (ii) diagonalisable? (iv) orthogonally diagonalisable? (b) [4 marks] Suppose that A ERn is orthogonally diagonalisable. Prove that A is symmetric. (c) [11 marks] Let A be the matrix 6 -2 Show that the eigenvalues are 7 and -6. Show that any corresponding eigenvectors vi and v2 are orthogonal with respect to the Euclidean inner product (d) [5 marks] Hence prove that the...
8. More generally, let X be any infinite-dimensional vector space equipped with an inner product ,) in such a way that the induced metric is complete. In particular, there is a norm on X defined by and the metric is given by d(r, y) yl Let A denote the unit ball A x E X < 1} We know that A is closed and bounded essentially from the definitions. Show that A is not compact. (Hint: Construct a sequence xn...
(f) Let A be symmetric square matrix of order n. Show that there exists an orthogonal matrix P such that PT AP is a diagonal matrix Hint : UseLO and Problem EK〗 (g) Let A be a square matrix and Rn × Rn → Rn is defined by: UCTION E AND MES FOR THE la(x, y) = хтАУ (i) Show that I is symmetric, ie, 14(x,y) = 1a(y, x), if a d Only if. A is symmetric (ii) Show that...
Let a vector z Rn be given. For X > 0 consider the problem (i) Show that for any λ 0 this problem has a unique solution「. (ii) Determine the unique solution「(as a function of λ and 2) Hint: Note that Λ is not differentiable everywhere. Remark: The solution of (ii) is really interesting and beautiful, since you will see that the solutions x\ are so-called sparse vectors, i.e. vector having many zero components. Indeed, χλ 0 whenever λ >...
Plese help me!!!(Conditioning of Problems and Stability of Algorithms) IA is an m x n matrix, and x is an n x 1 vector, then the linear transformation У-Ax maps Rn to Rm, so the linear transformation should have a condition number, condAar (x). Assume that ||l is a subordinate norm. a. Show that we can define condAx (x) = 11All 11제/IAxl for every x 0. IA is an m x n matrix, and x is an n x 1...