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A First Order Method for Solving Convex Bi-Level Optimization Problems

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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

TaskDatasetResultRank
Logistic Regression1,000 songs sample (train)
Lower-level Value0.3388
11
Least Squares Regression1,000 songs sample (train)
Lower-level Value0.0074
8
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