Homogenization and dehomogenization are key techniques for moving between affine and projective spaces in algebraic geometry. They allow us to convert varieties and polynomials, bridging the gap between these two fundamental settings.

These processes help simplify complex geometric problems by leveraging the advantages of . We can study behavior at infinity, apply powerful theorems, and gain deeper insights into the structure of algebraic varieties.

Homogenization vs Dehomogenization

The Process of Homogenization

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  • Homogenization converts an affine algebraic variety into a projective algebraic variety by introducing an additional variable
    • Example: An V(x2+y21)A2V(x^2 + y^2 - 1) \subseteq \mathbb{A}^2 can be homogenized to a projective variety V(x2+y2z2)P2V(x^2 + y^2 - z^2) \subseteq \mathbb{P}^2
  • To homogenize a polynomial f(x1,,xn)f(x_1, \ldots, x_n), introduce a new variable x0x_0 and multiply each monomial by an appropriate power of x0x_0 to make the degree of all monomials equal to the degree of ff
    • Example: The polynomial f(x,y)=x2+xy+yf(x, y) = x^2 + xy + y is homogenized to f(x0,x1,x2)=x12+x1x2+x0x2f^*(x_0, x_1, x_2) = x_1^2 + x_1x_2 + x_0x_2
  • The homogenization of an affine variety V(f1,,fs)AnV(f_1, \ldots, f_s) \subseteq \mathbb{A}^n is the projective variety V(f1,,fs)PnV(f_1^*, \ldots, f_s^*) \subseteq \mathbb{P}^n, where fif_i^* is the homogenization of fif_i

The Process of Dehomogenization

  • Dehomogenization converts a projective algebraic variety into an affine algebraic variety by setting one of the variables equal to 1
    • Example: A projective variety V(x2+y2z2)P2V(x^2 + y^2 - z^2) \subseteq \mathbb{P}^2 can be dehomogenized to an affine variety V(x2+y21)A2V(x^2 + y^2 - 1) \subseteq \mathbb{A}^2 by setting z=1z = 1
  • To dehomogenize a F(x0,x1,,xn)F(x_0, x_1, \ldots, x_n), set one of the variables (usually x0x_0) equal to 1 and simplify the resulting polynomial
    • Example: The homogeneous polynomial F(x0,x1,x2)=x12+x1x2+x0x2F(x_0, x_1, x_2) = x_1^2 + x_1x_2 + x_0x_2 is dehomogenized to f(x,y)=x2+xy+yf(x, y) = x^2 + xy + y by setting x0=1x_0 = 1
  • The dehomogenization of a projective variety V(F1,,Fs)PnV(F_1, \ldots, F_s) \subseteq \mathbb{P}^n with respect to x0x_0 is the affine variety V(F1(1,x1,,xn),,Fs(1,x1,,xn))AnV(F_1(1, x_1, \ldots, x_n), \ldots, F_s(1, x_1, \ldots, x_n)) \subseteq \mathbb{A}^n

Affine vs Projective Representations

Converting Affine Varieties to Projective Varieties

  • To convert an affine variety V(f1,,fs)AnV(f_1, \ldots, f_s) \subseteq \mathbb{A}^n to its projective closure, homogenize each polynomial fif_i to obtain fif_i^* and consider the projective variety V(f1,,fs)PnV(f_1^*, \ldots, f_s^*) \subseteq \mathbb{P}^n
    • Example: The affine variety V(x2+y21)A2V(x^2 + y^2 - 1) \subseteq \mathbb{A}^2 has the projective closure V(x2+y2z2)P2V(x^2 + y^2 - z^2) \subseteq \mathbb{P}^2
  • The projective closure of an affine variety VAnV \subseteq \mathbb{A}^n is the smallest projective variety in Pn\mathbb{P}^n containing VV

