Support Function for Set of Points in $\mathbb{R}^2$

In summary, the conversation discusses finding the support function for a set ##\left\{(x_1,x_2) \in \mathbb{R}^2: 0 \leq x_1 \leq 1 \; \text{and} \; 0 \leq x_2 \leq 1\right\}## using the definition of the support function ##\mu_s(p) = \inf\left\{p \cdot x: x \in S\right\}##. The support function is found to be ##\mu_S(x_1,x_2) = \begin{cases}x_1+x_2, & \text{if} \
  • #1
squenshl
479
4

Homework Statement


Let ##\left\{(x_1,x_2) \in \mathbb{R}^2: 0 \leq x_1 \leq 1 \; \text{and} \; 0 \leq x_2 \leq 1\right\}.## Find the support function ##\mu_s## for this set.

Homework Equations


We define the support function ##\mu_s: \mathbb{R}^n \rightarrow \mathbb{R} \cup \left\{-\infty\right\}## as ##\mu_s(p) = \inf\left\{p \cdot x: x \in S\right\}##.

The Attempt at a Solution


I know this is a square with vertices at ##(0,0)##, ##(0,1)##, ##(1,0)## and ##(1,1)##. I'll take a line that goes through ##(0,1)## and take a vector ##p## that is orthogonal to this. I get stuck after this in finding the support function

Someone please help!.
 
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  • #2
I think you need to find the maximum size of a vector in S, since the infimum of the dot product of p with an element x in S will be ## -|p| max_{x \in S}( |x| )##.
 
  • #3
Thanks. Here we are basically trying to maximise ##p_1x_1+p_2x_2## subject to the constraint ##p_1 \geq 0## and ##p_2 \leq 1.## The support function is
$$\mu_S(x_1,x_2) = \begin{cases}
x_1+x_2, & \text{if} \; x_1, x_2 \geq 0 \\
x_1, & \text{if} \; x_1 \geq 0, x_2 < 0 \\
x_2, & \text{if} \; x_1 < 0, x_2 \geq 0 \\
0 & \text{otherwise}
\end{cases}.$$
 

Related to Support Function for Set of Points in $\mathbb{R}^2$

1. What is the support function for a set of points in $\mathbb{R}^2$?

The support function for a set of points in $\mathbb{R}^2$ is a mathematical function that measures the maximum distance between a given point and the set of points. It is used to determine the convex hull of a set of points in $\mathbb{R}^2$.

2. How is the support function calculated?

The support function is calculated by finding the maximum inner product between a given vector and each point in the set. This can be done using linear programming techniques or by solving a system of equations.

3. What is the significance of the support function in geometric optimization?

The support function is an important tool in geometric optimization as it is used to determine the convex hull of a set of points. It can also be used to solve problems involving distance, separation, and containment of sets in $\mathbb{R}^2$.

4. Can the support function be extended to higher dimensions?

Yes, the support function can be extended to higher dimensions such as $\mathbb{R}^3$ or $\mathbb{R}^n$. The concept remains the same, but the calculations become more complex as the number of dimensions increases.

5. What are some applications of the support function?

The support function has various applications in fields such as computer graphics, robotics, and computational geometry. It is used to solve problems involving shape analysis, motion planning, and collision detection. It is also used in convex optimization and linear programming.

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