Linear, bilinear and quadratic formsandreea.arusoaie/Lectures... · 2019-08-04 · Symmetric...

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Linear, bilinear and quadratic forms Lecture 8 Mathematics - 1 st year, English Faculty of Computer Science, UAIC e-mail: [email protected] web: https://profs.info.uaic.ro/ ~ andreea.arusoaie/mathematics_en.html facebook: Adrian Zalinescu (group: FII - Matematica (2017-2018)) November 27, 2017 A. Z˘ alinescu (Ia¸ si) Lecture 8 November 27, 2017

Transcript of Linear, bilinear and quadratic formsandreea.arusoaie/Lectures... · 2019-08-04 · Symmetric...

Page 1: Linear, bilinear and quadratic formsandreea.arusoaie/Lectures... · 2019-08-04 · Symmetric bilinear forms De nition A bilinear form g : V V !™ is called symmetric if g(u,v) =

Linear, bilinear and quadratic formsLecture 8

Mathematics - 1st year, English

Faculty of Computer Science, UAIC

e-mail: [email protected]

web: https://profs.info.uaic.ro/~andreea.arusoaie/mathematics_en.html

facebook: Adrian Zalinescu (group: FII - Matematica (2017-2018))

November 27, 2017

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Outline of the lecture

1 Linear forms

2 Bilinear forms

3 Quadratic forms

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Linear forms

Definition

Let (V ,+, ·) be a linear space.

• A linear mapping f : V → � is called a linear form or a linear functional.

• The linear space L(V ;�) of all linear forms is called the dual of V and isdenoted V ∗.

Proposition

Let (V ,+, ·) be a finite-dimensional linear space. Then V ∗ is alsofinite-dimensional and dimV ∗ = dimV .

Proposition

Let (V ,+, ·) be a finite-dimensional linear space. If v ∈ V r {0V } then thereexists f ∈ V ∗ such that f (v) 6= 0.

Consequence. If u, v ∈ V and u 6= v then there exists f ∈ V ∗ such thatf (u) 6= f (v).

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Bidual and evaluation map

Definition

Let (V ,+, ·) be a linear space.

• The dual of V ∗, denoted by V ∗∗, is called the bidual of V .

• The function ψ : V → V ∗∗ defined by

ψ(v)(f ) := f (v), v ∈ V , f ∈ V ∗

is called the evaluation map.

The evaluation map is well-defined and it is linear:1. It is clear that ψ(v) : V ∗ → �. If α, β ∈ � and f , g ∈ V ∗, then

ψ(v)(αf + βg) = (αf + βg)(v) = αf (v) + βg(v)

= αψ(v)(f ) + βψ(v)(g).

Hence ψ(v) is linear, i.e. ψ(v) ∈ V ∗∗. Therefore, ψ is well-defined.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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2. If α, β ∈ � and u, v ∈ V , then

ψ(αu + βv)(f ) = f (αu + βv) = αf (u) + βf (v)

= αψ(u)(f ) + βψ(v)(f ), ∀f ∈ V ∗.

This means that ψ(αu + βv) = αψ(u) + βψ(v). In conclusion, ψ is linear.

3. If V is finite-dimensional, then ψ is a linear isomorphism.

Indeed, if v ∈ ker ψ, then

f (v) = 0, ∀f ∈ V ∗.

Supposing that v 6= 0V would contradict the existence of some f ∈ V ∗ suchthat f (v) 6= 0. Therefore, v should be equal to 0V . This implies thatker ψ = {0V }, i.e. ψ is injective.

On the other hand, dimV ∗∗ = dimV ∗ = dimV . By the dimension theorem,rank ψ = dimV = dimV ∗∗, so ψ is surjective, too.

In conclusion, ψ is a linear isomorphism. In this case, ψ is also called thecanonical isomorphism between V and V ∗∗.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Vector hyperplanes

Definition

Let (V ,+, ·) be a linear space. A linear subspace W ⊆ V is called a (vector)hyperplane if there exists f ∈ V ∗ r {0V ∗} such that ker f = W .

