Stable Complete Intersections - math.kth.se fileM. Torrente University of Genova (joint work with L....

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Stable Complete Intersections

M. TorrenteUniversity of Genova

(joint work with L. Robbiano)

MEGA 2011, Stockholm

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 1 / 22

Introduction

Introduction

Fact: A complete intersection of n polynomials in n indeterminates has onlya finite number of zeros.

Main question: how do the zeros change when the coefficients of thepolynomials are perturbed?

Motivation: problem of (”almost”) interpolating a set of points whosecoordinates are perturbed by errors.

Our main question can be rephrased as follows: is it possible to...

... devise a strategy to make the situation reasonably stable?

... change the generating polynomials s.t. the stability of the zeros increases?

Aim of the talk is to address these problems using a mixture of methodsfrom classical algebraic geometry and numerical algebra.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 2 / 22

Introduction

Introduction

Fact: A complete intersection of n polynomials in n indeterminates has onlya finite number of zeros.

Main question: how do the zeros change when the coefficients of thepolynomials are perturbed?

Motivation: problem of (”almost”) interpolating a set of points whosecoordinates are perturbed by errors.

Our main question can be rephrased as follows: is it possible to...

... devise a strategy to make the situation reasonably stable?

... change the generating polynomials s.t. the stability of the zeros increases?

Aim of the talk is to address these problems using a mixture of methodsfrom classical algebraic geometry and numerical algebra.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 2 / 22

Introduction

Introduction

Fact: A complete intersection of n polynomials in n indeterminates has onlya finite number of zeros.

Main question: how do the zeros change when the coefficients of thepolynomials are perturbed?

Motivation: problem of (”almost”) interpolating a set of points whosecoordinates are perturbed by errors.

Our main question can be rephrased as follows: is it possible to...

... devise a strategy to make the situation reasonably stable?

... change the generating polynomials s.t. the stability of the zeros increases?

Aim of the talk is to address these problems using a mixture of methodsfrom classical algebraic geometry and numerical algebra.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 2 / 22

Introduction

Introduction

Fact: A complete intersection of n polynomials in n indeterminates has onlya finite number of zeros.

Main question: how do the zeros change when the coefficients of thepolynomials are perturbed?

Motivation: problem of (”almost”) interpolating a set of points whosecoordinates are perturbed by errors.

Our main question can be rephrased as follows: is it possible to...

... devise a strategy to make the situation reasonably stable?

... change the generating polynomials s.t. the stability of the zeros increases?

Aim of the talk is to address these problems using a mixture of methodsfrom classical algebraic geometry and numerical algebra.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 2 / 22

Introduction

Introduction

Fact: A complete intersection of n polynomials in n indeterminates has onlya finite number of zeros.

Main question: how do the zeros change when the coefficients of thepolynomials are perturbed?

Motivation: problem of (”almost”) interpolating a set of points whosecoordinates are perturbed by errors.

Our main question can be rephrased as follows: is it possible to...

... devise a strategy to make the situation reasonably stable?

... change the generating polynomials s.t. the stability of the zeros increases?

Aim of the talk is to address these problems using a mixture of methodsfrom classical algebraic geometry and numerical algebra.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 2 / 22

Introduction

Motivating example - The Linear Case

I = (1.001 x + y − 1, x + y + 1) I = (x − 2000, x + y + 1)

Solution = (2000,−2001)

The first representation is ill-conditioned (almost parallel lines).

The second representation is much better (though not optimal).

Perturbations:I1 = (1.002 x + y − 1, x + y + 1) I2 = (x − 2000, x + y + 1.001)Solution = (1000,−1001) Solution = (2000,−2001.001).

Note that:

Only one solution has to be stabilized.

It is well-known that for a linear system with n equations and n unknowns,the most stable situation occurs when the coefficient matrix is orthonormal(the 2-norm condition number of the coefficient matrix is 1).

So another question arises: is there an analogue to orthonormality whenwe deal with polynomial systems?

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 3 / 22

Introduction

Motivating example - The Linear Case

I = (1.001 x + y − 1, x + y + 1) I = (x − 2000, x + y + 1)

Solution = (2000,−2001)

The first representation is ill-conditioned (almost parallel lines).

The second representation is much better (though not optimal).

Perturbations:I1 = (1.002 x + y − 1, x + y + 1) I2 = (x − 2000, x + y + 1.001)Solution = (1000,−1001) Solution = (2000,−2001.001).

Note that:

Only one solution has to be stabilized.

It is well-known that for a linear system with n equations and n unknowns,the most stable situation occurs when the coefficient matrix is orthonormal(the 2-norm condition number of the coefficient matrix is 1).

So another question arises: is there an analogue to orthonormality whenwe deal with polynomial systems?

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 3 / 22

Introduction

Motivating example - The Linear Case

I = (1.001 x + y − 1, x + y + 1) I = (x − 2000, x + y + 1)

Solution = (2000,−2001)

The first representation is ill-conditioned (almost parallel lines).

The second representation is much better (though not optimal).

Perturbations:I1 = (1.002 x + y − 1, x + y + 1) I2 = (x − 2000, x + y + 1.001)Solution = (1000,−1001) Solution = (2000,−2001.001).

Note that:

Only one solution has to be stabilized.

It is well-known that for a linear system with n equations and n unknowns,the most stable situation occurs when the coefficient matrix is orthonormal(the 2-norm condition number of the coefficient matrix is 1).

So another question arises: is there an analogue to orthonormality whenwe deal with polynomial systems?

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 3 / 22

Introduction

Motivating example - The Linear Case

I = (1.001 x + y − 1, x + y + 1) I = (x − 2000, x + y + 1)

Solution = (2000,−2001)

The first representation is ill-conditioned (almost parallel lines).

The second representation is much better (though not optimal).

Perturbations:I1 = (1.002 x + y − 1, x + y + 1) I2 = (x − 2000, x + y + 1.001)Solution = (1000,−1001) Solution = (2000,−2001.001).

Note that:

Only one solution has to be stabilized.

It is well-known that for a linear system with n equations and n unknowns,the most stable situation occurs when the coefficient matrix is orthonormal(the 2-norm condition number of the coefficient matrix is 1).

