Abstract:
A dual problem of linear programming (LP) is reduced to the unconstrained maximization of a concave piecewise quadratic function for sufficiently large values of a certain parameter. An estimate is given for the threshold value of the parameter starting from which the projection of a given point on the set of solutions of the dual LP problem in dual and auxiliary variables is easily found by means of a single solution of an unconstrained maximization problem. The unconstrained maximization is carried out by the generalized Newton method, which is globally convergent in a finite number of steps. The results of numerical experiments are presented for randomly generated large-scale LP problems.
Keywords:
linear programming problem, piecewise quadratic function, unconstrained maximization, generalized Newton method.
Citation:
A. I. Golikov, Yu. G. Evtushenko, “Generalized Newton method for linear optimization problems with inequality constraints”, Trudy Inst. Mat. i Mekh. UrO RAN, 19, no. 2, 2013, 98–108; Proc. Steklov Inst. Math. (Suppl.), 284, suppl. 1 (2014), 96–107
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\paper Generalized Newton method for linear optimization problems with inequality constraints
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\pages 98--108
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\jour Proc. Steklov Inst. Math. (Suppl.)
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\pages 96--107
\crossref{https://doi.org/10.1134/S0081543814020096}
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Linking options:
https://www.mathnet.ru/eng/timm936
https://www.mathnet.ru/eng/timm/v19/i2/p98
This publication is cited in the following 4 articles:
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V. I. Zabotin, Yu. A. Chernyaev, “Newton's method for minimizing a convex twice differentiable function on a preconvex set”, Comput. Math. Math. Phys., 58:3 (2018), 322–327
G. A. Amirkhanova, A. I. Golikov, Yu. G. Evtushenko, “On an inverse linear programming problem”, Proc. Steklov Inst. Math. (Suppl.), 295, suppl. 1 (2016), 21–27
A. I. Golikov, Yu. G. Evtushenko, “Regularization and normal solutions of systems of linear equations and inequalities”, Proc. Steklov Inst. Math. (Suppl.), 289, suppl. 1 (2015), 102–110