# All issues

Direct multiplicative methods for sparse matrices. Linear programming

pdf (487K)  / List of references

Multiplicative methods for sparse matrices are best suited to reduce the complexity of operations solving systems of linear equations performed on each iteration of the simplex method. The matrix of constraints in these problems of sparsely populated nonzero elements, which allows to obtain the multipliers, the main columns which are also sparse, and the operation of multiplication of a vector by a multiplier according to the complexity proportional to the number of nonzero elements of this multiplier. In addition, the transition to the adjacent basis multiplier representation quite easily corrected. To improve the efficiency of such methods requires a decrease in occupancy multiplicative representation of the nonzero elements. However, at each iteration of the algorithm to the sequence of multipliers added another. As the complexity of multiplication grows and linearly depends on the length of the sequence. So you want to run from time to time the recalculation of inverse matrix, getting it from the unit. Overall, however, the problem is not solved. In addition, the set of multipliers is a sequence of structures, and the size of this sequence is inconvenient is large and not precisely known. Multiplicative methods do not take into account the factors of the high degree of sparseness of the original matrices and constraints of equality, require the determination of initial basic feasible solution of the problem and, consequently, do not allow to reduce the dimensionality of a linear programming problem and the regular procedure of compression — dimensionality reduction of multipliers and exceptions of the nonzero elements from all the main columns of multipliers obtained in previous iterations. Thus, the development of numerical methods for the solution of linear programming problems, which allows to overcome or substantially reduce the shortcomings of the schemes implementation of the simplex method, refers to the current problems of computational mathematics.

In this paper, the approach to the construction of numerically stable direct multiplier methods for solving problems in linear programming, taking into account sparseness of matrices, presented in packaged form. The advantage of the approach is to reduce dimensionality and minimize filling of the main rows of multipliers without compromising accuracy of the results and changes in the position of the next processed row of the matrix are made that allows you to use static data storage formats.

As a direct continuation of this work is the basis for constructing a direct multiplicative algorithm set the direction of descent in the Newton methods for unconstrained optimization is proposed to put a modification of the direct multiplier method, linear programming by integrating one of the existing design techniques significantly positive definite matrix of the second derivatives.

Keywords: numerically stable direct multiplicative method, linear programming, the storage format of sparse matrices, parallel execution of matrix operations without unpacking, minimizing fill the main lines of multipliers, sparse matrices
Citation in English: Sviridenko A.B. Direct multiplicative methods for sparse matrices. Linear programming // Computer Research and Modeling, 2017, vol. 9, no. 2, pp. 143-165
Citation in English: Sviridenko A.B. Direct multiplicative methods for sparse matrices. Linear programming // Computer Research and Modeling, 2017, vol. 9, no. 2, pp. 143-165
DOI: 10.20537/2076-7633-2017-9-2-143-165
• . Direct multiplicative methods for sparse matrices. Newton methods. // Computer Research and Modeling. 2017. — V. 9, no. 5. — P. 679.
• . Direct multiplicative methods for sparse matrices. Quadratic programming. // Computer Research and Modeling. 2018. — V. 10, no. 4. — P. 407.
Please note that citation information may be incomplete as it includes data from Crossref cited-by program partners only.
Views (last year): 10. Citations: 2 (RSCI).

Indexed in Scopus

Full-text version of the journal is also available on the web site of the scientific electronic library eLIBRARY.RU

The journal is included in the Russian Science Citation Index

The journal is included in the RSCI

International Interdisciplinary Conference "Mathematics. Computing. Education"