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:''This article deals with [[linear map]]s from a [[vector space]] to its field of [[scalar (mathematics)|scalar]]s.  These maps '''may''' be [[functional (mathematics)|functionals]] in the traditional sense of functions of functions, but this is not necessarily the case.''
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In [[linear algebra]], a '''linear functional''' or '''linear form''' (also called a '''[[one-form]]''' or '''covector''') is a [[linear map]] from a [[vector space]] to its field of [[scalar (mathematics)|scalar]]s.&nbsp; In [[Euclidean space|'''R'''<sup>''n''</sup>]], if [[euclidean vector|vectors]] are represented as [[column vector]]s, then linear functionals are represented as [[row vector]]s, and their action on vectors is given by the [[dot product]], or the [[matrix product]] with the [[row vector]] on the left and the [[column vector]] on the right.&nbsp; In general, if ''V'' is a [[vector space]] over a [[field (mathematics)|field]] ''k'', then a linear functional ''f'' is a function from ''V'' to ''k'', which is linear:
 
:<math>f(\vec{v}+\vec{w}) = f(\vec{v})+f(\vec{w})</math> for all <math>\vec{v}, \vec{w}\in V</math>
:<math>f(a\vec{v}) = af(\vec{v})</math> for all <math>\vec{v}\in V, a\in k.</math>
 
The set of all linear functionals from ''V'' to ''k'', Hom<sub>''k''</sub>(''V'',''k''), is itself a vector space over ''k''.&nbsp; This space is called the [[dual space]] of ''V'', or sometimes the '''algebraic dual space''', to distinguish it from the [[continuous dual space]].&nbsp; It is often written ''V*'' or ''V′'' when the field ''k'' is understood.
 
== Continuous linear functionals ==
{{see also|Continuous linear operator}}
 
If ''V'' is a [[topological vector space]], the space of [[continuous function|continuous]] linear functionals &mdash;  the ''[[continuous dual space|continuous dual]]'' &mdash; is often simply called the dual space.&nbsp; If ''V'' is a [[Banach space]], then so is its (continuous) dual.&nbsp; To distinguish the ordinary dual space from the continuous dual space, the former is sometimes called the ''algebraic dual''.&nbsp; In finite dimensions, every linear functional is continuous, so the continuous dual is the same as the algebraic dual, although this is not true in infinite dimensions.
 
== Examples and applications ==
=== Linear functionals in R<sup>''n''</sup> ===
Suppose that vectors in the real coordinate space '''R'''<sup>''n''</sup> are represented as column vectors
 
:<math>\vec{x} = \begin{bmatrix}x_1\\ \vdots\\ x_n\end{bmatrix}.</math>
 
Then any linear functional can be written in these coordinates as a sum of the form:
 
:<math>f(\vec{x}) = a_1x_1 + \cdots + a_n x_n.</math>
 
This is just the matrix product of the row vector [''a''<sub>1</sub> ... ''a''<sub>''n''</sub>] and the column vector <math>\vec{x}</math>:
:<math>f(\vec{x}) = [a_1 \dots a_n] \begin{bmatrix}x_1\\ \vdots\\ x_n\end{bmatrix}.</math>
 
=== Integration ===
Linear functionals first appeared in [[functional analysis]], the study of [[function space|vector spaces of functions]].&nbsp; A typical example of a linear functional is [[integral|integration]]: the linear transformation defined by the [[Riemann integral]]
 
:<math>I(f) = \int_a^b f(x)\, dx</math>
 
is a linear functional from the vector space C[''a'',''b''] of continuous functions on the interval [''a'',&nbsp;''b''] to the real numbers.&nbsp; The linearity of ''I''(''f'') follows from the standard facts about the integral:
:<math>I(f+g) = \int_a^b(f(x)+g(x))\, dx = \int_a^b f(x)\, dx + \int_a^b g(x)\, dx = I(f)+I(g)</math>
:<math>I(\alpha f) = \int_a^b \alpha f(x)\, dx = \alpha\int_a^b f(x)\, dx = \alpha I(f).</math>
 
