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About Summary (Linear Algebra)
1. A vector space is a set that is closed under vector addition and scalar multiplication and satisfies other properties such as having an additive identity and inverse. The set of m x n matrices and Rn are examples of vector spaces. 2. A set of vectors is linearly dependent if one vector can be written as a linear combination of the others, otherwise it is linearly independent. The span of a set of vectors is the set of all their linear combinations. 3. Gaussian elimination puts a matrix in row echelon form by using elementary row operations. Every matrix is row equivalent to a matrix in echelon form. The column space of a matrix is the span of its columns and the nullspace is the
Detailed Information
| Author: | ['Kun Zhou'] |
|---|---|
| Publication Year: | 2014 |
| Pages: | 3 |
| Language: | English |
| Format: | |
| Price: | FREE |
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