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  2. File:A First Course in Linear Algebra for print.pdf - Wikipedia

    en.wikipedia.org/wiki/File:A_First_Course_in...

    Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.

  3. Matrix similarity - Wikipedia

    en.wikipedia.org/wiki/Matrix_similarity

    In linear algebra, two n-by-n matrices A and B are called similar if there exists an invertible n-by-n matrix P such that =. Similar matrices represent the same linear map under two (possibly) different bases, with P being the change-of-basis matrix.

  4. Linear algebra - Wikipedia

    en.wikipedia.org/wiki/Linear_algebra

    Download as PDF; Printable version; ... Linear algebra is the branch of mathematics concerning linear equations such as: ... Friedberg, Stephen H.; Insel, ...

  5. Quotient space (linear algebra) - Wikipedia

    en.wikipedia.org/.../Quotient_space_(linear_algebra)

    Formally, the construction is as follows. [1] Let be a vector space over a field, and let be a subspace of .We define an equivalence relation on by stating that iff .That is, is related to if and only if one can be obtained from the other by adding an element of .

  6. Rank–nullity theorem - Wikipedia

    en.wikipedia.org/wiki/Rank–nullity_theorem

    The rank–nullity theorem is a theorem in linear algebra, which asserts: the number of columns of a matrix M is the sum of the rank of M and the nullity of M ; and the dimension of the domain of a linear transformation f is the sum of the rank of f (the dimension of the image of f ) and the nullity of f (the dimension of the kernel of f ).

  7. Spectral theorem - Wikipedia

    en.wikipedia.org/wiki/Spectral_theorem

    In linear algebra and functional analysis, a spectral theorem is a result about when a linear operator or matrix can be diagonalized (that is, represented as a diagonal matrix in some basis). This is extremely useful because computations involving a diagonalizable matrix can often be reduced to much simpler computations involving the ...