Proposition

If (V ,+, ·) is a finite-dimensional linear space with dimV = n ∈ �∗, then a linearsubspace W ⊆ V is a hyperplane if and only if dimW = n− 1.

Proof.

[Proof: “⇒”] If W = ker f for some f ∈ V ∗ r {0V ∗}, then by the dimensiontheorem,

dimW = dim(ker f ) = dimV − dim(Im f ) = n− 1,

because f 6= 0V ∗ and thus Im f = �.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Proof.

[Proof: “⇐”] Conversely, if dimW = n− 1, there exists a basisB = {b1, . . . , bn−1, bn} of V such that Lin{b1, . . . , bn−1} = W . Takingf : V → � defined by

f (α1b1 + · · ·+ αnbn) := αn

for α1, . . . , αn ∈ �, we have f 6= 0V ∗ and

f (b1) = · · · = f (bn−1) = 0,

implying that W ⊆ ker f (i.e., f (v) = 0, ∀v ∈ W ). On the other hand, by thedirect implication, dim(ker f ) = n− 1 and consequently W = ker f .

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Let V be a finite-dimensional linear space and B = {b1, . . . , bn} a basis of V .

If W is a hyperplane with W = ker f , where f ∈ V ∗ r {0V ∗}, letβ1 := f (b1), . . . , βn := f (bn). Then v = x1b1 + · · ·+ xnbn ∈ ker f ischaracterized by the equation

(1) β1x1 + · · ·+ βnxn = 0.

Hence

(2) W = {x1b1 + · · ·+ xnbn ∈ V | β1x1 + · · ·+ βnxn = 0} .

Conversely, having β1, . . . , βn ∈ �, not all 0, the subset of V defined by theabove relation is a hyperplane of V .

One can show that any linear subspace of V (not only hyperplanes) can becharacterized by systems of equations of form (1).

If V = �n and B is the canonical basis, relation (2) can be written as

W = {(x1, . . . , xn) ∈ �n | β1x1 + · · ·+ βnxn = 0} .

In the particular cases n = 2 and n = 3, equation (1) becomes the equationof a line, respectively a plane passing through the origin.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Affine functionals

The following notion allows us to characterize all the lines (when n = 2) andplanes (when n = 3), not necessarily those passing through the origin.

Definition

Let (V ,+, ·) be a linear space. A function f : V → � is called an affinefunctional if there exist a linear functional f0 ∈ V ∗ and a constant c ∈ � suchthat f (v) = f0(v) + c , ∀v ∈ V .

For an affine functional f : V → � one can define its kernel in the same way asfor linear functionals, i.e. ker f := {v ∈ V | f (v) = 0}.

Definition

Let (V ,+, ·) be a linear space. A subset U ⊆ V is called an affine hyperplane ifthere exists a non-constant affine functional f : V → � such that ker f = U.

In other words, U is affine hyperplane if there exist a vector hyperplane Wand a vector v0 ∈ V such that

U = W + v0 := {v + v0 | v ∈ W }.A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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If V is finite-dimensional with a basis B = {b1, b2, . . . , bn}, then affinehyperplanes are given by subsets of the form

U = {x1b1 + · · ·+ xnbn ∈ V | β1x1 + · · ·+ βnxn + c = 0} ,

where c , β1, . . . , βn ∈ �.

In the cases n = 2 and n = 3, the affine hyperplanes are the lines,respectively the planes.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Bilinear forms

Definition

Let (V ,+, ·) and (W ,+, ·) two linear spaces. A function g : V ×W → � iscalled a bilinear form (bilinear map/mapping) on V ×W if the followingconditions are fulfilled:

1 g(αu + βv, w) = αg(u, w) + βg(v, w), ∀α, β ∈ �, ∀u, v ∈ V , ∀w ∈ W ;

2 g(v, λw + µz) = λg(v, w) + µg(v, z), ∀λ, µ ∈ �, ∀v ∈ V , ∀w, z ∈ W .

In the case W = V , a bilinear form on V × V is also called bilinear form(functional, map/mapping) on V .

1. Suppose now that V and W are finite-dimensional, with basesB = {b1, . . . , bn} and B = {b1, . . . , bm} on V , respectively W .

