Subspace Iteration Methods in terms of Matrix Product States

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A literature survey of low‐rank tensor approximation techniques (2013, neutral): “…CP [26,27]; • the (shift-and-invert) power and Lanczos methods combined with CP and Tucker [11,114]; • a restarted Lanczos method combined with TT [126]; • conjugate gradient type-methods for symmetric eigenvalue problems combined with truncation for low-rank matrices [180], as well as for TT…”
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives (2017, methodology): “In general, the GEVD problem can be formulated as the following trace optimization (maximization or minimization) problem max VPRIˆK tr(VTAV), s.t. VTBV = IK, (3.52) where the symmetric matrix, A P RIˆI , and symmetric positive-definite matrix, B P RIˆI , are known, while the objective is to estimat”