科研之友
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摘要
A problem of optimizing a matrix sparse in the joint Frobenius-Schatten norm is considered. The least rows are proposed to be truncated according to the lower bound to fight the ill-conditionality of the matrix. Truncation not only helps avoid incorrect termination of the algorithm but it also reduces the computational complexity. Convergence analysis ensures that a truncation algorithm finds an approximate solution to the problem. The numerical experiments show the advantage of the truncation method over the previous algorithm.
- 出版日期2018-5
- 单位南京航空航天大学