If v ∈ V and w ∈ W having α1, . . . , αn ∈ � and β1, . . . , βm ∈ � ascoordinates with respect to the bases B, respectively B, then

g(v, w) = g

(n

∑i=1

αibi ,m

∑j=1

βj bj

)=

n

∑i=1

m

∑j=1

αi βjg(bi , bj ).

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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The scalars aij := g(bi , bj ), 1 ≤ i ≤ n, 1 ≤ j ≤ m are called the coefficientsof the bilinear form g with respect to the bases B and B;

the matrix AgB,B

:= (aij ) 1≤i≤n1≤j≤m

in Mnm is called the matrix of the bilinear

form g with respect to the bases B, B.

2. If B ′ = {b′1, . . . , b′n} is another basis of V and B ′ = {b′1, . . . , b′m} is anotherbasis of W , let us denote S = (sij )1≤i ,j≤n ∈Mn the transition matrix from B toB ′ and S = (sij )1≤i ,j≤m ∈Mm the transition matrix from B to B ′.

Then the matrix of g with respect to the bases B ′ and B ′ can be written as

AgB ′,B ′

= S · AgB,B· ST.

It can be proven that rankAgB ′,B ′

= rankAgB,B

, so the rank of the matrix of

the bilinear form doesn’t depend on the bases of reference. This communvalue is called the rank of g and is denoted by rank g .

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Kernel of a bilinear form

Fixing w ∈ W , the bilinear form g : V ×W → � defines a linear functionalfw : V → �, by

fw(v) := g(v, w), v ∈ V .

Allowing now w to variate, the mapping w 7→ fw defines a linear operatorg ′ : W → V ∗.In a similar way, one can define a linear operator g ′′ : V → W ∗ byg ′′(v) := hv, where the linear functional hv ∈ W ∗ is introduced by

hv(w) := g(v, w), w ∈ V .

Definition

Let g : V ×W → � be a bilinear form and the associated linear operatorsg ′ : W → V ∗ and g ′′ : V → W ∗ introduced above. The linear subspaceker g ′ ⊆ W is called the right kernel of g , while the linear subspace ker g ′′ ⊆ V iscalled the left kernel of g .If Ker(g ′) = {0W } and Ker(g ′′) = {0V }, then the bilinear form g is callednon-degenerate.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Symmetric bilinear forms

Definition

A bilinear form g : V × V → � is called symmetric if

g(u, v) = g(v, u), ∀u, v ∈ V ,

respectively antisymmetric if

g(u, v) = −g(v, u), ∀u, v ∈ V .

Proposition

Let g : V × V → � be a symmetric bilinear form or an antisymmetric linear form.Then its right kernel coincides with its left kernel.

For such a bilinear form, the left kernel (which coincides with the right kernel) iscalled the kernel of g and is denoted by ker g .

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Dimension theorem for bilinear forms

Proposition

Let (V ,+, ·) be a finite-dimensional linear space and g : V × V → � asymmetric bilinear form. Then

rank g + dim (ker g) = dimV .

Remark. By the above result, a necessary and sufficient condition for a symmetricbilinear form to be non-degenerate is that rank g = dimV .

Definition

Let g : V × V → � be a symmetric bilinear form.

• Two vectors u, v ∈V are called orthogonal with respect to g if g(u, v) = 0.

• If U is a non-empty subset of V , we say that U is orthogonal with respect tog (or g -orthogonal) if g(u, v) = 0 for any distinct u, v ∈ U.

• If U is a non-empty subset of V , the set {v ∈ V | g(u, v) = 0, ∀u ∈ U} is alinear subspace of V , called the orthgonal complement of U with respect tog , denoted U⊥g .

Remark. If W is a finite dimensional subspace of V with {b1, . . . , bn} a basis ofW , then v ∈ W⊥g if and only if g(bk , v) = 0, ∀l ∈ {1, . . . , n}.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Sylvester’s law of inertia

Theorem

Let (V ,+, ·) be a finite-dimensional linear space and g : V × V → � asymmetric bilinear form. If {b1, . . . , bn} is a basis of V which is g -orthogonal,then rank g is precisely the number of elements amongg(b1, b1), g(b2, b2), ..., g(bn, bn) which are non-zero.

