LMSVD is a Matlab solver for computing truncated (dominant) singular value decompositions of relatively large matrices. The code uses a subspace optimization technique to achieve significant accelerations to the classic simultaneous subspace iterations method, and is typically much faster than the Matlab's default function "svds".
In the case you use LMSVD in your published work, please make a reference to the following paper.
Xin Liu, Zaiwen Wen and Yin Zhang, Limited Memory Block Krylov Subspace Optimization for Computing Dominant Singular Value Decompositions, SIAM Journal on Scientific Computing, 35-3 (2013), A1641-A1668.
Hi, does this svd code run for a complex matrix ?
I used this code to calculate singular values of large scale matrix. It is robust. More important, it is even faster than the svd of matlab built-in function.
Excellent！Help me a lot!
An excellent SVD solver. It is robust and efficient, especially for large scale problem. Really helps me a lot. Thanks to the author. Recommend!
Very very robust!!!
This script was of great help to me. Thank you very much!!
An excellent file!
a most excellent Svd solver I've ever used,thanks!
Very efficient !
I tested some large sparse instances, the solver performs so fast and efficient! Strongly recommend this solver.
The code can be even faster if the sparse matrix-dense vector products is efficient, for example using intel MKL:
I have tested on large sparse matrices, this function is at least twice faster than svds and the result is the same. Fantastic work! I will incorporate it into my program. Thanks!
An efficient svd solver for large problems. A very good alternative for the matlab svd function if you have large matrices.
An efficient SVD solver for large problems. A very good alternative for the buildin SVD function when the dimension is high.
friendly, efficient and robust!
The code can be more efficient using a suitable implementation for sparse matrix-dense vector multiplications, for example, "mkl_dcscmm" in intel MKL:
Can be orders of magnitude faster than Matlab SVDS on large matrices.
This code is really robust. Very good. Strongly recommend.
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