MHB Is the Putnam Solution for Var(fg) Inequality Correct?

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    2017
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The discussion revolves around proving the inequality for the variance of the product of two continuous functions, specifically that Var(fg) is bounded by a combination of the variances of f and g, scaled by the maximum values of each function. The proof utilizes the definitions of variance and maximum, along with properties of integrals and inequalities. Key steps include expressing the functions in terms of their means and establishing bounds through integration. The conclusion confirms that the inequality holds, validating the Putnam solution for Var(fg). This mathematical exploration emphasizes the relationship between function variances and their maximum values.
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Here is this week's POTW:

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For any continuous real-valued function $f$ defined on the interval $[0,1]$, let
\begin{gather*}
\mu(f) = \int_0^1 f(x)\,dx, \,
\mathrm{Var}(f) = \int_0^1 (f(x) - \mu(f))^2\,dx, \\
M(f) = \max_{0 \leq x \leq 1} \left| f(x) \right|.
\end{gather*}
Show that if $f$ and $g$ are continuous real-valued functions defined on the interval $[0,1]$, then
\[
\mathrm{Var}(fg) \leq 2 \mathrm{Var}(f) M(g)^2 + 2 \mathrm{Var}(g) M(f)^2.
\]

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No one answered this week's question, which was Problem B-4 in the 2013 Putnam archive. The solution, attributed to Kiran Kedlaya and associates, follows.

[sp]
$\newcommand{\Var}{\mathrm{Var}}$

Write $f_0(x) = f(x)-\mu(f)$ and $g_0(x) = g(x)-\mu(g)$, so that $\int_0^1 f_0(x)^2\,dx = \Var(f)$, $\int_0^1 g_0(x)^2\,dx = \Var(g)$, and $\int_0^1 f_0(x)\,dx = \int_0^1 g_0(x)\,dx = 0$. Now since $|g(x)| \leq M(g)$ for all $x$, $0\leq \int_0^1 f_0(x)^2(M(g)^2-g(x)^2)\,dx = \Var(f) M(g)^2-\int_0^1 f_0(x)^2g(x)^2\,dx$, and similarly $0 \leq \Var(g)M(f)^2-\int_0^1 f(x)^2g_0(x)^2\,dx$. Summing gives
\begin{equation}
\Var(f)M(g)^2+\Var(g)M(f)^2
\geq \int_0^1 (f_0(x)^2g(x)^2+f(x)^2g_0(x)^2)\,dx. \qquad (1)
\end{equation}
Now
\begin{align*}
&\int_0^1 (f_0(x)^2g(x)^2+f(x)^2g_0(x)^2)\,dx-\Var(fg) \\&= \int_0^1 (f_0(x)^2g(x)^2+f(x)^2g_0(x)^2-(f(x)g(x)-\int_0^1 f(y)g(y)\,dy)^2)\,dx;
\end{align*}
substituting $f_0(x)+\mu(f)$ for $f(x)$ everywhere and $g_0(x)+\mu(g)$ for $g(x)$ everywhere, and using the fact that $\int_0^1 f_0(x)\,dx = \int_0^1 g_0(x)\,dx = 0$, we can expand and simplify the right hand side of this equation to obtain
\begin{align*}
&\int_0^1 (f_0(x)^2g(x)^2+f(x)^2g_0(x)^2)\,dx-\Var(fg) \\
&= \int_0^1 f_0(x)^2g_0(x)^2\,dx \\
&-2\mu(f)\mu(g)\int_0^1 f_0(x)g_0(x)\,dx +(\int_0^1 f_0(x)g_0(x)\,dx)^2 \\
&\geq -2\mu(f)\mu(g)\int_0^1 f_0(x)g_0(x)\,dx.
\end{align*}
Because of (1), it thus suffices to show that
\begin{equation}
2\mu(f)\mu(g)\int_0^1 f_0(x)g_0(x)\,dx
\leq \Var(f)M(g)^2+\Var(g)M(f)^2. \qquad (2)
\end{equation}
Now since $(\mu(g) f_0(x)-\mu(f) g_0(x))^2 \geq 0$ for all $x$, we have
\begin{align*}
2\mu(f)\mu(g) \int_0^1 f_0(x)g_0(x)\,dx
& \leq \int_0^1 (\mu(g)^2 f_0(x)^2 + \mu(f)^2 g_0(x)^2) dx \\
& = \Var(f) \mu(g)^2 + \Var(g) \mu(f)^2 \\
& \leq \Var(f) M(g)^2 + \Var(g) M(f)^2,
\end{align*}
establishing (2) and completing the proof.
[/sp]