Accelerating inexact successive quadratic approximation for regularized optimization through manifold identification
C Lee - Mathematical Programming, 2023 - Springer
For regularized optimization that minimizes the sum of a smooth term and a regularizer that
promotes structured solutions, inexact proximal-Newton-type methods, or successive …
promotes structured solutions, inexact proximal-Newton-type methods, or successive …
Training structured neural networks through manifold identification and variance reduction
ZS Huang, C Lee - arXiv preprint arXiv:2112.02612, 2021 - arxiv.org
This paper proposes an algorithm (RMDA) for training neural networks (NNs) with a
regularization term for promoting desired structures. RMDA does not incur computation …
regularization term for promoting desired structures. RMDA does not incur computation …
Accelerated projected gradient algorithms for sparsity constrained optimization problems
JH Alcantara, C Lee - Advances in Neural Information …, 2022 - proceedings.neurips.cc
We consider the projected gradient algorithm for the nonconvex best subset selection
problem that minimizes a given empirical loss function under an $\ell_0 $-norm constraint …
problem that minimizes a given empirical loss function under an $\ell_0 $-norm constraint …
Global convergence and acceleration of projection methods for feasibility problems involving union convex sets
JH Alcantara, C Lee - Journal of Optimization Theory and Applications, 2025 - Springer
We prove global convergence of classical projection algorithms for feasibility problems
involving union convex sets, which refer to sets expressible as the union of a finite number of …
involving union convex sets, which refer to sets expressible as the union of a finite number of …
[引用][C] Global convergence and acceleration of fixed point iterations of union upper semicontinuous operators: proximal algorithms, alternating and averaged …
JH Alcantara, C Lee - arXiv preprint arXiv:2202.10052, 2022 - Technical report