Formalization of the General Model of the Green Economy at the Regional Level

The paper focuses on the study of the problems of the economic and mathematical modeling of the green economy at the regional level. The purpose of the research is the development of economic and mathematical tools for the economic and ecological systems’ modeling at the regional level on the basis of the principles of green economy. The hypothesis of the research is based on the thesis that in the conditions of the exhaustion of natural resources and depletion of natural capital, the technogenic fields, production and consumption waste could be considered as a resource basis for modernization. Such factors’ use leads to the elimination of accumulated environmental damage and substitution of natural resources. The paper describes the approaches to the system modeling problem-solving in order to develop the green economy both in the country and its regions. The urgency of the transition to a green economy is confirmed by the theoretical and practical research on the cyclical development of the socio-eco-economic systems. A number of formalized models and methods for solving the current environmental and economic issues including the economic valuation of accumulated environmental damage, eco-economic assessment of the efficiency of natural resource substitution with resource-substitute are proposed as well as the choice of an optimal set of resources-substitutes taking into account the financial and natural resource constraints. The authors research the typical model of green growth considering the exhaustion of natural resources, technogenic resources deposits involving in economic circulation through the implementation of investment projects on the elimination of accumulated environmental damage. The results could be used in the different regions of Russia for the justification and implementation of investment projects within the framework of the federal target program “Elimination of accumulated environmental damage” in 2015–2026 years.The research has been supported by the Grant of the Russian Foundation for Humanities, Project №14–02–00235а

Efficient Solving of Quantified Inequality Constraints over the Real Numbers

Let a quantified inequality constraint over the reals be a formula in the first-order predicate language over the structure of the real numbers, where the allowed predicate symbols are $\leq$ and $<$. Solving such constraints is an undecidable problem when allowing function symbols such $\sin$ or $\cos$. In the paper we give an algorithm that terminates with a solution for all, except for very special, pathological inputs. We ensure the practical efficiency of this algorithm by employing constraint programming techniques

Adapting Real Quantifier Elimination Methods for Conflict Set Computation

The satisfiability problem in real closed fields is decidable. In the context of satisfiability modulo theories, the problem restricted to conjunctive sets of literals, that is, sets of polynomial constraints, is of particular importance. One of the central problems is the computation of good explanations of the unsatisfiability of such sets, i.e.\ obtaining a small subset of the input constraints whose conjunction is already unsatisfiable. We adapt two commonly used real quantifier elimination methods, cylindrical algebraic decomposition and virtual substitution, to provide such conflict sets and demonstrate the performance of our method in practice

Solving Functional Constraints by Variable Substitution

Functional constraints and bi-functional constraints are an important constraint class in Constraint Programming (CP) systems, in particular for Constraint Logic Programming (CLP) systems. CP systems with finite domain constraints usually employ CSP-based solvers which use local consistency, for example, arc consistency. We introduce a new approach which is based instead on variable substitution. We obtain efficient algorithms for reducing systems involving functional and bi-functional constraints together with other non-functional constraints. It also solves globally any CSP where there exists a variable such that any other variable is reachable from it through a sequence of functional constraints. Our experiments on random problems show that variable elimination can significantly improve the efficiency of solving problems with functional constraints