This paper resolves a longstanding open question pertaining to the design of near-optimal
first-order algorithms for smooth and strongly-convex-strongly-concave minimax problems …

In this paper, we generalize the well-known Nesterov's accelerated gradient (AG) method,
originally designed for convex smooth optimization, to solve nonconvex and possibly …

On solving a convex-concave bilinear saddle-point problem (SPP), there have been many
works studying the complexity results of first-order methods. These results are all about …

Feature selection can alleviate the problem of the curse of dimensionality by selecting more
discriminative features, which plays an important role in multi-label learning. Recently …

This paper considers an important class of convex programming (CP) problems, namely, the
stochastic composite optimization (SCO), whose objective function is given by the …

Much recent research effort has been directed to the development of efficient algorithms for
solving minimax problems with theoretical convergence guarantees due to the relevance of …

We present a novel framework, namely, accelerated alternating direction method of
multipliers (AADMM), for acceleration of linearized ADMM. The basic idea of AADMM is to …

Functional constrained optimization is becoming more and more important in machine
learning and operations research. Such problems have potential applications in risk-averse …

Augmented Lagrangian method (ALM) has been popularly used for solving constrained
optimization problems. Practically, subproblems for updating primal variables in the …

We propose a practical inexact augmented Lagrangian method (iALM) for nonconvex
problems with nonlinear constraints. We characterize the total computational complexity of …