nuclearnormminimering
Nuclearnormminimering, often translated as nuclear norm minimization, is a mathematical technique used in various fields, particularly in machine learning and signal processing. It is a convex optimization method that seeks to find a low-rank matrix that best approximates a given matrix. The nuclear norm of a matrix is the sum of its singular values. Minimizing this norm encourages the resulting matrix to have a small number of non-zero singular values, which is a characteristic of low-rank matrices.
This technique is widely applied in matrix completion problems, where the goal is to reconstruct a complete
The optimization problem associated with nuclearnormminimering can be formulated as finding a matrix X that minimizes