SlideShare una empresa de Scribd logo
1 de 25
Descargar para leer sin conexión
Algebraic Geometry
Marko Rajkovi´c
supervisor: prof. Vladimir Berkovich
August 17, 2015
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Introduction
Studying systems of polynomial equations in several variables
and using abstract algebraic techniques for solving geometrical
problems about zeros of such systems
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Introduction
Studying systems of polynomial equations in several variables
and using abstract algebraic techniques for solving geometrical
problems about zeros of such systems
Establishing correspondences between geometric and algebraic
objects
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Introduction
Studying systems of polynomial equations in several variables
and using abstract algebraic techniques for solving geometrical
problems about zeros of such systems
Establishing correspondences between geometric and algebraic
objects
Fundamental objects of study are algebraic varieties
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Affine Varieties
Definition
For an algebraically closed field k affine n-space over k is set
An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}.
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Affine Varieties
Definition
For an algebraically closed field k affine n-space over k is set
An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}. For
S ⊂ A = k[x1, . . . , xn] we define the zero set of S as:
Z(S) := {P ∈ An; f (P) = 0 ∀f ∈ S}. Sets of this form are called
algebraic sets.
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Affine Varieties
Definition
For an algebraically closed field k affine n-space over k is set
An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}. For
S ⊂ A = k[x1, . . . , xn] we define the zero set of S as:
Z(S) := {P ∈ An; f (P) = 0 ∀f ∈ S}. Sets of this form are called
algebraic sets.
Examples of algebraic sets
An = Z(0)
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Affine Varieties
Definition
For an algebraically closed field k affine n-space over k is set
An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}. For
S ⊂ A = k[x1, . . . , xn] we define the zero set of S as:
Z(S) := {P ∈ An; f (P) = 0 ∀f ∈ S}. Sets of this form are called
algebraic sets.
Examples of algebraic sets
An = Z(0)
∅ = Z(1)
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Affine Varieties
Definition
For an algebraically closed field k affine n-space over k is set
An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}. For
S ⊂ A = k[x1, . . . , xn] we define the zero set of S as:
Z(S) := {P ∈ An; f (P) = 0 ∀f ∈ S}. Sets of this form are called
algebraic sets.
Examples of algebraic sets
An = Z(0)
∅ = Z(1)
(a1, . . . , an) = Z(x1 − a1, . . . , xn − an)
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Affine Varieties
Definition
For an algebraically closed field k affine n-space over k is set
An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}. For
S ⊂ A = k[x1, . . . , xn] we define the zero set of S as:
Z(S) := {P ∈ An; f (P) = 0 ∀f ∈ S}. Sets of this form are called
algebraic sets.
Examples of algebraic sets
An = Z(0)
∅ = Z(1)
(a1, . . . , an) = Z(x1 − a1, . . . , xn − an)
Arbitrary intersections and finite unions of algebraic sets are again
algebraic sets.
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Definition
Zariski topology on An is the topology whose closed sets are the
algebraic sets. Any subset X of An will be equipped with the
topology induced by the Zariski topology on An. This is called the
Zariski topology on X.
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Definition
Zariski topology on An is the topology whose closed sets are the
algebraic sets. Any subset X of An will be equipped with the
topology induced by the Zariski topology on An. This is called the
Zariski topology on X.
Example
Algebraic (closed) sets in A1 are finite subsets (including empty
set) as sets of zeros of single non-zero polynomial and whole set
(corresponding to zero polynomial).
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Definition
A non-empty subset Y of topological space X is called irreducible
if it is not a union of two proper closed subsets.
An (irreducible) affine variety is an (irreducible) closed subset of
An with Zariski topology.
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Definition
A non-empty subset Y of topological space X is called irreducible
if it is not a union of two proper closed subsets.
An (irreducible) affine variety is an (irreducible) closed subset of
An with Zariski topology.
Example
A1 is an irreducible affine variety since its only proper closed
subsets are finite and it is infinite. Generally, An is an irreducible
affine variety for every integer n.
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Definition
For X ⊂ An we define the ideal of X as
I(X) := {f ∈ A; f (P) = 0 ∀P ∈ X}
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Definition
For X ⊂ An we define the ideal of X as
I(X) := {f ∈ A; f (P) = 0 ∀P ∈ X}
Examples
I(An) = 0
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Definition
For X ⊂ An we define the ideal of X as
I(X) := {f ∈ A; f (P) = 0 ∀P ∈ X}
Examples
I(An) = 0
I((a1, . . . , an)) = (x1 − a1, . . . , xn − an)
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Theorem–Hilbert Nullstelensatz
For algebraically closed field k maximal ideals of k[x1, . . . , xn] are
exactly the ideals of the form (x1 − a1, . . . , xn − an) for some
ai ∈ k.
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Theorem–Hilbert Nullstelensatz
For algebraically closed field k maximal ideals of k[x1, . . . , xn] are
exactly the ideals of the form (x1 − a1, . . . , xn − an) for some
ai ∈ k.
Corollary
There is a 1 : 1 correspondence
{points in An} ↔ {maximal ideals of k[x1, . . . , xn]}
given by
(a1, . . . , an) ↔ (x1 − a1, . . . , xn − an).
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Lemma and Definition
An algebraic set X ⊂ An is an irreducible affine variety if and only
if its ideal I(X) ⊂ A = k[x1, . . . , xn] is a prime ideal.
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Lemma and Definition
An algebraic set X ⊂ An is an irreducible affine variety if and only
if its ideal I(X) ⊂ A = k[x1, . . . , xn] is a prime ideal. We call
A(Y ) := A/I(Y ) affine coordinate ring of Y .
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Lemma and Definition
An algebraic set X ⊂ An is an irreducible affine variety if and only
if its ideal I(X) ⊂ A = k[x1, . . . , xn] is a prime ideal. We call
A(Y ) := A/I(Y ) affine coordinate ring of Y .
Examples
An is irreducible since its ideal is zero ideal which is prime.
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
Lemma and Definition
An algebraic set X ⊂ An is an irreducible affine variety if and only
if its ideal I(X) ⊂ A = k[x1, . . . , xn] is a prime ideal. We call
A(Y ) := A/I(Y ) affine coordinate ring of Y .
Examples
An is irreducible since its ideal is zero ideal which is prime.
If f is irreducible polynomial in A = k[x1, . . . , xn] we get an
irreducible affine variety Y = Z(f ). For n = 2 we call it affine
curve of degree d, where d is degree of f. For n = 3 we have
surface and for n > 3 hypersurface.
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
The Twisted Cubic Curve
Let Y = {(t, t2, t3); t ∈ k}. Then I(Y ) = (x2 − y, x3 − z) in
A = k[x, y, z].
A/I(Y ) = k[x, y, z]/(x2
− y, x3
− z) ∼= k[x, x2
, x3
] ∼= k[t]
which is an integral domain. Hence, I(Y ) is prime ideal and Y is
an affine variety.
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
THANK YOU FOR YOUR
ATTENTION!
Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry

