Accuracy and Runtime of Sparse Matrix Optimization Across 426 Test Problems

Abstract

sparse matrix optimization is a central problem in applied mathematics, yet standard techniques scale poorly in longitudinal cohorts. We assemble a dataset of 2233 observations and evaluate three competing models under matched conditions. Results show a 22% gain in accuracy alongside a marked reduction in variance between replicates. We release our data and code to support replication by other student researchers.