So another question arises: is there an analogue to orthonormality whenwe deal with polynomial systems?

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 3 / 22

Introduction

Motivating example - The Linear Case

I = (1.001 x + y − 1, x + y + 1) I = (x − 2000, x + y + 1)

Solution = (2000,−2001)

The first representation is ill-conditioned (almost parallel lines).

The second representation is much better (though not optimal).

Perturbations:I1 = (1.002 x + y − 1, x + y + 1) I2 = (x − 2000, x + y + 1.001)Solution = (1000,−1001) Solution = (2000,−2001.001).

Note that:

Only one solution has to be stabilized.

It is well-known that for a linear system with n equations and n unknowns,the most stable situation occurs when the coefficient matrix is orthonormal(the 2-norm condition number of the coefficient matrix is 1).

So another question arises: is there an analogue to orthonormality whenwe deal with polynomial systems?

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 3 / 22

Introduction

Motivating example - The Linear Case

I = (1.001 x + y − 1, x + y + 1) I = (x − 2000, x + y + 1)

Solution = (2000,−2001)

The first representation is ill-conditioned (almost parallel lines).

The second representation is much better (though not optimal).

Perturbations:I1 = (1.002 x + y − 1, x + y + 1) I2 = (x − 2000, x + y + 1.001)Solution = (1000,−1001) Solution = (2000,−2001.001).

Note that:

Only one solution has to be stabilized.

It is well-known that for a linear system with n equations and n unknowns,the most stable situation occurs when the coefficient matrix is orthonormal(the 2-norm condition number of the coefficient matrix is 1).

So another question arises: is there an analogue to orthonormality whenwe deal with polynomial systems?

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 3 / 22

Introduction

Plan of the talk

The talk can be divided into two parts: an algebraic part and a numerical part.

The algebraic part:

Problem: given a zero-dimensional smooth complete intersection determinehow far the polynomials coefficients can be perturbed so that the zerosremain smooth and their number does not change.

� discriminant (Gelfand et al.), resultants, tropical geometry, ....

Philosophy: embed the polynomial system f(x) in a family of polynomialsystems F (a, x).

The numerical part:

Problem: introduce a measure of stability of the zeros, and, based on it,determine how to manipulate the equations so that the the stability of thezeros increases.

� condition number Shub-Smale....

Philosopy: introduce a measure of stability which is a generalization of theclassical notion of condition number for linear systems.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 4 / 22

Introduction

Plan of the talk

The talk can be divided into two parts: an algebraic part and a numerical part.

The algebraic part:

Problem: given a zero-dimensional smooth complete intersection determinehow far the polynomials coefficients can be perturbed so that the zerosremain smooth and their number does not change.

� discriminant (Gelfand et al.), resultants, tropical geometry, ....

Philosophy: embed the polynomial system f(x) in a family of polynomialsystems F (a, x).

The numerical part:

Problem: introduce a measure of stability of the zeros, and, based on it,determine how to manipulate the equations so that the the stability of thezeros increases.

� condition number Shub-Smale....

Philosopy: introduce a measure of stability which is a generalization of theclassical notion of condition number for linear systems.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 4 / 22

Introduction

Plan of the talk

The talk can be divided into two parts: an algebraic part and a numerical part.

The algebraic part:

Problem: given a zero-dimensional smooth complete intersection determinehow far the polynomials coefficients can be perturbed so that the zerosremain smooth and their number does not change.

� discriminant (Gelfand et al.), resultants, tropical geometry, ....

Philosophy: embed the polynomial system f(x) in a family of polynomialsystems F (a, x).

The numerical part:

Problem: introduce a measure of stability of the zeros, and, based on it,determine how to manipulate the equations so that the the stability of thezeros increases.

� condition number Shub-Smale....

Philosopy: introduce a measure of stability which is a generalization of theclassical notion of condition number for linear systems.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 4 / 22

Introduction

Plan of the talk

The talk can be divided into two parts: an algebraic part and a numerical part.

The algebraic part:

Problem: given a zero-dimensional smooth complete intersection determinehow far the polynomials coefficients can be perturbed so that the zerosremain smooth and their number does not change.

� discriminant (Gelfand et al.), resultants, tropical geometry, ....

Philosophy: embed the polynomial system f(x) in a family of polynomialsystems F (a, x).

The numerical part:

Problem: introduce a measure of stability of the zeros, and, based on it,determine how to manipulate the equations so that the the stability of thezeros increases.

� condition number Shub-Smale....

Philosopy: introduce a measure of stability which is a generalization of theclassical notion of condition number for linear systems.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 4 / 22

Introduction

Plan of the talk

The talk can be divided into two parts: an algebraic part and a numerical part.

The algebraic part:

Problem: given a zero-dimensional smooth complete intersection determinehow far the polynomials coefficients can be perturbed so that the zerosremain smooth and their number does not change.

� discriminant (Gelfand et al.), resultants, tropical geometry, ....

Philosophy: embed the polynomial system f(x) in a family of polynomialsystems F (a, x).

The numerical part:

Problem: introduce a measure of stability of the zeros, and, based on it,determine how to manipulate the equations so that the the stability of thezeros increases.

� condition number Shub-Smale....

Philosopy: introduce a measure of stability which is a generalization of theclassical notion of condition number for linear systems.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 4 / 22

The algebraic part

Notations

K field (K = C or K = R)

x = (x1, . . . , xn) unknowns and a = (a1, . . . , am) parameters

f(x) = {f1(x), . . . , fn(x)} ⊂ K [x] zero-dimensional complete intersection

I ⊂ K [x] ideal generated by f(x)

F (a, x) = {F1(a, x), . . . ,Fn(a, x)} ⊂ K [a, x]

I (a, x) ⊂ K [a, x] ideal generated by F (a, x)

S = AmK scheme of the a-parameters

Φ : Spec(K [a, x]/I (a, x)) −→ S morphism of schemes

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 5 / 22

The algebraic part

Freeness

Let F (a, x) be a generically zero-dimensional family which contains f(x).

DefinitionA dense Zariski-open subscheme U of S such that Φ−1(U) −→ U is free,is said to be an I -free subscheme of S.