Theorem (Sylvester’s law of inertia)

Let (V ,+, ·) be a finite-dimensional linear space and g : V ×V → � a symmetricbilinear form. Then there exist p, q, r ∈ � such that for every g -orthogonal basis{b1, . . . , bn} of V , p, q and r represent the number of positive, negative,respectively null elements among g(b1, b1), g(b2, b2), ..., g(bn, bn).

The numbers p and q are called the positive, respectively the negative indexof inertia.

The triple (p, q, r) is called the signature of g .

Of course, p + q + r = n (n = dimV ); moreover, rank g = p + q.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Quadratic forms

Definition

Let (V ,+, ·) be a linear space and g : V × V → � a symmetric bilinear form.The function h : V → �, defined by

h(v) := g(v, v), v ∈ V

is called the quadratic form (functional) associated to g .

Remark. Sinceh(u + v) = g(u + v, u + v) = g(u, u) + g(u, v) + g(v, u) + g(v, v) andg(u, v) = g(v, u), we have

h(u + v) = h(u) + 2g(u, v) + h(v), ∀u, v ∈ V .

From this formula we can retreive g from h:

g(u, v) =1

2[h(u + v)− h(u)− h(v)] , ∀u, v ∈ V

or

g(u, v) =1

4[h(u + v)− h(u− v)] , ∀u, v ∈ V .

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Suppose now that V is a finite-dimensional space and B = {b1, . . . , bn} is abasis of V .

Let AgB,B = (aij )1≤i ,j≤n be the matrix of g with respect to B. If

x1, . . . , xn ∈ � are the coefficients of a vector v ∈ V with respect to B, then

h(v) = h(x1b1 + · · ·+ xnbn) =n

∑i=1

n

∑j=1

aijxixj .

The right-hand side of this relation is a homogeneous polynomial of degree 2,called the quadratic polynomial associated to the quadratic form h and thebasis B.

The determinant of the symmetric matrix AgB,B is called the discriminant of h

with respect to the basis B. Its sign does not depend on the basis B.

We say that h is a non-degenerate quadratic form if g is a non-degeneratebilinear functional form, i.e. the discriminant of h (in any basis) is not zero(rankAg

B,B = rank g = n). Otherwise, we say that h is a degeneratequadratic form.

If (p, q, r) is the signature of g , we also call it the signature of the quadraticform h.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Reduced form of a bilinear form

Definition

Let (V ,+, ·) be a finite-dimensional linear space and h : V → V a quadratic formassociated to some symmetric bilinear form g : V × V → �.

• If B is a basis of V such that the matrix of g is diagonal, we call canonical(reduced) form of h the quadratic polynomial associated to h and B.

• A canonical form of h is called normal if the diagonal matrix associated to ghas on its diagonal only the elements 1, −1 and 0.

If B = {b1, . . . , bn} is a basis of V giving a canonical formω1x

21 + ω2x

22 + · · ·+ ωnx

2n of h, then B ′ = {c1b1, . . . , cnbn} gives a normal

form of h, where ci = 1 if ωi = 0, while ci =1√|ωi |

if ωi 6= 0, for 1 ≤ i ≤ n.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Gauss method

Theorem (Gauss method of reducing a quadratic form)

Let (V ,+, ·) be an n-dimensional linear space and h : V → � a quadratic form.Then there exists a basis {b1, . . . , bn} of V and ω1, . . . , ωn ∈ � such that forany x1, . . . , xn ∈ � we have

h(x1b1 + · · ·+ xnbn) = ω1x21 + ω2x

22 + · · ·+ ωnx

2n .

Remarks.

The quadratic polynomial ω1x21 + ω2x

22 + · · ·+ ωnx

2n is then a reduced form

of h (the matrix of g with respect to {b1, . . . , bn} is a diagonal matrix withentries ω1, . . . , ωn).

