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math.OC2025
A Low-rank Augmented Lagrangian Method for Polyhedral-SDP and Moment-SOS Relaxations of Polynomial Optimization
Di Hou, Tianyun Tang, Kim-Chuan Toh
Polynomial optimization problems (POPs) can be reformulated as geometric convex conic programs, as shown by Kim, Kojima, and Toh (SIOPT 30:1251-1273, 2020), though such formulation…
math.OC2025
A low-rank augmented Lagrangian method for doubly nonnegative relaxations of mixed-binary quadratic programs
Di Hou, Tianyun Tang, Kim-Chuan Toh
Doubly nonnegative (DNN) programming problems are known to be challenging to solve because of their huge number of constraints and variables. In this work, we int…