1, dynamic programming: decompose the original problem into several sub-problems, solve the sub-problems one by one from bottom to top, and finally get the solution of the original problem.
2. Greedy algorithm: Every step adopts the best or optimal (that is, the most favorable) choice in the current state, in the hope that the result will be the best or optimal algorithm.
3. Divide and conquer algorithm: divide a complex problem into two or more identical or similar subproblems, and then divide the subproblems into smaller subproblems until the final subproblem can be directly and simply solved. The solution of the original problem is the combination of the solutions of the subproblems.
4. Backtracking algorithm: Starting from the root, combining depth-first search and recursive exhaustion, when the current path is found to be infeasible, backtracking to the upper node and continuing to explore other paths.
5. Iterative algorithm: approximate the optimal solution or the solution that meets the conditions through continuous iteration.