Benchmark Results for Sparse Matrix Optimization Across 438 Test Problems

Abstract

Despite sustained attention from the research community, sparse matrix optimization remains difficult to study outside well-funded laboratories. We introduce a high-throughput framework and benchmark it against established baselines across 1782 trials. The proposed approach improves on the strongest baseline by 3% while reducing computational cost. We release our data and code to support replication by other student researchers.