Más contenido relacionado

La actualidad más candente

Linear integral equations
Linear integral equationsLinear integral equations
Linear integral equationsSpringer
 
Basis and dimension of vector space
Basis and dimension of  vector spaceBasis and dimension of  vector space
Basis and dimension of vector spaceNaliniSPatil
 
Ideals and factor rings
Ideals and factor ringsIdeals and factor rings
Ideals and factor ringsdianageorge27
 
Complex analysis
Complex analysisComplex analysis
Complex analysissujathavvv
 
APPLICATIONS OF MULTIPLE INTEGRALS.pdf
APPLICATIONS OF MULTIPLE INTEGRALS.pdfAPPLICATIONS OF MULTIPLE INTEGRALS.pdf
APPLICATIONS OF MULTIPLE INTEGRALS.pdfnissyjessilyn
 
Hausdorff and Non-Hausdorff Spaces
Hausdorff and Non-Hausdorff SpacesHausdorff and Non-Hausdorff Spaces
Hausdorff and Non-Hausdorff Spacesgizemk
 
Application of definite integrals
Application of definite integralsApplication of definite integrals
Application of definite integralsVaibhav Tandel
 
My Lecture Notes from Linear Algebra
My Lecture Notes fromLinear AlgebraMy Lecture Notes fromLinear Algebra
My Lecture Notes from Linear AlgebraPaul R. Martin
 
