Convex function and convex set

In summary, the conversation is about convex functions and convex sets in the context of nonlinear programming problems. The question asks for conditions under which a function or a set is locally convex in a neighborhood of a given point. The function and set in question are both twice continuously differentiable, but not necessarily positive definite or convex for all points. The specific questions are about the Hessian matrix of the function, and the conditions for a neighborhood of a feasible point to be locally convex. The asker has provided a .zip file with more details on their question.
  • #1
baiyang11
9
0
Convex function and convex set(#1 edited)

Please answer #4, where I put my questions more specific. Thank you very much!

The question is about convex function and convex set. Considering a constrained nonlinear programming (NLP) problem
\[min \quad f({\bf x}) \quad {\bf x}\in \mathbb{R}^{n} \]
\[s.t. \quad g_{i}({\bf x})\leq 0 \quad i=1,2,...,N \]
\[\quad\quad h_{j}({\bf x})=0 \quad j=1,2,...,M \]

Where \(g_{i}({\bf x})\) and \(h_{j}({\bf x})\) is twice continuously differentiable. The feasible region \( S=\{{\bf x}|g_{i}({\bf x}),h_{j}({\bf x}),\forall i,j\}\). It is known that if \(g_{i}({\bf x})\) is convex and \(h_{j}({\bf x})\) is affinely linear for \({\bf x}\in \mathbb{R}^{n}\), \(S\) is a convex set. However, in my problem, \(g_{i}({\bf x})\) and \(h_{j}({\bf x})\) is indefinite for \({\bf x}\in \mathbb{R}^{n}\). So I would like to ask if there is any theory may answer the following two questions:

(1)For any twice continuously differentiable but indefinite function \(g_{i}({\bf x})\), on what condition, \(g_{i}({\bf x})\) is convex in a neighborhood of a point \({\bf x_{0}}\in\mathbb{R}^{n}\) ? (A guess is that Hessian of \(g_{i}\) at \({\bf x_{0}}\) is positive semidefinite. Is that the case?)
View attachment 989
Just like the image above. The function is indefinite for all \(x\), but is locally convex in the neighborhood of \(x_{0}\), which is \((x_{1},x_{2})\).

(2)On what condition, a neighborhood in \(S\) of a feasible point \({\bf x_{0}}\in S\) is a convex set? (I suppose a sufficient condition is that every \(g_{i}({\bf x})\) and \(h_{j}({\bf x})\) is convex in a neighborhood of \({\bf x_{0}}\). But is that necessary?)
View attachment 990
Just like the image above. The set \(S\) is not convex, but is locally convex in the neighborhood of \({\bf x_{0}}\) (the red triangle set).
 

Attachments

  • Question.zip
    38.1 KB · Views: 64
  • 1.png
    1.png
    1.8 KB · Views: 67
  • 2.png
    2.png
    2.4 KB · Views: 55
Last edited:
Physics news on Phys.org
  • #2
baiyang11 said:
To be convenient, I wrote down my question in a .doc file as attachments. Because the doc file is out of max file size, so I made it .zip file. Please refer to the .zip attachment. Sorry for the inconvenience.
The question is about convex function and convex set.
Thanks very much!
Most people are very wary about opening zip files from unknown sources. If you have a genuine question, I suggest that you ask it in text form.
 
  • #3
Opalg said:
Most people are very wary about opening zip files from unknown sources. If you have a genuine question, I suggest that you ask it in text form.

Thank you! I've edited the #1 and ask the question in text form.
 
Last edited:
  • #4
(1) Given a twice continuously differentiable function [tex]f(x),x\in\mathbb{R}[/tex], it can be justified that [tex]f''(x)[/tex] is not always positive for [tex]\forall x\in\mathbb{R}[/tex]. However, if [tex]f''(x_0)>0[/tex], is [tex]f(x)[/tex] ("locally") convex in some epsilon distance around [tex]x_0[/tex]? (As shown in the 1st picutre in #1)

(2) Given a twice continously differentiable function [tex]f({\bf x}),{\bf x}\in\mathbb{R}^{n}[/tex], it can be justified that Hessian Matrix of [tex]f({\bf x})[/tex] is not always postive definite for [tex]\forall x\in\mathbb{R}^{n}[/tex]. However, if Hessian of [tex]f({\bf x})[/tex] at [tex]{\bf x_0}[/tex] is positve definite, is [tex]f({\bf x})[/tex] ("locally") convex in some epsilon neighborhood of [tex]{\bf x_0}[/tex]?

(3) Given a region [tex]S[/tex] defined by [tex]g_{i}({\bf x})\leq 0 \quad i=1,2,...,N[/tex] and [tex]h_{j}({\bf x})=0 \quad j=1,2,...,M[/tex] and [tex]{\bf x}\in\mathbb{R}^{n}[/tex] (usually [tex]S[/tex] defines the feasible region of a general constrained optimization problem), where every [tex]g_{i}({\bf x})[/tex] and [tex]h_{j}({\bf x})[/tex] is twice continously differentiable. Here [tex]g_{i}({\bf x})[/tex] is not convex for [tex]\forall {\bf x}\in\mathbb{R}^{n}[/tex], [tex]h_{j}({\bf x})[/tex] is not affinely linear, so [tex]S[/tex] is not a convex set "as a whole". But for a feasible point [tex]{\bf x_0}\in S[/tex], on what condition (I would like to know condition about [tex]g_{i}({\bf x})[/tex] and [tex]h_{j}({\bf x})[/tex], not just the "at + (1-t)b" definition of convexity set), a neighborhood of [tex]{\bf x_0}[/tex] in [tex]S[/tex] is ("locally") convex? (As shown in the 2nd picture in #1)
As to this question, if this kind of condition exists, Hessian of [tex]g_{i}({\bf x_0})[/tex] and [tex]h_{j}({\bf x_0})[/tex] is probably involved, as I guessed.

I believe these three questions make it easier for you to answer exactly. Thanks very much!
 

Related to Convex function and convex set

What is a convex function?

A convex function is a mathematical function that satisfies the property that a line segment drawn between any two points on the graph of the function lies entirely above or on the graph. In other words, the function is always "curving up" and does not have any "dips" or "valleys".

What are some examples of convex functions?

Examples of convex functions include linear functions, polynomial functions of even degree, exponential functions, and logarithmic functions.

What is a convex set?

A convex set is a set of points in a mathematical space where every line segment joining any two points in the set lies entirely within the set. In other words, the set is "curved" and does not have any "dips" or "holes".

What are some examples of convex sets?

Examples of convex sets include circles, spheres, and ellipsoids in two or three-dimensional space, as well as more complex shapes such as convex polyhedra and convex cones.

What are the applications of convex functions and convex sets?

Convex functions and convex sets have many applications in mathematics, engineering, economics, and computer science. They are used to model and optimize various real-world problems, such as minimizing cost, maximizing profit, and finding the best solution to a given problem.

Similar threads

Replies
3
Views
1K
Replies
5
Views
539
Replies
3
Views
1K
  • Calculus
Replies
13
Views
1K
Replies
18
Views
2K
Replies
2
Views
1K
Replies
2
Views
2K
Back
Top