Analyzing the Setup
Imagine you are standing on a coordinate plane, looking at three specific markers: (1,1), (2,4), and (3,9). As you trace these points, a familiar shape emerges—the parabola y=x2.
However, the problem states that f(x) is a twice-differentiable function that passes through these points. It does not necessarily mean f(x) is x2; it could be a complex, oscillating curve that happens to intersect these three targets.
Our goal is to uncover a hidden truth about the second derivative, f′′(x), using the elegance of calculus.
The Difference Engine
To tame this mystery, we introduce an auxiliary function:
Think of g(x) as a "difference engine" that measures the vertical gap between our unknown function f(x) and the standard parabola y=x2.
Since f(x) passes through (1,1), (2,4), and (3,9), we observe the following:
g(1)=1−12=0
g(2)=4−22=0
g(3)=9−32=0
Suddenly, we have a function g(x) that is zero at three distinct locations. This is the breakthrough we need.
The Rolle's Cascade
Now, we invoke the power of Rolle's Theorem. In the interval [1,2], g(x) starts at zero and ends at zero.
Rolle's Theorem guarantees there is some point c1∈(1,2) where g′(c1)=0. Similarly, in the interval [2,3], g(x) also starts at zero and ends at zero, so there must be some point c2∈(2,3) where g′(c2)=0.
We now have two roots for the derivative function g′(x). If we apply Rolle's Theorem again, this time to g′(x) on the interval [c1,c2], we are guaranteed that there exists some x∈(c1,c2) such that:
This is the "cascade"—three roots for g(x) lead to two roots for g′(x), which in turn lead to at least one root for g′′(x).
The Final Revelation
We defined g(x)=f(x)−x2. Differentiating this once gives:
Differentiating it a second time gives:
We just proved that g′′(x)=0 for some x in the interval (1,3). Substituting our expression, we get:
This simplifies beautifully to:
This is the hidden geometric reality of the problem: no matter how complex f(x) is, if it hits those three points, its second derivative must be 2 at some point. It is a stunning example of how Rolle's Theorem can extract deep information from minimal data.