A First Order Method for Solving Convex Bi-Level Optimization Problems
About
In this paper we study convex bi-level optimization problems for which the inner level consists of minimization of the sum of smooth and nonsmooth functions. The outer level aims at minimizing a smooth and strongly convex function over the optimal solutions set of the inner problem. We analyze a first order method which is based on an existing fixed-point algorithm. Global sublinear rate of convergence of the method is established in terms of the inner objective function values.
Shoham Sabach, Shimrit Shtern• 2017
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Logistic Regression | 1,000 songs sample (train) | Lower-level Value0.3388 | 11 | |
| Least Squares Regression | 1,000 songs sample (train) | Lower-level Value0.0074 | 8 |
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