Monday, 15:15 - 15:40 h, Room: H 0107

 

Yuan Shen
New augmented lagrangian-based proximal point algorithms for convex optimization with equality constraint

Coauthor: Bingsheng He

 

Abstract:
The Augmented Lagrangian method (ALM) is a classic and efficient method for solving constrained optimization
problem. It decomposes the original problem into a series of easy-to-solve subproblems to approach the solution of the original problem. However, its efficiency is still, to large extent, dependent on how efficient the subproblem can be solved. In general, the accurate solution of the subproblem can be expensive to compute, hence, it is more practical to relax the subproblem to make it easy to solve. When the objective has some favorable structure, the relaxed subproblem can be simple enough to have a closed form solution. Therefore, the resulting algorithm is efficient and practical for the low cost in each iteration. However, compared with the classic ALM, this algorithm can suffer from the slow convergence rate. Based on the same relaxed subproblem, we propose several new methods with faster convergence rate. We also report their numerical results in comparison to some state-of-the-art algorithms to demonstrate their efficiency.

 

Talk 1 of the contributed session Mon.3.H 0107
"Methods for nonlinear optimization III" [...]
Cluster 16
"Nonlinear programming" [...]

 

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