7 papers
Log-Averaged Mirror Prox for Fast, Large-Scale Optimal Transport in Linear Space
Matthew X. Burns, Jiaming Liang
We propose Log-Averaged Mirror Prox (LAMP), a linear-space primal-dual method for large-scale optimal transport. LAMP implements primal mirror prox updates by tracking an averaged…
Accuracy Certificates for Convex Optimization at Accelerated Rates via Primal-Dual Averaging
Matthew X. Burns, Jiaming Liang
Many works in convex optimization provide rates for achieving a small primal gap. However, this quantity is typically unavailable in practice. In this work, we show that solving a…
General Oscillator-Based Ising Machine Models with Phase-Amplitude Dynamics and Polynomial Interactions
Lianlong Sun, Matthew X. Burns, Michael C. Huang
We present an oscillator model with both phase and amplitude dynamics for oscillator-based Ising machines (OIMs). The model targets combinatorial optimization problems with polynom…
Limitations in Parallel Ising Machine Networks: Theory and Practice
Matthew X. Burns, Michael C. Huang
Analog Ising machines (IMs) occupy an increasingly prominent area of computer architecture research, offering high-quality and low latency/energy solutions to intractable computing…
Improved Analysis of Restarted Accelerated Gradient and Augmented Lagrangian Methods via Inexact Proximal Point Frameworks
Matthew X. Burns, Jiaming Liang
This paper studies a class of double-loop (inner-outer) algorithms for convex composite optimization. For unconstrained problems, we develop a restarted accelerated composite gradi…
Provable Accuracy Bounds for Hybrid Dynamical Optimization and Sampling
Matthew X. Burns, Qingyuan Hou, Michael C. Huang
Analog dynamical accelerators (DXs) are a growing sub-field in computer architecture research, offering order-of-magnitude gains in power efficiency and latency over traditional di…