Chapter 5 Graphs (1).ppt
Chapter 5 Graphs (1).pptChapter 5 Graphs (1).ppt
Chapter 5 Graphs (1).pptishan743441
 
Triple product of vectors
Triple product of vectorsTriple product of vectors
Triple product of vectorsguest581a478
 
Inverse trigonometric functions
Inverse trigonometric functionsInverse trigonometric functions
Inverse trigonometric functionsLeo Crisologo
 
Complex Numbers and Functions. Complex Differentiation
Complex Numbers and Functions. Complex DifferentiationComplex Numbers and Functions. Complex Differentiation
Complex Numbers and Functions. Complex DifferentiationHesham Ali
 
Linear algebra-Basis & Dimension
Linear algebra-Basis & DimensionLinear algebra-Basis & Dimension
Linear algebra-Basis & DimensionManikanta satyala
 
Isomorphism in Math
Isomorphism in MathIsomorphism in Math
Isomorphism in MathMahe Karim
 

La actualidad más candente (20)

Linear integral equations
Linear integral equationsLinear integral equations
Linear integral equations
 
Ring
RingRing
Ring
 
Group Theory
Group TheoryGroup Theory
Group Theory
 
Analytic function
Analytic functionAnalytic function
Analytic function
 
Basis and dimension of vector space
Basis and dimension of  vector spaceBasis and dimension of  vector space
Basis and dimension of vector space
 
Ideals and factor rings
Ideals and factor ringsIdeals and factor rings
Ideals and factor rings
 
Symmetrics groups
Symmetrics groupsSymmetrics groups
Symmetrics groups
 
Complex analysis
Complex analysisComplex analysis
Complex analysis
 
APPLICATIONS OF MULTIPLE INTEGRALS.pdf
APPLICATIONS OF MULTIPLE INTEGRALS.pdfAPPLICATIONS OF MULTIPLE INTEGRALS.pdf
APPLICATIONS OF MULTIPLE INTEGRALS.pdf
 
graph theory
graph theory graph theory
graph theory
 
Hausdorff and Non-Hausdorff Spaces
Hausdorff and Non-Hausdorff SpacesHausdorff and Non-Hausdorff Spaces
Hausdorff and Non-Hausdorff Spaces
 
1) Introduction to GeoGebra-PPT
1) Introduction to GeoGebra-PPT1) Introduction to GeoGebra-PPT
1) Introduction to GeoGebra-PPT
 
Application of definite integrals
Application of definite integralsApplication of definite integrals
Application of definite integrals
 
My Lecture Notes from Linear Algebra
My Lecture Notes fromLinear AlgebraMy Lecture Notes fromLinear Algebra
My Lecture Notes from Linear Algebra
 
Chapter 5 Graphs (1).ppt
Chapter 5 Graphs (1).pptChapter 5 Graphs (1).ppt
Chapter 5 Graphs (1).ppt
 
Triple product of vectors
Triple product of vectorsTriple product of vectors
Triple product of vectors
 
Inverse trigonometric functions
Inverse trigonometric functionsInverse trigonometric functions
Inverse trigonometric functions
 
Complex Numbers and Functions. Complex Differentiation
Complex Numbers and Functions. Complex DifferentiationComplex Numbers and Functions. Complex Differentiation
Complex Numbers and Functions. Complex Differentiation
 
Linear algebra-Basis & Dimension
Linear algebra-Basis & DimensionLinear algebra-Basis & Dimension
Linear algebra-Basis & Dimension
 
Isomorphism in Math
Isomorphism in MathIsomorphism in Math
Isomorphism in Math
 

Destacado

Algebird : Abstract Algebra for big data analytics. Devoxx 2014
Algebird : Abstract Algebra for big data analytics. Devoxx 2014Algebird : Abstract Algebra for big data analytics. Devoxx 2014
Algebird : Abstract Algebra for big data analytics. Devoxx 2014Samir Bessalah
 
Machine Learning In Production
Machine Learning In ProductionMachine Learning In Production
Machine Learning In ProductionSamir Bessalah
 
