Approximate FW Algorithm with a novel DMO method over Graph-structured Support Set
This work addresses optimization problems with graph structures, but it is incremental as it builds on existing methods with mixed results.
The paper tackled graph-structured convex optimization using an approximate Frank-Wolfe algorithm, where a backtracking line-search method reduced iteration count, but a new DMO method did not yield significant improvements.
In this project, we reviewed a paper that deals graph-structured convex optimization (GSCO) problem with the approximate Frank-Wolfe (FW) algorithm. We analyzed and implemented the original algorithm and introduced some extensions based on that. Then we conducted experiments to compare the results and concluded that our backtracking line-search method effectively reduced the number of iterations, while our new DMO method (Top-g+ optimal visiting) did not make satisfying enough improvements.