4 papers · 1 filter
A Fixed-Penalty Linearized Augmented Lagrangian Method with Classical Multiplier Updates
Benqi Liu, Kangkang Deng, Zichen Wang +1
Augmented Lagrangian methods are effective for nonlinear equality-constrained optimization, but solving their nonlinear primal subproblems can be expensive. For smooth nonconvex pr…
Retraction-Free Optimization over the Stiefel Manifold for the LoRA Fine-Tuning
Yuan Zhang, Jiang Hu, Zhijian Lai +2
Optimization over the Stiefel manifold plays a significant role in various machine learning tasks. Existing methods either use the retraction operators, requiring costly orthonorma…
Restarted Reflected Halpern Acceleration for Augmented Primal-Dual Methods
Benqi Liu, Ju Cao, Wotao Yin +1
We study linearly constrained composite convex optimization with a smooth term and a proximable nonsmooth term. We develop a unified augmented primal-dual framework with primal-dua…
Achieving double-logarithmic precision dependence in optimization-based quantum unstructured search
Zhijian Lai, Dong An, Jiang Hu +1
Grover's algorithm is a fundamental quantum algorithm that achieves a quadratic speedup for unstructured search problems of size . Recent studies have reformulated this task as…