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Logarithmic norm : ウィキペディア英語版
Logarithmic norm
In mathematics, the logarithmic norm is a real-valued functional on operators, and is derived from either an inner product, a vector norm, or its induced operator norm. The logarithmic norm was independently introduced by Germund Dahlquist〔Germund Dahlquist, "Stability and error bounds in the numerical integration of ordinary differential equations", Almqvist & Wiksell, Uppsala 1958〕 and Sergei Lozinskiĭ in 1958, for square matrices. It has since been extended to nonlinear operators and unbounded operators as well.〔Gustaf Söderlind, "The logarithmic norm. History and modern theory", ''BIT Numerical Mathematics'', 46(3):631-652, 2006〕 The logarithmic norm has a wide range of applications, in particular in matrix theory, differential equations and numerical analysis. In the finite dimensional setting it is also referred to as the matrix measure.
==Original definition==

Let A be a square matrix and \| \cdot \| be an induced matrix norm. The associated logarithmic norm \mu of A is defined
:\mu(A) = \lim \limits_ \frac
Here I is the identity matrix of the same dimension as A, and h is a real, positive number. The limit as h\rightarrow 0^- equals -\mu(-A), and is in general different from the logarithmic norm \mu(A), as -\mu(-A) \leq \mu(A) for all matrices.
The matrix norm \|A\| is always positive if A\neq 0, but the logarithmic norm \mu(A) may also take negative values, e.g. when A is negative definite. Therefore, the logarithmic norm does not satisfy the axioms of a norm. The name ''logarithmic norm,'' which does not appear in the original reference, seems to originate from estimating the logarithm of the norm of solutions to the differential equation
:\dot x = Ax.
The maximal growth rate of \log \|x\| is \mu(A). This is expressed by the differential inequality
:\frac \log \|x\| \leq \mu(A),
where \mathrm d/\mathrm dt^+ is the upper right Dini derivative. Using logarithmic differentiation the differential inequality can also be written
:\frac \leq \mu(A)\cdot \|x\|,
showing its direct relation to Grönwall's lemma. In fact, it can be shown that the norm of the state transition matrix \Phi(t, t_0) associated to the differential equation \dot x = A(t)x is bounded by
: \exp\left(-\int_^ \mu(-A(s)) ds \right) \le \|\Phi(t,t_0)\| \le \exp\left(\int_^ \mu(A(s)) ds \right)
for all t \ge t_0 .

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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