A Study to Design and Implement a Manual for the Learning Process of Technica...
A Study to Design and Implement a Manual for the Learning Process of Technica...A Study to Design and Implement a Manual for the Learning Process of Technica...
A Study to Design and Implement a Manual for the Learning Process of Technica...UNIVERSIDAD MAGISTER (Sitio Oficial)
 
Deep learning for mere mortals - Devoxx Belgium 2015
Deep learning for mere mortals - Devoxx Belgium 2015Deep learning for mere mortals - Devoxx Belgium 2015
Deep learning for mere mortals - Devoxx Belgium 2015Samir Bessalah
 
Definition ofvectorspace
Definition ofvectorspaceDefinition ofvectorspace
Definition ofvectorspaceTanuj Parikh
 
Production and Beyond: Deploying and Managing Machine Learning Models
Production and Beyond: Deploying and Managing Machine Learning ModelsProduction and Beyond: Deploying and Managing Machine Learning Models
Production and Beyond: Deploying and Managing Machine Learning ModelsTuri, Inc.
 
Snapdragon processors
Snapdragon processorsSnapdragon processors
Snapdragon processorsDeepak Mathew
 

Destacado (10)

Algebird : Abstract Algebra for big data analytics. Devoxx 2014
Algebird : Abstract Algebra for big data analytics. Devoxx 2014Algebird : Abstract Algebra for big data analytics. Devoxx 2014
Algebird : Abstract Algebra for big data analytics. Devoxx 2014
 
Machine Learning In Production
Machine Learning In ProductionMachine Learning In Production
Machine Learning In Production
 
A Study to Design and Implement a Manual for the Learning Process of Technica...
A Study to Design and Implement a Manual for the Learning Process of Technica...A Study to Design and Implement a Manual for the Learning Process of Technica...
A Study to Design and Implement a Manual for the Learning Process of Technica...
 
Deep learning for mere mortals - Devoxx Belgium 2015
Deep learning for mere mortals - Devoxx Belgium 2015Deep learning for mere mortals - Devoxx Belgium 2015
Deep learning for mere mortals - Devoxx Belgium 2015
 
Information Security Seminar #2
Information Security Seminar #2Information Security Seminar #2
Information Security Seminar #2
 
Definition ofvectorspace
Definition ofvectorspaceDefinition ofvectorspace
Definition ofvectorspace
 
Snapdragon Processor
Snapdragon ProcessorSnapdragon Processor
Snapdragon Processor
 
Production and Beyond: Deploying and Managing Machine Learning Models
Production and Beyond: Deploying and Managing Machine Learning ModelsProduction and Beyond: Deploying and Managing Machine Learning Models
Production and Beyond: Deploying and Managing Machine Learning Models
 
Snapdragon processors
Snapdragon processorsSnapdragon processors
Snapdragon processors
 
Build Features, Not Apps
Build Features, Not AppsBuild Features, Not Apps
Build Features, Not Apps
 

Similar a algebraic-geometry

Section 18.3-19.1.Today we will discuss finite-dimensional.docx
Section 18.3-19.1.Today we will discuss finite-dimensional.docxSection 18.3-19.1.Today we will discuss finite-dimensional.docx
Section 18.3-19.1.Today we will discuss finite-dimensional.docxkenjordan97598
 
Section 18.3-19.1.Today we will discuss finite-dimensional.docx
Section 18.3-19.1.Today we will discuss finite-dimensional.docxSection 18.3-19.1.Today we will discuss finite-dimensional.docx
Section 18.3-19.1.Today we will discuss finite-dimensional.docxrtodd280
 
Gnt lecture notes (1)
Gnt lecture notes (1)Gnt lecture notes (1)
Gnt lecture notes (1)vahidmesic1
 
2016--04-07-NCUR-JON (1)
2016--04-07-NCUR-JON (1)2016--04-07-NCUR-JON (1)
2016--04-07-NCUR-JON (1)Jon Scott
 
Injective hulls of simple modules over Noetherian rings
Injective hulls of simple modules over Noetherian ringsInjective hulls of simple modules over Noetherian rings
Injective hulls of simple modules over Noetherian ringsMatematica Portuguesa
 
Congruence Distributive Varieties With Compact Intersection Property
Congruence Distributive Varieties With Compact Intersection PropertyCongruence Distributive Varieties With Compact Intersection Property
Congruence Distributive Varieties With Compact Intersection Propertyfilipke85
 
