5 papers
Toward a Systematic Understanding and Interactive Search of Lyapunov-Style Proofs in Optimization
TaeHo Yoon, Jaewook J. Suh, Edward Duc Hien Nguyen +2
Lyapunov-style convergence proofs, which establish a nonincreasing sequence to provide a quantitative convergence rate for an algorithm, are popular and often considered desirable…
Exact worst-case convergence rates for Douglas--Rachford and Davis--Yin splitting methods
Edward Duc Hien Nguyen, Jaewook J. Suh, Xin Jiang +1
In this work, we aim to establish the exact worst-case convergence rates of Douglas--Rachford splitting (DRS) and Davis--Yin splitting (DYS) when applied to convex optimization pro…
Numerical Analysis of HiPPO-LegS ODE for Deep State Space Models
Jaesung R. Park, Jaewook J. Suh, Youngjoon Hong +1
In deep learning, the recently introduced state space models utilize HiPPO (High-order Polynomial Projection Operators) memory units to approximate continuous-time trajectories of…
An Adaptive and Parameter-Free Nesterov's Accelerated Gradient Method for Convex Optimization
Jaewook J. Suh, Shiqian Ma
We propose AdaNAG, an adaptive accelerated gradient method based on Nesterov's accelerated gradient method. AdaNAG is line-search-free, parameter-free, and achieves the accelerated…
Optimization Algorithm Design via Electric Circuits
Stephen P. Boyd, Tetiana Parshakova, Ernest K. Ryu +1
We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits. Given an optimization problem, the first stage of the methodology is…