3 papers
math.DS2025
A Frobenius-Optimal Projection for Enforcing Linear Conservation in Learned Dynamical Models
John M. Mango, Ronald Katende
We consider the problem of restoring linear conservation laws in data-driven linear dynamical models. Given a learned operator and a full-rank constraint matrix e…
math.NA2025
Preserving Extreme Singular Values with One Oblivious Sketch
John M. Mango, Ronald Katende
We study when a single linear sketch can control the largest and smallest nonzero singular values of every rank- matrix. Classical oblivious embeddings require $s=Θ(r/\varepsilo…
cs.LG2024
Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression
John Mango, Ronald Katende
This paper introduces a dynamic, error-bounded hierarchical matrix (H-matrix) compression method tailored for Physics-Informed Neural Networks (PINNs). The proposed approach reduce…