=== Evaluation ===
Let ''P<sub>n</sub>'' denote the vector space of real-valued polynomial functions of degree ≤''n'' defined on an interval [''a'',''b''].&nbsp; If ''c''&nbsp;∈&nbsp;[''a'',&nbsp;''b''], then let ev<sub>''c''</sub> : ''P<sub>n</sub>''&nbsp;→&nbsp;'''R''' be the '''evaluation functional''':
:<math>\operatorname{ev}_c f = f(c).</math>
The mapping ''f''&nbsp;→&nbsp;''f''(''c'') is linear since
:<math>(f+g)(c) = f(c) + g(c)</math>
:<math>(\alpha f)(c) = \alpha f(c).</math>
 
If ''x''<sub>0</sub>, ..., ''x<sub>n</sub>'' are ''n''+1 distinct points in [''a'',''b''], then the evaluation functionals ev''<sub>x<sub>i</sub></sub>'', ''i''=0,1,...,''n'' form a [[basis of a vector space|basis]] of the dual space of ''P<sub>n</sub>''.&nbsp; ({{harvtxt|Lax|1996}} proves this last fact using [[Lagrange interpolation]].)
 
=== Application to quadrature ===
The integration functional ''I'' defined above defines a linear functional on the [[linear subspace|subspace]] ''P<sub>n</sub>'' of polynomials of degree ≤&nbsp;''n''.&nbsp; If ''x''<sub>0</sub>,&nbsp;…, ''x''<sub>''n''</sub> are ''n''+1 distinct points in [''a'',''b''], then there are coefficients ''a''<sub>0</sub>,&nbsp;…, ''a''<sub>''n''</sub> for which
 
:<math>I(f) = a_0 f(x_0) + a_1 f(x_1) + \dots + a_n f(x_n)</math>
 
for all ''f''&nbsp;∈ ''P''<sub>''n''</sub>.&nbsp; This forms the foundation of the theory of [[numerical quadrature]].
 
This follows from the fact that the linear functionals ''ev<sub>x<sub>i</sub></sub>''&nbsp;:&nbsp;''f''&nbsp;→&nbsp;''f''(''x''<sub>''i''</sub>) defined above form a [[basis of a vector space|basis]] of the dual space of ''P''<sub>''n''</sub> {{harv|Lax|1996}}.
 
=== Linear functionals in quantum mechanics ===
Linear functionals are particularly important in [[quantum mechanics]].&nbsp; Quantum mechanical systems are represented by [[Hilbert space]]s, which are [[antilinear|anti]]-[[linear isomorphism|isomorphic]] to their own dual spaces.&nbsp; A state of a quantum mechanical system can be  identified with a linear functional.&nbsp; For more information see [[bra-ket notation]].
 
=== Distributions ===
In the theory of [[generalized function]]s, certain kinds of generalized functions called [[distribution (mathematics)|distributions]] can be realized as linear functionals on spaces of [[test function]]s.
 
== Properties ==
* Any linear functional ''L'' is either trivial (equal to 0 everywhere) or [[surjective]] onto the scalar field.&nbsp; Indeed, this follows since just as the image of a vector [[linear subspace|subspace]] under a linear transformation is a subspace, so is the image of ''V'' under ''L''.&nbsp; But the only subspaces (i.e., ''k''-subspaces) of ''k'' are {0} and ''k'' itself.
 
* A linear functional is continuous if and only if its [[Kernel (linear operator)|kernel]] is closed {{harv|Rudin|1991|loc=Theorem 1.18}}.
 
* Linear functionals with the same kernel are proportional.
 
* The absolute value of any linear functional is a [[seminorm]] on its vector space.
 
==Visualizing linear functionals==
[[File:Gradient 1-form.svg|thumb|160px|Geometric interpretation of a 1-form '''α''' as a stack of [[hyperplane]]s of constant value, each corresponding to those vectors that '''α''' maps to a given scalar value shown next to it along with the "sense" of increase. The zero plane ('''<span style="color:purple;">purple</span>''') is through the origin.]]
 
