Mathematical optimization, also known as mathematical programming, is a method to solve the optimal solution by using mathematical model and optimization theory. The core idea is to abstract specific problems into mathematical models, establish objective functions and constraints, and then solve the models by mathematical methods to get the optimal solution. In practical application, mathematical optimization is widely used in various fields such as industry, economy, society and science, which can provide scientific basis for decision-making and optimization scheme for resource planning, production scheduling and information management.

Mathematical optimization is widely used in modern society and plays an irreplaceable role. In the industrial field, mathematical optimization is applied to process optimization, equipment selection, production scheduling and other aspects to improve production efficiency, reduce costs and improve quality. In the financial field, mathematical optimization technology is widely used in risk management, asset allocation, investment decision-making and so on, in order to reduce investment risk and improve the rate of return. In the field of social management, mathematical optimization is widely used in resource allocation, personnel scheduling, urban planning and so on. Optimize resource allocation and improve service quality.

Limitation and future development of mathematical optimization

Although mathematical optimization has broad application prospects, it also has certain limitations. For example, mathematical optimization models are often based on hypothetical systems, so their accuracy and applicability are limited to some extent; At the same time, the error of numerical calculation will also affect the accuracy of the optimal solution. Therefore, it is necessary to make appropriate adjustments and corrections in practical applications. In the future, with the continuous progress of mathematical optimization technology and computing hardware, the application field of mathematical optimization will be broadened and play a more important role in a wider range of fields.