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When constructing regression models, one of the main problems is the selection of their structural specification. At present, there are already very large numbers of such specifications, and these numbers are constantly growing. The article focuses on the construction of linear multiplicative regressions belonging to the class of models that are linear in parameters, and in which various combinations of products of independent variables are used as regressors. Nowadays, the technology of organizing a "competition" of models is used to construct such models, which is to form a number of their alternative variants with predetermined properties and the subsequent choice of the most acceptable variant on the basis of a set of formal and substantive criteria. In this formulation, the problem is very time-consuming. Therefore, in order to reduce the time of its solution, the problem of constructing a linear multiplicative regression is formalized as a problem of partial-Boolean linear programming. With the help of linear constraints, one can control the nature of the occurrence of independent variables in the right-hand side of linear multiplicative regressions.

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