Commonly used algorithm strategies include the following aspects:

1. Greedy algorithm: Greedy algorithm is a method that takes the best or optimal algorithm in the current state at each step. (i.e., the most advantageous) choice, thereby hopefully leading to the best or optimal algorithm. The idea of ??the greedy algorithm is to start from the local optimal solution of the problem and try to achieve the global optimal solution as much as possible. The greedy algorithm may not necessarily get the optimal solution, but it can solve many problems in polynomial time, such as minimum spanning tree, shortest path, etc.

2. Dynamic programming algorithm: Dynamic programming algorithm is a method of solving complex problems by decomposing the original problem into relatively simple sub-problems. During the problem solving process, the solution to each subproblem is stored so that it can be reused when solving other subproblems. The dynamic programming algorithm is suitable for problems with overlapping subproblems and optimal substructure properties, such as the knapsack problem, the longest common subsequence, etc.

3. Backtracking algorithm: The backtracking algorithm is an algorithm that solves problems by exploring all possible solutions. In the backtracking algorithm, if it is found that the current solution does not meet the constraints of the problem, it will backtrack to the previous state and try other options. The backtracking algorithm is suitable for constraint satisfaction problems, such as the eight queens problem, graph coloring problems, etc.

Applications of algorithmic strategies:

1. Financial field: Algorithmic strategies are widely used in the financial field, such as stock trading, futures trading, foreign exchange trading, etc. Through algorithmic strategies, you can automatically analyze market data, predict market trends, and quickly make trading decisions to improve the efficiency and accuracy of trading.

2. Logistics field: In the logistics field, algorithm strategies can be used to optimize transportation routes, reduce transportation costs, and improve transportation efficiency. For example, through dynamic planning algorithms, the loading and transportation routes of goods can be reasonably arranged according to the destination and transportation needs of the goods, improving transportation efficiency and reducing costs.

3. The field of artificial intelligence: Algorithm strategy is an important part of the field of artificial intelligence and can be used in fields such as machine learning, natural language processing, and computer vision. For example, in machine learning, through strategies such as greedy algorithms and dynamic programming algorithms, models can be trained and model parameters optimized to improve the accuracy and generalization ability of the model.