# Questions tagged [nuclear-norm]

The nuclear norm of a matrix is the sum of its singular values.

115 questions
Filter by
Sorted by
Tagged with
8 views

72 views

274 views

I'm quite new to semi-definite programming (SDP), and I'd like to compute a program that has some conditions on the distance between two vectors. More precisely I have some fixed positive real $\... 2 votes 1 answer 196 views ### Proximal Operator of the Nuclear Norm with Non Negativity Constraints Let$L$and$R$be$n \times n$matrices. Consider the following minimization problem \begin{equation} \mathbf{L} = \min_{L \geq \mathbf{0}} \mu\|\mathbf{L}\|_* + \dfrac{1}{2\lambda}\|\mathbf{L-R}\|... 0 votes 0 answers 55 views ### Nuclear norm minimization of a matrix in the presence of non-negativity constraints [duplicate] Let$L$and$R$be$n \times n$matrices. Consider the following minimization problem \begin{equation} \mathbf{L} = \min_{L \geq \mathbf{0}} \mu\|\mathbf{L}\|_* + \dfrac{1}{2\lambda}\|\mathbf{L-R}\|... 0 votes 1 answer 65 views ### How to prove the lower bound of the amount of perturbation on nuclear norm after scaling a matrix? In my research, I need a lemma that gives the lower bound of the perturbation of nuclear norm. It is supposed to be Statement. For any real matrix$M_{n\times n}$and diagonal matrix$\Lambda_{n\...
Given $M \in \mathbb R^{n \times n}$, I would like to find its $k$-dimensional "leading singular vector space". $M$ is not necessarily symmetric. In contrast with the standard SVD problem, the left ...