Sets & Hyperplanes Homework: Convexity, Separability & More

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In summary: They can be separated but not strictly separated by ##x_1+x_2=1##.In summary, the sets A and B are both convex, closed, and compact. Their intersection is the empty set. They can be separated by the line x1+x2=2, but not strictly separated. They can be separated by the line x1+x2=1, but not strictly separated.
  • #1
squenshl
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Homework Statement


Consider the sets ##A = \left\{(x_1,x_2) \in\mathbb{R}^2: x_1+x_2 \leq 1\right\}## which is a straight line going through ##(0,1)## and ##(1,0)## and ##B = \left\{(x_1,x_2) \in\mathbb{R}^2: (x_1-3)^2+(x_2-3)^2 \leq 1 \right\}## which is a circle of radius ##1## centred at ##(3,3).##

1. Are the sets ##A## and ##B## convex? Are they closed? Are they compact?
2. What is ##A\cap B##?
3. Is it possible to find a hyperplane that separates ##A## and ##B##? That strictly separates them?
4. If they can be strictly separated then give one hyperplane that strictly separates the sets. If they can be separated but cannot be strictly separated then give one hyperplane that separates the sets and explain why they cannot be strictly separated. If they cannot be separated then explain why they cannot be separated.

Homework Equations

The Attempt at a Solution


1. I know they are convex, closed and compact by drawing a diagram. Is that sufficient enough or would I have to show it using maths? If so, how would I do that ? Some guidance would be great!
2. That is just the empty set.
3. Yes it is possible to separate them but not sure about if it strictly separates them.
4. The line ##x_1+x_2=2## is a hyperplane that strictly separates them I think. Is it possible 2 have sets that are both separated and strictly separated by a hyperplane. If not then obviously its just the first part of question 4 "If they can be strictly separated then give one hyperplane that strictly separates the sets".

Thanks a lot for the help!
 
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  • #2
squenshl said:

Homework Statement


Consider the sets ##A = \left\{(x_1,x_2) \in\mathbb{R}^2: x_1+x_2 \leq 1\right\}## which is a straight line going through ##(0,1)## and ##(1,0)##
One comment is that the set ## A## didn't mean the line, but the area below that line including itself.
 
  • #3
squenshl said:

Homework Statement


Consider the sets ##A = \left\{(x_1,x_2) \in\mathbb{R}^2: x_1+x_2 \leq 1\right\}## which is a straight line going through ##(0,1)## and ##(1,0)## and ##B = \left\{(x_1,x_2) \in\mathbb{R}^2: (x_1-3)^2+(x_2-3)^2 \leq 1 \right\}## which is a circle of radius ##1## centred at ##(3,3).##
Like Daeho Ro already said, set A is not just a line. Also, set B is not just the circle. It is a disk: the bounding circle and all of the points inside it.
squenshl said:
1. Are the sets ##A## and ##B## convex? Are they closed? Are they compact?
2. What is ##A\cap B##?
3. Is it possible to find a hyperplane that separates ##A## and ##B##? That strictly separates them?
Hyperplane? Your sets are in ##\mathbb{R}^2##. I've never heard of people referring to hyperplanes in spaces with a dimension less than 4. A "hyperplane" in ##\mathbb{R}^2## is just a line. Same comment in the next question.
squenshl said:
4. If they can be strictly separated then give one hyperplane that strictly separates the sets. If they can be separated but cannot be strictly separated then give one hyperplane that separates the sets and explain why they cannot be strictly separated. If they cannot be separated then explain why they cannot be separated.

Homework Equations

The Attempt at a Solution


1. I know they are convex, closed and compact by drawing a diagram. Is that sufficient enough or would I have to show it using maths? If so, how would I do that ? Some guidance would be great!
2. That is just the empty set.
3. Yes it is possible to separate them but not sure about if it strictly separates them.
4. The line ##x_1+x_2=2## is a hyperplane that strictly separates them I think. Is it possible 2 have sets that are both separated and strictly separated by a hyperplane. If not then obviously its just the first part of question 4 "If they can be strictly separated then give one hyperplane that strictly separates the sets".

Thanks a lot for the help!
 
  • #4
Cool thanks!

So they can only be strictly separated and not just separated or is it the other way around??
 
  • #5
This attachment will be helpful to you.
 

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  • #6
Great thanks. A & B can be separated but not strictly separated.
 
  • #7
Oops ##A## & ##B## can be strictly separated by ##x_1+x_2=2##
 

1. What is a set in mathematics?

A set is a collection of distinct objects, called elements, that are grouped together based on a specific criteria. Sets can be represented using curly braces { } and the elements are separated by commas. For example, the set of even numbers can be written as {2, 4, 6, 8, ...}.

2. What is a hyperplane?

A hyperplane is a geometric object in the n-dimensional space that divides the space into two half-spaces. In two-dimensional space, a hyperplane is a line that divides the plane into two regions. In three-dimensional space, a hyperplane is a flat surface that divides the space into two regions. Hyperplanes are commonly used in machine learning algorithms to separate data points into different classes.

3. What is convexity in sets?

Convexity in sets refers to the property of being convex. A set is convex if every line segment connecting two points in the set lies entirely within the set. In other words, a set is convex if it does not contain any indentations or holes. Convex sets have many useful properties, such as being closed under linear combinations and being easy to optimize over.

4. What does it mean for a set to be separable?

A set is separable if it can be divided into two disjoint subsets that do not share any common elements. In other words, the subsets are completely separated from each other. Separability is an important concept in machine learning, as it allows for the classification of data points into distinct groups.

5. How is convexity related to separability?

Convexity and separability are closely related concepts. A set is convex if and only if it is separable. This means that if a set is not convex, it is not separable, and vice versa. In machine learning, convexity and separability are important properties that are used to determine the feasibility and efficiency of different algorithms.

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