Simplex embedding method in decomposition of large sparse convex nondifferentiable optimization problems
We consider an adaptation of the simplex embedding method to the decomposition of large-scale convex problem with sparse block-wise constraint matrix. According to the Lagrangean relaxation technique such problem is reduced to the maximization of nondifferentiable concave function with subgradient that can be easily calculated at each feasible point. Simplex embedding method with modifications gives us the appropriate performance to optimize nondifferentiable function that will be demonstrated on the numerical tests. Copyright © by the paper's authors.
Библиографическая ссылка
Kolosnitsyn A. Simplex embedding method in decomposition of large sparse convex nondifferentiable optimization problems // CEUR Workshop Proceedings. Vol.2098. 2018. P.189-199.