If (p, q, r) is the signature of h, then among the coefficients ω1, . . . , ωn, pare positive, q are negative and r are equal to 0.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Jacobi method

Theorem (Jacobi method of reducing a quadratic form)

Let (V ,+, ·) be an n-dimensional linear space and h : V → � a quadratic form.Let ∆i , 1 ≤ i ≤ n the principal minors of the associated matrix (aij )1≤i ,j≤n withrespect to a basis of V , i.e.

∆i =

∣∣∣∣∣∣∣∣∣a11 . . . a1i

a21 . . . a2i...

...ai1 . . . aii

∣∣∣∣∣∣∣∣∣ , 1 ≤ i ≤ n.

If ∆i 6= 0, ∀i ∈ {1, . . . , n}, then h can be reduced to the canonical form

µ1x21 + µ2x

22 + · · ·+ µnx

2n ,

where µj =∆j−1

∆j, ∀j = {1, . . . , n}, with ∆0 = 1.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Definition

Let (V ,+, ·) be an n-dimensional linear space and h : V → � a quadratic formwith signature (p, q, r).

• If p = n, h is called a positive-definite quadratic form.

• If q = 0, the quadratic form h is called positive semidefinite.

• If q = n, h is called a negative-definite quadratic form.

• If p = 0, the quadratic form h is called negative semidefinite.

• The quadratic form h is called undefined if p > 0 and q > 0.

Let ∆i , 1 ≤ i ≤ n be the principal minors of the associated matrix with respect toan arbitrary basis. Then h is positive-definite if and only if

∆i > 0, ∀i ∈ {1, . . . , n}

and h is negative-definite if and only if

(−1)i∆i > 0, ∀i ∈ {1, . . . , n}.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Eigenvalues method

Theorem (Eigenvalues method of reducing a quadratic form)

Let (V , 〈·, ·〉) be a finite-dimensional prehilbertian space with dimV = n. Thenthere exists an orthonormal basis with respect to which h has the canonical form

λ1x21 + λ2x

22 + · · ·+ λnx

2n , x1, x2, . . . , xn ∈ �,

where λ1, λ2, . . . , λn ∈ �.

In fact, λ1, . . . , λn are the eigenvalues of the associated matrix with respectto any basis of V .

The method of the proof is similar to the diagonalization algorithm for linearoperators.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Non-homogeneous quadratic functionals

Definition

Let (V ,+, ·) be a linear space, h : V → � a quadratic form and f : V → � anaffine functional. The sum h+ f is called a non-homogeneous quadraticfunctional on V .

If V is finite-dimensional and B = {b1, . . . , bn} of basis of V , then for anyx1, . . . , xn ∈ �

(3) (h+ f )(x1b1 + · · ·+ xnbn) =n

∑i=1

n

∑j=1

aijxixj +n

∑i=1

bixi + c ,

where A = (aij )1≤i ,j≤n is the matrix associated to h and b1, . . . , bn, c ∈ �.

The right-hand side of this equality is called the quadratic polynomialassociated to h+ f (which is a polynomial of degree 2).If V = �n and B is its canonical basis, then (3) can be written as

(4) (h+ f )(x) = ρ(x) := 〈Ax, x〉+ 〈b, x〉+ c , ∀x ∈ �n,

where b = (b1, b2, . . . , bn) ∈ �n and the vectors x ∈ �n are interpreted ascolumn matrices.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Conversely, for arbitrary symmetric matrix A ∈Mn, b ∈ �n and c ∈ �, thefunction ρ : V → � defined by (4), i.e.

ρ(x) := 〈Ax, x〉+ 〈b, x〉+ c , ∀x ∈ �n

defines a non-homogeneous quadratic functional on V .

Moreover, A can be taken not necessarily symmetric, since

〈Ax, x〉 =1

2〈Ax, x〉+ 1

2〈x,Ax〉

=1

2〈Ax, x〉+ 1

2〈ATx, x〉 =

⟨1

2

(A+ AT

)x, x

⟩,

so the matrix A can be replaced by the symmetric matrix 12 (A+ AT).