Converting Projective Varieties to Affine Varieties

  • To convert a projective variety V(F1,,Fs)PnV(F_1, \ldots, F_s) \subseteq \mathbb{P}^n to its affine part with respect to x0x_0, dehomogenize each polynomial FiF_i by setting x0=1x_0 = 1 and consider the affine variety V(F1(1,x1,,xn),,Fs(1,x1,,xn))AnV(F_1(1, x_1, \ldots, x_n), \ldots, F_s(1, x_1, \ldots, x_n)) \subseteq \mathbb{A}^n
    • Example: The projective variety V(x2+y2z2)P2V(x^2 + y^2 - z^2) \subseteq \mathbb{P}^2 has the affine part V(x2+y21)A2V(x^2 + y^2 - 1) \subseteq \mathbb{A}^2 with respect to zz
  • The affine part of a projective variety VPnV \subseteq \mathbb{P}^n with respect to x0x_0 is the intersection of VV with the affine space An\mathbb{A}^n, obtained by setting x0=1x_0 = 1
  • The affine part of a projective variety and the projective closure of an affine variety are related by the operations of homogenization and dehomogenization

Applications of Homogenization

Simplifying the Study of Affine Varieties

  • Homogenization can simplify the study of affine varieties by working in the projective setting, where the geometry is more uniform and some computations are easier
    • Example: Bézout's theorem, which states that the number of intersection points of two plane curves (counting multiplicities) is equal to the product of their degrees, is more easily stated and proved in the projective setting
  • Homogenization can determine the behavior of an affine variety at infinity by studying the added points in the projective closure
    • Example: The affine variety V(xy1)A2V(xy - 1) \subseteq \mathbb{A}^2 has two branches that approach the lines x=0x = 0 and y=0y = 0 at infinity, which can be seen in its projective closure V(xyz2)P2V(xy - z^2) \subseteq \mathbb{P}^2

Applying Projective Results to Affine Varieties

  • Dehomogenization allows the application of results obtained in the projective setting to affine varieties
    • Example: If a projective variety is irreducible, then its affine part is also irreducible
  • Dehomogenization can analyze the local properties of a projective variety by considering its affine parts
    • Example: The singularities of a projective variety can be studied by examining the singularities of its affine parts
  • Homogenization and dehomogenization can establish a correspondence between affine and projective varieties, enabling the transfer of properties and results between the two settings

Benefits of Projective Space

Uniform and Symmetric Setting

  • Projective space provides a more uniform and symmetric setting for studying algebraic varieties, as it treats points at infinity on an equal footing with finite points
    • Example: In the projective plane, parallel lines always intersect at a point at infinity, whereas in the affine plane, they do not intersect
  • Many geometric properties and results are simpler and more elegant in the projective setting, such as Bézout's theorem and the of varieties

Compactness and Simplification

  • Projective space is compact, which can simplify certain arguments and proofs involving algebraic varieties
    • Example: The compactness of projective space can be used to prove that every non-constant polynomial map between projective varieties is surjective
  • Some computations, such as the computation of degrees and the application of resultants, are more straightforward in the projective setting
    • Example: The degree of a projective variety can be computed using the Hilbert polynomial, which is easier to work with than the degree of an affine variety

Analyzing Affine Varieties at Infinity

  • Working in projective space allows for the study of the behavior of affine varieties at infinity, providing a more complete understanding of their geometry
    • Example: The projective closure of the affine variety V(yx2)A2V(y - x^2) \subseteq \mathbb{A}^2 contains an additional point at infinity, (0:1:0)(0:1:0), which corresponds to the vertical asymptote of the parabola
  • Projective techniques, such as the use of homogeneous coordinates and the projective closure, can be used to analyze and solve problems involving affine varieties
    • Example: The intersection of two affine varieties can be computed by homogenizing their defining equations, computing the intersection of their projective closures, and then dehomogenizing the result

Key Terms to Review (12)