PropositionLet σ be a term ordering on Tn

let G (a, x) be the reduced σ-Grobner basis of I (a, x)K (a)[x]let d(a) be the l.c.m. of all denominators of the polys coefficients of G (a, x)let T = Tn \ LTσ(I (a, x)K (a)[x]). Then:

(a) The open subscheme U of S defined by d(a) 6= 0 is I -free.

(b) The multiplicity of each fiber over U coincides with the cardinality of T.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 6 / 22

The algebraic part

Freeness

Let F (a, x) be a generically zero-dimensional family which contains f(x).

DefinitionA dense Zariski-open subscheme U of S such that Φ−1(U) −→ U is free,is said to be an I -free subscheme of S.

PropositionLet σ be a term ordering on Tn

let G (a, x) be the reduced σ-Grobner basis of I (a, x)K (a)[x]let d(a) be the l.c.m. of all denominators of the polys coefficients of G (a, x)let T = Tn \ LTσ(I (a, x)K (a)[x]). Then:

(a) The open subscheme U of S defined by d(a) 6= 0 is I -free.

(b) The multiplicity of each fiber over U coincides with the cardinality of T.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 6 / 22

The algebraic part

Freeness

Let F (a, x) be a generically zero-dimensional family which contains f(x).

DefinitionA dense Zariski-open subscheme U of S such that Φ−1(U) −→ U is free,is said to be an I -free subscheme of S.

PropositionLet σ be a term ordering on Tn

let G (a, x) be the reduced σ-Grobner basis of I (a, x)K (a)[x]let d(a) be the l.c.m. of all denominators of the polys coefficients of G (a, x)let T = Tn \ LTσ(I (a, x)K (a)[x]). Then:

(a) The open subscheme U of S defined by d(a) 6= 0 is I -free.

(b) The multiplicity of each fiber over U coincides with the cardinality of T.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 6 / 22

The algebraic part

Smoothness

DefinitionA dense Zariski-open subscheme U of S such that Φ−1(U) −→ U is smooth,i.e. all the fibers of Φ−1(U) −→ U are zero-dimensional smooth completeintersections, is said to be an I -smooth subscheme of S.

TheoremLet D(a, x) = det(JacF (a, x)) be the det of the Jacobian of F (a, x) ∈ K [a][x]let J(a, x) = I (a, x) + (D(a, x)) in K [a, x]let H = J(a, x) ∩ K [a]. Then:

(a) There exists an I -smooth subscheme of S if and only if H 6= (0).

(b) Let 0 6= h(a) ∈ H; the open subscheme U of S defined by h(a) 6= 0 isI -smooth.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 7 / 22

The algebraic part

Smoothness

DefinitionA dense Zariski-open subscheme U of S such that Φ−1(U) −→ U is smooth,i.e. all the fibers of Φ−1(U) −→ U are zero-dimensional smooth completeintersections, is said to be an I -smooth subscheme of S.

TheoremLet D(a, x) = det(JacF (a, x)) be the det of the Jacobian of F (a, x) ∈ K [a][x]let J(a, x) = I (a, x) + (D(a, x)) in K [a, x]let H = J(a, x) ∩ K [a]. Then:

(a) There exists an I -smooth subscheme of S if and only if H 6= (0).

(b) Let 0 6= h(a) ∈ H; the open subscheme U of S defined by h(a) 6= 0 isI -smooth.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 7 / 22

The algebraic part

Freeness + Smoothness = Optimality

DefinitionA dense Zariski-open subscheme U of S such Φ−1(U) −→ Uis free and smooth is said to be I -optimal.

TheoremThere is an algorithm which computes an I -optimal subscheme of S.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 8 / 22

The algebraic part

An example

We consider the ideal I = (xy + 1, x2 + y2 − 5) ∈ R[x , y ] embedded into thefamily I (a, x , y) = (xy + a1x + 1, x2 + y2 + a2).

Freeness

We compute the reduced Lex-Grobner basis of I (a, x)K (a)[x , y ]

g1 = x − y3 − a1y2 − a2y − a1a2,

g2 = y4 + 2a1y3 + (a21 + a2)y2 + 2a1a2y + (a21a2 + 1)

There is no condition for the free locus.

Smoothness

We compute D(a, x , y) = det(JacF (a, x , y)) = −2x2 + 2y2 + 2a1y ,J(a, x , y) = I (a, x , y) + (D(a, x , y)) and H = J(a, x , y) ∩ K [a] = (h(a))

h(a) = a61a2 + 3a41a22 + a41 + 3a21a

32 + 20a21a2 + a42 − 8a22 + 16

Optimality

An I -optimal subscheme is U = {α ∈ A2R : h(α1, α2) 6= 0}.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 9 / 22

The algebraic part

An example

We consider the ideal I = (xy + 1, x2 + y2 − 5) ∈ R[x , y ] embedded into thefamily I (a, x , y) = (xy + a1x + 1, x2 + y2 + a2).

Freeness

We compute the reduced Lex-Grobner basis of I (a, x)K (a)[x , y ]

g1 = x − y3 − a1y2 − a2y − a1a2,

g2 = y4 + 2a1y3 + (a21 + a2)y2 + 2a1a2y + (a21a2 + 1)

There is no condition for the free locus.

Smoothness

We compute D(a, x , y) = det(JacF (a, x , y)) = −2x2 + 2y2 + 2a1y ,J(a, x , y) = I (a, x , y) + (D(a, x , y)) and H = J(a, x , y) ∩ K [a] = (h(a))

h(a) = a61a2 + 3a41a22 + a41 + 3a21a

32 + 20a21a2 + a42 − 8a22 + 16

Optimality

An I -optimal subscheme is U = {α ∈ A2R : h(α1, α2) 6= 0}.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 9 / 22

The algebraic part

An example

We consider the ideal I = (xy + 1, x2 + y2 − 5) ∈ R[x , y ] embedded into thefamily I (a, x , y) = (xy + a1x + 1, x2 + y2 + a2).

Freeness

We compute the reduced Lex-Grobner basis of I (a, x)K (a)[x , y ]

g1 = x − y3 − a1y2 − a2y − a1a2,

g2 = y4 + 2a1y3 + (a21 + a2)y2 + 2a1a2y + (a21a2 + 1)

There is no condition for the free locus.

