Property of lower semi-continuous maps

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In summary, a lower semi-continuous map is a mathematical function that has the property of mapping open sets to open sets. This property is important in mathematical analysis and optimization, as it allows for the identification of local minima and maxima and ensures the existence of solutions to certain problems. It is also related to convexity, as a lower semi-continuous map is convex if and only if it is a convex function. Some examples of lower semi-continuous maps include the absolute value function and the maximum function. In real-world applications, the property of lower semi-continuous maps is used in fields such as economics, engineering, and computer science to solve optimization problems and model real-world phenomena. It is also utilized in mathematical analysis to prove the
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Homework Statement



If X is a metric space then a map [itex] f: X \rightarrow \mathbb{R} [/itex] is called lower semi-continuous if for each ray of the type [itex] (a, + \infty) [/itex], the inverse image of the ray under f is also open. If X is a compact metric space, prove that f is bounded below, and that f actually obtains its minimum value for some [itex] x \in X [/itex].

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The Attempt at a Solution



Here is my proof:

Let [itex] U_a = (a, + \infty) [/itex]. Let [itex] U = \{U_a : a \in f(X)\} [/itex]. Let [itex] V_a = f^{-1}(U_a) [/itex] and let [itex] V = \{V_a : a \in f(X) \} [/itex]. Then V is an open cover of X and so it reduces to a finite subcovering [itex] \{ V_{a_1}, V_{a_2}, ... , V_{a_n} [/itex]. Let [itex] b = min \{a_1, a_2, ... , a_n [/itex]. Then [itex] f(V_b) = (b, + \infty) \supset (a_i, + \infty) [/itex] for [itex] a_i \neq b [/itex].

Now we show that f is bounded below by b. Choose any [itex] x \in X [/itex]. [itex] x \in V_{a_i} [/itex] for some [itex] i \in \{1, 2, ... , n \} [/itex]. Thus [itex] f(x) \in (a_i, + \infty) \subset (b, + \infty) [/itex]. Thus f is bounded below. Furthermore, since [itex] b \in U_b \subset f(X) [/itex], there is a c in X such that [itex] f(c) = b [/itex]. Thus f also attains its minimum value of b at the point c of X.

Is this proof correct?
 
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Your proof is correct and well-written. However, I would like to offer an alternative proof that may be easier to follow for some readers.

Proof:

Since X is a compact metric space, it is also sequentially compact. This means that for any sequence (x_n) in X, there exists a subsequence (x_{n_k}) that converges to some point x \in X. Now, let (a_n) be a sequence in f(X). Since f is lower semi-continuous, the inverse image of any ray (a_n, +\infty) is open. Therefore, for each n, there exists an open set V_n \subset X such that x \in V_n and f(V_n) \subset (a_n, +\infty).

Now, consider the sequence (V_n). Since X is sequentially compact, there exists a subsequence (V_{n_k}) that converges to some open set V \subset X. By construction, f(V_{n_k}) \subset (a_{n_k}, +\infty) for all k. Taking the limit as k \rightarrow \infty, we have f(V) \subset (a, +\infty) where a = \lim_{k \rightarrow \infty} a_{n_k}. This means that f is bounded below by a.

Since a \in f(X), there exists some c \in X such that f(c) = a. Therefore, f attains its minimum value at c.

I hope this alternative proof is helpful to you and other readers. Keep up the good work in your studies!
 

Related to Property of lower semi-continuous maps

What is a lower semi-continuous map?

A lower semi-continuous map is a mathematical function that satisfies the property that the preimage of any open set is open. In other words, for any open set in the range of the function, there exists an open set in the domain that maps to it.

Why is the property of lower semi-continuous maps important?

This property is important in mathematical analysis and optimization, as it allows for the identification of local minima and maxima of a function. It also ensures the existence of solutions to certain optimization problems.

How is the property of lower semi-continuous maps related to convexity?

A lower semi-continuous map is convex if and only if it is a convex function. This means that the line segment between any two points on the graph of the function lies entirely above or on the graph.

What are some examples of lower semi-continuous maps?

Some common examples include the absolute value function, the maximum function, and the indicator function of a closed set.

How is the property of lower semi-continuous maps used in real-world applications?

The property of lower semi-continuous maps is used in various fields such as economics, engineering, and computer science to solve optimization problems and model real-world phenomena. It is also utilized in mathematical analysis to prove the existence of solutions to equations and inequalities.

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