2 citations · 2 across the 14 of their papers we have counts for
5 papers · 1 filter
Convex Relaxations for the Optimization of Markov Processes
Hongyi Zhang, Yuehaw Khoo, Tianyun Tang
In this paper, we study the problem of optimizing Markov processes that interpolate between two prescribed probability distributions while minimizing a given cost. The main computa…
Convex relaxation approaches for high-dimensional optimal transport
Yuehaw Khoo, Tianyun Tang
Optimal transport (OT) is a powerful tool in mathematics and data science but faces severe computational and statistical challenges in high dimensions. We propose convex relaxation…
A Bregman ADMM for Bethe variational problem
Yuehaw Khoo, Tianyun Tang, Kim-Chuan Toh
In this work, we propose a novel Bregman ADMM with nonlinear dual update to solve the Bethe variational problem (BVP), a key optimization formulation in graphical models and statis…
Solving cluster moment relaxation with hierarchical matrix
Yi Wang, Rizheng Huang, Yuehaw Khoo
Convex relaxation methods are powerful tools for studying the lowest energy of many-body problems. By relaxing the representability conditions for marginals to a set of local const…
S-SOS: Stochastic Sum-Of-Squares for Parametric Polynomial Optimization
Richard L. Zhu, Mathias Oster, Yuehaw Khoo
Global polynomial optimization is an important tool across applied mathematics, with many applications in operations research, engineering, and physical sciences. In various settin…