Smoothness

We compute D(a, x , y) = det(JacF (a, x , y)) = −2x2 + 2y2 + 2a1y ,J(a, x , y) = I (a, x , y) + (D(a, x , y)) and H = J(a, x , y) ∩ K [a] = (h(a))

h(a) = a61a2 + 3a41a22 + a41 + 3a21a

32 + 20a21a2 + a42 − 8a22 + 16

Optimality

An I -optimal subscheme is U = {α ∈ A2R : h(α1, α2) 6= 0}.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 9 / 22

The algebraic part

An example

We consider the ideal I = (xy + 1, x2 + y2 − 5) ∈ R[x , y ] embedded into thefamily I (a, x , y) = (xy + a1x + 1, x2 + y2 + a2).

Freeness

We compute the reduced Lex-Grobner basis of I (a, x)K (a)[x , y ]

g1 = x − y3 − a1y2 − a2y − a1a2,

g2 = y4 + 2a1y3 + (a21 + a2)y2 + 2a1a2y + (a21a2 + 1)

There is no condition for the free locus.

Smoothness

We compute D(a, x , y) = det(JacF (a, x , y)) = −2x2 + 2y2 + 2a1y ,J(a, x , y) = I (a, x , y) + (D(a, x , y)) and H = J(a, x , y) ∩ K [a] = (h(a))

h(a) = a61a2 + 3a41a22 + a41 + 3a21a

32 + 20a21a2 + a42 − 8a22 + 16

Optimality

An I -optimal subscheme is U = {α ∈ A2R : h(α1, α2) 6= 0}.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 9 / 22

The algebraic part

What about real zeros?

Let f(x) be a zero-dimensional complete intersection in R[x]let F (a, x) ∈ R[a, x] be generically zero-dimensional family containing f(x).

TheoremAssume that there exists an I -optimal subscheme U of Am

R ;let αI ∈ U be the point in the parameter space corresponding to I = (f(x))let µR,I be the number of real points in the fiber over αI (= zeroes of I )

Then:there exists an open semi-algebraic subscheme V of U s.t.∀ α ∈ V the number of real points in the fiber over α is µR,I .

� proof Shape Lemma + theorem Basu-Pollack-Roy

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 10 / 22

The algebraic part

What about real zeros?

Let f(x) be a zero-dimensional complete intersection in R[x]let F (a, x) ∈ R[a, x] be generically zero-dimensional family containing f(x).

TheoremAssume that there exists an I -optimal subscheme U of Am

R ;let αI ∈ U be the point in the parameter space corresponding to I = (f(x))let µR,I be the number of real points in the fiber over αI (= zeroes of I )

Then:there exists an open semi-algebraic subscheme V of U s.t.∀ α ∈ V the number of real points in the fiber over α is µR,I .

� proof Shape Lemma + theorem Basu-Pollack-Roy

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 10 / 22

The algebraic part

What about real zeros?

Let f(x) be a zero-dimensional complete intersection in R[x]let F (a, x) ∈ R[a, x] be generically zero-dimensional family containing f(x).

TheoremAssume that there exists an I -optimal subscheme U of Am

R ;let αI ∈ U be the point in the parameter space corresponding to I = (f(x))let µR,I be the number of real points in the fiber over αI (= zeroes of I )

Then:there exists an open semi-algebraic subscheme V of U s.t.∀ α ∈ V the number of real points in the fiber over α is µR,I .

� proof Shape Lemma + theorem Basu-Pollack-Roy

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 10 / 22

The algebraic part

An example: continuation 1

Going back to the example ...

An I -optimal subscheme isU = {α ∈ A2

R : h(α) 6= 0}Therefore, for h(a) 6= 0 each fiberis smooth and has multiplicity 4hence it consists of 4 distinct complexpoints.

What about real points?

αI = (0,−5) ∈ U and µR,I = 4

The real curve h(a) = 0 is the unionof two branches and point (0, 2).

Region R1 (with the exception of (0, 2)) : four complex non-real points;

Regions R2 and R3: two complex non-real points and two real points;

Region R4: four real points.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 11 / 22

The algebraic part

An example: continuation 1

Going back to the example ...

An I -optimal subscheme isU = {α ∈ A2

R : h(α) 6= 0}Therefore, for h(a) 6= 0 each fiberis smooth and has multiplicity 4hence it consists of 4 distinct complexpoints.

What about real points?

αI = (0,−5) ∈ U and µR,I = 4

The real curve h(a) = 0 is the unionof two branches and point (0, 2).

Region R1 (with the exception of (0, 2)) : four complex non-real points;

Regions R2 and R3: two complex non-real points and two real points;

Region R4: four real points.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 11 / 22

The algebraic part

An example: continuation 1

Going back to the example ...

An I -optimal subscheme isU = {α ∈ A2

R : h(α) 6= 0}Therefore, for h(a) 6= 0 each fiberis smooth and has multiplicity 4hence it consists of 4 distinct complexpoints.

What about real points?

αI = (0,−5) ∈ U and µR,I = 4

The real curve h(a) = 0 is the unionof two branches and point (0, 2).

Region R1 (with the exception of (0, 2)) : four complex non-real points;

Regions R2 and R3: two complex non-real points and two real points;

Region R4: four real points.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 11 / 22

The algebraic part

An example: continuation 1

Going back to the example ...

An I -optimal subscheme isU = {α ∈ A2

R : h(α) 6= 0}Therefore, for h(a) 6= 0 each fiberis smooth and has multiplicity 4hence it consists of 4 distinct complexpoints.

What about real points?

αI = (0,−5) ∈ U and µR,I = 4

The real curve h(a) = 0 is the unionof two branches and point (0, 2).

Region R1 (with the exception of (0, 2)) : four complex non-real points;

Regions R2 and R3: two complex non-real points and two real points;

Region R4: four real points.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 11 / 22

The algebraic part

An example: continuation 1

Going back to the example ...

An I -optimal subscheme isU = {α ∈ A2

R : h(α) 6= 0}Therefore, for h(a) 6= 0 each fiberis smooth and has multiplicity 4hence it consists of 4 distinct complexpoints.

What about real points?

