In this paper, we propose a feasible-direction method for large-scale nonconvex programs, where the gradient projection on a linear subspace defined by the active constraints of the original problem is determined by dual decomposition. Results are extended for dynamical problems which include distributed delays and constraints both in state and control variables. The approach is compared with other feasible-direction approaches, and the method is applied to a power generation problem. Some computational results are included.
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This work was supported by the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico, Brasilia, Brasil, and by the Fundaçao de Amparo a Pesquisa do Estado de Sao Paulo, Sao Paulo, Brazil.
On leave from UNICAMP, Campinas, Brazil.
Communicated by P. Varaiya
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Geromel, J.C., Baptistella, L.F.B. Feasible direction method for large-scale nonconvex programs: Decomposition approach. J Optim Theory Appl 35, 231–249 (1981). https://doi.org/10.1007/BF00934578
- Optimal control
- nonconvex programming
- constrained optimization
- feasible direction methods
- dual decomposition