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- W1570762167 abstract "We present a rank-adaptive optimization strategy for finding low-rank solutions of matrix optimization problems involving a quadratic objective function. The algorithm combines a greedy outer iteration that increases the rank and a smooth Riemannian algorithm that further optimizes the cost function on a fixed-rank manifold. While such a strategy is not especially novel, we show that it can be interpreted as a perturbed gradient descent algorithms or as a simple warm-starting strategy of a projected gradient algorithm on the variety of matrices of bounded rank. In addition, our numerical experiments show that the strategy is very efficient for recovering full rank but highly ill-conditioned matrices that have small numerical rank." @default.
- W1570762167 created "2016-06-24" @default.
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- W1570762167 date "2015-05-01" @default.
- W1570762167 modified "2023-10-06" @default.
- W1570762167 title "Greedy rank updates combined with Riemannian descent methods for low-rank optimization" @default.
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- W1570762167 doi "https://doi.org/10.1109/sampta.2015.7148925" @default.
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