Proving the Equivalence of Local and Global Maxima for Concave Functions

In summary, the concept of proving the equivalence of local and global maxima for concave functions is based on the idea that the highest point on a local scale is equivalent to the highest point on a global scale. This is proved through mathematical proofs and equations, assuming the function is continuous, differentiable, and concave. This concept is specific to concave functions and is important in scientific research as it allows for accurate determination of the maximum value of a function and can lead to further advancements in various fields of study.
  • #1
pitaly
6
1
TL;DR Summary
Proof of theorem. Intuition: local maxima and global maxima coincide for concave functions
Consider the following theorem:

Theorem: Let ##f## be a concave differentiable function and let ##g## be a concave function. Then: ##y \in argmax_{x} {f(x)+g(x)}## if and only if ##y \in argmax_{x} {f(y)+f'(y)(x-y)+g(x)}.##

The intuition is that local maxima and global maxima coincide for concave functions. But can anyone help me with a formal proof? Thanks in advance!
 
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  • #2
Very interesting. Suggests me Intermediate Value Theorem and Mean Value Theorem, and this picture:
20211027_091904.jpg
 

FAQ: Proving the Equivalence of Local and Global Maxima for Concave Functions

What is the definition of a concave function?

A concave function is a function where the line segment connecting any two points on the graph of the function lies above or on the graph itself.

What is the significance of proving the equivalence of local and global maxima for concave functions?

This proof is important because it shows that for concave functions, the highest point on the graph can be found at any point, not just at the highest point. This allows for more flexibility in finding the maximum value of a function.

How is the equivalence of local and global maxima for concave functions proven?

The proof involves using the definition of a concave function and the properties of derivatives to show that any local maximum point is also a global maximum point, and vice versa.

Why is it necessary to assume that the function is a concave function in this proof?

The proof relies on the properties of concave functions, so assuming that the function is concave is necessary in order to make the proof valid.

What are some real-world applications of this proof?

This proof has applications in various fields such as economics, engineering, and finance, where concave functions are commonly used to model relationships between variables. It can also be used in optimization problems to find the maximum value of a concave function.

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