Is Lambda a Convex Mapping?

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  • Thread starter Euge
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    2016
In summary, a convex mapping is a function that preserves the convexity of sets in its domain. In this context, $\Lambda$ refers to a specific function or mapping being considered. Convexity in a mapping can be determined by looking at the curvature of the mapping, with positive curvature indicating convexity. Examples of convex mappings include linear functions, quadratic functions with positive leading coefficients, and exponential functions. Convexity is important in mathematics for its applications in optimization problems and various fields such as economics, statistics, and machine learning.
  • #1
Euge
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Here is this week's POTW:

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Let $\Lambda :\Bbb R \to \Bbb R$ be a mapping such that for all bounded measurable mappings $f : [0,1]\to \Bbb R$,

$$\Lambda\left(\int_0^1 f(x)\, dx\right) \le \int_0^1 \Lambda(f(x))\, dx.$$

Show that $\Lambda$ is a convex mapping.

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  • #2
Congratulations to Opalg for his correct solution! You can find it below.

Given $a < b$ in $\mathbb{R}$ and $0 < \lambda < 1$, apply the inequality $$\Lambda\left(\int_0^1 f(x)\, dx\right) \leqslant \int_0^1 \Lambda(f(x))\, dx $$ to the function $$f(x) = \begin{cases}a &(0\leqslant x \leqslant \lambda), \\b &(\lambda < x \leqslant 1).\end{cases}$$ On the left side, \(\displaystyle \int_0^1f(x)\,dx = \int_0^\lambda a\,dx + \int_\lambda^1 b\,dx = \lambda a + (1-\lambda)b. \)

On the right side, \(\displaystyle \int_0^1 \Lambda(f(x))\, dx = \int_0^\lambda \Lambda(f(x))\, dx + \int_\lambda^1 \Lambda(f(x))\, dx = \int_0^\lambda \Lambda(a)\, dx + \int_\lambda^1 \Lambda(b)\, dx = \lambda\Lambda(a) + (1-\lambda)\Lambda(b). \)

Putting those values into the given inequality gives \(\displaystyle \Lambda\bigl( \lambda a + (1-\lambda)b\bigr) \leqslant \lambda\Lambda(a) + (1-\lambda)\Lambda(b).\) Since that holds whenever $a < b$ and $0 < \lambda < 1$, it follows that $\Lambda$ is convex.
 

Related to Is Lambda a Convex Mapping?

1. What is a convex mapping?

A convex mapping is a function that preserves the convexity of sets in its domain. In other words, the image of a convex set under a convex mapping is also a convex set.

2. What is $\Lambda$ in this context?

In this context, $\Lambda$ refers to a specific function or mapping being considered. It is often used as a placeholder for a general function in mathematical equations.

3. How is convexity determined in a mapping?

Convexity in a mapping can be determined by looking at the curvature of the mapping. A convex mapping will have a positive curvature, meaning that it curves outward, while a concave mapping will have a negative curvature, curving inward.

4. What are some examples of convex mappings?

Some examples of convex mappings include linear functions, quadratic functions with positive leading coefficients, and exponential functions. Any function that has a positive curvature and preserves the convexity of sets can be considered a convex mapping.

5. Why is convexity important in mathematics?

Convexity is important in mathematics because it allows for the use of powerful optimization techniques. Many real-world problems can be formulated as optimization problems, and convexity ensures that these problems have a unique global minimum. Additionally, convexity has applications in fields such as economics, statistics, and machine learning.

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