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Dr. Yang Liu is a researcher in numerical optimization and scientific computing, with a focus on scalable algorithms for large-scale nonconvex problems. His work integrates numerical linear algebra, tensor methods, and Krylov subspace techniques to advance efficient optimization frameworks. He has contributed to the development of Newton-MR methods, pseudoinverse solution recovery, and adaptive regularization strategies for higher-order tensor models. His research emphasizes matrix-free and high-performance implementations, with contributions incorporated into advanced solver libraries. Dr. Yang Liu actively engages in international collaborations and contributes to peer-reviewed journals and conferences in optimization and applied mathematics.
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