54. A random active set method for strictly convex quadratic problem with simple bounds
Invited abstract in session MD-35: Nonlinear Optimization Algorithms and Applications: 3, stream Continuous and mixed-integer nonlinear programming: theory and algorithms.
Monday, 14:30-16:00Room: Michael Sadler LG15
Authors (first author is the speaker)
| 1. | Ran Gu
|
| School of Statistics and Data Science, Nankai University |
Abstract
The active set method aims at finding the correct active set of the optimal solution and it is a powerful method for solving strictly convex quadratic problem with bound constraints. To guarantee the finite step convergence, the existing active set methods all need strict conditions or some additional strategies, which greatly affect the efficiency of the algorithm. In this talk, we propose a random active set method which introduces randomness in the update of active set. We prove that it can converge in finite number of iterations with probability one without extra conditions on the problem or any additional strategies. Numerical experiments show that the algorithm can obtain the correct active set within a few iterations, and it has better efficiency and robustness than the existing methods.
Keywords
- Programming, Quadratic
- Convex Optimization
Status: accepted
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