A Projection Method for Metric-Constrained Optimization
This addresses practical scalability issues in machine learning and graph clustering applications, though it appears incremental as it builds on existing projection methods.
The paper tackles the challenge of solving metric-constrained optimization problems, which are difficult due to high memory demands, by developing a general solver based on a projection algorithm, enabling solutions for problems with up to 10^8 variables and 10^11 constraints.
We outline a new approach for solving optimization problems which enforce triangle inequalities on output variables. We refer to this as metric-constrained optimization, and give several examples where problems of this form arise in machine learning applications and theoretical approximation algorithms for graph clustering. Although these problem are interesting from a theoretical perspective, they are challenging to solve in practice due to the high memory requirement of black-box solvers. In order to address this challenge we first prove that the metric-constrained linear program relaxation of correlation clustering is equivalent to a special case of the metric nearness problem. We then developed a general solver for metric-constrained linear and quadratic programs by generalizing and improving a simple projection algorithm originally developed for metric nearness. We give several novel approximation guarantees for using our framework to find lower bounds for optimal solutions to several challenging graph clustering problems. We also demonstrate the power of our framework by solving optimizing problems involving up to 10^{8} variables and 10^{11} constraints.