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Unformatted text preview: EECS 227A: Nonlinear and Convex Optimization Fall 2009 Linear algebra/Analysis and Calculus review 1 Chapter 1 Linear Algebra 1.1 Matrices 1.1.1 Basics Nullspace The nullspace (or, kernel) of a m n matrix A is the following subspace of R n : N ( A ) := { x R n : Ax = 0 } . Range and rank The range (or, image) of a m n matrix A is defined as the following subset of R m : R ( A ) := { Ax : x R n } . The range is simply the span of the columns of A . The dimension of the range is called the rank of the matrix. As we will see later, the rank cannot exceed any one of the dimensions of the matrix A : r min( m,n ). It is equal to n minus the dimension of its nullspace. A basic result of linear algebra states that any vector in R n can be decomposed as x = y + z , with y N ( A ), z R ( A T ), and z,y are orthogonal. (One way to prove this is via the singular value decomposition, seen later.) Symmetric Matrices : A square matrix A R n n is symmetric if and only if A = A T . The set of symmetric n n matrices is denoted S n . Orthogonal matrices. A square, n n matrix U = [ u 1 ,...,u n ] is orthogonal if its columns form an orthonormal basis. The condition u T i u j = 0 if i 6 = j , and 1 otherwise, translates in matrix terms as U T U = I n with I n the n n identity matrix. Unitary matrix: An n n matrix U is unitary if UU * = U * U = I where U * is the transpose of the conjugate of U . Normal matrix: An n n matrix A is normal if AA * = A * A 2 EECS 227A Fall 2009 1.1.2 Eigenvalue decomposition A fundamental result of linear algebra states that any symmetric matrix can be decomposed as a weighted sum of normalized dyads that are orthogonal to each other. Precisely, for every A S n , there exist numbers 1 ,..., n and an orthonormal basis ( u 1 ,...,u n ), such that A = n X i =1 i u i u T i . In a more compact matrix notation, we have A = U U T , with = diag ( 1 ,..., n ), and U = [ u 1 ,...,u n ]. The numbers 1 ,..., n are called the eigenvalues of A , and are the roots of the charac teristic equation det( I A ) = 0 , where I n is the n n identity matrix. Eigenvalues and eigenvectors satisfies Au i = i u i , some other properties of eigenvalues det ( A ) = Q n i =1 i Tr ( A ) = n i =1 i For arbitrary square matrices, eigenvalues can be complex. In the symmetric case, the eigenvalues are always real. There are only n (possibly distinct) solutions to the above equation. It is interesting to see what the eigenvalue decomposition of a given symmetric matrix A tells us about the corresponding quadratic form, q A ( x ) := x T Ax . With A = U U T , we have q A ( x ) = ( U T x ) T ( U T x ) = n X i =1 i ( u T i x ) 2 ....
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This note was uploaded on 09/03/2011 for the course EE 227A taught by Professor Staff during the Spring '11 term at University of California, Berkeley.
 Spring '11
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