Gram matrix: Difference between revisions

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	In ] and ], a '''Gramian matrix''' is a ] ] ] that can be used to test for ] of ]. The Gramian matrix of a set of functions <math>\{l_i(\cdot),\,i=1,\dots,n\}</math> is defined as		In ] and ], a '''Gramian matrix''' is a ] ] ] that can be used to test for ] of ]. The Gramian matrix of a set of functions <math>\{l_i(\cdot),\,i=1,\dots,n\}</math> is defined as
	:<math>G=,\,\,G_{ij}=\int_{t_0}^{t_f} l_i(\tau)l_j(\tau)\, d\tau </math>		:<math>G=,\,\,G_{ij}=\int_{t_0}^{t_f} l_i(\tau)l_j(\tau)\, d\tau </math>
	If the functions are linearly independent, then <math>G</math> is ]. Its ] is known as the '''Gram determinant'''.		If the functions are linearly independent, then <math>G</math> is ]. Its ] is known as the '''Gram determinant'''.

Revision as of 20:20, 25 September 2004

In systems theory and linear algebra, a Gramian matrix is a real symmetric matrix that can be used to test for linear independence of functions. The Gramian matrix of a set of functions $\{l_{i}(\cdot ),\,i=1,\dots ,n\}$ is defined as

G=,\,\,G_{ij}=\int _{t_{0}}^{t_{f}}l_{i}(\tau )l_{j}(\tau )\,d\tau

If the functions are linearly independent, then $G$ is nonsingular. Its determinant is known as the Gram determinant.

In fact this is a special case of a quantitative measure of linear independence of vectors, available in any Hilbert space.