Sentence examples for solving interval from inspiring English sources

An interval linear programming was developed, and a method for solving interval linear programming was presented.

To avoid the limitations of indirect algorithms for solving interval optimization models, we have proposed a direct interval optimization algorithm for uncertain structures by introducing the concept of the degree of interval constraint violation (DICV) [27] based on Hu's "center first halfwidth next" interval order relation [28].

Otherwise, increase the iteration number of the outer layer GA by 1 and go to Step 4. Two illustrative examples are investigated in this section to verify the effectiveness of the proposed approach for directly solving interval robust optimization problems and its applicability in engineering practice.

Similar to [11], if the coefficients Q, (B_{1}(cdot)), (B_{2}(cdot)) are Malliavin differentiable, then this ABSDE can be solved interval by interval in Malliavin's sense to get its unique solution ((Y_{cdot},Z_{cdot})).

Finally, an application to solve interval differential equations is shown in two different aspects: in the first approach, the interval problem has a unique solution, which is obtained by solving an ordinary differential equation, and the results in the second approach are equivalent to the obtained results using the gH-differentiability concept.

Fan and Huang ([2012]) developed a robust two-step method (RTSM) to solve interval-parameter linear programming through incorporating additional constraints into solution procedures.

The conventional indirect approaches for solving the interval optimization model will result in different optimal solutions when prescribing different satisfactory degrees of interval constraints and also deviates from the original intention of modeling the optimization problem based on interval theory.

This paper proposes a new methodology for solving the interval bilevel linear programming problem in which all coefficients of both objective functions and constraints are considered as interval numbers.

The second issue of interest is solving linear interval equation systems, which is often an ill-conditioned problem.

Present indirect approaches for solving such interval optimization models by converting them into deterministic ones will result in the loss of uncertainty information and deviate from the original intention of realistically modeling engineering optimization problems.

In this study, we present two new simple mathematical proofs of the vertex solution theorem using Cramer's rule for solving linear interval equations, different from the other proof methods, to find the upper and lower bounds on the set of solutions.