αI = (0,−5) ∈ U and µR,I = 4

The real curve h(a) = 0 is the unionof two branches and point (0, 2).

Region R1 (with the exception of (0, 2)) : four complex non-real points;

Regions R2 and R3: two complex non-real points and two real points;

Region R4: four real points.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 11 / 22

The algebraic part

An example: continuation 2

To describe the four regions algebraically, we usethe Sturm-Habicht sequence ofg2 = y4 + 2a1y

3 + (a21 + a2)y2 + 2a1a2y + (a21a2 + 1) ∈ R(a)[y ].

The leading monomials of the sequence are

y4, 4y3, 4r(a)y2, −8`(a)y , 16h(a)

where r(a) = a21 − 2a2 and `(a) = a41a2 + 2a21a22 + 2a21 + a32 − 4a2.

To get the total number of real roots we countthe sign changes in the sequence at −∞ and +∞;in particular, we observe that in the parameter spacethe ideal I corresponds to the point αI = (0,−5) ∈ R4.

We getR4 = {α ∈ R2 | r(α) > 0, `(α) < 0, h(α) > 0}

which is semi-algebraic open, not Zariski-open.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 12 / 22

The algebraic part

An example: continuation 2

To describe the four regions algebraically, we usethe Sturm-Habicht sequence ofg2 = y4 + 2a1y

3 + (a21 + a2)y2 + 2a1a2y + (a21a2 + 1) ∈ R(a)[y ].

The leading monomials of the sequence are

y4, 4y3, 4r(a)y2, −8`(a)y , 16h(a)

where r(a) = a21 − 2a2 and `(a) = a41a2 + 2a21a22 + 2a21 + a32 − 4a2.

To get the total number of real roots we countthe sign changes in the sequence at −∞ and +∞;in particular, we observe that in the parameter spacethe ideal I corresponds to the point αI = (0,−5) ∈ R4.

We getR4 = {α ∈ R2 | r(α) > 0, `(α) < 0, h(α) > 0}

which is semi-algebraic open, not Zariski-open.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 12 / 22

The algebraic part

An example: continuation 2

To describe the four regions algebraically, we usethe Sturm-Habicht sequence ofg2 = y4 + 2a1y

3 + (a21 + a2)y2 + 2a1a2y + (a21a2 + 1) ∈ R(a)[y ].

The leading monomials of the sequence are

y4, 4y3, 4r(a)y2, −8`(a)y , 16h(a)

where r(a) = a21 − 2a2 and `(a) = a41a2 + 2a21a22 + 2a21 + a32 − 4a2.

To get the total number of real roots we countthe sign changes in the sequence at −∞ and +∞;in particular, we observe that in the parameter spacethe ideal I corresponds to the point αI = (0,−5) ∈ R4.

We getR4 = {α ∈ R2 | r(α) > 0, `(α) < 0, h(α) > 0}

which is semi-algebraic open, not Zariski-open.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 12 / 22

The algebraic part

An example: continuation 2

To describe the four regions algebraically, we usethe Sturm-Habicht sequence ofg2 = y4 + 2a1y

3 + (a21 + a2)y2 + 2a1a2y + (a21a2 + 1) ∈ R(a)[y ].

The leading monomials of the sequence are

y4, 4y3, 4r(a)y2, −8`(a)y , 16h(a)

where r(a) = a21 − 2a2 and `(a) = a41a2 + 2a21a22 + 2a21 + a32 − 4a2.

To get the total number of real roots we countthe sign changes in the sequence at −∞ and +∞;in particular, we observe that in the parameter spacethe ideal I corresponds to the point αI = (0,−5) ∈ R4.

We getR4 = {α ∈ R2 | r(α) > 0, `(α) < 0, h(α) > 0}

which is semi-algebraic open, not Zariski-open.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 12 / 22

The numerical part

Admissible Perturbations

Let f(x) be a zero-dimensional smooth complete intersection in R[x]let µR,I be the number of real solutions of f(x) = 0

DefinitionLet ε(x) = {ε1(x), . . . , εn(x)} be a set of polynomials in R[x].Let V ⊂ Am

R be an open semi-algebraic subset of U such that

αI ∈ V∀ α ∈ V the number of real roots of F (α, x) = 0 is µR,I .

If there exists α ∈ V such that (f + ε)(x) = F (α, x),then ε(x) is called an admissible perturbation of f(x) .

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 13 / 22

The numerical part

Admissible Perturbations

Let f(x) be a zero-dimensional smooth complete intersection in R[x]let µR,I be the number of real solutions of f(x) = 0

DefinitionLet ε(x) = {ε1(x), . . . , εn(x)} be a set of polynomials in R[x].Let V ⊂ Am

R be an open semi-algebraic subset of U such that

αI ∈ V∀ α ∈ V the number of real roots of F (α, x) = 0 is µR,I .

If there exists α ∈ V such that (f + ε)(x) = F (α, x),then ε(x) is called an admissible perturbation of f(x) .

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 13 / 22

The numerical part

Example of admissible perturbation

Let f = {f1, f2} ∈ R[x , y ] and I = (f1, f2) where

f1 = xy − 6f2 = x2 + y2 − 13

and ZR(f) = {(−3,−2), (3, 2), (−2,−3), (2, 3)}

We embed f(x) in F (a, x) = {xy + a1, x2 + a2y

2 + a3}.The semi-algebraic open set

V = {α ∈ R3 | α23 − 4α2

1α2 > 0, α2 > 0, α3 < 0}

is a subset of the I -optimal scheme U = {α ∈ A3R | α2(α2

3 − 4α21α2) 6= 0}

αI = (−6, 1,−13) ∈ Vthe fiber over each α ∈ V consists of 4 real points

The polynomial set ε(x , y) = {2, 54y

2} is an admissible perturbation of f(x , y).The real roots of (f + ε)(x) = 0 are

ZR(f + ε) ={(−3,− 4

3

),(3, 43

), (−2,−2), (2, 2)

}

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 14 / 22

The numerical part

Example of admissible perturbation

Let f = {f1, f2} ∈ R[x , y ] and I = (f1, f2) where

f1 = xy − 6f2 = x2 + y2 − 13

and ZR(f) = {(−3,−2), (3, 2), (−2,−3), (2, 3)}

We embed f(x) in F (a, x) = {xy + a1, x2 + a2y

2 + a3}.

