3 papers
math.OC2023
Self-adaptive ADMM for semi-strongly convex problems
Tianyun Tang, Kim-Chuan Toh
In this paper, we develop a self-adaptive ADMM that updates the penalty parameter adaptively. When one part of the objective function is strongly convex i.e., the problem is semi-s…
math.OC2023
A Riemannian Dimension-reduced Second Order Method with Application in Sensor Network Localization
Tianyun Tang, Kim-Chuan Toh, Nachuan Xiao +1
In this paper, we propose a cubic-regularized Riemannian optimization method (RDRSOM), which partially exploits the second order information and achieves the iteration complexity o…
math.OC2023
A feasible method for solving an SDP relaxation of the quadratic knapsack problem
Tianyun Tang, Kim-Chuan Toh
In this paper, we consider an SDP relaxation of the quadratic knapsack problem (QKP). After using the Burer-Monteiro factorization, we get a non-convex optimization problem, whose…