This approach involves using dynamic programming to store solutions to subproblems in a table and build up to the solution of the original problem. By doing so, we can avoid redundant calculations and achieve a more efficient solution.
Time Complexity: O(n)
Space Complexity: O(n)
1// JavaScript code exampleThis JavaScript solution uses an array to dynamically compute each required subproblem in a manner that builds up to an efficient overall solution.
A greedy algorithm is an approach that constructs a solution by choosing the best option at each step. This approach may not always yield the optimal global solution, but for certain problems, especially those with optima formed by greedy choices, it can be very efficient.
Time Complexity: O(n log n) /* or other depending on the specific problem */
Space Complexity: O(1) /* if in-place, depending on conditions */
1// C# code exampleThis C# solution applies a greedy algorithm approach that looks at each element sequentially, deciding on the optimal choice at each stage.