In finite dimensions, a linear functional can be visualized in terms of its [[level set]]s.&nbsp; In three dimensions, the level sets of a linear functional are a family of mutually parallel planes; in higher dimensions, they are parallel [[hyperplane]]s.&nbsp; This method of visualizing linear functionals is sometimes introduced in [[general relativity]] texts, such as [[Gravitation (book)|Gravitation]] by {{harvtxt|Misner|Thorne|Wheeler|1973}}.
{{-}}
 
== Dual vectors and bilinear forms ==
{{See also|Hodge dual}}
[[File:1-form linear functional.svg|thumb|400px|Linear functionals (1-forms) '''α''', '''β''' and their sum '''σ''' and vectors '''u''', '''v''', '''w''', in [[three-dimensional space|3d]] [[Euclidean space]]. The number of (1-form) [[hyperplane]]s intersected by a vector equals the [[inner product]].<ref>{{cite book|title=Gravitation|author=J.A. Wheeler, C. Misner, K.S. Thorne|publisher=W.H. Freeman & Co|year=1973|page=57|isbn=0-7167-0344-0}}</ref>]]
 
Every non-degenerate [[bilinear form]] on a finite-dimensional vector space ''V'' gives rise to an [[isomorphism]] from ''V'' to ''V*''.  Specifically, denoting the bilinear form on ''V'' by <&nbsp;,&nbsp;> (for instance in [[Euclidean space]] <''v'',''w''>&nbsp;=&nbsp;''v''•''w'' is the [[dot product]] of ''v'' and ''w''), then there is a natural isomorphism  <math>V\to V^*:v\mapsto v^*</math> given by
 
: <math> v^*(w) := \langle v,  w\rangle.</math>
 
The inverse isomorphism is given by <math>V^* \to V : f \mapsto f^* </math> where ''f*'' is the unique element of ''V'' for which for all ''w''&nbsp;∈&nbsp;''V''
 
: <math> \langle f^*, w\rangle = f(w).</math>
 
The above defined vector ''v''*&nbsp;∈&nbsp;''V*'' is said to be the '''dual vector''' of ''v''&nbsp;∈&nbsp;''V''.
 
In an infinite dimensional [[Hilbert space]], analogous results hold by the [[Riesz representation theorem]].&nbsp; There is a mapping ''V''&nbsp;→&nbsp;''V*'' into the ''continuous dual space'' ''V*''.&nbsp; However, this mapping is [[antilinear]] rather than linear.
{{-}}
 
==Bases in finite dimensions==
 
===Basis of the dual space in finite dimensions===
Let the vector space ''V'' have a basis <math>\vec{e}_1, \vec{e}_2,\dots,\vec{e}_n</math>, not necessarily [[orthogonal]].&nbsp; Then the [[dual space]] ''V*'' has a basis <math>\tilde{\omega}^1,\tilde{\omega}^2,\dots,\tilde{\omega}^n</math> called the [[dual basis]] defined by the special property that
 
:<math> \tilde{\omega}^i (\vec e_j) = \left\{\begin{matrix} 1 &\mathrm{if}\ i=j\\ 0 &\mathrm{if}\ i\not=j.\end{matrix}\right. </math>
 
Or, more succinctly,
 
:<math> \tilde{\omega}^i (\vec e_j) = \delta^i_j </math>
 
where δ is the [[Kronecker delta]].&nbsp; Here the superscripts of the basis functionals are not exponents but are instead [[covariance and contravariance|contravariant]] indices.
 
