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4. Find and interpret \( C=g(f(n)) \)

Ask by Wood Burton. in the United States
Jan 26,2025

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Answer

To find \( C = g(f(n)) \), first apply function \( f \) to \( n \), then apply function \( g \) to the result. This means \( C \) is the composition of \( g \) and \( f \) applied to \( n \).

Solution

To address the problem **"4. Find and interpret \( C = g(f(n)) \)"**, we'll explore the concept of function composition and how it applies in this context. ### Understanding Function Composition **Function composition** involves applying one function to the result of another function. In mathematical terms, if you have two functions: - \( f: X \rightarrow Y \) - \( g: Y \rightarrow Z \) The composition \( g \circ f \) is a new function defined by: \[ (g \circ f)(n) = g(f(n)) \] This means you first apply \( f \) to \( n \), and then apply \( g \) to the result of \( f(n) \). ### Finding \( C = g(f(n)) \) Given the expression \( C = g(f(n)) \), here's how you can find \( C \): 1. **Apply Function \( f \) to \( n \):** - Calculate \( f(n) \). This gives you an intermediate result. 2. **Apply Function \( g \) to the Result of \( f(n) \):** - Take the value obtained from \( f(n) \) and apply \( g \) to it, resulting in \( g(f(n)) \). 3. **Assign the Result to \( C \):** - Set \( C = g(f(n)) \). **Example:** Let's consider specific functions to illustrate this process. - Let \( f(n) = 2n + 3 \) - Let \( g(m) = m^2 \) **Step 1: Apply \( f \) to \( n \)** \[ f(n) = 2n + 3 \] **Step 2: Apply \( g \) to \( f(n) \)** \[ g(f(n)) = g(2n + 3) = (2n + 3)^2 \] **Step 3: Assign to \( C \)** \[ C = (2n + 3)^2 \] ### Interpretation of \( C = g(f(n)) \) The composition \( C = g(f(n)) \) represents a two-step transformation of the variable \( n \): 1. **First Transformation (\( f \))**: The function \( f \) modifies \( n \) in a specific way (e.g., scaling and shifting). 2. **Second Transformation (\( g \))**: The function \( g \) takes the output of \( f(n) \) and transforms it further (e.g., squaring the result). The overall effect is a combined transformation where \( n \) undergoes both \( f \) and \( g \) sequentially. This can be useful in various applications, such as mathematical modeling, computer science algorithms, and more, where complex operations are built from simpler, composed functions. ### General Points to Consider - **Domain and Range**: Ensure that the range of \( f \) is compatible with the domain of \( g \) for the composition to be valid. - **Order Matters**: \( g(f(n)) \) is generally not the same as \( f(g(n)) \) unless the functions satisfy certain commutativity conditions. - **Simplification**: Sometimes, the composition can be simplified algebraically to a single expression, as shown in the example. If you have specific forms for \( f(n) \) and \( g(n) \), feel free to provide them, and I can offer a more detailed solution tailored to those functions!

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The Deep Dive

To analyze \( C = g(f(n)) \), you start by identifying the functions \( f(n) \) and \( g(x) \) where \( x = f(n) \). Suppose \( f(n) \) represents a growth function, like \( n^2 \), and \( g(x) \) is a specific transformation, such as \( x + 3 \). This would lead to \( C = g(n^2) = n^2 + 3 \). Interpreting this means that as \( n \) increases, \( C \) will grow predominantly based on the behavior of \( f(n) \), providing insight into the overall complexity or outcome of the system described. This formulation is often seen in computer science, particularly in algorithm analysis where \( f(n) \) could represent the number of operations as a function of input size \( n \), and \( g(x) \) could encapsulate additional overhead or constant factors. Understanding how these functions intertwine can help in optimizing performance and predicting resource usage effectively.

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