词条 | Min-max theorem |
释义 |
In linear algebra and functional analysis, the min-max theorem, or variational theorem, or Courant–Fischer–Weyl min-max principle, is a result that gives a variational characterization of eigenvalues of compact Hermitian operators on Hilbert spaces. It can be viewed as the starting point of many results of similar nature. This article first discusses the finite-dimensional case and its applications before considering compact operators on infinite-dimensional Hilbert spaces. We will see that for compact operators, the proof of the main theorem uses essentially the same idea from the finite-dimensional argument. In the case that the operator is non-Hermitian, the theorem provides an equivalent characterization of the associated singular values. The min-max theorem can be extended to self-adjoint operators that are bounded below. MatricesLet {{mvar|A}} be a {{math|n × n}} Hermitian matrix. As with many other variational results on eigenvalues, one considers the Rayleigh–Ritz quotient {{math|RA : Cn \\ {0} → R}} defined by where {{math|(⋅, ⋅)}} denotes the Euclidean inner product on {{math|Cn}}. Clearly, the Rayleigh quotient of an eigenvector is its associated eigenvalue. Equivalently, the Rayleigh–Ritz quotient can be replaced by For Hermitian matrices, the range of the continuous function RA(x), or f(x), is a compact subset [a, b] of the real line. The maximum b and the minimum a are the largest and smallest eigenvalue of A, respectively. The min-max theorem is a refinement of this fact. Min-max theoremLet {{mvar|A}} be an {{math|n × n}} Hermitian matrix with eigenvalues {{math|λ1 ≤ ... ≤ λk ≤ ... ≤ λn}} then and in particular, and these bounds are attained when {{mvar|x}} is an eigenvector of the appropriate eigenvalues. Also note that the simpler formulation for the maximal eigenvalue λn is given by: Similarly, the minimal eigenvalue λ1 is given by: ProofSince the matrix {{mvar|A}} is Hermitian it is diagonalizable and we can choose an orthonormal basis of eigenvectors {u1, ..., un} that is, ui is an eigenvector for the eigenvalue λi and such that (ui, ui) = 1 and (ui, uj) = 0 for all i ≠ j. If U is a subspace of dimension k then its intersection with the subspace {{math|span{uk, ..., un} }} isn't zero (by simply checking dimensions) and hence there exists a vector {{math|v ≠ 0}} in this intersection that we can write as and whose Rayleigh quotient is (as all for i=k,..,n) and hence Since this is true for all U, we can conclude that This is one inequality. To establish the other inequality, chose the specific k-dimensional space {{math|V {{=}} span{u1, ..., uk} }}, for whichbecause is the largest eigenvalue in V. Therefore, also In the case where U is a subspace of dimension n-k+1, we proceed in a similar fashion: Consider the subspace of dimension k, {{math|span{u1, ..., uk}.}} Its intersection with the subspace U isn't zero (by simply checking dimensions) and hence there exists a vector v in this intersection that we can write as and whose Rayleigh quotient is and hence Since this is true for all U, we can conclude that Again, this is one part of the equation. To get the other inequality, note again that the eigenvector u of is contained in {{math|U {{=}} span{uk, ..., un} }}so that we can conclude the equality. Counterexample in the non-Hermitian caseLet N be the nilpotent matrix Define the Rayleigh quotient exactly as above in the Hermitian case. Then it is easy to see that the only eigenvalue of N is zero, while the maximum value of the Rayleigh ratio is {{math|{{sfrac|1|2}}}}. That is, the maximum value of the Rayleigh quotient is larger than the maximum eigenvalue. ApplicationsMin-max principle for singular valuesThe singular values {σk} of a square matrix M are the square roots of the eigenvalues of M*M (equivalently MM*). An immediate consequence{{Citation needed|reason=claim is unreferenced and maybe suspicious|date=April 2014}} of the first equality in the min-max theorem is: Similarly, Here denotes the kth entry in the increasing sequence of σ's, so that . Cauchy interlacing theorem{{Main|Poincaré separation theorem}}Let {{mvar|A}} be a symmetric n × n matrix. The m × m matrix B, where m ≤ n, is called a compression of {{mvar|A}} if there exists an orthogonal projection P onto a subspace of dimension m such that P*AP = B. The Cauchy interlacing theorem states: Theorem. If the eigenvalues of {{mvar|A}} are {{math|α1 ≤ ... ≤ αn}}, and those of B are {{math|β1 ≤ ... ≤ βj ≤ ... ≤ βm}}, then for all {{math|j ≤ m}}, This can be proven using the min-max principle. Let βi have corresponding eigenvector bi and Sj be the j dimensional subspace {{math|Sj {{=}} span{b1, ..., bj},}} then According to first part of min-max, {{math|αj ≤ βj.