A linear functional <math>\tilde{u}</math> belonging to the dual space <math>\tilde{V}</math> can be expressed as a [[linear combination]] of basis functionals, with coefficients ("components") ''u<sub>i</sub>'',
:<math>\tilde{u} = \sum_{i=1}^n u_i \, \tilde{\omega}^i. </math>
Then, applying the functional <math>\tilde{u}</math> to a basis vector ''e<sub>j</sub>'' yields
:<math>\tilde{u}(\vec e_j) = \sum_{i=1}^n (u_i \, \tilde{\omega}^i) \vec e_j = \sum_i u_i (\tilde{\omega}^i (\vec e_j)) </math>
due to linearity of scalar multiples of functionals and pointwise linearity of sums of functionals.&nbsp; Then
:<math> \tilde{u}({\vec e}_j) = \sum_i u_i (\tilde{\omega}^i ({\vec e}_j)) = \sum_i u_i \delta^i {}_j = u_j </math>
that is
:<math>\tilde{u} (\vec e_j) = u_j.</math>
This last equation shows that an individual component of a linear functional can be extracted by applying the functional to a corresponding basis vector.
 
=== The dual basis and inner product ===
When the space ''V'' carries an [[inner product]], then it is possible to write explicitly a formula for the dual basis of a given basis.&nbsp; Let ''V'' have (not necessarily orthogonal) basis <math>\vec{e}_1,\dots, \vec{e}_n</math>.&nbsp; In three dimensions (''n'' = 3), the dual basis can be written explicitly
:<math> \tilde{\omega}^i(\vec{v}) = {1 \over 2} \, \left\langle  { \sum_{j=1}^3\sum_{k=1}^3\varepsilon^{ijk} \, (\vec e_j \times \vec e_k) \over \vec  e_1 \cdot \vec e_2 \times \vec e_3} , \vec{v} \right\rangle.</math>
for ''i'' = 1, 2, 3, where ε is the [[Levi-Civita symbol]] and <math>\langle,\rangle</math> the inner product (or [[dot product]]) on ''V''.
 
In higher dimensions, this generalizes as follows
:<math> \tilde{\omega}^i(\vec{v}) = \left\langle \frac{\underset{{}^{1\le i_2<i_3<\dots<i_n\le n}}{\sum}\varepsilon^{ii_2\dots i_n}(\star \vec{e}_{i_2}\wedge\dots\wedge\vec{e}_{i_n})}{\star(\vec{e}_1\wedge\dots\wedge\vec{e}_n)}, \vec{v} \right\rangle </math>
where <math>\star</math> is the [[Hodge star operator]].
 
==See also==
*[[Discontinuous linear map]]
*[[Positive linear functional]]
*[[Bilinear form]]
 
==References==
*{{citation|first1=Richard|last1=Bishop|first2=Samuel|last2=Goldberg|year=1980|title=Tensor Analysis on Manifolds|publisher=Dover Publications|chapter=Chapter 4|isbn=0-486-64039-6}}
* {{citation|first=Paul|last=Halmos|authorlink=Paul Halmos|title=Finite dimensional vector spaces|year=1974|publisher=Springer|isbn=0-387-90093-4}}
* {{citation|authorlink=Peter Lax|first=Peter|last=Lax|title=Linear algebra|year=1996|publisher=Wiley-Interscience|isbn=978-0-471-11111-5}}
* {{Citation|first=Charles W. | last=Misner | first2=Kip. S. |last2=Thorne | first3=John A. | last3=Wheeler |title=Gravitation | publisher= W. H. Freeman | year=1973 | isbn=0-7167-0344-0 }}
*{{Citation | last1=Rudin | first1=Walter | author1-link=Walter Rudin | title=Functional Analysis | publisher=McGraw-Hill Science/Engineering/Math | isbn=978-0-07-054236-5 | year=1991}}
* {{citation|first=Bernard|last=Schutz|year=1985|title=A first course in general relativity|publisher=Cambridge University Press|location=Cambridge, UK|chapter=Chapter 3|isbn=0-521-27703-5}}
 
{{reflist}}
 
{{Functional Analysis}}
 
[[Category:Functional analysis]]
[[Category:Linear algebra]]
[[Category:Linear operators]]

Revision as of 22:16, 25 February 2014

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These diets are largely vegetarian diets plus don't suggest eating a lot of meat. Like the low-carb diets, you are able to eat limitless amounts of certain foods. Because we can't eat a lot of meat, these diets are deficient in zinc, vitamin B12, and necessary fatty acids.

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