Convexity of continuous real function, midpoint convex

In summary, The conversation discusses how to prove that a continuous real function defined in a certain interval is convex. The person asking the question provides their attempt at a solution using a set E and asks for help in proving that E is dense in [p,q]. Another person suggests a simpler way to prove it by taking the endpoints of an open interval where the function is greater than a straight line connecting two points. The original person thanks them for pointing out this solution.
  • #1
boombaby
130
0

Homework Statement


Assume f is a continuous real function defined in (a,b) such that f([tex]\frac{x+y}{2}[/tex])<=[tex]\frac{f(x)+f(y)}{2}[/tex] for all x,y in(a,b) then f is convex.


Homework Equations





The Attempt at a Solution


my attempt is to suppose there are 3 points p<r<q such that f(r)>g(r), where g(x) is the straight line connecting (p,f(p)) and (q,f(q)), and get a contradiction.
1, There exists a neighborhood of r such that any x in N(r) implies that f(x)>g(x), followed by continuity.
2, Let E_0={p,q}, define E_1 = E_0[tex]\cup[/tex]{x | x is a mid point of any two points in E_0}. we continue this procedure and let E= union of all E_n.
3, For any points x in E, f(x)<=g(x), which can be proved straightforwardly
4, then we can find a point t in E which is also in N(r) such that f(x)<=g(x), if E is proved to be dense in [p,q].
5, we get a contradiction.


Question 1, I got stuck when I came to prove that E is dense in [p,q], it is jammed...Could anyone give me some hint to prove E is dense?

Question 2, Is there any other way(easier or harder are both welcome) to prove it?

Question 3, by the way, how can I determine whether a point belongs to Cantor set or not? Say, 1/4?1/5?

Thanks a lot!
 
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  • #2
well, I think I know how to prove that E is dense in [p,q] by the following steps:
suppose there exist a point in [p,q] and a neighborhood associated with it, such that N_0[tex]\cap[/tex]E = empty, and diam(N_1)=d, then I can always find another segment N_1, where diam(N_1)=2d... suppose we have find that N_n such that diam(N_n)=(2^n)*d. If diam(N_n)>(q-p)/2, the point (q+p)/2 is in N_n, contradicting that (q+p)/2 is also in E.
Hence E is dense in [p,q].

so, any other way to prove it? I think it could be done in an easier way...
Thanks!
 
  • #3
I think you can prove it a lot more simply. You've picked r so f(r)>g(r). The set of all x such that f(x)>g(x) is open (since both functions are continuous). So there's an open interval around r in the set. Take the endpoints of the open interval to be x and y. Doesn't that work? I don't see why you have to worry about any set being dense.
 
  • #4
Dick said:
The set of all x such that f(x)>g(x) is open (since both functions are continuous)

This is cool! I didn't realize it... Thanks a lot
 

What is convexity of a continuous real function?

The convexity of a continuous real function refers to the shape of the graph of the function. A function is considered convex if any line segment between any two points on the graph of the function lies above or on the graph. In simpler terms, it means the function is always "curving upwards" and does not have any "dips."

What does it mean for a function to be midpoint convex?

A function is considered midpoint convex if the midpoint of any two points on the graph of the function lies on or above the graph. This property is also known as Jensen's inequality and is a key concept in convex optimization.

How do you determine if a function is convex?

To determine if a function is convex, you can use the second derivative test. If the second derivative of the function is always positive, then the function is convex. Another way is to check if the function satisfies the definition of convexity, which states that the value of the function at any point on the line segment connecting two points should be less than or equal to the midpoint of the function values at those two points.

What are the applications of convexity in real life?

Convexity has many applications in real life, especially in fields such as economics, engineering, and machine learning. It is used to optimize problems with linear constraints, such as in portfolio management, production planning, and resource allocation. In machine learning, convexity is used to find the best fitting model for a given dataset.

How can convexity be useful in data analysis?

Convexity is useful in data analysis because it allows for efficient and reliable optimization of functions. It also guarantees that the solution found is a global minimum, meaning it is the best possible solution. In data analysis, convex functions are often used to model relationships between variables and can help identify key patterns and trends in the data.

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