二段階非線形最適化のための微分可能な拡張ラグランジュ法
A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization
拡張ラグランジュ法と自動微分を組み合わせ、二段階最適化問題に適した汎用非線形最適化ソルバを開発し、ロバスト制御と接触系のパラメータ推定で有効性を示した。
著者: Benoit Landry, Zachary Manchester, Marco Pavone
分類: cs.RO, math.OC
原文アブストラクト
Many problems in modern robotics can be addressed by modeling them as bilevel optimization problems. In this work, we leverage augmented Lagrangian methods and recent advances in automatic differentiation to develop a general-purpose nonlinear optimization solver that is well suited to bilevel optimization. We then demonstrate the validity and scalability of our algorithm with two representative robotic problems, namely robust control and parameter estimation for a system involving contact. We stress the general nature of the algorithm and its potential relevance to many other problems in robotics.
