Chapter 3: Problem 80
Let \(f(0)=1\) and \(f^{\prime}(0)=2\). Find the derivative of \(f(f(x)-1)\) at \(x=0\).
Short Answer
Expert verified
The derivative is 4.
Step by step solution
01
Define the function
Let \( g(x) = f(f(x) - 1) \). We need to find \( g'(x) \). By the chain rule, \( g'(x) = f'(f(x) - 1) \cdot (f'(x)) \).
02
Evaluate at x = 0
Since we want the derivative at \( x = 0 \), we find \( g'(0) \). First, determine \( f(x) - 1 \) at \( x = 0 \). Substitute \( x = 0 \) in \( f(x) - 1 \) to get \( f(0) - 1 = 1 - 1 = 0 \).
03
Apply composition derivative at x = 0
Next, evaluate \( g(x) = f(f(x) - 1) \) at \( f(x) - 1 = 0 \). Therefore, \( g(x) = f(0) \) when \( f(x) - 1 = 0 \). Finding \( g'(0) \) involves applying the chain rule: \( g'(0) = f'(f(0) - 1) \cdot f'(0) = f'(0) \cdot f'(0) \).
04
Plug in known values
From the question, \( f(0) = 1 \) and \( f'(0) = 2 \). Therefore, \( g'(0) = 2 \times 2 = 4 \).
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Key Concepts
These are the key concepts you need to understand to accurately answer the question.
Derivative Calculation
Calculating derivatives is one of the foundational skills in calculus. Derivatives measure how a function changes as its input changes. In simpler terms, it provides the rate at which a particular function is changing at any given point.
Derivatives are not just theoretical but have practical applications such as forecasting in economics and natural sciences. Overall, derivatives are crucial for understanding relationships within mathematical models.
- Think of a derivative as the slope of a curve at a specific point;
- It tells you if the function is increasing, decreasing, or staying constant.
Derivatives are not just theoretical but have practical applications such as forecasting in economics and natural sciences. Overall, derivatives are crucial for understanding relationships within mathematical models.
Chain Rule
The chain rule is an essential technique for calculating the derivative of composite functions. Composite functions are those where one function is nested inside another. Essentially, the chain rule allows you to differentiate these types of functions by differentiating each component separately and then combining the results.
To apply the chain rule, follow three steps:
To apply the chain rule, follow three steps:
- Differentiating the outer function first;
- Then multiply by the derivative of the inner function;
- Keep the "chain" intact by focusing on each part.
Function Composition
Function composition is all about stacking functions, meaning taking the output of one function and using it as the input for another. It’s like putting on socks first, then shoes—the socks are crucial for the shoes to be worn correctly. In notation terms, for two functions \( f(x) \) and \( g(x) \), the composition is written as \( f(g(x)) \).
Function composition is a powerful way to create more complex functions from simpler ones. It can also help model real-world scenarios where several steps or transformations are applied sequentially.
Function composition is a powerful way to create more complex functions from simpler ones. It can also help model real-world scenarios where several steps or transformations are applied sequentially.
- Composing functions can also simplify problems;
- By breaking them down into smaller, more manageable parts.
Evaluating Derivatives at a Point
Evaluating the derivative of a function at a specific point reveals the behavior of the function exactly at that point. It's like zooming in to understand precisely how fast the function is moving at just that instance. This is particularly useful in finding tangent lines or instantaneous rates of change.
To evaluate a derivative at a point, follow these steps:
This meticulous process gives us insights into the precise change rate, which is infinitely valuable in analytical scenarios.
To evaluate a derivative at a point, follow these steps:
- Calculate the general derivative;
- Plug in the specific x-value you are interested in.
This meticulous process gives us insights into the precise change rate, which is infinitely valuable in analytical scenarios.