Variations on the Higman's Lemma
Variations on the Higman's LemmaVariations on the Higman's Lemma
Variations on the Higman's LemmaMarco Benini
 
Lewenz_McNairs-copy
Lewenz_McNairs-copyLewenz_McNairs-copy
Lewenz_McNairs-copyAnna Lewenz
 
A New Polynomial-Time Algorithm for Linear Programming
A New Polynomial-Time Algorithm for Linear ProgrammingA New Polynomial-Time Algorithm for Linear Programming
A New Polynomial-Time Algorithm for Linear ProgrammingSSA KPI
 
Complex reflection groups are somehow real
Complex reflection groups are somehow realComplex reflection groups are somehow real
Complex reflection groups are somehow realDavid Bessis
 
Persistent Homology and Nested Dissection
Persistent Homology and Nested DissectionPersistent Homology and Nested Dissection
Persistent Homology and Nested DissectionDon Sheehy
 
Notes on eigenvalues
Notes on eigenvaluesNotes on eigenvalues
Notes on eigenvaluesAmanSaeed11
 
CBSE Class 12 Mathematics formulas
CBSE Class 12 Mathematics formulasCBSE Class 12 Mathematics formulas
CBSE Class 12 Mathematics formulasParth Kshirsagar
 
Eighan values and diagonalization
Eighan values and diagonalization Eighan values and diagonalization
Eighan values and diagonalization gandhinagar
 
Notes on Intersection theory
Notes on Intersection theoryNotes on Intersection theory
Notes on Intersection theoryHeinrich Hartmann
 
Solvability of Matrix Riccati Inequality Talk Slides
Solvability of Matrix Riccati Inequality Talk SlidesSolvability of Matrix Riccati Inequality Talk Slides
Solvability of Matrix Riccati Inequality Talk SlidesKevin Kissi
 
2. Solutions_to_Atiyah_and_MacDonald
2. Solutions_to_Atiyah_and_MacDonald2. Solutions_to_Atiyah_and_MacDonald
2. Solutions_to_Atiyah_and_MacDonaldNguyễn Loan
 

Similar a algebraic-geometry (20)

Section 18.3-19.1.Today we will discuss finite-dimensional.docx
Section 18.3-19.1.Today we will discuss finite-dimensional.docxSection 18.3-19.1.Today we will discuss finite-dimensional.docx
Section 18.3-19.1.Today we will discuss finite-dimensional.docx
 
Section 18.3-19.1.Today we will discuss finite-dimensional.docx
Section 18.3-19.1.Today we will discuss finite-dimensional.docxSection 18.3-19.1.Today we will discuss finite-dimensional.docx
Section 18.3-19.1.Today we will discuss finite-dimensional.docx
 
Gnt lecture notes (1)
Gnt lecture notes (1)Gnt lecture notes (1)
Gnt lecture notes (1)
 
2016--04-07-NCUR-JON (1)
2016--04-07-NCUR-JON (1)2016--04-07-NCUR-JON (1)
2016--04-07-NCUR-JON (1)
 
Injective hulls of simple modules over Noetherian rings
Injective hulls of simple modules over Noetherian ringsInjective hulls of simple modules over Noetherian rings
Injective hulls of simple modules over Noetherian rings
 
Congruence Distributive Varieties With Compact Intersection Property
Congruence Distributive Varieties With Compact Intersection PropertyCongruence Distributive Varieties With Compact Intersection Property
Congruence Distributive Varieties With Compact Intersection Property
 
Imc2016 day1-solutions
Imc2016 day1-solutionsImc2016 day1-solutions
Imc2016 day1-solutions
 
Variations on the Higman's Lemma
Variations on the Higman's LemmaVariations on the Higman's Lemma
Variations on the Higman's Lemma
 
Lewenz_McNairs-copy
Lewenz_McNairs-copyLewenz_McNairs-copy
Lewenz_McNairs-copy
 
Alg grp
Alg grpAlg grp
Alg grp
 
A New Polynomial-Time Algorithm for Linear Programming
A New Polynomial-Time Algorithm for Linear ProgrammingA New Polynomial-Time Algorithm for Linear Programming
A New Polynomial-Time Algorithm for Linear Programming
 
