2 papers
math.OC2026
First-Order Projected Algorithms With the Same Linear Convergence Rate Bounds as Their Unconstrained Counterparts
Mengmou Li, Ioannis Lestas, Masaaki Nagahara
In this paper, we propose a systematic approach for extending first-order optimization algorithms, originally designed for unconstrained strongly convex problems, to handle closed…
math.OC2025
Exponential Convergence of Augmented Primal-dual Gradient Algorithms for Partially Strongly Convex Functions
Mengmou Li, Masaaki Nagahara
We show that the augmented primal-dual gradient algorithms can achieve global exponential convergence with partially strongly convex functions. In particular, the objective functio…