Distributed Computing for Huge-Scale Linear Programming
| Autor: | Tao, Luoyi |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | This study develops an algorithm for distributed computing of linear programming problems of huge-scales. Global consensus with single common variable, multiblocks, and augmented Lagrangian are adopted. The consensus is used to partition the constraints of equality and inequality into multi consensus blocks, the subblocks of each consensus block are employed to partition the primal variables into $M$ sets of disjoint subvectors. The block-coordinate Gauss-Seidel method, the proximal point method (via $\Vert\cdot\Vert_{1}$ and $\Vert\cdot\Vert^2$), and ADMM are used to update the primal variables. The dual variables are partitioned into group 1 and group 2; descent models are used to update group 1 to guarantee convergence of the algorithm. The algorithm converges to feasible points. How to update group 2 of the dual is to be explored which is linked to whether a feasible is an optimal. Comment: 9 pages |
| Databáze: | arXiv |
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