Accelerated Inexact First-Order Methods for Solving Nonconvex Composite Optimization Problems
arXiv:2104.09685
Abstract
This thesis focuses on developing and analyzing accelerated and inexact first-order methods for solving or finding stationary points of various nonconvex composite optimization (NCO) problems. The main tools mainly come from variational and convex analysis, and the key results are in the form of iteration complexity bounds and how these bounds compare to other ones in the literature.
References in corpus (7)
- Efficient Algorithms for Smooth Minimax Optimization
- Sparse PCA with Oracle Property
- Calibrated Elastic Regularization in Matrix Completion
- A global dual error bound and its application to the analysis of linearly constrained nonconvex optimization
- Iteration-complexity of an inexact proximal accelerated augmented Lagrangian method for solving linearly constrained smooth nonconvex composite optimization problems
- A Doubly Accelerated Inexact Proximal Point Method for Nonconvex Composite Optimization Problems
- Lower Bounds for Smooth Nonconvex Finite-Sum Optimization