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To bypass the curse of dimensionality in high-dimensional eigenvalue problems arising in quantum systems and PDEs, the authors reformulate the Rayleigh–Ritz minimization over bounded-rank Tensor Train (TT) formats using a smooth slack-variable parametrization. This construction turns non-smooth low-rank tensor constraints into a tractable Riemannian manifold optimization problem that directly exploits Kronecker-product operator structures. Across benchmark physics models including the Schrödinger equation and Bose–Einstein condensates, the method matches full-space eigensolver accuracy, automatically performs numerical rank reduction, and remains robust even when ranks are substantially over-estimated.
Over-estimating tensor ranks no longer destabilizes extreme eigensolvers: a smooth slack-variable TT parametrization recovers high-dimensional ground states at full-space fidelity without ever materializing ambient-dimensional vectors.
High-dimensional extreme eigenvalue problems often arise from molecular vibrational models, electronic structure calculations, and quantum mechanics. Directly solving these problems suffers from the curse of dimensionality. Instead of tackling the problem in high-dimensional ambient space, we reformulate the problem through low-rank tensor formats, which can significantly reduce the computational cost and storage. Specifically, we consider the Rayleigh--Ritz problem on bounded-rank tensors in the tensor train format, which enables a more flexible choice of rank parameters. Moreover, we consider a smooth parametrization for bounded-rank tensors by introducing slack variables, leading to a smooth manifold structure. The original Rayleigh--Ritz problem is therefore transferred to an optimization problem on the manifold. We develop the Riemannian geometry and propose optimization methods for solving extreme eigenvalue problems. In practice, the Kronecker-product structure in Hamiltonian and PDE operators is employed to simplify the computation. Numerical experiments on the harmonic oscillator, the Laplace operator, the Schr\"odinger equation, the layered cluster problem, and the Bose--Einstein model demonstrate that the proposed method achieves accuracy comparable to full-space eigensolvers with reduced computation time and avoids storing full vectors. The results also show numerical rank reduction during iteration and robustness when the rank parameter is over-estimated. In addition, the fixed-point iterations illustrate that the proposed methods are able to serve as an inner eigensolver for solving nonlinear eigenvalue problems.