1, Knowledge reserve
preface
Don't worry that you are Xiaobai now. Don't worry that you are not ready now. This article will take you counter attack and don't worry anymore. Just look at my article! Of course, if you have better information to read together, why do I set up this column, because I try every article myself and write the code line by line. Unlike others who copy it all from pdf for everyone to see, I explain it in my own saliva to ensure that Xiaobai can understand it.
No matter what level you are at this time, this article is worth reading. It's better to check leaks and fill vacancies, or start from scratch. In the last few days of the sprint, all your thoughts will be put on it, and you will be able to learn very well. If you have any questions about learning, you can add my contact information on the home page. If you don't say it can help you, let's discuss it together.
1, Optimization class
Linear programming (transportation problem, assignment problem, duality theory, sensitivity analysis) Integer programming (branch and bound, enumeration heuristic, Monte Carlo) Nonlinear programming (constrained extremum, unconstrained extremum) Objective planning (single objective, multi-objective) Dynamic programming (dynamic, static, linear dynamic, regional dynamic, tree dynamic, knapsack dynamic) Dynamic optimization (variational method) Optimization algorithms (greedy algorithm, tabu search, simulated annealing, genetic algorithm, artificial neural network, ant colony algorithm, particle swarm optimization algorithm, crowd search algorithm, artificial immune algorithm, integrated algorithm TSP Problems QAP Problems JSP Questions) Fuzzy approximation algorithm
2, Graph theory
Minimum spanning tree( prim Algorithm Kruskal Algorithm) Minimum spanning tree( prim Algorithm Kruskal Algorithm) Matching problem (Hungarian algorithm) Euler Figure and Hamilton chart Network flow (maximum flow problem, minimum cost maximum flow problem)
III IV. forecast and statistics
GM(1，1)Gray prediction Time series model (deterministic time series, stationary time series, moving average, exponential smoothing Winter method Time series model (deterministic time series, stationary time series, moving average, exponential smoothing Winter Methods ARIMA Model) Regression (univariate linear regression, multivariate linear regression) MLR,Nonlinear regression, multiple stepwise regression MSR,Principal component regression PCR,Partial least squares regression PLSR)((key) Bayes Statistical prediction Classification model (logistic regression, decision tree, neural network) Classification model (logistic regression, decision tree, neural network) Discriminant analysis model (distance discrimination Fisher Discrimination Bayes Discrimination) Parameter estimation (point estimation, maximum likelihood estimation Bayes (estimated) Hypothesis test( U-Inspection T-Inspection, chi square inspection F-Test, optimality test, distribution fitting test Analysis of variance (univariate, multivariate, correlation test) Empirical distribution function orthogonal test Fuzzy mathematics (fuzzy classification, fuzzy decision) Random forest
5, Data processing
image processing Interpolation and fitting( Lagrange Interpolation Newton Interpolation Hermite Interpolation, cubic spline interpolation, linear least squares) Search algorithm (backtracking, divide and conquer, sorting, grid, exhaustive) Numerical analysis methods (solving equations, matrix operation, numerical integration, successive approximation method, Newton iteration method) Fuzzy approximation Dynamic weighting Sequence analysis principal component analysis factor analysis cluster analysis Grey correlation analysis Data envelopment analysis( DEA)
6, Evaluation category
Analytic hierarchy process( AHP) Fuzzy comprehensive evaluation Fuzzy comprehensive evaluation based on Analytic Hierarchy Process Dynamic weighted comprehensive evaluation TEIZ theory
7, Graphics (key)
algorithm flow chart Bar chart histogram Scatter diagram Pie chart Line chart Stem leaf diagram Box diagram Venn chart Vector graph Error analysis diagram Probability distribution diagram 5w1h analytical method funnel model Pyramid Model Fishbone Diagram Contour surface Mind map
8, Simulation and simulation
Monte Carlo Cellular automata
9, Equation