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Normal form of non-homogeneous quadratic functionals

Let us now consider an affine change of coordinates, i.e. a transformation of theform

x′ = Sx + x0,

where S ∈Mn is a non-singular matrix and x0 ∈ �n. Then

ρ(x) =⟨AS−1(x′ − x0),S

−1(x′ − x0)⟩+⟨

b,S−1(x′ − x0)⟩+ c

=

⟨(S−1

)TAS−1x′, x′

⟩−⟨

2(S−1

)TAS−1x0 +

(S−1

)Tb, x′

⟩+(c −

⟨b,S−1x0

⟩).

Suppose now that S is the transition matrix from the canonical basis to anorthonormal basis giving the canonical form in eigenvalues method of reduction.Therefore, S is an orthonormal matrix (S−1 = ST) andSAST = D := diag(λ1, . . . , λn), where λ1, . . . , λn are the eigenvalues of A.Consequently, we have:

ρ(x) = 〈Dx′, x′〉 − 2

⟨S

(AST x0 +

1

2b

), x′⟩+(c −

⟨b,S−1x0

⟩).

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If A is non-singular, we can take x0 := − 12SA

−1b, obtaining

ρ(x) = 〈Dx′, x′〉+ c0,

where c0 := 〈Dx0, x0〉 − 〈Sb, x0〉+ c . Therefore, by the change ofcoordinates x′ = Sx− 1

2SA−1b, we obtain

ρ(x) =n

∑i=1

λi (x′i )

2 + c0, ∀x ∈ �n,

where x ′i are the coordinates of x with respect to the new orthogonal basis.If detA = 0, then by letting x0 := 0, we obtain

ρ(x) = 〈Dx′, x′〉+ 〈Sb, x′〉+ c0,

where c0 := −〈Sb, x0〉+ c .If (p, q, r) is the signature of h, we have r > 0 and n− r is the rank of A;one can further find an adequate basis B ′′ such that

ρ(x) =n−r∑i=1

λi (x′′i )

2 + γx ′′n−r+1,

where x ′′1 , . . . , x ′′n are the coordinates of x with respect to this new basis andγ ∈ �.

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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Geometric classification

From the geometric point of view,

ker ρ := {x ∈ �n | ρ(x) = 0}

is a conic in the case n = 2, a quadric if n = 3, a hyperquadric if n ≥ 4.

1. Case n = 1: the normal forms of ρ are:

x2 + 1 (ker ρ = ∅: two “imaginary” points);

x2 − 1 (ker ρ = {−1, 1}: two distinct points);

x2 (ker ρ = {0}: two identical points).

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2. Case n = 2: we have nine types of conics, according to the normal form of ρ:

x21 + x2

2 + 1 = 0 (∅: “imaginary” ellipse);

x21 − x2

2 + 1 = 0 (hyperbola);

x21 + x2

2 − 1 = 0 (ellipse);

x21 − 2x2 = 0 (parabola);

x21 + x2

2 = 0 (a point: two “imaginary”, conjugate lines);

x21 − x2

2 = 0 (two intersecting lines);

x21 + 1 = 0 (∅: two “imaginary” lines);

x21 − 1 = 0 (two parallel lines);

x21 = 0 (two identical lines).

A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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3. Case n = 3: we have 17 types of quadrics, characterized by the followingnormal forms:

x21 + x2

2 + x23 + 1 = 0 (“imaginary” ellipsoid);

x21 + x2

2 + x23 − 1 = 0 (ellipsoid);

x21 + x2

2 − x23 − 1 = 0 (hyperboloid of one sheet);

x21 − x2

2 − x23 − 1 = 0 (hyperboloid of two sheets);

x21 + x2

2 + x23 = 0 (a point: “imaginary” cone);

x21 + x2

2 − x23 = 0 (cone);

x21 + x2

2 − 2x3 = 0 (elliptic paraboloid);

x21 − x2

2 − 2x3 = 0 (hyperbolic paraboloid).

The remaining 9 normal forms are the same as those in the case n = 2, which in�3 represent cylinders of different types: elliptic, hyperbolic or parabolic.The first 6 quadrics are non-singular quadrics, while the others are singularquadrics.

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A. Zalinescu (Iasi) Lecture 8 November 27, 2017

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A. Zalinescu (Iasi) Lecture 8 November 27, 2017