Affine coordinates: Affine coordinates are a system of coordinates used in affine geometry that define points in a space relative to a set of basis vectors. They allow for the representation of geometric objects and transformations without needing to consider distances or angles, focusing instead on the relationships between points. This is particularly useful in the context of algebraic geometry, especially during homogenization and dehomogenization, where the interplay between projective and affine spaces becomes crucial.
Affine variety: An affine variety is a subset of affine space that is defined as the common zero set of a collection of polynomials. It represents the solution set to polynomial equations, allowing for the study of geometric properties using algebraic techniques, and serves as a fundamental building block in algebraic geometry.
Algorithm for homogenization: An algorithm for homogenization is a systematic method used to convert a polynomial equation into a homogeneous polynomial by introducing an additional variable, typically denoted as 't'. This transformation allows the study of properties of the polynomial in projective space, making it easier to analyze solutions and their geometric interpretations, especially when dealing with intersection theory and algebraic varieties.
Computational methods: Computational methods are techniques and algorithms used to solve mathematical problems through numerical approximations and simulations rather than purely analytical solutions. These methods are essential for processing and analyzing complex mathematical structures, such as polynomials and geometric objects, in a way that is often more feasible than traditional symbolic computations. By utilizing computational methods, mathematicians can explore, visualize, and manipulate algebraic varieties, making them invaluable in the study of algebraic geometry.
Dual varieties: Dual varieties are geometric constructs that relate to the original varieties by representing the set of hyperplanes tangent to the original variety at all its points. This concept plays a crucial role in the context of homogenization and dehomogenization, connecting projective spaces to their duals and allowing for the analysis of properties such as tangent spaces and singularities. Understanding dual varieties enhances the study of intersection theory and helps in various applications of algebraic geometry.
Homogeneous Polynomial: A homogeneous polynomial is a polynomial whose terms all have the same total degree. This property allows it to have a consistent form when represented in projective space, enabling various applications in geometry, algebra, and computational methods.
Homogenization process: The homogenization process refers to the technique used in algebraic geometry to convert a polynomial into a homogeneous polynomial by introducing an additional variable, typically denoted as 't'. This transformation helps in studying the properties of the polynomial at infinity and allows for the application of projective geometry techniques.
Intersection Theory: Intersection theory is a branch of algebraic geometry that studies the intersection of algebraic varieties, focusing on the properties and dimensions of their intersections. It provides a framework to count and analyze how geometric objects intersect, which is essential for solving polynomial equations and understanding the structure of varieties. This theory connects algebraic concepts with geometric intuition, making it a powerful tool in various mathematical contexts.
Parametrization of curves: Parametrization of curves refers to the representation of a curve using a parameter, usually denoted as 't', which describes the coordinates of points on the curve in terms of this single variable. This method allows for a more flexible and comprehensive description of curves, making it easier to analyze and manipulate them mathematically. By converting a curve into parametric equations, it's possible to express not only geometric properties but also their behavior under transformations like homogenization and dehomogenization.
Projective Coordinates: Projective coordinates are a system of coordinates used in projective geometry that enable the representation of points in a projective space. Unlike traditional Cartesian coordinates, projective coordinates introduce a notion of 'points at infinity,' allowing for a unified treatment of parallel lines and facilitating the study of geometric properties invariant under projection. This concept is crucial when dealing with homogenization and dehomogenization processes, which transform coordinates to handle the complexities of projective spaces.
Projective Space: Projective space is a fundamental concept in algebraic geometry that extends the notion of Euclidean space by adding 'points at infinity' to allow for a more comprehensive study of geometric properties. This extension allows for the unification of various types of geometric objects, facilitating intersection theory, transformations, and various algebraic structures.
Resultant computation: Resultant computation is a mathematical technique used to eliminate variables from a system of polynomial equations, helping to find solutions that satisfy all equations simultaneously. This process is significant in algebraic geometry for determining conditions under which polynomials share common roots, and it connects deeply with concepts like homogenization, where equations are transformed into a uniform degree, and the historical development of algebraic methods.
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