Differential equation( Malthus Population model Logistic Model, war model) Steady state model( Volterra Model) Solution of ordinary differential equations (discretization Euler Methods Runge—Kutta Methods (linear multistep) Difference equation (cobweb model, genetic model) Numerical solution of partial differential equations (definite solution problem, difference method, finite element analysis)
10, Data modeling & machine learning method
Cloud model Logistic regression principal component analysis Support vector machine( SVM) K-Mean( K-Means) Nearest neighbor method Cloud model Logistic regression principal component analysis Support vector machine( SVM) K-Mean( K-Means) Nearest neighbor method
11, Other models
queuing theory Game theory Chu cunlun probability model Markov chain model Decision theory Single objective decision making: uncertain decision making, risk decision making, utility function, decision tree, sensitivity analysis) Multi objective decision making: hierarchical sequence method, multi-objective linear programming, analytic hierarchy process) System engineering modeling( ISM Interpretation model, network planning model, system evaluation, decision analysis) Cross validation method( Holdout Verification K-fold cross-validation,Leave one for verification) Proportional relationship Functional relationship Geometric simulation Analogical analysis Physical law modeling
2, Tools and data
English revision tool (used to check the English grammar and other errors of the paper)
Download address: https://pc.qq.com/detail/18/detail_13078.html
Brainless installation is mainly to log in, all in Chinese.
It is recommended to use Weibo to scan the QR code to log in and download a Sina Weibo. I registered with my email and said my password was wrong
Textstudio software (for paper typesetting)
Country data: https://data.stats.gov.cn/
matlab software system and lingo software (for programming)
3, Download of test questions and papers over the years
Very detailed. Go in and have a look:
https://www.shumo.com/wiki/doku.php?id=start
4, Literature search
You can use Google search, can't use Google search, hurry to prepare. The things found by Baidu engine are too lj, a pile of advertisements
China HowNet: https://www.cnki.net/ Google academic: codechina.csdn.net/weixin_46211269/test Baidu Scholar: https://xueshu.baidu.com/ Wanfang Data: https://www.wanfangdata.com.cn/index.html Foreign: https://www.sciencedirect.com/
5, Game must know
- After you get the topic, you need to determine the topic type as soon as possible
- Be sure to pay attention to the official forum of mathematical modeling
- Be sure to look at the references given by the title
- Don't wait for others to give results before you start writing a paper
- There are several questions about mathematical modeling. The first two questions can also be done. If you can't solve the following problems, you should write down your ideas clearly, even if the final result can't come out.
- All that can be made into a diagram must be made into a diagram (the diagram can be made without a table)
- After a small question is finished, it needs to be tested and optimized. It's really impossible to write down the ideas clearly. Refer to excellent articles. It should be summarized later in that chapter.
- Believe that those online generation gunmen help to do papers. Don't cheat, don't cheat, and don't go to high-level people to help. If the model is excellent and doesn't meet your level, you won't get a prize.
4, Are you Xiaobai? Can't you learn? don't worry!
If you need courseware and pdf explanation, official account: Kawakawa Natori reply: mathematical modeling
ABCDE thesis reply over the years: mathematical modeling thesis over the years
Or you can add me, I'll send it to you, and spell it in the last few days!
I started from scratch in the mathematical modeling column and explained it in my simplest words to ensure that Xiaobai can understand it as much as possible, because I also wrote notes while learning. Of course, I didn't write completely, because I spent a lot of time writing other blogs, but enough basic knowledge of modeling. Because the information was found in station b, I talked about it well, but I had to pay for it. I didn't read it publicly and talked about it. Then I looked at my own information. The school should organize training in recent days. We must see it!
For Xiaobai, I have opened a column in mathematical modeling. I have talked about more than 20 basic articles and 20 models and algorithms. Although they are not complete, I explain them in detail. I hope I can help you.
Portal: Mathematical modeling from little white to big man series