Abstract
We introduce a first-order Mirror-Descent (MD) type algorithm for solving nondifferentiable convex problems having a combination of simple constraint set X (ball, simplex, etc.) and an additional functional constraint. The method is tuned to exploit the structure of X by employing an appropriate non-Euclidean distance-like function. Convergence results and efficiency estimates are derived. The performance of the algorithm is demonstrated by solving certain image deblurring problems.
Original language | English |
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Pages (from-to) | 493-498 |
Number of pages | 6 |
Journal | Operations Research Letters |
Volume | 38 |
Issue number | 6 |
DOIs | |
State | Published - Nov 2010 |
Externally published | Yes |
Keywords
- Convex optimization
- Gradient-based methods
- Mirror Descent
- Non-Euclidean projection