Unifying Asymptotic Complexity and Real-World Performance in Matrix-Based Network Computations

David Gleich from Purdue University discusses methods to bridge the gap between theoretical asymptotic complexity and actual performance in large-scale matrix-based network computations, highlighting both theoretical insights and experimental results.