The semi-algebraic open set

V = {α ∈ R3 | α23 − 4α2

1α2 > 0, α2 > 0, α3 < 0}

is a subset of the I -optimal scheme U = {α ∈ A3R | α2(α2

3 − 4α21α2) 6= 0}

αI = (−6, 1,−13) ∈ Vthe fiber over each α ∈ V consists of 4 real points

The polynomial set ε(x , y) = {2, 54y

2} is an admissible perturbation of f(x , y).The real roots of (f + ε)(x) = 0 are

ZR(f + ε) ={(−3,− 4

3

),(3, 43

), (−2,−2), (2, 2)

}

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 14 / 22

The numerical part

Example of admissible perturbation

Let f = {f1, f2} ∈ R[x , y ] and I = (f1, f2) where

f1 = xy − 6f2 = x2 + y2 − 13

and ZR(f) = {(−3,−2), (3, 2), (−2,−3), (2, 3)}

We embed f(x) in F (a, x) = {xy + a1, x2 + a2y

2 + a3}.The semi-algebraic open set

V = {α ∈ R3 | α23 − 4α2

1α2 > 0, α2 > 0, α3 < 0}

is a subset of the I -optimal scheme U = {α ∈ A3R | α2(α2

3 − 4α21α2) 6= 0}

αI = (−6, 1,−13) ∈ Vthe fiber over each α ∈ V consists of 4 real points

The polynomial set ε(x , y) = {2, 54y

2} is an admissible perturbation of f(x , y).The real roots of (f + ε)(x) = 0 are

ZR(f + ε) ={(−3,− 4

3

),(3, 43

), (−2,−2), (2, 2)

}

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 14 / 22

The numerical part

Example of admissible perturbation

Let f = {f1, f2} ∈ R[x , y ] and I = (f1, f2) where

f1 = xy − 6f2 = x2 + y2 − 13

and ZR(f) = {(−3,−2), (3, 2), (−2,−3), (2, 3)}

We embed f(x) in F (a, x) = {xy + a1, x2 + a2y

2 + a3}.The semi-algebraic open set

V = {α ∈ R3 | α23 − 4α2

1α2 > 0, α2 > 0, α3 < 0}

is a subset of the I -optimal scheme U = {α ∈ A3R | α2(α2

3 − 4α21α2) 6= 0}

αI = (−6, 1,−13) ∈ V

the fiber over each α ∈ V consists of 4 real points

The polynomial set ε(x , y) = {2, 54y

2} is an admissible perturbation of f(x , y).The real roots of (f + ε)(x) = 0 are

ZR(f + ε) ={(−3,− 4

3

),(3, 43

), (−2,−2), (2, 2)

}

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 14 / 22

The numerical part

Example of admissible perturbation

Let f = {f1, f2} ∈ R[x , y ] and I = (f1, f2) where

f1 = xy − 6f2 = x2 + y2 − 13

and ZR(f) = {(−3,−2), (3, 2), (−2,−3), (2, 3)}

We embed f(x) in F (a, x) = {xy + a1, x2 + a2y

2 + a3}.The semi-algebraic open set

V = {α ∈ R3 | α23 − 4α2

1α2 > 0, α2 > 0, α3 < 0}

is a subset of the I -optimal scheme U = {α ∈ A3R | α2(α2

3 − 4α21α2) 6= 0}

αI = (−6, 1,−13) ∈ Vthe fiber over each α ∈ V consists of 4 real points

The polynomial set ε(x , y) = {2, 54y

2} is an admissible perturbation of f(x , y).The real roots of (f + ε)(x) = 0 are

ZR(f + ε) ={(−3,− 4

3

),(3, 43

), (−2,−2), (2, 2)

}

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 14 / 22

The numerical part

Example of admissible perturbation

Let f = {f1, f2} ∈ R[x , y ] and I = (f1, f2) where

f1 = xy − 6f2 = x2 + y2 − 13

and ZR(f) = {(−3,−2), (3, 2), (−2,−3), (2, 3)}

We embed f(x) in F (a, x) = {xy + a1, x2 + a2y

2 + a3}.The semi-algebraic open set

V = {α ∈ R3 | α23 − 4α2

1α2 > 0, α2 > 0, α3 < 0}

is a subset of the I -optimal scheme U = {α ∈ A3R | α2(α2

3 − 4α21α2) 6= 0}

αI = (−6, 1,−13) ∈ Vthe fiber over each α ∈ V consists of 4 real points

The polynomial set ε(x , y) = {2, 54y

2} is an admissible perturbation of f(x , y).The real roots of (f + ε)(x) = 0 are

ZR(f + ε) ={(−3,− 4

3

),(3, 43

), (−2,−2), (2, 2)

}M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 14 / 22

The numerical part

Local Condition Number

Let p be a nonsingular real solution of f(x) = 0let ε(x) be an admissible perturbation of f(x)

DefinitionThe number κ(f, p) = ‖ Jacf(p)−1‖‖ Jacf(p)‖is called the local condition number of f(x) at p.

CorollaryLet p + ∆p be the real solution of (f + ε)(x) = 0 corresponding to p. Then

0 = (f + ε)(p + ∆p) ≈ ε(p) + Jacf+ε(p) ∆p (1)

and the approximate solution of (1) is denoted by ∆p1 = − Jacf+ε(p)−1ε(p).

Theorem (Local Condition Number)Under the condition ‖ Jacf(p)−1 Jacε(p)‖ < 1, we have

‖∆p1‖‖p‖

≤ Λ(f, ε, p) κ(f, p)

(‖ Jacε(p)‖‖ Jacf(p)‖

+‖ε(0)− ε≥2(0, p)‖‖f(0)− f≥2(0, p)‖

)(2)

where Λ(f, ε, p) = 1/(1− ‖ Jacf(p)−1 Jacε(p)‖).

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 15 / 22

The numerical part

Local Condition Number

Let p be a nonsingular real solution of f(x) = 0let ε(x) be an admissible perturbation of f(x)

DefinitionThe number κ(f, p) = ‖ Jacf(p)−1‖‖ Jacf(p)‖is called the local condition number of f(x) at p.

