Applications of singular-value decomposition (SVD)

نویسندگان

  • Alkiviadis G. Akritas
  • Gennadi I. Malaschonok
چکیده

Let A be an m × n matrix with m ≥ n. Then one form of the singular-value decomposition of A is A = UΣV, where U and V are orthogonal and Σ is square diagonal. That is, UUT = Irank(A), V V T = Irank(A), U is rank(A)×m, V is rank(A)× n and Σ =   σ1 0 · · · 0 0 0 σ2 · · · 0 0 .. .. . . . .. .. 0 0 · · · σrank(A)−1 0 0 0 · · · 0 σrank(A)   is a rank(A)× rank(A) diagonal matrix. In addition σ1 ≥ σ2 ≥ · · · ≥ σrank(A) > 0. The σi’s are called the singular values of A and their number is equal to the rank of A. The ratio σ1 σrank(A) can be regarded as a condition number of the matrix A. It is easily verified that the singular-value decomposition can be also written as

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عنوان ژورنال:
  • Mathematics and Computers in Simulation

دوره 67  شماره 

صفحات  -

تاریخ انتشار 2004