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// Java code exampleUsing a similar approach in Java, we leverage array structures to keep track of computed subproblem results, leading up to solving the overall problem efficiently.
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 exampleIn this C++ implementation, a greedy algorithm is used to make problem-solving faster, processing elements and making the best possible local choices without looking back.