We will update the thinking model and code throughout this competition, please check the business card at the end of the article to get
Information and assists related to the previous national games can be viewed
The update process of our national competition is as follows:
Question A idea:
(Updated as soon as the game starts)
Question B idea:
(Updated as soon as the game starts)
Question C ideas:
(Updated as soon as the game starts)
Question D ideas:
(Updated as soon as the game starts)
Summary of Common Algorithms for National Competition Modeling
Before the start of the national competition, I summarized the common algorithms of mathematical modeling for everyone, and you can refer to them for reference.
Frequently asked questions about mathematical modeling in the national competition are divided into:
1. Classification problem
2. Prediction problem
3. Optimization problem
4. Evaluation Questions
4.1 Classification problem
Discriminant analysis
distance discriminant
Fisher's test
Bayes discriminant method
step-by-step method
Cluster analysis
Hierarchical clustering method (hierarchical clustering method)
Fast clustering method (K-means clustering method)
Two-step clustering method (smart clustering method)
fuzzy cluster analysis
Clustering methods combined with genetic algorithms, neural networks or gray theory
Neural Network Classification Method
4.2 Prediction problem
regression analysis
time series analysis
gray forecasting method
BP neural network method
Portfolio Forecasting
4.3 Optimization problem
mathematical programming model
differential equation model
Graph theory and network optimization problems
probability model
Combinatorial optimization classic problem
Multidimensional Knapsack Problem (MKP)
Two-dimensional assignment problem (QAP)
Traveling Salesman Problem (TSP)
Vehicle Routing Problem (VRP)
Job Shop Scheduling Problem (JSP)
4.4 Evaluation Questions
Analytical Hierarchy Process (AHP)
Gray comprehensive evaluation method (gray correlation degree analysis)
fuzzy comprehensive evaluation method
BP Neural Network Comprehensive Evaluation Method
Data envelopment method (DEA)
Portfolio Evaluation
How to Become a Mathematical Modeling Expert
1. A solid foundation
The so-called foundation here does not refer to the foundation of mathematics alone, but refers to some basic knowledge, which may be some common sense, including mathematics, physics, chemistry, biology, geography, etc. Of course, these knowledge are not necessarily learned in the classroom, some come from life. Maybe everyone is good at modeling, but not everyone can build an excellent model. When you find that you have no ideas for some small problems in real life, it is not that you do not have the talent for mathematics, but that you lack the understanding of life. accumulation of knowledge. Don't ask what is the use of learning calculus from the beginning,
all you have to do is to learn it first, even if you write it down, so that you will not encounter problems like "doing the most with the least money" "I feel overwhelmed with the most common questions like this. So what we have to do is to dabble in as much knowledge as possible, not just stick to our major.
2. Rich imagination
Don't stick to a fixed way of thinking. When you encounter a problem, you should think of several solutions to the problem and try a method that others have never thought of. Don't classify the problems as soon as you get them. Many people are willing to classify the problems as soon as they come up, such as optimization problems, combination problems, equation problems, and so on. Then use some methods related to this category to solve the problem. Many practical problems are very complex, and simple classification sometimes does not make any sense. Doing so not only limits your thinking, but also makes you more stubborn. A rich imagination will bring you closer to the problem, and an open mind will allow you to see all sides of the problem. Of course, rich imagination is based on rich knowledge.
3. The simplest is the best.
This may be a rule followed by all sciences. In the eyes of Einstein, the complex principle of mass-energy conversion is just a simple formula: E=mc2. Simple methods are easier to understand, easier to implement, and easier to maintain. When encountering a problem, give priority to the simplest solution, and consider complex solutions only when the simple solution cannot meet the requirements. Of course, even if you want to apply a complex solution, you must adopt a step-by-step thinking, and gradually improve the previous solution, and don't try a very complicated solution at the beginning.
4. Don’t be too stubborn
When you encounter obstacles, you might as well stay away from the problem for a while, look at the scenery outside the window, listen to light music, chat with friends, or read a few novels. When I encounter a problem, I usually go to chat with my friends. Some kind suggestions or even encouragement from my friends will give my brain a full rest. When I start working again, I will find that those difficult problems can be easily solved now.
5. Desire for answers
The history of the development of human natural science is a process of longing for answers. Even if we only know a small part of the answers, it is worth our effort. As long as you have a firm belief and must find the answer to the question, you will put in the energy to explore. Even if you don't get the answer in the end, you will learn a lot in the process.
6. Communicate more with others.
Threesomes must have my teacher. Maybe in a casual conversation with others, a spark of inspiration can burst out. Go online more and see what other people think about the same issue, it will give you a lot of inspiration. Of course, don't regard the purpose of communicating with others as getting answers to questions, even the exchange of learning methods is beneficial to you.
7. Good programming literacy.
With the continuous progress of science, more and more subjects are inseparable from computers. As one of the main means to solve practical problems, mathematical modeling is of course inseparable from computers. Some people may think that those who engage in mathematical modeling only need to write some simple programs, but I hold a negative attitude towards this. For programming, regardless of the size of the program, it is a project. Since it is a project, it must be done according to quality standards. Isn't there ISO9000 quality standard? That standard applies to programming as well. Only when the quality of programming is guaranteed, can the computer, a tool, truly become a powerful weapon for modeling.
8. Resilience and perseverance
This may be the biggest difference between "masters" and ordinary people. Masters are not geniuses, they are tempered in countless days and nights. Success can bring us great joy, but the process is extremely boring. You might as well take a test and insist on going to the library to read books or materials related to mathematical modeling for one hour every day for half a year. If you can complete this work without interruption, you can meet this requirement.