Affine hull and affine combinations equivalence

In summary: I'm not sure what is meant by affine combination, other than all possible ##\vec{c} + \sum_i \alpha_i \,\vec{0x_i}## which is possibly wrong here. It should presumably be ##\sum_{i,j} \alpha_{ij} \,(\vec{0x_i}-\vec{0x_j})\,.##
  • #1
Mr Davis 97
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Let ##X = \{x_1 , \dots , x_n\}##. Then ##\text{aff}(X) = \text{intersection of all affine spaces containing X}##. Let ##C(X)## be the set of all affine combinations of elements of ##X##. We want to show that these two sets are equal. First we focus on the ##\text{aff}(X) \subseteq C(X)## inclusion. If we can show that ##C(X)## is affine and that it contains ##X## then this inclusion will hold. Let's just say that it's obvious that it contains ##X##. So I want to prove that ##C(X)## is affine. Here is where I hit a roadblock. In my reference it only states that an affine space is a translate of a linear subspace. This is really the only definition I am given. Does this mean that I have to show that ##C(X)## is the translate of some linear subspace?
 
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  • #2
Mr Davis 97 said:
Let ##X = \{x_1 , \dots , x_n\}##. Then ##\text{aff}(X) = \text{intersection of all affine spaces containing X}##. Let ##C(X)## be the set of all affine combinations of elements of ##X##. We want to show that these two sets are equal. First we focus on the ##\text{aff}(X) \subseteq C(X)## inclusion. If we can show that ##C(X)## is affine and that it contains ##X## then this inclusion will hold. Let's just say that it's obvious ...
or just note that ##x_i=0+x_i## is an affine combination
... that it contains ##X##. So I want to prove that ##C(X)## is affine. Here is where I hit a roadblock. In my reference it only states that an affine space is a translate of a linear subspace. This is really the only definition I am given. Does this mean that I have to show that ##C(X)## is the translate of some linear subspace?
Yes, and again ##0 + \operatorname{span}(X)## is a linear and as such an affine space, too.
 
  • #3
fresh_42 said:
or just note that ##x_i=0+x_i## is an affine combination
Yes, and again ##0 + \operatorname{span}(X)## is a linear and as such an affine space, too.
Are you saying that ##C(X) = 0 + \text{span}(X)##?
 
  • #4
Mr Davis 97 said:
Are you saying that ##C(X) = 0 + \text{span}(X)##?
You're right, that wasn't a valid argument. But ##0+\operatorname{span}(X) \subseteq C(X)## whereas ##0+\operatorname{span}(X) \nsubseteq \operatorname{aff}(X)##. What do I miss here? Are you sure the equation holds? I guess I'm currently a bit confused.

Edit: Assume we have two points in the plane. Then ##\operatorname{aff}(\{x,y\})## is the one straight line through ##x## and ##y##. But any affine combination ##\alpha x + \beta y = c## yields the entire plane.
 
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  • #5
Mr Davis 97 said:
Are you saying that ##C(X) = 0 + \text{span}(X)##?

What is span(X)? X are elements in an affine space: writing span around them makes no sense.

Can you give your definition of affine space? I saw it as a set on which a vector space acts satisfying some axioms.
 
  • #6
Math_QED said:
What is span(X)? X are elements in an affine space: writing span around them makes no sense.
It does. The linear span of all points in ##X## is still an affine space.
 
  • #7
fresh_42 said:
It does. The linear span of all points in ##X## is still an affine space.

Span is something we write for vectors. The elements of X are not necessarily vectors if one treats the theory of affine geometry generally. It's why I asked for the OP's definition.
 
  • #8
Math_QED said:
Span is something we write for vectors. The elements of X are not necessarily vectors if one treats the theory of affine geometry generally. It's why I asked for the OP's definition.
##\vec{0x_i}## are the corresponding vectors. A space is affine if all ##\vec{0x_i} - \vec{0x_j}## are within. This is the case for ##(0+\vec{0x_i}) - (0+\vec{0x_j})##. Nevertheless, I'm not sure what is meant by affine combination, other than all possible ##\vec{c} + \sum_i \alpha_i \,\vec{0x_i}## which is possibly wrong here. It should presumably be ##\sum_{i,j} \alpha_{ij} \,(\vec{0x_i}-\vec{0x_j})\,.##
 

1. What is the difference between affine hull and affine combinations?

The affine hull of a set of points is the smallest affine space that contains all of those points. An affine space is a vector space that has been shifted and translated, but maintains its linear structure. An affine combination is a linear combination of points in an affine space, where the coefficients add up to 1. In other words, the affine combination of a set of points is a point in the affine hull of those points.

2. Can you give an example of affine hull and affine combinations?

Let's say we have three points in a 2-dimensional space: (1,1), (2,3), and (4,5). The affine hull of these points is the entire 2-dimensional space, since any point in this space can be written as an affine combination of these three points. For example, the point (3,4) can be written as (1,1) + 2*(2,3) + (-1)*(4,5). This is an affine combination of the three points, with coefficients that add up to 1.

3. How is affine hull and affine combinations equivalence useful in mathematics?

Affine hull and affine combinations equivalence is useful in many areas of mathematics, including linear algebra, functional analysis, and optimization. It allows us to study the properties of an affine space by looking at the properties of its affine hull. This can simplify and generalize many mathematical concepts and proofs.

4. Are there any real-world applications of affine hull and affine combinations equivalence?

Affine hull and affine combinations equivalence have many real-world applications, particularly in computer graphics and computer vision. Affine transformations, which are used to manipulate and transform images, are based on the concept of affine combinations. Additionally, affine hulls are used in data analysis and machine learning to find patterns and relationships in large datasets.

5. How does affine hull and affine combinations equivalence relate to convexity?

Affine hull and affine combinations equivalence are closely related to convexity. In fact, a set is convex if and only if its affine hull is equal to the set itself. This means that any point in a convex set can be written as an affine combination of points in that set. This property is useful in optimization problems, where finding the convex hull of a set of points can help to simplify and solve the problem.

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