The set of all for which is equal to
Detailed Explanation
1. Domain of the Logarithm
For a real logarithm, the inside must be positive:
2. Turn the Inequality into a Friendlier Function
Move everything to one side: Define
3. Analyze the Shape with Derivatives
Differentiate to see where rises or falls:
- If , then → decreasing.
- If , then → increasing.
Thus has a minimum at .
4. Evaluate the Minimum Point
Because is never below this minimum, we get for every . That translates back to
5. Final Interval
Combine with the domain: the complete solution set is
Simple Explanation (ELI5)
Imagine Climbing a Hill
- Think of the hill as the graph of two paths:
- The log path that shows how high you get with
$\log(1+x)$. - The straight path that simply goes up with
$x$.
- The log path that shows how high you get with
- We want to check where the log path never rises above the straight path (that is,
$\log(1+x) \le x$). - First rule of the log: you’re only allowed to use it when the inside is positive, so
1 + xmust be more than 0. That means$x>-1$. - At
$x = 0$, both paths meet at the same spot ($\log(1)=0$). - Any step left of 0 (but still more than -1) makes the log path drop faster than the straight path, so the log path stays below.
- Any step right of 0 makes the straight path climb faster, so again the log path stays below.
- Conclusion: Every place you can legally stand (
$x>-1$) works! The only forbidden spot is .
So the answer is all numbers bigger than -1.
Step-by-Step Solution
Step 1: State Domain
Step 2: Rewrite Inequality
Define
Step 3: Differentiate
Step 4: Monotonicity
- For , → decreases.
- For , → increases. Hence is the minimum.
Step 5: Evaluate Minimum
Since the minimum is and everywhere else, the inequality holds for the whole domain.
Step 6: Combine with Domain
(Equality only at .)
Examples
Example 1
Economics: The inequality models diminishing returns—log revenue grows slower than linear cost for any positive additional input.
Example 2
Signal Processing: For small signal gains, using a log amplifier ensures the output never exceeds the linear approximation error.
Example 3
Computer Science: Comparing a logarithmic-time algorithm to a linear-time one shows log grows more slowly for any positive input size.