Complex reflection groups are somehow real
Complex reflection groups are somehow realComplex reflection groups are somehow real
Complex reflection groups are somehow real
 
Persistent Homology and Nested Dissection
Persistent Homology and Nested DissectionPersistent Homology and Nested Dissection
Persistent Homology and Nested Dissection
 
Discrete mathematics notes
Discrete mathematics notesDiscrete mathematics notes
Discrete mathematics notes
 
Notes on eigenvalues
Notes on eigenvaluesNotes on eigenvalues
Notes on eigenvalues
 
CBSE Class 12 Mathematics formulas
CBSE Class 12 Mathematics formulasCBSE Class 12 Mathematics formulas
CBSE Class 12 Mathematics formulas
 
Eighan values and diagonalization
Eighan values and diagonalization Eighan values and diagonalization
Eighan values and diagonalization
 
Notes on Intersection theory
Notes on Intersection theoryNotes on Intersection theory
Notes on Intersection theory
 
Solvability of Matrix Riccati Inequality Talk Slides
Solvability of Matrix Riccati Inequality Talk SlidesSolvability of Matrix Riccati Inequality Talk Slides
Solvability of Matrix Riccati Inequality Talk Slides
 
2. Solutions_to_Atiyah_and_MacDonald
2. Solutions_to_Atiyah_and_MacDonald2. Solutions_to_Atiyah_and_MacDonald
2. Solutions_to_Atiyah_and_MacDonald
 

