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Mathematics · পরিভাষা

quadratic form কী?

অন্য নাম: polarization identity · congruent matrices

Definition 12.1 University Mathematics — Year 2 · অধ্যায় 12 — Quadratic Forms

A symmetric bilinear form on a real vector space EE is a bilinear φ ⁣:E×E→R\varphi \colon E \times E \to \R with φ(x,y)=φ(y,x)\varphi(x, y) = \varphi(y, x); the associated quadratic form is q(x)=φ(x,x)q(x) = \varphi(x, x). The form φ\varphi is recovered from qq by polarization:

φ(x,y)=12(q(x+y)−q(x)−q(y)).\varphi(x, y) = \tfrac12\bigl(q(x + y) - q(x) - q(y)\bigr).

In a basis (ei)(e_i), the matrix of φ\varphi is the symmetric B=(φ(ei,ej))B = (\varphi(e_i, e_j)), with q(x)=XTBXq(x) = X^{\mathsf T} B X; a change of basis with matrix PP replaces BB by PTBPP^{\mathsf T} B P (congruence — not similarity!). The rank of qq is rk⁡B\operatorname{rk} B (invariant: congruence multiplies by invertible matrices).

উদাহরণ

Example 12.3 (Congruence in action)

Take q(x,y)=x2+4xy+y2q(x, y) = x^2 + 4xy + y^2 (matrix B=(1221)B = \begin{pmatrix} 1 & 2\\ 2 & 1\end{pmatrix}) and the new basis e1′=(1,1)e_1' = (1, 1), e2′=(1,−1)e_2' = (1, -1), i.e. P=(111−1)P = \begin{pmatrix} 1 & 1\\ 1 & -1\end{pmatrix}. Then

PTBP=(111−1)(1221)(111−1)=(600−2):P^{\mathsf T}BP = \begin{pmatrix} 1 & 1\\ 1 & -1\end{pmatrix} \begin{pmatrix} 1 & 2\\ 2 & 1\end{pmatrix} \begin{pmatrix} 1 & 1\\ 1 & -1\end{pmatrix} = \begin{pmatrix} 6 & 0\\ 0 & -2\end{pmatrix} :

in the coordinates (u,v)(u, v) along the new basis, q=6u2−2v2q = 6u^2 - 2v^2 — check: x=u+vx = u + v, y=u−vy = u - v gives x2+4xy+y2=6u2−2v2x^2 + 4xy + y^2 = 6u^2 - 2v^2 directly. Note the new diagonal entries 6,−26, -2 are not the eigenvalues 3,−13, -1 of BB: congruence rescales, only similarity preserves spectra — but the signs agree, as Sylvester’s theorem demands. (The basis here is orthogonal but not orthonormal; normalizing it by 12\frac{1}{\sqrt2} would divide the diagonal by 22 and recover the eigenvalues.)

Example 12.4 (Gram determinants measure area)

For v1,v2v_1, v_2 in a Euclidean space, the Gram matrix G=(⟨vi,vj⟩)G = \bigl(\langle v_i, v_j\rangle\bigr) packages lengths and angle; its determinant packages area:

det⁡G=∥v1∥2∥v2∥2−⟨v1,v2⟩2=∥v1∥2∥v2∥2(1−cos⁡2θ)=(∥v1∥ ∥v2∥sin⁡θ)2,\det G = \norm{v_1}^2\norm{v_2}^2 - \langle v_1, v_2\rangle^2 = \norm{v_1}^2\norm{v_2}^2\bigl(1 - \cos^2\theta\bigr) = \bigl(\norm{v_1}\,\norm{v_2}\sin\theta\bigr)^2 ,

the squared area of the parallelogram on v1,v2v_1, v_2 — and Cauchy–Schwarz is exactly the statement det⁡G≥0\det G \geq 0. Worked instance: v1=(1,2,2)v_1 = (1, 2, 2), v2=(2,1,−2)v_2 = (2, 1, -2) in R3\R^3:

G=(9009),det⁡G=81:G = \begin{pmatrix} 9 & 0\\ 0 & 9 \end{pmatrix}, \qquad \det G = 81 :

the vectors are orthogonal of length 33, spanning a parallelogram (here, a square) of area 81=9\sqrt{81} = 9. Closing insight: no cross product and no dimension-33 magic was used — det⁡G\sqrt{\det G} measures kk-dimensional volume in any dimension, the starting point of the weekend problem’s Part I and of the surface-area integrals later in this volume.

Example 12.16

A=(2112)A = \begin{pmatrix} 2 & 1\\ 1 & 2 \end{pmatrix}: eigenvalues 33 (eigenvector 12(1,1)\frac{1}{\sqrt2}(1,1)) and 11 (12(1,−1)\frac{1}{\sqrt2}(1,-1)). The quadratic form 2x2+2xy+2y22x^2 + 2xy + 2y^2 becomes 3X2+Y23X^2 + Y^2 in the rotated orthonormal frame: an ellipse’s principal axes, computed. The Gauss reduction reaches a diagonal form too, but only the spectral theorem reaches it without distorting lengths.

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