A primal dual projection algorithm for efficient constraint preconditioning
- We consider a linear iterative solver for large scale linearly constrained quadratic minimization problems that arise, for example, in optimization with PDEs. By a primal-dual projection (PDP) iteration, which can be interpreted and analysed as a gradient method on a quotient space, the given problem can be solved by computing sulutions for a sequence of constrained surrogate problems, projections onto the feasible subspaces, and Lagrange multiplier updates. As a major application we consider a class of optimization problems with PDEs, where PDP can be applied together with a projected cg method using a block triangular constraint preconditioner. Numerical experiments show reliable and competitive performance for an optimal control problem in elasticity.
Author: | Anton Schiela, Matthias Stöcklein, Martin WeiserORCiD |
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Document Type: | Article |
Parent Title (English): | SIAM Journal on Scientific Computing |
Volume: | 43 |
Issue: | 6 |
First Page: | A4095 |
Last Page: | A4120 |
Year of first publication: | 2021 |
DOI: | https://doi.org/10.1137/20M1380739 |