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  2. Kernel (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(linear_algebra)

    The left null space of A is the same as the kernel of A T. The left null space of A is the orthogonal complement to the column space of A, and is dual to the cokernel of the associated linear transformation. The kernel, the row space, the column space, and the left null space of A are the four fundamental subspaces associated with the matrix A.

  3. Row and column spaces - Wikipedia

    en.wikipedia.org/wiki/Row_and_column_spaces

    It follows that the null space of A is the orthogonal complement to the row space. For example, if the row space is a plane through the origin in three dimensions, then the null space will be the perpendicular line through the origin. This provides a proof of the rank–nullity theorem (see dimension above).

  4. HackerspaceSG - Wikipedia

    en.wikipedia.org/wiki/HackerspaceSG

    HackerspaceSG is a 826-square-foot (76.7 m 2) technology community center and hackerspace in Singapore. [1] While predominantly an open working space for software projects, HackerspaceSG is also a landmark of the Singapore DIY movement, [2] and also hosts a range of events from technology classes to biology, computer hardware, and manufacturing.

  5. Nullspace - Wikipedia

    en.wikipedia.org/?title=Nullspace&redirect=no

    This page was last edited on 29 September 2013, at 13:22 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.

  6. Moore–Penrose inverse - Wikipedia

    en.wikipedia.org/wiki/Moore–Penrose_inverse

    The vector space of ⁠ ⁠ matrices over ⁠ ⁠ is denoted by ⁠ ⁠. For ⁠ A ∈ K m × n {\displaystyle A\in \mathbb {K} ^{m\times n}} ⁠ , the transpose is denoted ⁠ A T {\displaystyle A^{\mathsf {T}}} ⁠ and the Hermitian transpose (also called conjugate transpose ) is denoted ⁠ A ∗ {\displaystyle A^{*}} ⁠ .

  7. Nullspace property - Wikipedia

    en.wikipedia.org/wiki/Nullspace_property

    The non-convex-minimization problem, ‖ ‖ subject to =, is a standard problem in compressed sensing. However, -minimization is known to be NP-hard in general. [2] As such, the technique of -relaxation is sometimes employed to circumvent the difficulties of signal reconstruction using the -norm.

  8. Null space (matrix) - Wikipedia

    en.wikipedia.org/?title=Null_space_(matrix...

    This page was last edited on 30 September 2013, at 19:23 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.

  9. Null space - Wikipedia

    en.wikipedia.org/?title=Null_space&redirect=no

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