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The Simulation Models in Grid Methods of Uniform Probing

The article provides an overview of modern problems of improving large-scale systems solved by means of using grid methods of uniform probing. It establishes a connection between approximate optimization algorithms for poorly formalized systems with the method of finite difference of mathematical physics. The finite difference method is known since the time of Euler, but began to develop rapidly only with the advent of high-speed computing. This method has also name of the grid method, widely used for solving partial differential equations. Suggested algorithms on the base of grid methods of uniform probing extend the capabilities of grid methods for the study of systems, which have in their description a set of non-differentiable functions. The appointment of the stages of grid methods of uniform probing with the use of simulation models to describe the investigated or projected systems is analyzed. A set of examples of approximate optimization of dynamic stochastic systems described by means of simulation statistical models demonstrate the features of the considered approaches. Models were checked for adequacy, and program implementations were created on the basis of high-speed procedures.