collaborators

5 papers

math.RA2026

Unitary-Invariant Decomposition of Reducible Total Least Squares Core Problems

Sijia Yu, Bruno Carpentieri, Yan-Fei Jing

The analysis of a total least square problem (TLS) can be reduced to that of an associated core problem, which typically has lower dimension and improved solubility properties. Nev…

nlin.CD2026

FEG-Pro: Forecast-Error Growth Profiling for Finite-Horizon Instability Analysis of Nonlinear Time Series

Andrei Velichko, N'Gbo N'Gbo, Bruno Carpentieri +1

Estimating the largest Lyapunov exponent from a scalar time series is difficult when the governing equations, tangent dynamics, and full state vector are unavailable. We propose FE…

math.NA2026

Interpretable AI-Assisted Early Reliability Prediction for a Two-Parameter Parallel Root-Finding Scheme

Bruno Carpentieri, Andrei Velichko, Mudassir Shams +1

We propose an interpretable AI-assisted reliability diagnostic framework for parameterized root-finding schemes based on kNN-LLE proxy stability profiling and multi-horizon early p…

math.NA2026

Direct Finite-Time Contraction (Step-Log) Profiling--Driven Optimization of Parallel Schemes for Nonlinear Problems on Multicore Architectures

Mudassir Shams, Andrei Velichko, Bruno Carpentieri

Efficient computation of all distinct solutions of nonlinear problems is essential in many scientific and engineering applications. Although high-order parallel iterative schemes o…

math.NA2026

Optimizing Parallel Schemes with Lyapunov Exponents and kNN-LLE Estimation

Mudassir Shams, Andrei Velichko, Bruno Carpentieri

Inverse parallel schemes remain indispensable tools for computing the roots of nonlinear systems, yet their dynamical behavior can be unexpectedly rich, ranging from strong contrac…