CorollaryLet p + ∆p be the real solution of (f + ε)(x) = 0 corresponding to p. Then

0 = (f + ε)(p + ∆p) ≈ ε(p) + Jacf+ε(p) ∆p (1)

and the approximate solution of (1) is denoted by ∆p1 = − Jacf+ε(p)−1ε(p).

Theorem (Local Condition Number)Under the condition ‖ Jacf(p)−1 Jacε(p)‖ < 1, we have

‖∆p1‖‖p‖

≤ Λ(f, ε, p) κ(f, p)

(‖ Jacε(p)‖‖ Jacf(p)‖

+‖ε(0)− ε≥2(0, p)‖‖f(0)− f≥2(0, p)‖

)(2)

where Λ(f, ε, p) = 1/(1− ‖ Jacf(p)−1 Jacε(p)‖).

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 15 / 22

The numerical part

Local Condition Number

Let p be a nonsingular real solution of f(x) = 0let ε(x) be an admissible perturbation of f(x)

DefinitionThe number κ(f, p) = ‖ Jacf(p)−1‖‖ Jacf(p)‖is called the local condition number of f(x) at p.

CorollaryLet p + ∆p be the real solution of (f + ε)(x) = 0 corresponding to p. Then

0 = (f + ε)(p + ∆p) ≈ ε(p) + Jacf+ε(p) ∆p (1)

and the approximate solution of (1) is denoted by ∆p1 = − Jacf+ε(p)−1ε(p).

Theorem (Local Condition Number)Under the condition ‖ Jacf(p)−1 Jacε(p)‖ < 1, we have

‖∆p1‖‖p‖

≤ Λ(f, ε, p) κ(f, p)

(‖ Jacε(p)‖‖ Jacf(p)‖

+‖ε(0)− ε≥2(0, p)‖‖f(0)− f≥2(0, p)‖

)(2)

where Λ(f, ε, p) = 1/(1− ‖ Jacf(p)−1 Jacε(p)‖).

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 15 / 22

The numerical part

Local Condition Number

Let p be a nonsingular real solution of f(x) = 0let ε(x) be an admissible perturbation of f(x)

DefinitionThe number κ(f, p) = ‖ Jacf(p)−1‖‖ Jacf(p)‖is called the local condition number of f(x) at p.

CorollaryLet p + ∆p be the real solution of (f + ε)(x) = 0 corresponding to p. Then

0 = (f + ε)(p + ∆p) ≈ ε(p) + Jacf+ε(p) ∆p (1)

and the approximate solution of (1) is denoted by ∆p1 = − Jacf+ε(p)−1ε(p).

Theorem (Local Condition Number)Under the condition ‖ Jacf(p)−1 Jacε(p)‖ < 1, we have

‖∆p1‖‖p‖

≤ Λ(f, ε, p) κ(f, p)

(‖ Jacε(p)‖‖ Jacf(p)‖

+‖ε(0)− ε≥2(0, p)‖‖f(0)− f≥2(0, p)‖

)(2)

where Λ(f, ε, p) = 1/(1− ‖ Jacf(p)−1 Jacε(p)‖).M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 15 / 22

The numerical part

Generalization of the Linear Case

The notion of local condition number is a generalization of the classical notion ofcondition number of linear systems.

f(x) linear implies f(x) = Ax− b with A ∈ Matn(R) invertibleJacf(x) = A

and so κ(f, p) = ‖ Jacf(p)−1‖‖ Jacf(p)‖ = ‖A−1‖‖A‖

Further, using the perturbation ε(x) = ∆Ax−∆b, relation (2) becomes

‖∆p‖‖p‖

≤ 1

1− ‖A−1‖ ‖∆A‖‖A−1‖ ‖A‖

(‖∆A‖‖A‖

+‖∆b‖‖b‖

)which is the relation that quantifies the sensitivity of the Ax = b problem.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 16 / 22

The numerical part

Generalization of the Linear Case

The notion of local condition number is a generalization of the classical notion ofcondition number of linear systems.

f(x) linear implies f(x) = Ax− b with A ∈ Matn(R) invertibleJacf(x) = A

and so κ(f, p) = ‖ Jacf(p)−1‖‖ Jacf(p)‖ = ‖A−1‖‖A‖

Further, using the perturbation ε(x) = ∆Ax−∆b, relation (2) becomes

‖∆p‖‖p‖

≤ 1

1− ‖A−1‖ ‖∆A‖‖A−1‖ ‖A‖

(‖∆A‖‖A‖

+‖∆b‖‖b‖

)which is the relation that quantifies the sensitivity of the Ax = b problem.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 16 / 22

The numerical part

Generalization of the Linear Case

The notion of local condition number is a generalization of the classical notion ofcondition number of linear systems.

f(x) linear implies f(x) = Ax− b with A ∈ Matn(R) invertibleJacf(x) = A

and so κ(f, p) = ‖ Jacf(p)−1‖‖ Jacf(p)‖ = ‖A−1‖‖A‖

Further, using the perturbation ε(x) = ∆Ax−∆b, relation (2) becomes

‖∆p‖‖p‖

≤ 1

1− ‖A−1‖ ‖∆A‖‖A−1‖ ‖A‖

(‖∆A‖‖A‖

+‖∆b‖‖b‖

)which is the relation that quantifies the sensitivity of the Ax = b problem.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 16 / 22

The numerical part

Generalization of the Linear Case

The notion of local condition number is a generalization of the classical notion ofcondition number of linear systems.

f(x) linear implies f(x) = Ax− b with A ∈ Matn(R) invertibleJacf(x) = A

and so κ(f, p) = ‖ Jacf(p)−1‖‖ Jacf(p)‖ = ‖A−1‖‖A‖

Further, using the perturbation ε(x) = ∆Ax−∆b, relation (2) becomes

‖∆p‖‖p‖

≤ 1

1− ‖A−1‖ ‖∆A‖‖A−1‖ ‖A‖

(‖∆A‖‖A‖

+‖∆b‖‖b‖

)which is the relation that quantifies the sensitivity of the Ax = b problem.

