You are standing at position 0 on an infinite number line. There is a destination at position target.
You can make some number of moves numMoves so that:
ith move (starting from i == 1 to i == numMoves), you take i steps in the chosen direction.Given the integer target, return the minimum number of moves required (i.e., the minimum numMoves) to reach the destination.
Example 1:
Input: target = 2 Output: 3 Explanation: On the 1st move, we step from 0 to 1 (1 step). On the 2nd move, we step from 1 to -1 (2 steps). On the 3rd move, we step from -1 to 2 (3 steps).
Example 2:
Input: target = 3 Output: 2 Explanation: On the 1st move, we step from 0 to 1 (1 step). On the 2nd move, we step from 1 to 3 (2 steps).
Constraints:
-109 <= target <= 109target != 0This approach involves breaking down the problem into smaller subproblems, solving each subproblem recursively, and then combining the results. This is a classic Divide and Conquer approach which can be applied to a variety of problems, such as sorting algorithms (e.g., Merge Sort).
This C code implements the Merge Sort algorithm using the Divide and Conquer strategy. The array is divided into two halves, each is sorted recursively, and then the two halves are merged together. The merge function combines two sorted subarrays into a single sorted array.
C++
Java
Python
C#
JavaScript
Time Complexity: O(n log n)
Space Complexity: O(n)
Dynamic Programming (DP) is an approach that solves complex problems by breaking them down into simpler subproblems and storing the results to avoid recomputing. It's particularly useful for optimization problems where decisions depend on previous decisions.
This C code demonstrates a dynamic programming solution for computing Fibonacci numbers. It uses memoization to store previously calculated results in the dp array, avoiding redundant calculations.
C++
Java
Python
C#
JavaScript
Time Complexity: O(n)
Space Complexity: O(n)
| Approach | Complexity |
|---|---|
| Approach 1: Divide and Conquer | Time Complexity: O(n log n) |
| Approach 2: Dynamic Programming | Time Complexity: O(n) |
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