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  • Orthogonality and projection

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  • Inner products in coordinate spaces
  • Norm and length
  • Orthogonality
  • Projection
  • Least squares
  • Inner products

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  • Definition of (orthogonal) projection of one vector onto another vector
  • Formula for the (orthogonal) projection of one vector onto another vector
  • Definition of (not necessarily orthogonal) projection onto a component of a direct sum
  • Definition of (orthogonal) projection onto a subspace
  • The projection of a vector which is in a subspace is the vector itself.
  • The (orthogonal) projection of a vector onto a subspace is the point in the subspace closest to the vector.
  • Formula for the coordinates of the projection of a vector onto a subspace
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  1. Projection

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  • Proof of several equivalences for nonsingular matrix#23644
  • QR decomposition#23630
  • Rank and mullity#23672
  • Rank and nullity#23882
  • Removing a linearly dependent vector from a set does not change the span of the set.#23982
  • R^n is a vector space.#24097
  • Row equivalence is an equivalence relation#23480
  • Row equivalent matrices have the same row space.#23881
  • Row equivalent matrices represent equivalent linear systems#23769
  • Row operations#23474
  • Row operations are given by multiplication by elementary matrices.#23559
  • Row operations do not necessarily preserve the column space.#23878
  • Scalar multiplication#23470
  • Similarity of matrices#23634
  • Similarity of matrices in an equivalence relation.#23636
  • Similar matrices have the same eigenvalues and the same characteristic polynomials.#23638
  • Spans#23804
  • Subspaces#23811
  • Subspaces associated to a linear transformation#24055
  • Subspaces associated to a matrix#23872
  • Switching two rows multiplies the determinant by -1.#23738
  • Symmetric matrices#23571
  • Symmetric matrices are square.#23573
  • Terminology#23754
  • The 0 scalar multiplied by any vector equals the 0 vector.#23960
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  • Aspen Summer 2016 - Entanglement Matters#1213
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