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{{Short description|Type of homogeneous polynomial of degree 2}}
In [[mathematics]], a '''definite quadratic form''' is a [[quadratic form]] over some [[Real number|real]] [[vector space]] {{
A '''semidefinite''' (or
An '''indefinite''' quadratic form takes on both positive and negative values and is called an [[isotropic quadratic form]].
More generally, the definition applies to a vector space over an [[ordered field]].<ref>Milnor & Husemoller (1973) p. 61</ref>▼
▲More generally,
==Associated symmetric bilinear form==
Quadratic forms correspond one-to-one to [[symmetric bilinear form]]s over the same space.<ref>This is true only over a field of [[characteristic (algebra)|characteristic]] other than 2, but here we consider only [[ordered field]]s, which necessarily have characteristic 0.</ref> A symmetric bilinear form is also described as '''definite''', '''semidefinite''', etc. according to its associated quadratic form. A quadratic form {{
:<math>\begin{align}
Q(x) &= B(x, x) \\
\end{align}</math>
▲:<math>\, B(x,y) = B(y,x) = \tfrac{1}{2} (Q(x + y) - Q(x) - Q(y)) </math>
==Examples==
As an example, let <math>V = \mathbb{R}^2 </math>, and consider the quadratic form
:<math> Q(x) = c_1{x_1}^2 + c_2{x_2}^2 </math>
where
In general a quadratic form in two variables will also involve a cross-product term in
:<math> Q(x) = c_1 {x_1}^2 + c_2 {x_2}^2 +
This quadratic form is positive
This bivariate quadratic form appears in the context of [[conic section]]s centered on the origin. If the general quadratic form above is equated to 0, the resulting equation is that of an [[ellipse]] if the quadratic form is positive or negative
The square of the [[Euclidean norm]] in
:<math> {x_1}^2 +\cdots + {x_n}^2 ~.</math>
In two dimensions this means that the distance between two points is the square root of the sum of the squared distances along the <math>x_1</math> axis and the <math>x_2</math> axis.
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A quadratic form can be written in terms of [[matrix (mathematics)|matrices]] as
:<math>x^\mathsf{T}
where
Positive or negative
==Optimization==
Definite quadratic
:<math>x^
where
:<math>
giving
:<math> x = -\tfrac{1}{2}\,A^{-1}b
assuming {{mvar|A}} is [[nonsingular matrix|nonsingular]]. If the quadratic form, and hence
An important example of such an optimization arises in [[multiple regression]], in which a vector of estimated parameters is sought which minimizes the sum of squared deviations from a perfect fit within the dataset.
==See also==
*[[
*[[Positive
*[[Positive
*[[Polarization identity]]
==
{{reflist}}
==References==
*{{cite book
| last=Kitaoka | first=Yoshiyuki
| year=1993
| title=Arithmetic of quadratic forms
| series=Cambridge Tracts in Mathematics
| volume=106
| publisher=Cambridge University Press
| isbn=0-521-40475-4
| zbl=0785.11021
}}
*{{Lang Algebra
| edition=3r2004
| page=578
}}.
*{{cite book
| first1=J. | last1=Milnor | author1-link=John Milnor
| first2=D. | last2=Husemoller
| year=1973
| title=Symmetric Bilinear Forms
| series=[[Ergebnisse der Mathematik und ihrer Grenzgebiete]]
| volume=73
| publisher=Springer
| isbn=3-540-06009-X
| zbl=0292.10016
}}
[[Category:Quadratic forms]]
[[Category:Linear algebra]]
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