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 16 / 22

The numerical part

Further properties and Optimization

Further properties

‖ · ‖ is and induced matrix norm ⇒ κ(f, p) ≥ 1

κ2(f, p) = σmax(Jacf (p))σmin(Jacf (p))

κ2(f, p) = 1 ⇐⇒ Jacf(p) orthonormal

κ(f, p) invariant under multiplication by unique nonzero scalar γ ∈ R

PropositionSuppose that deg(f1) = . . . = deg(fn)let C = (cij) ∈ Matn(R) invertible, and g be defined by gtr = C · ftr .Then the following conditions are equivalent:

(a) κ2(g, p) = 1, the minimum possible

(b) C tC = (Jacf(p) Jacf(p)t)−1

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 17 / 22

The numerical part

Further properties and Optimization

Further properties

‖ · ‖ is and induced matrix norm ⇒ κ(f, p) ≥ 1

κ2(f, p) = σmax(Jacf (p))σmin(Jacf (p))

κ2(f, p) = 1 ⇐⇒ Jacf(p) orthonormal

κ(f, p) invariant under multiplication by unique nonzero scalar γ ∈ R

PropositionSuppose that deg(f1) = . . . = deg(fn)let C = (cij) ∈ Matn(R) invertible, and g be defined by gtr = C · ftr .Then the following conditions are equivalent:

(a) κ2(g, p) = 1, the minimum possible

(b) C tC = (Jacf(p) Jacf(p)t)−1

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 17 / 22

Experiments

Experiments I

We consider f = {f1, f2} ∈ R[x , y ] and I = (f1, f2) where

f1 = 14x

2y + xy2 + 14y

3 + 15x

2 − 58xy + 13

40y2 + 9

40x −35y + 1

40

f2 = x3 + 1413xy

2 + 5752x

2 − 2552xy + 8

13y2 − 11

52x −413y −

413

and the point p = (0, 1) ∈ ZR(f).

the system g = {f1, 6316 f1 −6516 f2} is an alternative representation of I

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 18 / 22

Experiments

Experiments I

We consider f = {f1, f2} ∈ R[x , y ] and I = (f1, f2) where

f1 = 14x

2y + xy2 + 14y

3 + 15x

2 − 58xy + 13

40y2 + 9

40x −35y + 1

40

f2 = x3 + 1413xy

2 + 5752x

2 − 2552xy + 8

13y2 − 11

52x −413y −

413

and the point p = (0, 1) ∈ ZR(f).

the system g = {f1, 6316 f1 −6516 f2} is an alternative representation of I

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 18 / 22

Experiments

Experiments I

-0,5 -0,25 0 0,25 0,5

0,75

1

1,25

-0,5 -0,25 0 0,25 0,5

0,75

1

1,25

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 19 / 22

Experiments

Experiments I

We consider f = {f1, f2} ∈ R[x , y ] and I = (f1, f2) where

f1 = 14x

2y + xy2 + 14y

3 + 15x

2 − 58xy + 13

40y2 + 9

40x −35y + 1

40

f2 = x3 + 1413xy

2 + 5752x

2 − 2552xy + 8

13y2 − 11

52x −413y −

413

and the point p = (0, 1) ∈ ZR(f).

the system g = {f1, 6316 f1 −6516 f2} is an alternative representation of I

q ∈ ZR(f + ε) and r ∈ ZR(g + ε) are perturbations of pfor different admissible perturbations ε

κ2(f, p) ‖q−p‖2‖p‖2

8 0.000097

κ2(g, p) ‖r−p‖2‖p‖2

1 0.000023

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 20 / 22

Experiments

Experiments II

We consider f = {f1, f2, f3} ∈ R[x , y , z ] and I = (f1, f2, f3) where

f1 = 617x

2 + xy − 2485x −

885y −

685

f2 = 3989x

2 + 7089xy + yz − 39

89x + 1089y

f3 = y2 + 2xz + z2 − z

and the point p = (1, 0, 0) ∈ ZR(f).

the system g = {f1, 7564123 f1 −7565123 f2, f3} is an alternative representation of I

q ∈ ZR(f + ε) and r ∈ ZR(g + ε) are perturbations of pfor different admissible perturbations ε

κ2(f, p) ‖q−p‖2‖p‖2

123 0.0436

κ2(g, p) ‖r−p‖2‖p‖2

1 0.0221

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 21 / 22

Experiments

Experiments II

We consider f = {f1, f2, f3} ∈ R[x , y , z ] and I = (f1, f2, f3) where

f1 = 617x

2 + xy − 2485x −

885y −

685

f2 = 3989x

2 + 7089xy + yz − 39

89x + 1089y

f3 = y2 + 2xz + z2 − z

and the point p = (1, 0, 0) ∈ ZR(f).

the system g = {f1, 7564123 f1 −7565123 f2, f3} is an alternative representation of I

q ∈ ZR(f + ε) and r ∈ ZR(g + ε) are perturbations of pfor different admissible perturbations ε

κ2(f, p) ‖q−p‖2‖p‖2

123 0.0436

κ2(g, p) ‖r−p‖2‖p‖2

1 0.0221

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 21 / 22

Experiments

Experiments II

We consider f = {f1, f2, f3} ∈ R[x , y , z ] and I = (f1, f2, f3) where

f1 = 617x

2 + xy − 2485x −

885y −

685

f2 = 3989x

2 + 7089xy + yz − 39

89x + 1089y

f3 = y2 + 2xz + z2 − z

and the point p = (1, 0, 0) ∈ ZR(f).

the system g = {f1, 7564123 f1 −7565123 f2, f3} is an alternative representation of I

q ∈ ZR(f + ε) and r ∈ ZR(g + ε) are perturbations of pfor different admissible perturbations ε

κ2(f, p) ‖q−p‖2‖p‖2

123 0.0436

κ2(g, p) ‖r−p‖2‖p‖2

1 0.0221

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 21 / 22

Future work

Future work

Optimization in the case of arbitrary degrees

Definition of global condition number

. . .

Thank you!

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 22 / 22

Future work

Future work

Optimization in the case of arbitrary degrees

Definition of global condition number

. . .

Thank you!

M. Torrente (University of Genova) Stable Complete Intersections MEGA 2011 22 / 22

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