17 citations · 17 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
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…
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…
A Bernoulli-barycentric rational matrix collocation method with preconditioning for a class of evolutionary PDEs
Wei-Hua Luo, Xian-Ming Gu, Bruno Carpentieri +1
We propose a Bernoulli-barycentric rational matrix collocation method for two-dimensional evolutionary partial differential equations (PDEs) with variable coefficients that combine…
A model-driven approach for a new generation of adaptive libraries
Marco Cianfriglia, Flavio Vella, Cedric Nugteren +2
Efficient high-performance libraries often expose multiple tunable parameters to provide highly optimized routines. These can range from simple loop unroll factors or vector sizes…