# Globally Convergent Primal-Dual Active-Set Methods with Inexact Subproblem Solves

@article{Curtis2016GloballyCP, title={Globally Convergent Primal-Dual Active-Set Methods with Inexact Subproblem Solves}, author={Frank E. Curtis and Zheng Han}, journal={SIAM J. Optim.}, year={2016}, volume={26}, pages={2261-2283} }

We propose primal-dual active-set (PDAS) methods for solving large-scale instances of an important class of convex quadratic optimization problems (QPs). The iterates of the algorithms are partitions of the index set of variables, where corresponding to each partition there exist unique primal-dual variables that can be obtained by solving a (reduced) linear system. Algorithms of this type have recently received attention when solving certain QPs and linear complementarity problems since, with…

## 3 Citations

### A dual gradient-projection method for large-scale strictly convex quadratic problems

- Computer Science, MathematicsComput. Optim. Appl.
- 2017

The details of a solver for minimizing a strictly convex quadratic objective function subject to general linear constraints are presented and how the linear algebra may be arranged to take computational advantage of sparsity in the second-derivative matrix is shown.

### Primal-Dual Active-Set Methods for Isotonic Regression and Trend Filtering

- Computer ScienceArXiv
- 2015

It is proved that, like the PAV algorithm, the proposed primal-dual active-set (PDAS) algorithm for IR is convergent and has a work complexity of O(n), though the numerical experiments suggest that the PDAS algorithm is often faster than PAV.

### Primal-Dual Active-Set Methods for Convex Quadratic Optimization with Applications

- Mathematics, Computer Science
- 2015

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