Animated Solution for Mathematics - Differentiation: If f(x)=∫0x2et(t−2)(t−3)dt for all x∈(0,∞), then
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Visualized Solution
Understanding the Function f(x)
Given function: f(x)=∫0x2et(t−2)(t−3)dt
Domain: x∈(0,∞)
Objective: Analyze local extrema and monotonicity of f(x).
The Newton-Leibniz Rule
To find f′(x), we use the Newton-Leibniz Rule.
dxd∫g(x)h(x)F(t)dt=F(h(x))⋅h′(x)−F(g(x))⋅g′(x)
Applying Leibniz Rule to f(x)
Substitute h(x)=x2 and g(x)=0.
f′(x)=[ex2(x2−2)(x2−3)]⋅dxd(x2)−0
f′(x)=ex(x2−2)(x2−3)⋅2x
Simplifying f′(x)
Rearranging terms: f′(x)=2xex(x2−2)(x2−3)
For x>0, the terms 2x and ex are always strictly positive.
The sign of f′(x) depends entirely on (x2−2)(x2−3).
Finding Critical Points
Set f′(x)=0 to find critical points.
2xex(x2−2)(x2−3)=0
Since 2xex=0, we solve: x2−2=0⟹x=2
And x2−3=0⟹x=3
Sign Analysis: 0<x<2
Interval: x∈(0,2)
Here, x2<2, so (x2−2)<0 and (x2−3)<0.
Product: (x2−2)(x2−3)>0⟹f′(x)>0.
f(x) is increasing on (0,2).
Sign Analysis: 2<x<3
Interval: x∈(2,3)
Here, 2<x2<3.
So, (x2−2)>0 and (x2−3)<0.
Product: (x2−2)(x2−3)<0⟹f′(x)<0.
f(x) is decreasing on (2,3).
Sign Analysis: x>3
Interval: x∈(3,∞)
Here, x2>3.
So, (x2−2)>0 and (x2−3)>0.
Product: (x2−2)(x2−3)>0⟹f′(x)>0.
f(x) is increasing on (3,∞).
Local Maxima at x=2
At x=2, f′(x) changes sign from positive to negative.
By the First Derivative Test, f(x) has a local maximum at x=2.
Local Minima at x=3
At x=3, f′(x) changes sign from negative to positive.
By the First Derivative Test, f(x) has a local minimum at x=3.
Existence of f′′(c)=0
We know f′(2)=0 and f′(3)=0.
f′(x) is continuous and differentiable for x>0.
By Rolle's Theorem on [2,3]:
There exists at least one c∈(2,3) such that f′′(c)=0.
Final Conclusion
Summary of Results:
1. Local maximum at x=2 (Correct)
2. Decreasing on (2,3) (Correct)
3. f′′(c)=0 for some c∈(0,∞) (Correct)
4. Local minimum at x=3 (Correct)
All four options are correct.
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The Sigma Insight: Maxima and Minima
Solution Diagram
The Calculus Landscape
Unveiling the Hidden Geometry
Welcome, fellow traveler of the mathematical realm! Today, we are not just solving a problem; we are exploring the topography of a function.
We are given f(x)=∫0x2et(t−2)(t−3)dt. At first glance, this integral might look like a mountain too steep to climb.
In JEE Advanced, we don't always need to conquer the mountain; sometimes, we just need to understand its peaks and valleys.
Phase 1
The Power of the Leibniz Rule
We want to know where this function increases, where it decreases, and where it hits its local maximums and minimums. To do this, we need the derivative, f′(x).
We invoke the Newton-Leibniz Rule. This rule is our secret weapon, stating that if we have a function defined by an integral with a variable upper limit h(x), the derivative is the integrand evaluated at h(x), multiplied by the derivative of h(x).
Applying this to our function, where the upper limit is x2, we get:
f′(x)=[ex2(x2−2)(x2−3)]⋅dxd(x2)−0
Simplifying this, we arrive at our derivative:
f′(x)=2xex(x2−2)(x2−3)
Phase 2
The Sign Analysis
Now, look at this expression. It is a product of three parts: 2x, ex, and the polynomial (x2−2)(x2−3).
Since we are restricted to x>0, both 2x and ex are always positive. They are like the wind at our back—they don't change the direction of our journey.
The real action happens in the polynomial part. We find critical points where f′(x)=0, which occurs when x2=2 or x2=3. Since x>0, our critical points are x=2 and x=3.
Let's map the behavior:
1. In the interval (0,2): Here, x2<2. Both (x2−2) and (x2−3) are negative. A negative times a negative is a positive, so f′(x)>0, and our function is climbing.
2. In the interval (2,3): Here, 2<x2<3. Now, (x2−2) is positive, but (x2−3) is negative. A positive times a negative is a negative, so our function is descending.
3. In the interval (3,∞): Here, x2>3. Both brackets are positive. The function is climbing again.
Phase 3
The Final Synthesis
By the First Derivative Test, we have a local maximum at x=2 because the function switches from increasing to decreasing.
We have a local minimum at x=3 because it switches from decreasing to increasing.
Because f′(x) is continuous and differentiable, Rolle's Theorem guarantees that between our two critical points, there must be a point c where the slope of the derivative is zero, meaning f′′(c)=0.
The beauty of this problem lies not in the complexity of the integration, but in the elegance of the derivative's behavior. Keep this perspective, and you will find that even the most daunting calculus problems are just landscapes waiting to be mapped.