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ADAPTIVE PROXIMAL GRADIENT METHODS FOR CONVEX BILEVEL OPTIMIZATION
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| 概要 | Bilevel optimization is a comprehensive framework that bridges single- and multi-objective optimization. It encompassess many general formulations, such as, but not limited to, standard nonlinear prog...rams. This work demonstrates how elementary proximal gradient iterations can be used to solve a wide class of convex bilevel optimization problems without involving subroutines. Compared to and improving upon existing methods, ours (1) can handle a much wider class of problems, including both constraints and nonsmooth terms, (2) does not require strong convexity or Lipschitz smoothness assumptions, and (3) provides a systematic adaptive stepsize selection strategy with no need of function evaluations. A linesearch-free variant is also proposed that eliminates wasteful backtracking trials at the sole expense of cost evaluations.続きを見る |
| 目次 | Bilevel optimization Setup & goals Algorithmic literature Examples An adaptive proximal gradient solver Precursors adaBiM staBiM Simulations Logistic regression Integral equations Minimum ℓ1-norm problems Number of backtracks Conclusions続きを見る |
詳細
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| 登録日 | 2023.06.16 |
| 更新日 | 2023.06.16 |