Orthogonality

Orthogonal set and basis

An orthogonal set is a set in which all pairs of distinct vectors are orthogonal. Similarly, an orthogonal basis is a basis that is an orthogonal set.

If {v1,...,vn}\{\vec{v_1}, ..., \vec{v_n}\} is an orthogonal basis for a subspace WW and w\vec{w} is a vector in WW, the scalars ci,...,cnc_i, ..., c_n that express ww as a linear combination of {v1,...,vn}\{\vec{v_1}, ..., \vec{v_n}\} are given by:

ci=wvivivifor i=1,...,nc_i = \frac{w \cdot v_i}{v_i \cdot v_i} \text{for } i = 1, ..., n

Orthonormal set and basis

A set of vectors in RnR^n is called an orthonormal set if it is an orthogonal set of unit vectors. Likewise, an orthonormal basis is an orthonormal set that is a basis for some subspace. You can easily obtain an orthonormal set from an orthogonal set by normalizing each vector in the set.

If {q1,q2,...,qn}\{\vec{q_1}, \vec{q_2}, ..., \vec{q_n}\} is an orthonormal basis for subspace WW and w\vec{w} is in WW, then w\vec{w} can be expressed as a combination of projections:

w=(wq1)q1+(wq2)q2+...+(wqn)qnw = (w \cdot q_1)q_1 + (w \cdot q_2)q_2 + ... + (w \cdot q_n)q_n

Orthogonal Matrix

Somewhat confusingly, a square matrix in which the columns form an orthonormal set is called an orthogonal matrix. There is no name for a non-square matrix with orthonormal columns.

An orthogonal matrix some nice properties. Some of these are a bit obvious but it's nice to have them in a list. For any orthogonal matrix QQ with dimension n×nn \times n:

  • Q1=QTQ^{-1} = Q^T.

  • Qx=x||Q\vec{x}|| = ||\vec{x}|| for any xx in RnR^n.

  • Any eigenvalues λ\lambda of QQ have λ=1|\lambda| = 1.

  • QxQy=xyQ \vec{x} \cdot Q \vec{y} = \vec{x} \cdot \vec{y} for every xx and yy in RnR^n.

  • Q1Q^{-1} is orthogonal.

  • det(Q)=±1det(Q) = \pm 1.

Orthogonal Projection

If WW is some subspace of RnR^n and {u1,...,un}\{\vec{u_1}, ..., \vec{u_n}\} is an orthogonal basis for WW, for any v\vec{v} in RnR^n, the orthogonal projection of v onto W is defined as:

projW(v)=(u1vu1u1)u1+...+(unvunun)unproj_W (v) = (\frac{u_1 \cdot v}{u_1 \cdot u_1})u_1 + ... + (\frac{u_n \cdot v}{u_n \cdot u_n})u_n

The component of v orthogonal to W is the vector:

perpW(v)=vprojW(v)perp_W (v) = v - proj_W (v)

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