Complexity of an inexact proximal-point penalty method for constrained smooth non-convex optimization
In this paper, an inexact proximal-point penalty method is studied for constrained
optimization problems, where the objective function is non-convex, and the constraint …
optimization problems, where the objective function is non-convex, and the constraint …
Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization
First-order methods have been studied for nonlinear constrained optimization within the
framework of the augmented Lagrangian method (ALM) or penalty method. We propose an …
framework of the augmented Lagrangian method (ALM) or penalty method. We propose an …
Stochastic inexact augmented Lagrangian method for nonconvex expectation constrained optimization
Many real-world problems not only have complicated nonconvex functional constraints but
also use a large number of data points. This motivates the design of efficient stochastic …
also use a large number of data points. This motivates the design of efficient stochastic …
Iteration complexity of an inner accelerated inexact proximal augmented Lagrangian method based on the classical Lagrangian function
This paper establishes the iteration complexity of an inner accelerated inexact proximal
augmented Lagrangian (IAIPAL) method for solving linearly constrained smooth nonconvex …
augmented Lagrangian (IAIPAL) method for solving linearly constrained smooth nonconvex …
Oracle complexity of single-loop switching subgradient methods for non-smooth weakly convex functional constrained optimization
We consider a non-convex constrained optimization problem, where the objective function is
weakly convex and the constraint function is either convex or weakly convex. To solve this …
weakly convex and the constraint function is either convex or weakly convex. To solve this …
Iteration complexity of a proximal augmented Lagrangian method for solving nonconvex composite optimization problems with nonlinear convex constraints
This paper proposes and analyzes a proximal augmented Lagrangian (NL-IAPIAL) method
for solving constrained nonconvex composite optimization problems, where the constraints …
for solving constrained nonconvex composite optimization problems, where the constraints …
A single-loop gradient descent and perturbed ascent algorithm for nonconvex functional constrained optimization
S Lu - International Conference on Machine Learning, 2022 - proceedings.mlr.press
Nonconvex constrained optimization problems can be used to model a number of machine
learning problems, such as multi-class Neyman-Pearson classification and constrained …
learning problems, such as multi-class Neyman-Pearson classification and constrained …
Algorithms for difference-of-convex programs based on difference-of-moreau-envelopes smoothing
In this paper, we consider minimization of a difference-of-convex (DC) function with and
without linear equality constraints. We first study a smooth approximation of a generic DC …
without linear equality constraints. We first study a smooth approximation of a generic DC …
First-Order Methods for Problems with (1) Functional Constraints Can Have Almost the Same Convergence Rate as for Unconstrained Problems
Y Xu - SIAM Journal on Optimization, 2022 - SIAM
First-order methods (FOMs) have recently been applied and analyzed for solving problems
with complicated functional constraints. Existing works show that FOMs for functional …
with complicated functional constraints. Existing works show that FOMs for functional …
An adaptive superfast inexact proximal augmented Lagrangian method for smooth nonconvex composite optimization problems
A Sujanani, RDC Monteiro - Journal of Scientific Computing, 2023 - Springer
This work presents an adaptive superfast proximal augmented Lagrangian (AS-PAL)
method for solving linearly-constrained smooth nonconvex composite optimization …
method for solving linearly-constrained smooth nonconvex composite optimization …