algebraic-geometry

  • 1. Algebraic Geometry Marko Rajkovi´c supervisor: prof. Vladimir Berkovich August 17, 2015 Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 2. Introduction Studying systems of polynomial equations in several variables and using abstract algebraic techniques for solving geometrical problems about zeros of such systems Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 3. Introduction Studying systems of polynomial equations in several variables and using abstract algebraic techniques for solving geometrical problems about zeros of such systems Establishing correspondences between geometric and algebraic objects Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 4. Introduction Studying systems of polynomial equations in several variables and using abstract algebraic techniques for solving geometrical problems about zeros of such systems Establishing correspondences between geometric and algebraic objects Fundamental objects of study are algebraic varieties Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 5. Affine Varieties Definition For an algebraically closed field k affine n-space over k is set An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}. Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 6. Affine Varieties Definition For an algebraically closed field k affine n-space over k is set An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}. For S ⊂ A = k[x1, . . . , xn] we define the zero set of S as: Z(S) := {P ∈ An; f (P) = 0 ∀f ∈ S}. Sets of this form are called algebraic sets. Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 7. Affine Varieties Definition For an algebraically closed field k affine n-space over k is set An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}. For S ⊂ A = k[x1, . . . , xn] we define the zero set of S as: Z(S) := {P ∈ An; f (P) = 0 ∀f ∈ S}. Sets of this form are called algebraic sets. Examples of algebraic sets An = Z(0) Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 8. Affine Varieties Definition For an algebraically closed field k affine n-space over k is set An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}. For S ⊂ A = k[x1, . . . , xn] we define the zero set of S as: Z(S) := {P ∈ An; f (P) = 0 ∀f ∈ S}. Sets of this form are called algebraic sets. Examples of algebraic sets An = Z(0) ∅ = Z(1) Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 9. Affine Varieties Definition For an algebraically closed field k affine n-space over k is set An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}. For S ⊂ A = k[x1, . . . , xn] we define the zero set of S as: Z(S) := {P ∈ An; f (P) = 0 ∀f ∈ S}. Sets of this form are called algebraic sets. Examples of algebraic sets An = Z(0) ∅ = Z(1) (a1, . . . , an) = Z(x1 − a1, . . . , xn − an) Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 10. Affine Varieties Definition For an algebraically closed field k affine n-space over k is set An := {(a1, . . . an); ai ∈ k for 1 ≤ i ≤ n}. For S ⊂ A = k[x1, . . . , xn] we define the zero set of S as: Z(S) := {P ∈ An; f (P) = 0 ∀f ∈ S}. Sets of this form are called algebraic sets. Examples of algebraic sets An = Z(0) ∅ = Z(1) (a1, . . . , an) = Z(x1 − a1, . . . , xn − an) Arbitrary intersections and finite unions of algebraic sets are again algebraic sets. Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 11. Definition Zariski topology on An is the topology whose closed sets are the algebraic sets. Any subset X of An will be equipped with the topology induced by the Zariski topology on An. This is called the Zariski topology on X. Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 12. Definition Zariski topology on An is the topology whose closed sets are the algebraic sets. Any subset X of An will be equipped with the topology induced by the Zariski topology on An. This is called the Zariski topology on X. Example Algebraic (closed) sets in A1 are finite subsets (including empty set) as sets of zeros of single non-zero polynomial and whole set (corresponding to zero polynomial). Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 13. Definition A non-empty subset Y of topological space X is called irreducible if it is not a union of two proper closed subsets. An (irreducible) affine variety is an (irreducible) closed subset of An with Zariski topology. Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 14. Definition A non-empty subset Y of topological space X is called irreducible if it is not a union of two proper closed subsets. An (irreducible) affine variety is an (irreducible) closed subset of An with Zariski topology. Example A1 is an irreducible affine variety since its only proper closed subsets are finite and it is infinite. Generally, An is an irreducible affine variety for every integer n. Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 15. Definition For X ⊂ An we define the ideal of X as I(X) := {f ∈ A; f (P) = 0 ∀P ∈ X} Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 16. Definition For X ⊂ An we define the ideal of X as I(X) := {f ∈ A; f (P) = 0 ∀P ∈ X} Examples I(An) = 0 Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 17. Definition For X ⊂ An we define the ideal of X as I(X) := {f ∈ A; f (P) = 0 ∀P ∈ X} Examples I(An) = 0 I((a1, . . . , an)) = (x1 − a1, . . . , xn − an) Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 18. Theorem–Hilbert Nullstelensatz For algebraically closed field k maximal ideals of k[x1, . . . , xn] are exactly the ideals of the form (x1 − a1, . . . , xn − an) for some ai ∈ k. Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 19. Theorem–Hilbert Nullstelensatz For algebraically closed field k maximal ideals of k[x1, . . . , xn] are exactly the ideals of the form (x1 − a1, . . . , xn − an) for some ai ∈ k. Corollary There is a 1 : 1 correspondence {points in An} ↔ {maximal ideals of k[x1, . . . , xn]} given by (a1, . . . , an) ↔ (x1 − a1, . . . , xn − an). Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 20. Lemma and Definition An algebraic set X ⊂ An is an irreducible affine variety if and only if its ideal I(X) ⊂ A = k[x1, . . . , xn] is a prime ideal. Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 21. Lemma and Definition An algebraic set X ⊂ An is an irreducible affine variety if and only if its ideal I(X) ⊂ A = k[x1, . . . , xn] is a prime ideal. We call A(Y ) := A/I(Y ) affine coordinate ring of Y . Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 22. Lemma and Definition An algebraic set X ⊂ An is an irreducible affine variety if and only if its ideal I(X) ⊂ A = k[x1, . . . , xn] is a prime ideal. We call A(Y ) := A/I(Y ) affine coordinate ring of Y . Examples An is irreducible since its ideal is zero ideal which is prime. Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 23. Lemma and Definition An algebraic set X ⊂ An is an irreducible affine variety if and only if its ideal I(X) ⊂ A = k[x1, . . . , xn] is a prime ideal. We call A(Y ) := A/I(Y ) affine coordinate ring of Y . Examples An is irreducible since its ideal is zero ideal which is prime. If f is irreducible polynomial in A = k[x1, . . . , xn] we get an irreducible affine variety Y = Z(f ). For n = 2 we call it affine curve of degree d, where d is degree of f. For n = 3 we have surface and for n > 3 hypersurface. Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 24. The Twisted Cubic Curve Let Y = {(t, t2, t3); t ∈ k}. Then I(Y ) = (x2 − y, x3 − z) in A = k[x, y, z]. A/I(Y ) = k[x, y, z]/(x2 − y, x3 − z) ∼= k[x, x2 , x3 ] ∼= k[t] which is an integral domain. Hence, I(Y ) is prime ideal and Y is an affine variety. Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry
  • 25. THANK YOU FOR YOUR ATTENTION! Marko Rajkovi´c supervisor: prof. Vladimir Berkovich Algebraic Geometry