Undergrad Is Lambda a Convex Mapping?

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    2016
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SUMMARY

The discussion centers on proving that the mapping $\Lambda :\Bbb R \to \Bbb R$ is a convex mapping under the condition that for all bounded measurable mappings $f : [0,1]\to \Bbb R$, the inequality $\Lambda\left(\int_0^1 f(x)\, dx\right) \le \int_0^1 \Lambda(f(x))\, dx$ holds. The proof involves demonstrating that this inequality satisfies the definition of convexity for mappings. Opalg provided the correct solution, confirming the convex nature of $\Lambda$.

PREREQUISITES
  • Understanding of convex functions and mappings
  • Familiarity with measurable functions and integrals
  • Knowledge of bounded functions on the interval [0,1]
  • Basic principles of mathematical proofs and inequalities
NEXT STEPS
  • Study the properties of convex functions in real analysis
  • Explore the implications of Jensen's inequality in the context of convex mappings
  • Investigate measurable functions and their integrability
  • Learn about functional analysis and its applications in convex analysis
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Mathematicians, students of real analysis, and anyone interested in the properties of convex mappings and their applications in various fields of mathematics.

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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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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, $$\int_0^1f(x)\,dx = \int_0^\lambda a\,dx + \int_\lambda^1 b\,dx = \lambda a + (1-\lambda)b. $$

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

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

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