}} On the other hand, if we define {{math|Sm−j+1 {{=}} span{bj, ..., bm},}} then where the last inequality is given by the second part of min-max. Notice that, when {{math|n − m {{=}} 1}}, we have {{math|αj ≤ βj ≤ αj+1}}, hence the name interlacing theorem. Compact operatorsLet {{mvar|A}} be a compact, Hermitian operator on a Hilbert space H. Recall that the spectrum of such an operator (the set of eigenvalues) is a set of real numbers whose only possible cluster point is zero. It is thus convenient to list the positive eigenvalues of {{mvar|A}} as where entries are repeated with multiplicity, as in the matrix case. (To emphasize that the sequence is decreasing, we may write .) When H is infinite-dimensional, the above sequence of eigenvalues is necessarily infinite. We now apply the same reasoning as in the matrix case. Letting Sk ⊂ H be a k dimensional subspace, we can obtain the following theorem. Theorem (Min-Max). Let {{mvar|A}} be a compact, self-adjoint operator on a Hilbert space {{mvar|H}}, whose positive eigenvalues are listed in decreasing order {{math|... ≤ λk ≤ ... ≤ λ1}}. Then: A similar pair of equalities hold for negative eigenvalues. Proof: {{hidden|(Click "show" at right to see the proof of this theorem or "hide" to hide it.) |2= Let S' be the closure of the linear span . The subspace S' has codimension k − 1. By the same dimension count argument as in the matrix case, S' ∩ Sk is non empty. So there exists x ∈ S' ∩ Sk with . Since it is an element of S' , such an x necessarily satisfy Therefore, for all Sk But {{mvar|A}} is compact, therefore the function f(x) = (Ax, x) is weakly continuous. Furthermore, any bounded set in H is weakly compact. This lets us replace the infimum by minimum: So Because equality is achieved when , This is the first part of min-max theorem for compact self-adjoint operators. Analogously, consider now a {{math|(k − 1)}}-dimensional subspace Sk−1, whose the orthogonal complement is denoted by Sk−1⊥. If S' = span{u1...uk}, So This implies where the compactness of A was applied. Index the above by the collection of k-1-dimensional subspaces gives Pick Sk−1 = span{u1, ..., uk−1} and we deduce }} Self-adjoint operatorsThe min-max theorem also applies to (possibly unbounded) self-adjoint operators.[1] [2] Recall the essential spectrum is the spectrum without isolated eigenvalues of finite multiplicity. Sometimes we have some eigenvalues below the essential spectrum, and we would like to approximate the eigenvalues and eigenfunctions. Theorem (Min-Max). Let A be self-adjoint, and let be the eigenvalues of A below the essential spectrum. Then . If we only have N eigenvalues and hence run out of eigenvalues, then we let (the bottom of the essential spectrum) for n>N, and the above statement holds after replacing min-max with inf-sup. Theorem (Max-Min). Let A be self-adjoint, and let be the eigenvalues of A below the essential spectrum. Then . If we only have N eigenvalues and hence run out of eigenvalues, then we let (the bottom of the essential spectrum) for n>N, and the above statement holds after replacing max-min with sup-inf. The proofs[1][2] use the following results about self-adjoint operators: Theorem. Let A be self-adjoint. Then for if and only if . Theorem. If A is self-adjoint, then and . See also
References1. ^1 G. Teschl, Mathematical Methods in Quantum Mechanics (GSM 99) http://www.mat.univie.ac.at/~gerald/ftp/book-schroe/schroe.pdf 2. ^1 Lieb-Loss, Analysis 2nd ed. (GSM 14)
4 : Articles containing proofs|Theorems in functional analysis|Spectral theory|Operator theory |
随便看 |
|
开放百科全书收录14589846条英语、德语、日语等多语种百科知识,基本涵盖了大多数领域的百科知识,是一部内容自由、开放的电子版国际百科全书。