Generally speaking, the mathematical programming model refers to the optimization problem model. Optimization problems can be divided into discrete or continuous, constrained or unconstrained. Constrained optimization problem is more difficult to solve than unconstrained optimization problem. Let's talk about sensitivity analysis in mathematical modeling. Sensitivity analysis refers to the influence of mathematical model on the final optimal solution after some changes are made to the constraint conditions or correlation coefficients.

In other words, the hypothesis condition has become an influencing factor in the modeling process, and sensitivity analysis is to test the advantages and disadvantages of the model after it is established. Generally speaking, the sensitivity analysis done by Lingo can reach an ideal level, but it still needs to be studied according to the model itself. I suggest you study numerical analysis first, which is very useful for sensitivity analysis of modeling. Then, according to the numerical analysis method, do the sensitivity analysis of M-C (Monte Carlo) method, and you will soon master it. It can also be understood in this way, because modeling only builds a system according to logic, and there is only one output result. However, there are many uncertainties in reality, so the sensitivity analysis of input variables in different situations will get different results.