The rankings are as follows:
1. Monte Carlo algorithm (This algorithm is also called a random simulation algorithm. It is an algorithm to solve problems through computer simulation. At the same time, it can be used to test the correctness of one's own model through simulation. It is a method that must be used in competitions)
2. Data fitting, parameter estimation, interpolation and other data processing algorithms (A large amount of data usually needs to be processed in competitions, and the key to processing data lies in these algorithms. Matlab is usually used as a tool)
3. Planning problems such as linear programming, integer programming, multi-variable programming, quadratic programming (Most problems in modeling competitions belong to optimization problems. In many cases, these problems can be described by mathematical programming algorithms. Lindo and Lingo software are usually used to implement them)
4. Graph theory algorithms (This type of algorithm can be divided into many types, including shortest path, network flow, bipartite graph and other algorithms. Problems involving graph theory can be solved by these methods. Serious preparation is required)
5. Computer algorithms such as dynamic programming, backtracking search, divide-and-conquer algorithm, branch and bound (These algorithms are relatively commonly used methods in algorithm design and can be used in many competitions)
6. Three non-classical algorithms of optimization theory: simulated annealing method, neural network, genetic algorithm (These problems are algorithms used to solve some more difficult optimization problems. They are very helpful for some problems, but the implementation of the algorithm is relatively difficult. Be cautious in use)
7. Grid algorithm and exhaustive method (Both the grid algorithm and the exhaustive method are brute-force search algorithms for the optimal point. They are applied in many competition problems. When focusing on the model itself and neglecting the algorithm, this brute-force scheme can be used. It is best to use some high-level languages as programming tools)
8. Some continuous-discretization methods (Many problems come from reality. The data can be continuous, but computers only recognize discrete data. Therefore, the idea of discretizing it and then using difference instead of differential, summation instead of integration, etc. is very important)
9. Numerical analysis algorithms (If high-level languages are used for programming in competitions, then some commonly used algorithms in numerical analysis such as system of equations solving, matrix operation, function integration and other algorithms need to be additionally compiled into library functions for calling)
10. Image processing algorithms (There is a type of problem related to graphics in the competition questions. Even if it is not related to graphics, there should be plenty of pictures in the paper. How to display and process these graphics is the problem to be solved. Matlab is usually used for processing)
1. Monte Carlo algorithm (This algorithm is also called a random simulation algorithm. It is an algorithm to solve problems through computer simulation. At the same time, it can be used to test the correctness of one's own model through simulation. It is a method that must be used in competitions)
2. Data fitting, parameter estimation, interpolation and other data processing algorithms (A large amount of data usually needs to be processed in competitions, and the key to processing data lies in these algorithms. Matlab is usually used as a tool)
3. Planning problems such as linear programming, integer programming, multi-variable programming, quadratic programming (Most problems in modeling competitions belong to optimization problems. In many cases, these problems can be described by mathematical programming algorithms. Lindo and Lingo software are usually used to implement them)
4. Graph theory algorithms (This type of algorithm can be divided into many types, including shortest path, network flow, bipartite graph and other algorithms. Problems involving graph theory can be solved by these methods. Serious preparation is required)
5. Computer algorithms such as dynamic programming, backtracking search, divide-and-conquer algorithm, branch and bound (These algorithms are relatively commonly used methods in algorithm design and can be used in many competitions)
6. Three non-classical algorithms of optimization theory: simulated annealing method, neural network, genetic algorithm (These problems are algorithms used to solve some more difficult optimization problems. They are very helpful for some problems, but the implementation of the algorithm is relatively difficult. Be cautious in use)
7. Grid algorithm and exhaustive method (Both the grid algorithm and the exhaustive method are brute-force search algorithms for the optimal point. They are applied in many competition problems. When focusing on the model itself and neglecting the algorithm, this brute-force scheme can be used. It is best to use some high-level languages as programming tools)
8. Some continuous-discretization methods (Many problems come from reality. The data can be continuous, but computers only recognize discrete data. Therefore, the idea of discretizing it and then using difference instead of differential, summation instead of integration, etc. is very important)
9. Numerical analysis algorithms (If high-level languages are used for programming in competitions, then some commonly used algorithms in numerical analysis such as system of equations solving, matrix operation, function integration and other algorithms need to be additionally compiled into library functions for calling)
10. Image processing algorithms (There is a type of problem related to graphics in the competition questions. Even if it is not related to graphics, there should be plenty of pictures in the paper. How to display and process these graphics is the problem to be solved. Matlab is usually used for processing)